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The rise of electric vehicles—2020 status and future expectations

Matteo Muratori 13,1 , Marcus Alexander 2 , Doug Arent 1 , Morgan Bazilian 3 , Pierpaolo Cazzola 4 , Ercan M Dede 5 , John Farrell 1 , Chris Gearhart 1 , David Greene 6 , Alan Jenn 7 , Matthew Keyser 1 , Timothy Lipman 8 , Sreekant Narumanchi 1 , Ahmad Pesaran 1 , Ramteen Sioshansi 9 , Emilia Suomalainen 10 , Gil Tal 7 , Kevin Walkowicz 11 and Jacob Ward 12

Published 25 March 2021 • © 2021 IOP Publishing Ltd Progress in Energy , Volume 3 , Number 2 Focus on Transport Electrification Citation Matteo Muratori et al 2021 Prog. Energy 3 022002 DOI 10.1088/2516-1083/abe0ad

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1 National Renewable Energy Laboratory, Golden, CO, United States of America

2 Electric Power Research Institute, Palo Alto, CA, United States of America

3 Colorado School of Mines, Golden, CO, United States of America

4 International Transport Forum in Paris, France

5 Toyota Research Institute of North America, Ann Arbour, MI, United States of America

6 University of Tennessee, Knoxville, TN, United States of America

7 University of California, Davis, CA, United States of America

8 University of California, Berkeley, CA, United States of America

9 The Ohio State University, Columbus, OH, United States of America

10 Institut VEDECOM, Versailles, France

11 Calstart, Pasadena, CA, United States of America

12 Carnegie Mellon University, Pittsburgh, PA, United States of America

Author notes

13 Author to whom any correspondence should be addressed.

Matteo Muratori https://orcid.org/0000-0003-1688-6742

Doug Arent https://orcid.org/0000-0002-4219-3950

Morgan Bazilian https://orcid.org/0000-0003-1650-8071

Ahmad Pesaran https://orcid.org/0000-0003-0666-1021

Emilia Suomalainen https://orcid.org/0000-0002-6339-2932

Gil Tal https://orcid.org/0000-0001-7843-3664

Jacob Ward https://orcid.org/0000-0002-8278-8940

• Received 3 August 2020
• Accepted 27 January 2021
• Published 25 March 2021

Peer review information

Method : Single-anonymous Revisions: 3 Screened for originality? Yes

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Electric vehicles (EVs) are experiencing a rise in popularity over the past few years as the technology has matured and costs have declined, and support for clean transportation has promoted awareness, increased charging opportunities, and facilitated EV adoption. Suitably, a vast body of literature has been produced exploring various facets of EVs and their role in transportation and energy systems. This paper provides a timely and comprehensive review of scientific studies looking at various aspects of EVs, including: (a) an overview of the status of the light-duty-EV market and current projections for future adoption; (b) insights on market opportunities beyond light-duty EVs; (c) a review of cost and performance evolution for batteries, power electronics, and electric machines that are key components of EV success; (d) charging-infrastructure status with a focus on modeling and studies that are used to project charging-infrastructure requirements and the economics of public charging; (e) an overview of the impact of EV charging on power systems at multiple scales, ranging from bulk power systems to distribution networks; (f) insights into life-cycle cost and emissions studies focusing on EVs; and (g) future expectations and synergies between EVs and other emerging trends and technologies. The goal of this paper is to provide readers with a snapshot of the current state of the art and help navigate this vast literature by comparing studies critically and comprehensively and synthesizing general insights. This detailed review paints a positive picture for the future of EVs for on-road transportation, and the authors remain hopeful that remaining technology, regulatory, societal, behavioral, and business-model barriers can be addressed over time to support a transition toward cleaner, more efficient, and affordable transportation solutions for all.

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This article was updated on 29 April 2021 to add the name of the fifth author and to correct the name of the eighth author.

1. Introduction

First introduced at the end of the 1800s, electric vehicles (EVs) 12 have been experiencing a rise in popularity over the past few years as the technology has matured and costs (especially of batteries) have declined substantially. Worldwide support for clean transportation options (i.e. low emissions of greenhouse gasses [GHG] to mitigate climate change and criteria pollutants) has promoted awareness, increased charging opportunities, and facilitated adoption of EVs. EVs present numerous advantages compared to fossil-fueled internal-combustion-engine vehicles (ICEVs), inter alia: zero tailpipe emissions, no reliance on petroleum, improved fuel economy, lower maintenance, and improved driving experience (e.g. acceleration, noise reduction, and convenient home and opportunity recharging). Further, when charged with clean electricity, EVs provide a viable pathway to reduce overall GHG emissions and decarbonize on-road transportation. This decarbonization potential is important, given limited alternative options to liquid fossil fuels. The ability of EVs to reduce GHG emissions is dependent, however, upon clean electricity. Therefore, EV success is intertwined closely with the prospect of abundant and affordable renewable electricity (in particular solar and wind electricity) that is poised to transform power systems (Jacobson et al 2015 , Kroposki et al 2017 , Gielen et al 2019 , IEA 2020b ). Coordinated actions can produce beneficial synergies between EVs and power systems and support renewable-energy integration to optimize energy systems of the future to benefit users and offer decarbonization across sectors (CEM 2020 ). A cross-sectoral approach across the entire energy system is required to realise clean future transformation pathways (Hansen et al 2019 ). EVs are expected to play a critical role in the power system of the future (Muratori and Mai).

EV success is increasing rapidly since the mid-2010s. EV sales are breaking previous records every year, especially for light-duty vehicles (LDVs), buses, and smaller vehicles such as three-wheelers, mopeds, kick-scooters, and e-bikes (IEA 2017 , 2018a , 2019 , 2020 ). To date, global automakers are committing more than $140 billion to transportation electrification, and 50 light-duty EV models are available commercially in the U.S. market (Moore and Bullard 2020 ). Approximately 130 EV models are anticipated by 2023 (AFDC 2020 , Moore and Bullard 2020 ). Future projections of the role of EVs in LDV markets vary widely, with estimates ranging from limited success (∼10% of sales in 2050) to full market dominance, with EVs accounting for 100% of LDV sales well before 2050. Many studies project that EVs will become economically competitive with ICEVs in the near future or that they are already cost-competitive for some applications (Weldon et al 2018 , Sioshansi and Webb 2019 , Yale E360 2019 , Kapustin and Grushevenko 2020 ). However, widespread adoption requires more than economic competitiveness, especially for personally owned vehicles. Behavioral and non-financial preferences of individuals on different technologies and mobility options are also important (Lavieri et al 2017 , Li et al 2017 , McCollum et al 2018 , Ramea et al 2018 ). EV adoption beyond LDVs has been focused on buses, with significant adoption in several regions (especially China). Electric trucks also are receiving great attention, and Bloomberg New Energy Finance (BloombergNEF) projects that by 2025, alternative fuels will compete with, or outcompete, diesel in long-haul trucking applications (Moore and Bullard 2020 ). These recent successes are being driven by technological progress, especially in batteries and power electronics, greater availability of charging infrastructure, policy support driven by environmental benefits, and consumer acceptance. EV adoption is engendering a virtuous circle of technology improvements and cost reductions that is enabled and constrained by positive feedbacks arising from scale and learning by doing, research and development, charging-infrastructure coverage and utilization, and consumer experience and familiarity with EVs.

Vehicle electrification is a game-changer for the transportation sector due to major energy and environmental implications driven by high vehicle efficiency (EVs are approximately 3–4 times more efficient than comparable ICEVs), zero tailpipe emissions, and reduced petroleum dependency (great fuel diversity and flexibility exist in electricity production). Far-reaching implications for vehicle-grid integration extend to the electricity sector and to the broader energy system. A revealing example of the role of EVs in broader energy-transformation scenarios is provided by Muratori and Mai, who summarize results from 159 scenarios underpinning the special report on Global Warming of 1.5 °C (SR1.5) by Intergovernmental Panel on Climate Change (IPCC). Muratori and Mai also show that transportation represents only ∼2% of global electricity demand currently (with rail responsible for more than two-thirds of this total). They show that electricity is projected to provide 18% of all transportation-energy needs by 2050 for the median IPCC scenario, which would account for 10% of total electricity demand. Most of this electricity use is targeted toward on-road vehicle electrification. These projections are the result of modeling and simulations that are struggling to keep pace with the EV revolution and its role in energy-transformation scenarios as EV technologies and mobility are evolving rapidly (McCollum et al 2017 , Venturini et al 2019 , Muratori et al 2020 ). Recent studies explore higher transportation-electrification scenarios: for example, Mai et al ( 2018 ) report a scenario in which 75% of on-road miles are powered by electricity, and transportation represents almost a quarter of total electricity use during 2050.

Vehicle electrification is a disruptive element in energy-system evolution that radically changes the roles of different sectors, technologies, and fuels in long-term transformation scenarios. Traditionally, energy-system-transformation studies project minimal end-use changes in transportation-energy use over time (limited mode shifting and adoption of alternative fuels), and the sector is portrayed as a 'roadblock' to decarbonization. In many decarbonization scenarios, transportation is seen traditionally as one of the biggest hurdles to achieve emissions reductions (The White House 2016 ). These scenarios rely on greater changes in the energy supply to reduce emissions and petroleum dependency (e.g. large-scale use of bioenergy, often coupled to carbon capture and sequestration) rather than demand-side transformations (IPCC 2014 , Pietzcker et al 2014 , Creutzig et al 2015 , Muratori et al 2017 , Santos 2017 ). In most of these studies, electrification is limited to some transportation modes (e.g. light-duty), and EVs are not expected to replace ICEVs fully (The White House 2016 ). More recently, however, major mobility disruptions (e.g. use of ride-hailing and vehicle ride-sharing) and massive EV adoption across multiple applications are proposed (Edelenbosch et al 2017 , Van Vuuren et al 2017 , Hill et al 2019 , E3 2020 , Zhang and Fujimori 2020 ). These mobility disruptions allow for more radical changes and increase the decarbonization role of transportation and highlight the integration opportunities between transportation and energy supply, especially within the electricity sector. For example, Zhang and Fujimori ( 2020 ) highlight that for vehicle electrification to contribute to climate-change mitigation, electricity generation needs to transition to clean sources. They note that EVs can reduce mitigation costs, implying a positive impact of transport policies on the economic system (Zhang and Fujimori 2020 ). In-line with these projections, many countries are establishing increasingly stringent and ambitious targets to support transport electrification and in some cases ban conventional fossil fuel vehicles (Wentland 2016 , Dhar et al 2017 , Coren 2018 , CARB 2020 , State of California 2020 ).

EV charging undoubtedly will impact the electricity sector in terms of overall energy use, demand profiles, and synergies with electricity supply. Mai et al ( 2018 ) show that in a high-electrification scenario, transportation might grow from the current 0.2% to 23% of total U.S. electricity demand in 2050 and significantly impact system peak load and related capacity costs if not controlled properly. Widespread vehicle electrification will impact the electricity system across the board, including generation, transmission, and distribution. However, expected changes in U.S. electricity demand as a result of vehicle electrification are not greater than historical growth in load and peak demand. This finding suggests that bulk-generation capacity is expected to be available to support a growing EV fleet as it evolves over time, even with high EV-market growth (U.S. DRIVE 2019 ). At the same time, many studies have shown that 'smart charging' and vehicle-to-grid (V2G) services create opportunities to reduce system costs and facilitate the integration of variable renewable energy (VRE). Charging infrastructure that enables smart charging and alignment with VRE generation, as well as business models and programs to compensate EV owners for providing charging flexibility, are the most pressing required elements for successfully integrating EVs with bulk power systems. At the local level, EV charging could increase and change electricity loads significantly, which could impact distribution networks and power quality and reliability (FleetCarma 2019 ). Distribution-network impacts can be particularly critical for high-power charging and in cases in which many EVs are concentrated in a specific location, such as clusters of residential LDV charging and possibly fleet depots for commercial vehicles (Muratori 2018 ).

This paper provides a timely status of the literature on several aspects of EV markets, technologies, and future projections. The paper focuses on multiple facets that characterize technology status and the role of EVs in transportation decarbonization and broader energy-transformation pathways focusing on the U.S. context. As appropriate, global context is provided as well. Hybrid EVs (for which liquid fuel is the only source of energy) and fuel cell EVs (that power an electric powertrain with a fuel cell and on-board hydrogen storage) have some similarities with EVs and could complement them for many applications. However, these technologies are not reviewed in detail here. The remainder of this paper is structured as follows. Section 2 focuses on the status of the light-duty-EV market and provides a comparison of projections for future adoption. Section 3 provides insights on market opportunities beyond LDVs. Section 4 offers a review of cost and performance evolution for batteries, power electronics, and electric machines that are key components of EV success. Section 5 reviews charging-infrastructure status and focuses on modeling and analysis studies used to project charging-infrastructure requirements, the economics of public charging, and some considerations on cybersecurity and future technologies (e.g. wireless charging). Section 6 provides an overview of the impact of EV charging on power systems at multiple scales, ranging from bulk power systems to distribution networks. Section 7 provides insights into life-cycle cost and emissions studies focusing on EVs. Finally, section 8 touches on future expectations.

1.1. Summary of take-away points

1.1.1. ev adoption.

• The global rate of adoption of light-duty EVs (passenger cars) has increased rapidly since the mid-2010s, supported by three key pillars: improvements in battery technologies; a wide range of supportive policies to reduce emissions; and regulations and standards to promote energy efficiency and reduce petroleum consumption.
• Adoption of advanced technologies has been underestimated historically in modeling and analyses; EV adoption is projected to remain limited until 2030, and high uncertainty is shown afterward with widely different projections from different sources. However, EVs hold great promise to replace conventional LDVs affordably.
• Barriers to EV adoption to date include consumer skepticism toward new technology, high purchase prices, limited range and lack of charging infrastructure, and lack of available models and other supply constraints.
• A major challenge facing both manufacturers and end-users of medium- and heavy-duty EVs is the diverse set of operational requirements and duty cycles that the vehicles encounter in real-world operation.
• EVs appear to be well suited for short-haul trucking applications such as regional and local deliveries. The potential for battery-electric models to work well in long-haul on-road applications has yet to be established, with different studies indicating different opportunities.

1.1.2. Batteries and other EV technologies

• Over the last 10 years, the price of lithium-ion battery packs has dropped by more than 80% (from over $1000 kWh −1 to $156 kWh −1 at the end of 2019, BloombergNEF 2020 ). Further price reduction is needed to achieve EV purchase-price parity with ICEVs.
• Over the last 10 years, the specific energy of a lithium-ion battery cell has almost doubled, reaching 240 Wh kg −1 (BloombergNEF 2020 ), reducing battery weight significantly.
• Reducing or eliminating cobalt in lithium-ion batteries is an opportunity to lower costs and reduce reliance on a rare material with controversial supply chains.
• While batteries are playing a key role in the rise of EVs, power electronics and electric motors are also key components of an EV powertrain. Recent trends toward integration promise to deliver benefits in terms of increased power density, lower losses, and lower costs.

1.1.3. Charging infrastructure

• With a few million light-duty EVs on the road, currently, there is about one public charge point per ten battery electric vehicles (BEVs) in U.S. (although most vehicles have access to a residential charger).
• Given the importance of home charging (and the added convenience compared to traditional refueling at public stations), charging solutions in residential areas comprising attached or multi-unit dwellings is likely to be essential for EVs to be adopted at large scale.
• Although public charging infrastructure is clearly important to EV purchasers, how best to deploy charging infrastructure in terms of numbers, types, locations, and timing remains an active area for research.
• The economics of public charging vary with location and station configuration and depend critically on equipment and installation costs, incentives, non-fuel revenues, and retail electricity prices, which are heavily dependent on station utilization.
• The electrification of medium- and heavy-duty commercial trucks and buses might introduce unique charging and infrastructure requirements compared to those of light-duty passenger vehicles.
• Wireless charging, specifically high-power wireless charging (beyond level-2 power), could play a key role in providing an automated charging solution for tomorrow's automated vehicles.

1.1.4. Power system integration

• Accommodating EV charging at the bulk power-system level (generation and transmission) is different in each region, but there are no major known technical challenges or risks to support a growing EV fleet, especially in the near term (approximately one decade).
• At the local level, however, EV charging can increase and change electricity loads significantly, causing possible negative impacts on distribution networks, especially for high-power charging.
• The integration of EVs into power systems presents opportunities for synergistic improvement of the efficiency and economics of electromobility and electric power systems, and EVs can support grid planning and operations in several ways.
• There are still many challenges for effective EV-grid integration at large scale, linked not only to the technical aspects of vehicle-grid-integration (VGI) technology but also to societal, economic, business model, security, and regulatory aspects.
• VGI offers many opportunities that justify the efforts required to overcome these challenges. In addition to its services to the power system, VGI offers interesting perspectives for the full exploitation of synergies between EVs and VRE as both technologies promise large-scale deployment in the future.

1.1.5. Life-cycle cost and emissions

• Many factors contribute to variability in EV life-cycle emissions, mostly the carbon intensity of electricity, charging patterns, vehicle characteristics, and even local climate. Grid decarbonization is a prerequisite for EVs to provide major GHG-emissions reductions.
• Existing literature suggests that future EVs can offer 70%–90% lower GHG emissions compared to today's ICEVs, most obviously due to broad expectations for continued grid decarbonization.
• Operational costs of EVs (fuel and maintenance) are typically lower than those of ICEVs, largely because EVs are more efficient than ICEVs and have fewer moving parts.

1.1.6. Synergies with other technologies and future expectations

• Vehicle electrification fits in broader electrification and mobility macro-trends, including micro-mobility in urban areas, new mobility business models regarding ride-hailing and car-sharing, and automation that complement well with EVs.
• While EVs are a relatively new technology and automated vehicles are not readily available to the general public, the implications and potential synergies of these technologies operating in conjunction are significant.
• The coronavirus pandemic is impacting transportation markets negatively, including those for EVs, but long-term prospects remain undiminished.
• Several studies project major roles for EVs in the future, which is reflected in massive investment in vehicle development and commercialization, charging infrastructure, and further technology improvement. Consumer adoption and acceptance and technology progress form a virtuous self-reinforcing circle of technology-component improvements and cost reductions.
• EVs hold great promise to replace ICEVs affordably for a number of on-road applications, eliminating petroleum dependence, improving local air quality and enabling GHG-emissions reductions, and improving driving experiences.
• Forecasting the future, including technology adoption, remains a daunting task. However, this detailed review paints a positive picture for the future of EVs across a number of on-road applications.

2. Status of electric-LDV market and future projections

This section provides a current snapshot of the electric-LDV market in a global and U.S. context, but focuses on the latter. The global rate of adoption of electric LDVs has increased rapidly since the mid-2010s 13 . By the end of 2019, the global EV fleet reached 7.3 million units—up by more than 40% from 2018—with more than 1.25 million electric LDVs in the U.S. market alone (IEA 2020 ). EV sales totaled more than 2.2 million in 2019, exceeding the record level that was attained in 2018, despite mixed performances in different markets. Electric-LDV sales increased in Europe and stagnated or declined in other major markets, particularly in China (with a significant slowdown due to changes in Chinese subsidy policy in July 2019), Japan, and U.S. U.S. EV adoption varies greatly geographically—nine counties in California currently see EVs accounting for more than 10% of sales (8% on average for California as a whole), but national-level sales remain at less than 3% (Bowermaster 2019 ). BEV sales exceeded those of plug-in hybrid electric vehicles (PHEVs) in all regions.

The rapid increase in EV adoption is underpinned by three key pillars:

• (a)   Improvements and cost reductions in battery technologies, which were enabled initially by the large-scale application of lithium-ion batteries in consumer electronics and smaller vehicles (e.g. scooters, especially in China, IEA 2017 ). These developments offer clear and growing opportunities for EVs and HEVs to deliver a reduced total cost of ownership (TCO) in comparison with ICEVs.
• (b)   A wide range of supportive policy instruments for clean transportation solutions in major global markets (Axsen et al 2020 ), which are mirrored by private-sector investment. These developments are driven by environmental goals (IPCC 2014 ), including reduction of local air pollution. These policy instruments support charging-infrastructure deployment (Bedir et al 2018 ) and provide monetary (e.g. rebates and vehicle-registration discounts) and non-monetary (e.g. access to high-occupancy-vehicle lanes and preferred parking) incentives to support EV adoption (IEA 2018a , AFDC 2020 ).
• (c)   Regulations and standards that support high-efficiency transportation solutions and reduce petroleum consumption (e.g. fuel-economy standards, zero-emission-vehicle mandates, and low-carbon-fuel standards). These regulations are being supported by technology-push measures, consisting primarily of economic instruments (e.g. grants and research funds) that aim to stimulate technological progress (especially batteries), and market-pull measures (e.g. public-procurement programs) that aim to support the deployment of clean-mobility technologies and enable cost reductions due to technology learning, scale, and risk mitigation.

Transport electrification also has started a virtuous self-reinforcing circle. Battery-technology developments and cost reductions triggered by EV adoption provide significant economic-development opportunities for the companies and countries intercepting the battery and EV value chains. Adoption of alternative vehicles both is enabled and constrained by powerful positive feedback arising from scale and learning by doing, research and development, consumer experience and familiarity with technologies (e.g. neighborhood effect), and complementary resources, such as fueling infrastructure (Struben and Sterman 2008 ). In this context, more diversity in make and model market offerings is supporting vehicle adoption. As of April 2020, there are 50 EV models available commercially in U.S. markets (AFDC 2020 ), and ∼130 are anticipated by 2023 (Moore and Bullard 2020 ).

Measures that support transport electrification have been, and increasingly shall be, accompanied by policies that control for the unwanted consequences. Thus, the measures need to be framed in the broader energy and industry contexts.

When looking at the future, EV-adoption forecasts remain highly uncertain. Technology-adoption projections are used by a number of stakeholders to guide investments, inform policy design and requirements (Kavalec et al 2018 ), assess benefits of previous and ongoing efforts (Stephens et al 2016 ), and develop long-term multi-sectoral assessments (Popp et al 2010 , Kriegler et al 2014 ). However, projecting the future, including technology adoption, is a daunting task. Past projections often have turned out to be inaccurate. Still, progress has been made to address projection uncertainty (Morgan and Keith 2008 , Reed et al 2019 ) and contextualize scenarios to explore alternative futures in a useful way. Scenario analysis is used largely in the energy-environment community to explore the possible implications of different judgments and assumptions by considering a series of 'what if' experiments (BP 2019 ).

Adoption of advanced technologies historically has been underestimated in modeling and analysis results (e.g. Creutzig et al 2017 ), which fail to capture the rapid technological progress and its impact on sales. Historical experiences suggest that technology diffusion, while notoriously difficult to predict, can occur rapidly and with an extensive reach (Mai et al 2018 ). Projecting personally owned LDV sales is particularly challenging because decisions are made by billions of independent (not necessarily rational) decision-makers valuing different vehicle attributes based on incomplete information (e.g. misinformation and skepticism toward new technologies) and limited financial flexibility.

Many studies make projections for future EV sales (see figure 1 for a comparison of different projections). Some organizations (e.g. Energy Information Administration [EIA]) historically have been conservative in projecting EV success, mostly due to scenario constraints and assumptions. Still, U.S. EV-sales projections from EIA in recent years are much higher than in the past. Others (e.g. BloombergNEF and Electric Power Research Institute [EPRI]) consistently have been more optimistic in terms of EV sales and continue to adjust sales projections upward. Policy ambition for EV adoption is also optimistic. For example, in September 2020, California passed new legislation that requires that by 2035 all new car and passenger-truck sales be zero-emission vehicles (and that all medium- and heavy-duty vehicles be zero-emission by 2045) (California, 2020). Projected EV sales and outcomes from major energy companies vary widely, ranging from somewhat limited EV adoption (e.g. ExxonMobil) to full market success (e.g. Shell). A survey from Columbia University (Kah 2019 ) considers 17 studies and shows that 'EV share of the global passenger vehicle fleet is not projected to be substantial before 2030 given the long lead time in turning over the global automobile fleet' and that 'the range of EVs in the 2040 fleet is 10% to 70%'. The studies compared in figure 1 show an even greater variability for 2050 projections, ranging from 13% to 100% of U.S. EV adoption for LDVs.

Figure 1.  Electric LDV (BEV and PHEV) new sales projections from numerous international sources. Unless otherwise noted, data refer to new U.S. sales. AEO2015 = EIA Annual Energy Outlook 2015, Reference Scenario. AEO2017 = EIA Annual Energy Outlook 2017, Reference Scenario. AEO2020 = EIA Annual Energy Outlook 2020, Reference Scenario. AEO2020HO = EIA Annual Energy Outlook 2020, High Oil Scenario. EFS Med = National Renewable Energy Laboratory (NREL) Electrification Futures Study, Medium Scenario. EFS High = NREL Electrification Futures Study, High Scenario. EPRI Med = EPRI Plug-in Electric Vehicle Projections: Scenarios and Impacts, Medium Scenario. EPRI High = EPRI Plug-in Electric Vehicle Projections: Scenarios and Impacts, High Scenario. EPRI NEA = EPRI National Electrification Assessment, Reference Scenario. GEVO NP = IEA Global EV Outlook 2019, New Policies Scenario. GEVO CEM = IEA Global EV Outlook 2019, Clean Energy Ministerial 30@30 Campaign Scenario. BNEF = BloombergNEF EV Outlook 2020. Equinor Riv = Equinor 2019 Energy Perspectives, Rivalry Scenario. Equinor Ren = Equinor 2019 Energy Perspectives, Renewal Scenario. Shell Sky = Shell Sky Scenario. ExxonMobil = 2019 ExxonMobil Outlook for Energy. IEEJ Ref = The Institute of Energy Economics, Japan. 2019 Outlook, Reference Scenario (global sales). IEEJ Adv = The Institute of Energy Economics, Japan. 2019 Outlook, Advanced Technologies Scenario (global sales). CA ZEV Mandate = California zero-emission vehicle (ZEV) Executive Order N-79-20 (September 2020).

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The future remains uncertain, but there is a clear trend in projections of light-duty EV sales toward more widespread adoption as the technology improves, consumers become more familiar with the technology, automakers expand their offerings, and policies continue to support the market.

A number of studies analyze the drivers of EV adoption (Vassileva and Campillo 2017 , Priessner et al 2018 ) and highlight several barriers for EVs to achieve widespread success, including consumer skepticism for new technologies (Egbue and Long 2012 ); uncertainty around environmental benefits (consumers wonder whether EVs actually are green; see section 7 for more clarity on the environmental benefits of EVs) and continued policy support; unclear battery aging/resale value; high costs (Haddadian et al 2015 , Rezvani et al 2015 , She et al 2017 ); lack of charging infrastructure (Melaina et al 2017 , Narassimhan and Johnson 2018 ); range anxiety (the fear of being unable to complete a trip) associated with shorter-range EVs; longer refueling times compared to conventional vehicles (Franke and Krems 2013 , Neubauer and Wood 2014 ; Melaina et al 2017 ); dismissive and deceptive car dealerships (De Rubens et al 2018 ); and other EV-supply considerations, such as limited model availability and limited supply chains.

A recent review of 239 articles published in top-tier journals focusing on EV adoption draws attention to 'relatively neglected topics such as dealership experience, charging infrastructure resilience, and marketing strategies as well as identifies much-studied topics such as charging infrastructure development, TCO, and purchase-based incentive policies' (Kumar and Alok 2019 ). Similar reviews published recently focus on different considerations, such as market heterogeneity (Lee et al 2019a ), incentives and policies (Hardman 2019 , Tal et al 2020 ), and TCO (Hamza et al 2020 ). Other than some limited discussions on business models and TCO, the literature is focused on one side of the story, namely demand. However, the availability (makes and models) of EVs is extremely limited compared to ICEVs (AFDC 2020 ). This is justified, in part, by new technologies requiring time to be introduced, and, in part, by the higher manufacturer revenues associated with selling and providing maintenance for ICEVs. Moreover, slow turnover in legacy industry (Morris 2020 ) and other supply constraints can be a major barrier to widespread EV uptake (Wolinetz and Axsen 2017 , De Rubens et al 2018 ). Kurani ( 2020 ) argues that in most cases, 'Results of large sample surveys and small sample workshops mutually reinforce the argument that continued growth of PEV markets faces a barrier in the form of the inattention to plug-in electric vehicles (PEVs) of the vast majority of car-owning and new-car-buying households even in a place widely regarded as a leader. Most car-owning households are not paying attention to PEVs or the idea of a transition to electric-drive.'

3. EVs beyond light-duty applications

While much of the recent focus on vehicle electrification is with LDVs and small two- or three-wheelers (primarily in China), major progress also is being made with the electrification of medium- and heavy-duty vehicles. This includes heavy-duty trucks of various types, urban transit buses, school buses, and medium-duty vocational vehicles. As of the end of 2019, there were about 700 000 medium- and heavy-duty commercial EVs in use around the world (EV Volumes 2020 , IEA 2020 ).

A major challenge facing both manufacturers and end-users of medium- and heavy-duty vehicles is the diverse set of operational requirements and duty cycles that the vehicles encounter in real-world operation. When designing powertrain configurations and on-board energy-storage needs for new technologies, it is of critical importance to represent vehicle behavior accurately for different operations, including possible changes triggered by electrification (Delgado-Neira 2012 ). Medium- and heavy-duty vehicles can require a large number of powertrain and battery configurations, control strategies, and charging solutions. These needs depend on vehicle type (covering the full U.S. gross vehicle weight ratings [GVWR] spectrum from class 3 to class 8, 10 001–80 000 lb [4536–36 287 kg]), commercial operational situations and activities, and diverse drive cycles and charging opportunities (e.g. depot-based operations vs. long-haul). An example of this potential variability and its effect on the required battery capacity across multiple vehicle vocations is shown in figure 2 (Smith et al 2019 ).

Figure 2.  Battery capacity requirements vs. weight class for medium- and heavy-duty vehicles (Smith et al 2019 ).

Another example of the highly variable use cases for medium- and heavy-duty EVs shows energy efficiencies range between 0.8 kWh mile −1 and 3.2 kWh mile −1 (0.5–2.5 kWh km −1 ) (Gao et al 2018 ). If the on-board energy-storage needs for these vehicles are considered, assuming a daily operational range of between 50 miles and 200 miles (80–322 km), this results in battery-size requirements between 40 kWh and 640 kWh (assuming that the vehicle is recharged once daily). If additional charging strategies are considered (with their variability in expected charge times and associated power ratings), the range of vehicle-hardware and charging-infrastructure possibilities increases further. When adding variability across use cases with respect to temperature effects, battery-capacity degradation, payload, and road grade, it becomes clear that medium- and heavy-duty truck manufacturers face a significant challenge in designing, developing, and manufacturing systems that are able to meet the diverse operational requirements.

There are potential synergies between components of light-duty and medium- and heavy-duty electric vehicles. However, the requirements of medium- and heavy-duty vehicles place much greater burdens on powertrain components. The power and energy needs in heavy-duty applications are much larger than in light-duty applications. Heavy-duty vehicles could demand twice the peak power, four times the torque, and can consume more than five times the energy per mile (or km) driven compared to LDVs. In addition to using more energy per mile (or km) driven, typically, commercial vehicles drive many more miles (or km) per day, requiring much larger batteries and possibly much higher-power charging. Moreover, heavy-duty-vehicle users expect their vehicles to last more than a million miles, pointing to significantly higher durability requirements for heavy-duty-vehicle components (Smith et al 2019 ). Overall, these requirements, in combination with the needs for very high durability and very high-power drivelines and charging, may cause battery chemistries of heavy-duty vehicle batteries to diverge from those that are used in LDVs, hindering economies of scale. Demands for high efficiency, high power, and lower weight will put pressure on commercial vehicles to work at higher voltages than LDVs do. While LDVs are designed typically with powertrains that operate at a few hundred volts, it may be desirable to design large EVs with kilovolt powertrains. This will have a particularly significant impact on power electronics and could drive the development of wide-bandgap power electronics.

Historically, EVs have not been considered capable alternatives to heavy-duty diesel trucks (above 33 000 lb [14 969 kg] GVWR) due to high capital costs, high energy and power requirements, and weight and range-related battery constraints. International Council on Clean Transportation (ICCT), for example, suggests that while conventional EV-charging methods may be sufficient for small urban commercial vehicles, overhead catenary or in-road charging are required for heavier vehicles (Moultak et al 2017 ). Recent studies dispute this, anticipating a much greater opportunity for EVs to replace diesel trucks in the short-term, even for long-haul applications (Mai et al 2018 , McCall and Phadke 2019 , Borlaug et al Forthcoming ), but the potential for battery-electric models to work well in long-haul applications has yet to be established (NACFE 2018 ). Studies show that a significant amount of payload capacity will be consumed by batteries, potentially up to 7 tons or 28% of capacity in a truck with a 500 mile (805 km) range with 1100 kWh battery capacity (Burke and Fulton 2019 ). Thus, batteries would reduce significantly the amount of cargo that can be carried. Other studies suggest this could be much less―on the order of 4% of lost payload capacity for 500 mile range (805 km) trucks and with overall lower TCO than diesel trucks (Phadke et al 2019 ). For short-haul applications, such as port drayage and regional or local deliveries, EVs appear well suited and battery weight may not affect the cargo or payload capacity adversely. Several heavy-duty battery-electric trucks for short- and medium-haul applications have been developed and tested in recent years by Balqon, Daimler Trucks NA, Peterbilt, TransPower, Tesla, US Hybrid, Volvo, and others (AFDC 2020 ).

Urban buses are also a major emerging market for electrification. In California, Innovative Clean Transit rules require transit agencies to transition completely to zero-emission technologies (batteries or fuel cells), with all new bus purchases being zero-emission by 2029 (CARB 2018 ). Eight of the ten largest transit agencies in California already are adopting zero-emission technologies into their fleets (CARB 2018 ). In a comparative study of urban buses running on diesel, compressed natural gas, diesel hybrid, fuel cells, and batteries, the battery buses are estimated to have the lowest CO 2 emissions in both California and Finland bus duty cycles at the time of the study (Lajunen and Lipman 2016 ). This study also shows that battery buses have only slightly higher overall costs per mile (or km) than fossil-fuel-based alternatives. Future projections out to 2030 show that electric buses have the lowest overall life-cycle costs, especially when CO 2 costs are included (Lajunen and Lipman 2016 ).

Medium-duty delivery vehicles (typically 10 000–33 000 lb [4536–14 969 kg] GVWR) are another attractive emerging area for electrification. The goods-delivery market is growing at approximately 9% per year in recent years, with a projected $343 billion global industry value in 2020 (Accenture 2015 ). The 'last mile' delivery vehicles that are needed for this market are undergoing changes and present good opportunities for electrification. Amazon, for example, has announced plans to purchase 100 000 custom-designed Rivian electric delivery vans by 2030, with 10 000 of the vehicles delivered by late 2022 (Davies 2019 ).

A significant challenge with electrifying these heavy- and medium-duty vehicles revolves around the installation of the required charging infrastructure (either at depots or along highways). While LDVs typically charge at power levels of 3 kW–10 kW, and potentially 50 kW–250 kW with DC fast chargers (DCFCs), a heavy-duty vehicle may require higher-power charging, depending on its duty cycle. Fleets of these vehicles charging in one location, such as a truck depot or travel center, may require several megawatts of power. This requires expensive charging infrastructure, potentially including costly and time-consuming distribution-grid upgrades, to provide the higher voltage and current levels that are needed. For example, a single 350 kW DCFC that may be suitable for heavy-duty applications costs almost $150 000 today (Nelder and Rogers 2019 , Nicholas 2019 ). These costs would, in turn, impact the business case for vehicle electrification. Potential costs of grid upgrades to support these new electrical loads would be additional expenses that may or may not be supported by the local utility, depending on the circumstances. To enable reliable, low-cost charging, which is crucial when considering the TCO for a fleet owner, the installation and operational costs of the charging infrastructure must be optimized, requiring engagement with power-supply stakeholders.

4. Batteries, power electronics, and electric machines

Electrification is a key aspect of modern life, and electric motors and machines are prevalent in manufacturing, consumer electronics, robotics, and EVs (Zhu and Howe 2007 ). One reason for the recent success and rise in adoption of EVs is the use of advanced lithium-ion batteries with improved performance, life, and lower cost. Improved energy and power performance, increased cycle and calendar life, and lower costs are leading to EVs with longer electric range and better acceleration at lower cost premia that are attracting consumers. This section summarizes the state-of-the-art for batteries and for power electronics, electric machines, and electric traction drives in terms of cost, performance, power and energy density, and reliability, and highlights some research challenges, pathways, and targets for the future.

4.1. Batteries

Over the last 10 years, the price of a lithium-ion battery pack has dropped by almost 90% from over $1000 kWh −1 in 2010 to $156 kWh −1 at the end of 2019 (BloombergNEF 2020 ). Meanwhile, the specific energy of a lithium-ion battery cell has almost doubled from 140 Wh kg −1 to 240 Wh kg −1 during that same window of time (BloombergNEF 2020 ). The improvement in performance and cost comes mainly from engineering improvements, use of materials with higher capacities and voltages, and development of methods to increase stability for longer life and improved safety. Improvements in cell, module, and pack design also help to improve performance and lower costs. Increases in manufacturing volume due to EV sales contribute significantly to cost reductions (Nykvist and Nilsson 2015 , Nykvist et al 2019 ). However, further reductions in battery costs, along with a reduction in the cost of electric machines and power electronics, are needed for EVs to achieve purchase-price parity with ICEVs. This parity is estimated by U.S. Department of Energy (DOE) to be achieved at battery costs of ∼$100 kWh −1 (preferably less than $80 kWh −1 ) (VTO, 2020 ). At that point, EVs should have both a purchase- and a lifetime-operating-cost benefit over ICEVs. Such cost benefits are likely to trigger drastic increases in EV sales. Figure 3 shows the observed price of lithium-ion battery packs from 2010 to 2018, as well as estimated prices through 2030. BloombergNEF projects that by 2024 the price for original equipment manufacturers (OEMs) to acquire battery packs will go below $100 kWh −1 and reach ∼$60 kWh −1 by 2030 if high levels of investments continue in the future (BloombergNEF 2020 ).

Figure 3.  Evolution of battery prices over the last 10 years and future projections (Goldie-Scot 2019 ). BloombergNEF 2019.

The typical anode material that is used in most lithium-ion EV batteries is graphite (Ahmed et al 2017 ). Research is underway to utilize silicon, in addition to graphite, due to its higher specific-energy capacity. For cathodes, there is more variety (Lee et al 2019 , Manthiram 2020 ). Consumer electronics such as mobile phones and computers almost exclusively have used lithium cobalt oxide, LiCoO 2 , due to its high specific-energy density (Keyser et al 2017 ). Most EV manufacturers (except Tesla) have avoided using LiCoO 2 in EVs due to its high cost and safety concerns. Lithium iron phosphate also has been used for electric cars and buses because of its long life and better safety and power capabilities. However, due to its low specific-energy density (110 Wh kg −1 ) when paired with a graphite anode, lithium iron phosphate is not used commonly for light-duty EVs in U.S. In recent years, battery makers and vehicle OEMs have moved to lithium nickel manganese cobalt oxides (NMC) with varying ratios of the three transition metals. Initially, OEMs used NMC111 (the numbers represent the molar fractions of nickel, manganese, and cobalt, which are equal in this case), but they have transitioned to NMC532 and utilize NMC622 now while working to stabilize the NMC811 cathode structure. The goal is eventually to reduce the amount of cobalt in the cathode to less than 5% and perhaps even eliminate the use of cobalt. The use of these cathodes with higher specific-energy density and less cobalt leads to lower battery cost per unit energy ($ kWh −1 ). Table 1 shows the specific energy and estimated (bottom-up) cost from Argonne National Laboratory's BatPaC Battery Performance and Cost model (Ahmed et al 2016 ) based on large-volume material processing, cell manufacturing, and pack manufacturing.

Table 1.  Calculated specific energy and cost of advanced lithium-ion batteries with different cathode/anode chemistries. Numbers are from BatPaC (Ahmed et al 2016 ) and are intended for relative comparison only. Final values can change depending on the components used and production volume, and costs reported do not reflect what a negotiated price could be between a battery and EV maker.

The cost of batteries is expected to decline in the future due to improved capacity of materials (such as Si anodes), increased percentage of active material components, use of lower-cost elements (no cobalt), improved packaging, and continued automation to increase yield while leading to a longer electric range. However, price increases for certain metals such as Ni and Li could prevent achieving those lower-battery-cost projections. Moreover, different battery chemistries can lead to very different costs and specific energies. For example, table 1 shows results obtained from bottom-up calculations with Argonne National Laboratory's BatPaC Battery Performance and Cost Model (Ahmed et al 2016 ), for a 100 kWh battery pack showing great variability in battery cost and performance for different chemistries.

Opportunities to improve performance and reduce costs further are being pursued in a number of major research areas. The battery community is investigating a number of materials, with the aim of reducing the cost and increasing the energy density of battery systems (Deign and Pyper 2018 ). Future work will involve utilizing silicon (Salah et al 2019 ) or lithium metal (Zhang et al 2020 ) as the anode while utilizing high-energy cathodes, such as NMC811 or lithium sulfur (Zhu et al 2019 ). Reducing the amount of critical material in lithium-ion batteries, especially cobalt, is an opportunity to lower the cost of batteries and improve supply-chain resilience. The private and public sectors are working toward developing new cathode materials along these lines (Li et al 2009 , 2017b ). Research and development (R&D) projects are underway to develop infrastructure and recycling technologies to collect batteries and recover the key battery materials economically and environmentally (Harper et al 2019 ). Reuse of end-of-life batteries from EVs would delay the need for additional battery materials, which should have positive environmental benefits (Neubauer et al 2012 ). Different battery technologies also are being explored. To increase energy density, reduce cost, and improve safety, the battery community is pursuing development of solid-state batteries with solid-state electrolytes (Randau et al 2020 ) that have ionic conductivities approaching those of today's liquid electrolyte systems. Solid-state lithium batteries enable the use of metallic lithium anodes, together with solid electrolytes and high-energy cathodes (such as high-nickel NMC or sulfur). Lithium-metal batteries based on solid electrolytes can, in principle, alleviate the safety concerns with current lithium-ion batteries with a flammable organic electrolyte. The main challenges facing lithium-metal anodes are dendritic growth, especially at low temperatures and higher current rates. Dendritic growth could lead to short circuit and thermal runaway and low Coulombic efficiency leading to poor cycle life (Xia et al 2019 ). Slow ion transport through the solid-state electrolyte leading to low power densities and manufacturing challenges, including poor mechanical integrity, pose additional challenges. Significant R&D activities are focused on developing solid-state electrolytes that prevent dendrite growth, have high ionic conductivity, good voltage-stability windows, and low impedance at the electrode–electrolyte interface. Recent cathode formulations in Li-S cells overcome the polysulfide problem, which could lead to lower efficiency and cycle life. Nevertheless, the deployment of cells with lower electrolyte-to-sulfur ratios for scale-up to large sizes is a remaining challenge. It may take another 5 to 10 years to mass-produce solid-state lithium batteries for EV applications.

As is discussed in section 5 , a network of fast chargers and batteries that can handle high charging-power rates is needed to address any potential barriers to widespread EV adoption. Research is focusing on developing batteries that can be charged very quickly (e.g. 80% of capacity in less than 15 min). A number of challenges to high-power charging, such as lithium plating, thermal management, and poor cycle life, need to be addressed (Ahmed et al 2017 ; DOE 2017 , Michelbacher et al 2017 ). Significant efforts also have focused on developing electrochemical and thermal modeling of batteries for EV applications (Kim et al 2011 , Chen et al 2016 , Keyser et al 2017 , Zhang et al 2017 ) to improve battery lifetime and efficiency in real-world applications. These efforts include lifetime-estimation and degradation modeling under different real-world climate and driving conditions (Hoke et al 2014 , Neubauer and Wood 2014 , Liu et al 2017b , Harlow et al 2019 , Li et al 2019 ); simplified models for control and diagnostics (e.g. state-of-charge estimation) (Muratori et al 2010 , Fan et al 2013 , Cordoba-Arenas et al 2015 , Bartlett et al 2016 ); and developing effective thermal management and control strategies (Pesaran 2001 , Serrao et al 2011 ).

Besides EV applications, batteries can offer energy-storage solutions for hybrid- or distributed-energy systems. These solutions include the use of batteries in integrated configurations with wind or solar photovoltaic (PV) systems or with EV fast-charging stations (Bernal-Agustín and Dufo-Lopez 2009 , Badwawi et al 2015 , Muratori et al 2019a ). Batteries also can provide stabilization and flexibility and can improve resilience and efficiency for power systems in general, especially for critical services or when a high share of variable power generation (e.g. from solar or wind) is expected (Divya and Østergaard 2009 , Denholm et al 2013 ; De Sisternes et al 2016 ). Lithium-ion batteries that have been developed for EV applications have found their way into stationary applications (Pellow et al 2020 ) because of their lower cost and modularity compared to other energy-storage technologies (Chen et al 2020 ). Moreover, EV batteries can be reused or repurposed at the end of their 'vehicle life' (usually considered when energy storage capacity drops below 70%–80% of the original nominal value, (Podias et al 2018 )) for stationary applications, improving their economic and environmental performance (Assuncao et al 2016 , Ahmadi et al 2017 , Martinez-Laserna et al 2018 , Olsson et al 2018 , Kamath et al 2020 ).

4.2. Power electronics, electric machines, and electric-traction-drive systems

While batteries are playing a key role in the rise of EVs, power electronics and electric motors and machines are also key components of an EV powertrain. Traditionally, the motor and power electronics drive were separate components in an EV. However, recent trends toward integration promise to deliver benefits in terms of increased power density, lower losses, and lower costs compared with separate motor and motor-drive solutions (Reimers et al 2019 ). Figure 4 shows the 2020 power density for power electronics, electric machines, and electric-traction-drive system from some example commercial vehicles as well as the 2025 DOE and U.S. DRIVE Partnership targets for near-term improvements (U.S. DRIVE 2017 , Chowdhury et al 2019 ). Commercially available vehicles exceed the 2020 power-density target. However, the 2025 target is at least a factor of six to eight higher than current commercial baselines. U.S. DRIVE Partnership also proposes electric-traction-drive-cost targets for 2020 and 2025: $8 kW −1 and $6 kW −1 , respectively, both of which are challenging targets (U.S. DRIVE 2017 , Chowdhury et al 2019 ). The authors are not aware of commercial systems meeting the 2020 target, and the 2025 target represents a further 33% reduction.

Figure 4.  Integrated electric-drive system and inverter power density for several commercial light-duty vehicles and DOE targets (data from U.S. DRIVE 2017 , Chowdhury et al 2019 ).

Improvements in compact power electronics and electric machines are applicable to novel emerging wheel-integrated solutions as well (Iizuka and Akatsu 2017 , Fukuda and Akatsu 2019 ). The development of advanced electric traction drive with improved efficiency is a strategy for increasing the range of electric-drive vehicles. In addition to this, chassis light-weighting is another strategy that is being pursued by the industry and the research community for increasing EV driving ranges. There are several technical challenges in meeting the DOE power-density targets (shown in figure 4 ). Challenges in meeting related DOE cost targets remain as well. A range of integration approaches are proposed in the literature, including surface mounting the power electronics on the motor housing (Nakada, Ishikawa, and Oki 2014 ), mounting on the motor stator iron (Wheeler et al 2005 ), and piecewise integration. Piecewise integration involves modularizing both power modules and machine stators into smaller units (Brown et al 2007 ). In all cases, the close physical positioning of the power electronics relative to the machine and the associated harsh thermal environment necessitate new concepts related to the active cooling of both components. A first strategy may be to isolate the power electronics from the machine thermally using parallel cooling mechanisms (Wheeler et al 2005 ). Another approach may be to use a fully integrated, series-connected, active-cooling loop (Tenconi et al 2008 , Gurpinar et al 2018 ). In either case, cost benefits may be realized through the possible elimination or combination of cooling loops. Significant research also has been focused on reducing rare-earth and heavy-rare-earth materials within the electric machines because that is an additional important pathway to reduce costs (U.S. DRIVE 2017 ).

Higher levels of integration go hand-in-hand with the utilization of wide-bandgap (WBG) semiconductor devices, which may be used at higher operational temperatures (e.g. >200 °C versus 150 °C for silicon) with reduced switching loss (Millán et al 2014 ). However, the adoption of WBG devices requires new packaging technologies to support the end goals of high temperature, high frequency, higher voltages, and more compact footprints. High-performance electrical interconnects (Cheng et al 2013 ), die-attach (Liu et al 2020 ), encapsulation (Cao et al 2010 ), and power-module-substrate technologies (Stockmeier et al 2011 ), along with thermal management and reliability of these technologies (Moreno et al 2014 , Paret et al 2016 , 2019 ), are critical aspects to consider. The new materials, devices, and components must be cost-effective and high-temperature-capable to be compatible with WBG devices. The downsizing of passive electrical components is another added benefit of adopting WBG devices and a further necessity for integrated machine-drive packaging solutions. Fortunately, the higher switching frequencies that are supported by WBG devices enable the downsizing of both the inductors and capacitors found in a traditional power-control unit (Hamada et al 2015 ). The development of economically viable and high-temperature-capable passives, capacitors in particular (Caliari et al 2013 ), is an area of great interest.

Besides EV applications, power electronics and electric machines with low cost, high performance, and high reliability are important for numerous energy-efficiency and renewable-energy applications, such as solar inverters, generators and electric drives for wind, grid-tied medium-voltage power electronics, and sensors and electronics for high-temperature geothermal applications (PowerAmerica 2020 ).

5. Charging infrastructure

Infrastructure planning and deploying an ecosystem of cost-effective and convenient public and private chargers is central to supporting EV adoption (CEM 2020 ). The lack of a sufficient refueling infrastructure has hampered many past efforts to promote alternatives to petroleum fuels (McNutt and Rodgers 2004 ). Extensive research is being done to address the diverse challenges that are posed by a transition from fossil-fuelled ICEVs to EVs and the special role of charging infrastructure in this transition (Muratori et al 2020b ).

At the end of 2019, there were an estimated 7.3 million EV chargers (or plugs) worldwide, of which almost 0.9 million were public, including approximately 264 000 public DCFCs (81% in China) (IEA 2020 ). Significant government support and private investments are helping to expand the network of public charging stations worldwide. With about 7.2 million light-duty BEVs on the road, there is about one public charger per 10 light-duty BEVs, and most vehicles have access to a residential charger. However, the number of public chargers per BEV varies widely among the 10 countries with the most BEVs (figure 5 ) because of different strategies for deploying fast versus slow public chargers. In addition to these LDV chargers, IEA estimated there are 184 000 fast chargers dedicated to electric buses (95% in China).

Figure 5.  Public charging availability by country in 2019, measured as Level-1 and Level-2 chargers per BEV and DCFC per 10 BEVs (Data from IEA 2020 ).

Studies show consistently that today's EVs do the majority (50%–80%) of their charging at home, followed by at work (15%–25% when workers use their vehicles to commute), and using public chargers (only about 5% of charging) (Hardman et al 2018 ). PHEVs conduct more charging at home than BEVs do, and they rely more on level-1 charging (Tal et al 2019 ). While single-household detached residences readily can accommodate level-1 or -2 charging, multi-unit dwellings require curbside public charging or installations in shared parking facilities (Hall and Lutsey 2017 ). Historical data on the charging behavior of California BEV owners reveals that 11% of their charging sessions were at level 1, 72% were at level 2, and 17% used DCFCs (Tal et al 2019 ). Use of DCFCs is lowest for BEVs with less than 100 miles (161 km) of range, highest for medium-range BEVs, and lower again for BEVs with ranges of 300 miles (483 km) or more.

5.1. Charging-siting modeling

Public charging infrastructure is clearly important to EV purchasers and supports EV sales by adding value (Narassimhan and Johnson 2018 , Greene et al 2020 ). However, how best to deploy charging infrastructure, in terms of numbers, types, locations, and timing remains an active area for research (Ko et al 2017 , Funke et al 2019 provide reviews). The literature includes many examples of geographically and temporally detailed models to optimize the location, number, and types of charging stations (e.g. Wood et al 2017 , Wu and Sioshansi 2017 , Zhao et al 2019 ). Geographically and temporally detailed data recording the movements of PEVs and their charging behavior are scarce. With few exceptions (e.g. Gnann et al 2018 ), simulation analyses rely on conventional ICEV databases (e.g. Dong et al 2014 , Wood et al 2015 , 2018 ), which do not reflect the changes PEV owners will make to maximize the utility of PEVs.

Given the importance of home charging, access to chargers for on-street parking in residential areas comprising attached or multi-unit dwellings is likely to be essential for PEVs to be adopted at large scale. Grote et al ( 2019 ) employ heuristic methods with geographical-information systems to locate curbside chargers in urban areas using a combination of census and parking data. The works of Nie and Ghamami ( 2013 ), Ghamami et al ( 2016 ), and Wang et al ( 2019 ) are examples of the variety of optimization methods that are applied to design DCFC networks to support intercity travel. Despite these examples, applied research is hindered by the scarcity of data on long-distance vehicle travel by PEVs (Eisenmann and Plötz 2019 ). Jochem et al ( 2019 ) estimate that 314 DCFC stations could provide minimum coverage of EU intercity routes with approximately 0.7 charging points per 1000 BEVs. Using a database of simulated U.S. intercity travel, He et al ( 2019 ) employ a mixed-integer model to optimize the location and number of DCFCs. They conclude that 250 stations could serve 98% of the long-distance miles of BEVs with ranges of 150 miles (241 km) or greater but only 73% of the long-distance miles of 100 mile range (161 km range) BEVs. Similarly, Wood et al ( 2017 ) estimate that 400 DCFC stations are required to cover the U.S. interstate-highway network with a 40 mile (64 km) spacing between stations. Others consider the optimal location of dynamic, wireless charging in combination with stationary charging (Liu and Wang 2017 ).

Optimization models for locating chargers to support commercial PEV fleets also appear in the literature (Jung et al 2014 , Shahraki et al 2015 ). In the future, if vehicle sharing becomes much more common, the downtime for charging could be an important disadvantage for PEVs. Using an integer model to optimize station allocation and PEV assignment, Roni et al ( 2019 ) find that charging time represents 72%–75% of vehicle downtime but that charging time could be reduced by almost 50% by optimal deployment of charging stations.

5.2. Beyond LDV charging

The electrification of medium- and heavy-duty commercial trucks and buses introduces unique charging and infrastructure requirements compared to those of LDVs. These requirements stem from the significantly higher battery capacities required on-board the vehicles, potentially shorter charging-dwell times (due to the in-service time requirements of the vehicles), and the potential of large facility charging loads (due to multiple trucks or buses charging in one location). One challenge is to understand the costs associated with the multitude of charging scenarios for commercial vehicles for current operations as well as future operations. It is expected that on-road freight vehicle miles (or km) traveled will increase by 75% from 2012 to 2045 (McCall and Phadke 2019 ). This increase may bring about new business models and potentially new charging-infrastructure approaches to meet this demand with electrified trucks. California's Innovative Clean Transit regulation, which will require California transit agencies to adopt zero-emission buses by 2040, is likely to drive large charging-infrastructure investments for buses (CARB 2018 ).

Today's commercial diesel-powered trucks in small fleets typically are fueled at publicly available on-road fueling stations, while nearly half of trucks in fleets of 10+ vehicles use company-owned facilities (Davis and Boundy 2020 ). Likewise, commercial EVs are charged primarily in fleet-owned facilities as their daily schedule allows (most often overnight). This depot-charging approach, which enables seamless integration of EVs into fleet logistics, might limit the electrification of some vehicle segments in the long term due to the battery capacity that is needed to satisfy their daily-range requirements (the need to complete their full-day function) and return to the facility to recharge fully 14 . Some studies suggest that long-haul battery-electric trucks are technically feasible and economically compelling (Phadke et al 2019 ) while others are more skeptical (Held et al 2018 ). Publicly available, high-power charging or en-route charging infrastructure for commercial vehicles could enable electrification for longer-distance vehicles (by enabling smaller on-board battery-capacity needs), but this scenario has cost challenges. En-route, high-power charging of over 1 MW might be needed to enable 500 miles (805 km) or more of daily driving. Installation of a 20 MW truck-charging station in California (capable of multiple 1.5 MW charge events for heavy-duty freight vehicles) is estimated to cost as much as 15 million USD. McCall and Phadke ( 2019 ) estimate that as many as 750 of these stations are needed to electrify the fleet of California Class-8 combination trucks. Charging commercial vehicles at depots requires additional infrastructure costs to install lower-power EV-supply equipment networks (e.g. 50 kW–100 kW) capable of charging multiple vehicles at these lower rates. These depot charging systems also will challenge existing facility electrical systems by adding a significant load that was not planned previously at the facility (Borlaug et al Forthcoming ).

5.3. Economics of public charging

PEV-charging economics vary with location and station configuration and depend critically on equipment and installation costs and retail electricity prices, which are dependent on utilization (Muratori et al 2019b , Borlaug et al 2020 ). In the early stages of market development, when there are relatively few vehicles, future demand is uncertain, and most charging is done at an EV's home base (Nigro and Frades 2015 , Madina et al 2016 ). Public charging stations tend to be lightly used during these initial stages (e.g. INL 2015 ), which poses a difficult challenge for private investment. Understanding and quantifying the value of public charging is hindered by lack of experience with PEVs on the part of consumers (Ito et al 2013 , Greene et al 2020 , Miele et al 2020 ) and the complexity of network effects in the evolution of alternative-fuel-vehicle markets (Li et al 2017a ). Nevertheless, it is likely that DCFCs will be profitable with sufficient demand. Considering vehicle ranges of between 100 km and 300 km and charging-power levels of between 50 kW and 150 kW, Gnann et al ( 2018 ) conclude that charger-usage fees could be between 0.05 € kWh −1 and 0.15 € kWh −1 in addition to the cost of electricity. The estimates were based on simulations with average daily occupancy of charging points of 10%–25% and peak-hour utilization of 20%–70%. In their simulations, utilization rates increase with increasing charger power and decrease with increased EV range. For intercity travel along European Union highways, Jochem et al ( 2019 ) estimate that a surcharge of 0.05 € kWh −1 of DCFC would make a minimal coverage of 314 stations (with 20 charge plugs each) profitable, even for station capital costs of one million EUR. He et al ( 2019 ) optimize DCFC locations along U.S. intercity routes and conclude that providing an adequate nationwide charging network for long-distance travel by 100 mile (161 km) range BEVs is more economical than increasing vehicle range and reducing the number of charging stations. Muratori et al ( 2019a ) consider a set of charging scenarios from real-world data and thousands of U.S. electricity retail rates. They conclude that batteries can be highly effective at mitigating electricity costs associated with demand charges and low station utilization, thereby reducing overall DCFC costs.

Early estimates show that the cost of public DCFC in U.S. can vary widely based on the station characteristics and level of use (Muratori et al 2019a ). Numerous new technology options are being explored to provide lower-cost electricity for light-duty passenger and medium- and heavy-duty commercial BEVs. Increasing the range of EVs through higher-power public charging stations as well as accommodating new potential BEV business models, such as transportation-network companies or automated vehicles, are driving new charging-technology solutions. Managed charging solutions that are available today can provide increased value to the BEV owner (lower electricity costs), charging station owner (lower operating costs), or grid operator (lower infrastructure-investment costs). For example, a managed-charging solution has been adopted and is currently in operation at a Santa Clara Valley Transportation Authority depot to charge a fleet of Proterra electric buses optimally to ensure minimal stress on the grid (Ross 2018 ).

5.4. Emerging charging technologies

Wireless charging, specifically high-power wireless charging (beyond level-2 power levels), could play a key role in providing an automated charging solution for tomorrow's automated vehicles (Lukic and Pantic 2013 , Qiu et al 2013 , Miller et al 2015 , Feng et al 2020 ). Wireless charging also can enable significant electric range for BEVs by providing en-route opportunity charging (static or dynamic charging opportunities). If a network of wireless charging options is available to provide convenient and fast en-route charging, it could help reduce the amount of battery that is needed on-board a vehicle and reduce the cost of ownership for a BEV owner. Wireless charging is being developed for power levels of up to 300 kW for LDVs, 500 kW for medium-duty vehicles, and 1000 kW for heavy-duty vehicles. Bidirectional functionality, improved efficiency, interoperability of different systems, improved cybersecurity, and increased human-safety factors continue to be developed (Ozpineci et al 2019 ).

Connectivity and communication advances will enable new BEV-charging infrastructure and managed charging solutions. However, emerging cybersecurity threats also are being identified and should be addressed. There are concerns associated with data exchange, communications network, infrastructure, and firmware/software elements of the EV infrastructure (Chaudhry and Bohn 2012 ), and new charging-system security requirements and protocols are being developed to address these concerns (ElaadNL 2017 ). New emulation and simulation platforms also are being developed to address these threats and help understand the consequences and value of mitigating cyberattacks that could affect BEVs, electric-vehicle-supply equipment, or the electric grid (Sanghvi et al 2020 ).

6. Vehicle-grid integration (VGI)

Connecting millions of EVs to the power system, as may occur in the coming decades in major cities, regions, and countries around the world, introduces two fundamental themes: (a) challenges to meet reliably overall energy and power requirements, considering temporal load variations, and (b) VGI opportunities that leverage flexible vehicle charging ('smart charging') or V2G services to provide power-system services from connected vehicles. Multiple studies, which are reviewed in detail below, investigate the potential load growth, impact on load shapes, and infrastructure implications of increased EV adoption. These works focus especially on impact on distribution systems and opportunities for flexible charging to reshape aggregate power loads. Mai et al ( 2018 ), for example, shows that in a high-electrification scenario, transportation might grow from the current 0.2% to 23% of total U.S. electricity demand by 2050. This growth would impact system peak load and related capacity costs significantly if not controlled properly. In-depth analytics indicate a complex decision framework that requires critical understanding of potential future mobility demands and business models (e.g. ride-hailing, vehicle sharing, and mobility as a service), technology evolution, electricity-market and retail-tariff design, infrastructure planning (including charging), and policy and regulatory design (Codani et al 2016 , Eid et al 2016 , Knezovic et al 2017 , Borne et al 2018 , Hoarau and Perez 2019 , Gomes et al 2020 , Muratori and Mai 2020 , Thompson and Perez 2020 ).

While accommodating EV charging at the bulk-power (generation and transmission) level will be different in each region, no major technical challenges or risks have been identified to support a growing EV fleet, especially in the near term (FleetCarma 2019 , U.S. DRIVE 2019 , Doluweera et al 2020 ). At the same time, many studies show that smart charging and V2G create opportunities to reduce system costs and facilitate VRE integration (Sioshansi and Denholm 2010 , Weiller and Sioshansi 2014 , IRENA 2019 , Zhang et al 2019 ). Therefore, charging infrastructure that enables smart charging (e.g. widespread residential and workplace charging) and alignment with VRE generation and business models and programs to compensate EV owners for providing charging flexibility are critical elements for successful integration of EVs with bulk power systems.

6.1. Impact of EV loads on distribution systems

At the local level, EV charging can increase and change electricity loads significantly, having possible negative impacts on distribution networks (e.g. cables and distribution transformers) and power quality or reliability (Khalid et al 2019 ). Residential EV charging represents a significant increase in household electricity consumption that can require upgrades of the household electrical system which, unless managed properly, may exceed the maximum power that can be supported by distribution systems, especially for legacy infrastructure and during times of high electricity utilization (e.g. peak hours and extreme days) (IEA 2018b ). The impact of EVs on distribution systems also is influenced by the simultaneous adoption of other distributed energy resources, e.g. rooftop PV panels. While this interdependency complicates assessing the impact of EV charging, Fachrizal et al ( 2020 ) show that the two technologies support one other. Similarly, Vopava et al ( 2020 ) show that line overloads caused by rooftop PV panels can be reduced (but not avoided) by increasing EV adoption and vice versa.

The impact of EV charging on distribution systems is particularly critical for high-power charging and in cases in which many EVs are concentrated in specific locations, such as clusters of residential LDV charging and possibly fleet depots for commercial vehicles (Saarenpää et al 2013 , Liu et al 2017a , Muratori 2018 ). Smart charging, by which EV charging is timed based on signals from the grid and electricity prices that vary over time, or other forms of control, can help to minimize the impact of EV charging on distribution networks. However, smart charging requires both appropriate business models and signals (with related communication and distributed-control challenges). The market for distribution-system operators to provide such services is not mature yet (Everoze 2018 , Crozier, Morstyn, and McCulloch 2020 ). Time-varying pricing schemes, which are effective at influencing the timing of EV charging (PG&E 2017 ), typically do not include any distribution-level considerations. Thus, while consumers are responsive to such signals, the business models to include distribution-level metrics still are lacking. Moreover, price signals are offered usually to a large consumer base with the intent of reshaping the overall system load. At the local level, however, multiple consumers responding to the same signal might cause 'rebound peaks' (Li et al 2012 , Muratori and Rizzoni 2016 ) that can overstress distribution systems, calling for coordination among consumers connected to the same distribution network (e.g. direct EV-charging control from an intermediate aggregator).

Charging of larger commercial vehicles and highway fast-charging stations typically involves higher power levels: DCFC is typically at 50 kW/plug today, but power levels are increasing rapidly. Commercial charging locations with multiple plugs co-located at a specific location may lead to possible MW-level loads, which is roughly equivalent to the peak load of a large hotel. Commercial DCFC may require costly upgrades to distribution systems that can impact the cost-effectiveness of public fast charging heavily, especially if stations experience low utilization (Garrett and Nelder 2016 , Muratori et al 2019b ). While charging timing and speed at commercial stations is less flexible (consumers want to charge and leave or commercial fleets must meet business requirements), business models are often already in place to incentivize curbing maximum peak power from commercial installations. For example, demand charges (a fixed monthly payment that is proportional to the peak power that is drawn during a given month) are fairly common in U.S. retail tariffs and provide a reason to limit peak power. Furthermore, Muratori et al ( 2019a ) show that distributed batteries can be effective at mitigating the cost associated with demand charges by up to 50%, especially for 'peaky' or low-utilization EV-charging loads. Batteries also can facilitate coupling EV-charging stations with local solar electricity production or can provide grid services (Megel, Mathieu, and Andersson 2015 ), generating additional revenue.

6.2. Value of managed ('smart') EV charging for power systems

The integration of EVs into power systems presents several opportunities for synergistic improvement of the efficiency and economics of electromobility and electric power systems. These synergies stem from two inherent characteristics of EVs and power systems. Demand response and other forms of demand-side flexibility can be of value for power-system planning and operations (Albadi and El-Saadany 2007 , 2008 , Su and Kirschen 2009 , Muratori et al 2014 ). Contemporaneously, most personal-vehicle driving patterns entail vehicle-use for mobility purposes a relatively small proportion of the time (Kempton and Letendre 1997 ). If EVs are grid-connected for extended periods of time, they can provide demand-side flexibility in the form of smart charging or V2G services. Such use of an EV can improve its economics by leveraging cheaper electricity at little incremental cost (e.g. the costs of monitoring, communication, and control equipment that are needed to manage smart charging). EVs can support grid planning and operations in a number of ways. Figure 6 summarizes the key support services that EVs can provide. These services include reducing peak load and generation-, transmission-, and distribution-capacity requirements, deferring system upgrades, providing load response, supporting power-system dispatch (including VRE integration and real-time energy and operating reserves), providing energy arbitrage, and supporting power quality and end retail consumers.

Figure 6.  Summary of opportunities for EVs to provide demand-side flexibility to support power system planning and operations across multiple timescales.

Habib et al ( 2015 ) and Thompson and Perez ( 2020 ) provide detailed surveys of different potential uses of EVs for smart charging and V2G services. This includes active- and reactive-power services, load balancing, power-quality-related services (e.g. managing flicker and harmonics), retail-bill management, resource adequacy, and network deferral. In addition, Habib et al ( 2015 ) discuss different standards and technology needs relating to V2G services.

Kempton and Letendre ( 1997 ) provide the first description of the concept of EVs providing grid services, either in the form of smart charging or bidirectional V2G services (which can involve discharging EV batteries). Denholm and Short ( 2006 ) study the benefits of controlled overnight charging of PHEVs for valley-filling purposes. They demonstrate that with proper control of vehicle charging, up to 50% of the vehicle fleet could be electrified without needing new generation capacity to be built and at substantial savings compared to using liquid fuels for transportation. They show also that under conservative utility-planning practices, PHEVs could replace a significant portion of low-capacity-factor generating capacity by providing peaking V2G services. Tomić and Kempton ( 2007 ) examine the economics of using EVs for the provision of frequency reserves and demonstrate that such services can yield substantive revenues to vehicle owners in a variety of wholesale markets. Thompson and Perez ( 2020 ) conduct a meta-analysis of V2G services and value streams and find that power-focused services are of greater value than energy-focused services. They distinguish the two types of services based on the extent to which EV batteries must be discharged and degraded. Sioshansi and Denholm ( 2010 ) come to a similar conclusion in comparing the value of using PHEV batteries for energy arbitrage and operating reserves.

Another important synergy between EVs and power systems is using the flexibility of EV charging to manage the integration of VRE into power systems (Mwasilu et al 2014 , Weiller and Sioshansi 2014 ). Hoarau and Perez ( 2018 ) develop a framework for examining the synergies between EV charging and the integration of photovoltaic-solar resources into power systems. They find that the spatial footprint across which solar resources and EVs are deployed and the regulatory, policy, and market barriers to cooperation between solar resources and EVs are critical to realizing these synergies. Szinai et al ( 2020 ) find that controlled EV charging in California under its 2025 renewable-portfolio standards can reduce operational costs and renewable curtailment compared to unmanaged charging. They find that properly designed time-of-use retail tariffs can achieve some, but not all, of the benefits of controlled EV charging. They show also that these two approaches to managing EV charging (controlling EV charging directly and time-of-use tariffs) reduce the cost of infrastructure that is necessary to accommodate EV charging relative to a case of uncontrolled EV charging. Chandrashekar et al ( 2017 ) conduct an analysis of the Texas power system and find similar benefits of controlled EV charging in reducing wind-integration costs. Coignard et al ( 2018 ) show that under California's 2020 renewable-portfolio standards, controlled EV charging can deliver the same renewable-integration benefits that California's energy-storage mandate does but at substantially lower costs. They show that bidirectional V2G services deliver up to triple the value of controlled EV charging. Kempton and Tomić ( 2005 ) show that high penetrations of wind energy in U.S. could be accommodated at relatively low costs if 3% of the vehicle fleet provides frequency reserves and 8%–38% of the fleet provides operating reserves and energy-storage services to avoid wind curtailment. Loisel et al ( 2014 ) and Zhang et al ( 2019 ) conduct more forward-looking analyses of the synergies between EVs and renewables. The former examines German systems, and the latter examines California systems under potential renewable-deployment scenarios in the year 2030.

An important assumption underlying these works is that EV owners (or aggregations of EVs) are exposed to prices that signal the value of these services and that there are regulatory and business models that allow such services to be exploited (i.e. consumer are willing to engage in these programs and are compensated properly for providing flexible charging). Several pilot studies suggest that EV owners have interest in participating in utility-run controlled-charging programs and that a set of different compensation strategies beyond time-varying electricity pricing might maximize engagement (Geske and Schumann 2018 , Hanvey 2019 , Küfeoğlu et al 2019 , Delmonte et al 2020 ).

Niesten and Alkemade ( 2016 ) survey the literature on these topics and numerous European and U.S. pilot programs in terms of value generation for V2G services. They find that the ability of an aggregator to scale is related to its ability to develop a financially viable business model for V2G services. Another important consideration is the availability of control and communication technologies to manage EV charging based on power-system conditions. Key considerations in the design of control strategies are robustness in the face of uncertainty (e.g. renewable availability, EV-arrival times and charge levels upon arrivals, and EV-departure times), data privacy, and robustness to communication or other failures. Le Floch et al ( 2015a ), Le Floch et al ( 2015b ), ( 2016 ), develop a variety of distributed and partial-differential-equation-based algorithms for controlling EV charging. Rotering and Ilic ( 2011 ) develop a dynamic-optimization-based approach to control EV charging and bidirectional V2G services (with a focus on the provision of ancillary services). Donadee and Ilic ( 2014 ) develop a Markov decision process to optimize the offering behavior of EVs that participate in wholesale electricity markets to provide frequency reserves.

6.3. Remaining challenges for effective vehicle-grid integration at scale

There are still many challenges to tackle before smart charging and V2G can be deployed effectively at large scale. These challenges are linked to the technical aspects of VGI technology but also to societal, economic, security, resilience, and regulatory questions (Noel et al 2019a ). With regard to the technical challenges of VGI, existing barriers notably include battery degradation, charger availability and efficiency, communication standards, cybersecurity, and aggregation issues (Eiza and Ni 2017 , Sovacool et al 2017 , Noel et al 2019a ).

While the technical aspects of VGI are studied widely, this is much less the case for its key societal aspects. Societal issues include the environmental performance of VGI, its impact on natural resources, consumer acceptance and awareness, financing and business models, and social justice and equity (Sovacool et al 2018 ). There are also various regulatory and political challenges linked to clarifying the regulatory frameworks applicable to VGI as well as market-design issues, such as the proper valuation of VGI services and double taxation (Noel et al 2019a ) and the trade-offs between bulk power and distribution-system needs. Regulatory changes may be required to enable distribution-network operators and EV owners (or aggregators) to take a more active role in electricity markets. The Parker project, an experimental project on balancing services from an EV fleet, underlines some of the barriers to providing ancillary services, such as metering requirements (Andersen et al 2019 ). It is argued that insufficient regulatory action might keep us from attaining the full economic and environmental benefits of V2G (Thompson and Perez 2020 ) and that regulations are lagging behind technological developments (Freitas Gomes et al 2020 ). The lack of defined business models is seen by many experts as a key impediment (Noel et al 2019b ).

Major challenges that are linked to data-related aspects of VGI, including who has the right to access data from EVs (e.g. the state of EV batteries and charging) and how these data can be exploited, remain. Privacy concerns are one of the major obstacles to user acceptance (as is fear of loss of control over charging) (Bailey and Axsen 2015 ). In addition, there are also questions linked to cybersecurity (Noel et al 2019a ).

Nevertheless, VGI offers many opportunities that justify the efforts required to overcome these challenges. In addition to its services to the power system, VGI offers interesting perspectives for the full exploitation of synergies between EVs and renewable energy sources as both technologies promise large-scale deployment in the future (Kempton and Tomić 2005 , Lund and Kempton 2008 ). Exploiting EV batteries for VGI also is appealing from a life-cycle perspective, as the manufacturing of EV batteries has a non-trivial environmental footprint (Hall and Lutsey 2018 ). However, there are a few future developments that might compromise the potential of VGI, most notably cheaper batteries (including second-life EV batteries) that might compete with EVs for many potential services (Noel et al 2019b ). In addition, the impacts of new mobility business models, such as the rise of vehicle- and ride-sharing, on grid services remain unclear. Although smart charging will come first in the path toward grid integration, V2G services have the potential to provide additional value (Thingvad et al 2016 ).

7. Life-cycle cost and emissions

EVs differ from conventional ICEVs on an emissions basis. While the operation of gasoline- or diesel-powered ICEVs produces GHG and pollutant emissions that are discharged from the vehicle tailpipe, EVs have no tailpipe emissions. In a broader context, EVs still can be associated with so-called 'upstream' emissions from the processes that generate, transmit, and distribute the electricity that is used for their charging. Fueling an ICEV also involves upstream 'fuel-cycle' emissions from the raw-material extraction and transportation, refining, and final-product-delivery processes that make gasoline or diesel fuel available at a retail pump. These fuel-cycle emissions give rise to the colloquial jargon 'well-to-pump' emissions. Accordingly, a 'well-to-wheels' (WTW) life-cycle analysis (LCA) is an appropriate framework for comparing EV and ICEV emissions. WTW considers both upstream emissions from the fuel cycle ('well-to-pump') and direct emissions from vehicle operation ('pump-to-wheels') for a standardized functional unit and temporal period. WTW studies have a history of over three decades of use to evaluate direct and indirect emissions related to fuel production and vehicle operations (Wang 1996 ). WTW emissions are expressed typically on a per-mile or per-kilometer basis over a vehicle's assumed lifetime.

WTW analyses typically focus only on fuel production and vehicle operation. Some studies consider broader system boundaries that include vehicle production and decommissioning (i.e. recycling and scrappage) in an LCA framework. This broader system boundary considers what is commonly called the 'vehicle cycle' and provides a so-called 'cradle-to-grave' (or 'C2G') analysis. Vehicle-cycle emissions typically account for 5%–20% of today's ICEV C2G emissions and can be as low as 15% or as high as 80% of today's BEV emissions, depending on the underlying electricity-generation mix. Lower-carbon mixes result in vehicle-cycle emissions accounting for a greater portion of total emissions. As an extreme illustrative example, the case of zero-carbon electricity implies that vehicle-cycle emissions account for 100% of C2G emissions. In general, BEV vehicle-cycle emissions are 25% to 100% higher than their ICEV counterpart (Samaras and Meisterling 2008 , Ambrose and Kendall 2016 , Elgowainy et al 2016 , Hall and Lutsey 2018 , Ricardo 2020 ). As this section explores, higher initial BEV vehicle-cycle emissions almost always are counterbalanced by lower emissions during vehicle operation (with notable exceptions in cases in which BEVs are charged from especially high-emissions electricity).

Even including upstream emissions, EVs are championed as a critical technology for decarbonizing transportation (in line with anticipated widespread grid decarbonization). National Research Council ( 2013 ) identifies EVs as one of several technologies that could put U.S. on a path to reducing transportation-sector GHG emissions to 80% below 2005 levels in 2050. Furthermore, National Research Council ( 2013 ) estimates that BEVs would reduce emissions by 53%–72% compared to ICEVs in 2030. IEA ( 2019 ) contends, similarly, that EVs can reduce WTW GHG emissions by half versus equivalent ICEVs in 2030. Recently published literature also agrees, even on a C2G basis, estimating that future EV pathways offer 70%–90% lower GHG emissions compared to today's ICEVs (Elgowainy et al 2018 ). As such, the broad view across national, international, and academic-research perspectives is that EVs offer the potential to reduce transportation-related GHG emissions by 53% to 90% in the future.

Several studies find that EVs already reduce WTW GHG emissions today by as little as 10% or as much as 41% on average versus comparable ICEVs based on current electricity-production mixes. Samaras and Meisterling ( 2008 ), who are among the first to relate a range of potential electricity carbon intensities to associated EV-lifecycle emissions explicitly, estimate a 38%–41% GHG emissions benefit for EVs powered by the average 2008 U.S. grid. Hawkins et al ( 2012a ), informed by a meta-study of 51 previous LCAs, highlight great variations based on different electricity generation assumptions and vehicle lifetime. Hawkins et al ( 2012b ) estimate a decline of 10%–24% global warming potential (a measure proportional to GHG emissions) for EVs powered by the average 2012 European electricity mix. Elgowainy et al 2016 , 2018 ) estimate that EVs emit 20%–35% fewer GHG emissions when operating on the average 2014 U.S. grid mix.

Many factors contribute to variability in EV WTW emissions and estimated reduction opportunities compared to ICEVs—electricity-carbon intensity, charging patterns, vehicle characteristics, and even local climate (Noshadravan et al 2015 , Requia et al 2018 ). To illustrate these variabilities, figure 7 compares WTW GHG emissions of EVs versus comparable ICEVs. Relative emissions reductions are generally larger for larger vehicles. Woo et al ( 2017 ) find that electrifying SUVs reduces emissions more than electrifying sub-compact vehicles on a WTW basis versus comparable ICEVs (30%–45% and 10%–20%, respectively, assuming median national grid mixes). Ellingsen et al ( 2016 ) find that large EVs emit proportionally less than small EVs compared to comparable ICEVs on a C2G basis (27% and 19%, respectively).

Figure 7.  WTW GHG emissions for EVs versus comparable ICEVs on average and with illustrative variability by market segment, electricity generation pathway, grid mix, and ambient temperature.

Low-carbon electricity can lead to greater reductions in EV emissions. Electricity that is produced from coal, which has a high carbon intensity, can increase EV emissions by as much as 40% or decrease EV emissions by as much as 5% compared to an ICEV (depending on other assumptions). Conversely, electricity from hydropower, nuclear, solar, or wind, all of which offer near-zero carbon intensities, can decrease EV emissions by more than 95% compared to an ICEV (Woo et al 2017 ). Such variability in electricity-generation pathways affects the relative benefits of real-world grid mixes. For example, while EVs offer 30%–65% lower emissions versus comparable ICEVs on average in Europe (Woo et al 2017 , Moro and Lonza 2018 ), in individual countries relative emissions can range from as much as 95% lower to 60% higher (Orsi et al 2016 , Moro and Lonza 2018 ). Typically, U.S. EVs provide emissions reductions, but in some regions EV emissions are higher compared to an efficient ICEV (Reichmuth 2020 ). Changes in regional climate and daily weather add further variability: EV emissions can vary between 40% and 50% lower than a comparable ICEV even when charged from the same grid mix (Yuksel et al 2016 ). While outside the scope of a typical WTW comparison, the additional consideration of refueling infrastructure (i.e. gasoline stations for ICEVs and recharging equipment for EVs) is estimated to increase EV emissions by 4%–8% compared to a more modest 0.3%–0.7% increase for ICEV emissions (Lucas et al 2012 ).

When assessing EV emissions, average or marginal grid-emission factors are considered (Anair and Mahmassani 2012 , Traut et al 2013 , EPRI 2015 , Nealer and Hendrickson 2015 , Nealer et al 2015 , Elgowainy et al 2018 ), leading to significantly different results. Average emissions factors consider all electricity loads as equivalent, while marginal emission factors consider EVs as an additional load on top of existing electricity demands and estimate the associated incremental generation emissions. Marginal emissions could be higher or lower than average, depending on the relative emissions of marginal plants compared to the average in different regions. Different questions lead to using average or marginal metrics. Proper assessment of indirect EV emissions associated with electricity generation is complicated by numerous factors, including timescale (short or long term, aggregate or temporally explicit), system boundaries, impact of EV loads on power-system-expansion and -operation decisions, and non-trivial supply-demand synergies and allocation complexities. Yang ( 2013 ) reviews different grid-emissions-allocation methods concluding that there is no ideal approach to the allocation of emissions to specific end-use and stressing how different assumptions make it difficult to determine EV emissions and compare them to other alternatives and across studies. Nealer and Hendrickson ( 2015 ) discuss whether it is more appropriate to use marginal or average grid-emission factors to estimate EV emissions, concluding that 'average emissions may be the most accessible for long-term comparisons given the assumptions that must be made about the future of the electricity grid.'

Just as EVs offer typically a WTW-emissions reduction compared to ICEVs while shifting those emissions from the tailpipe to upstream, EVs shift costs as well. Operational (fuel and maintenance) costs of EVs are typically lower than those of ICEVs, largely because EVs are more efficient than ICEVs and have fewer moving parts. While data are still scarce, a recent Consumer Reports study estimates that maintenance and repair costs for EVs are about half over the life of the vehicle and that a typical EV owner who does most fueling at home can expect to save an average of $800 to $1000 a year on fuel costs over an equivalent ICEV (Harto 2020 ). Insideevs ( 2018 ) estimates a saving of 23% in servicing costs over the first 3 years and 60 000 miles (96 561 km). Borlaug et al ( 2020 ) estimate fuel savings between $3000 and $10 500 compared with gasoline vehicles (over a 15 year time horizon). However, vehicle capital costs for EVs are higher (principally due to the relatively high cost of EV batteries). In general, studies use a TCO metric to combine and compare initial capital costs with operational costs over a vehicle's lifetime. While some studies find that EVs are typically cost-competitive with ICEVs (Weldon et al 2018 ), others find that EVs are still more costly, even on a TCO basis (Breetz and Salon 2018 , Elgowainy et al 2018 ), or that the relative cost depends on other contextual factors, such as vehicle lifetime and use, economic assumptions, and projected fuel prices. Longer travel distance and smaller vehicle sizes favor relatively lower EV TCO (Wu et al 2015 ), as do lower relative electricity-versus-gasoline price differentials (Lévay et al 2017 ). Despite these differences regarding TCO conclusions across studies, there is general agreement that future EV costs will decline (Dumortier et al 2015 , Wu et al 2015 , Elgowainy et al 2018 ).

The existing literature suggests future EV emissions will decline, in large part due to expectations for continued grid decarbonization (Elgowainy et al 2016 , 2018 , Woo et al 2017 , Cox et al 2018 ). For example, Ambrose et al ( 2020 ) anticipate that evolution in vehicle types and designs could accelerate future decreases for EV GHG emissions. Several studies also posit repurposing used EV batteries for stationary applications could accrue additional GHG benefits (Ahmadi et al 2014 , 2017 , Olsson et al 2018 , Kamath et al 2020 ). Cox et al ( 2018 ) suggest future connectivity and automation technologies will enable energy-optimized EV-recharging behavior and associated lower carbon emissions. Similarly, future EV costs also are expected to decline as battery costs continue to decline (cf section 4 ), and new mobility modes such as ride-hailing lead to higher vehicle use that favors the business case for highly efficient EVs compared to ICEVs.

8. Synergies with other technologies, macro trends, and future expectations

Vehicle electrification fits within broader electrification trends, including power-system decarbonization and mobility changes. The latter include micro-mobility in urban areas, new mobility business models revolving around 'shared' services as opposed to vehicle ownership (e.g. ride-hailing and car-sharing), ride pooling, and automation. These trends are driven partially by the larger availability of efficient and cost-effective electrified technologies (Mai et al 2018 ) and the prospect of abundant and affordable renewable electricity and by other technological and behavioral changes (e.g. real-time communication). Abundant and affordable renewable electricity is a conditio sine qua non for EVs to provide a pathway to decarbonize road transportation. Direct use of PV on-board vehicles (i.e. PV-powered or solar vehicles) also is being considered. However, this concept still faces many challenges (Rizzo 2010 , Aghaei et al 2020 ). Yamaguchi et al ( 2020 ) show potential synergies for integration but also highlight that for this technology to be successful, the development of high-efficiency (>30%), low‐cost, and flexible PV modules is essential.

Urban micro-mobility is emerging recently as an alternative to traditional mobility modes providing consumers in most metropolitan areas worldwide with convenient options for last-mile transportation (Clewlow 2019 , Zarif et al 2019 , Tuncer and Brown 2020 ). Virtually all micro-mobility solutions use all-electric powertrains. Shared electric scooters and bikes (often dockless), e.g. those pioneered by Lime and Bird in the U.S., are experiencing rapid success and are 'the fastest-ever U.S. companies to reach billion-USD valuations, with each achieving this milestone within a year of inception' (Ajao 2019 ). Future expectations for micro-mobility remain uncertain due to issues related to sidewalk congestion, safety, and vandalism (heavily impacting the business case for these technologies). However, the nexus with EVs has not been questioned.

Similarly, ride-hailing—matching drivers with passengers at short notice for one-off rides through a smartphone application, which date back to Uber's introducing the concept in 2009—is an attractive alternative to traditional transportation solutions. These mobility-as-a-service solutions cater to the consumer's need for quick, convenient, and cost-effective transportation and may lead to drops in car-ownership and driver-licensure rates (Garikapati et al 2016 , Clewlow and Mishra 2017 , Movmi 2018 , Walmsley 2018 , Henao and Marshall 2019 , Arevalo 2020 ). After just over 10 years, ride-hailing is widely available and extremely successful, with hundreds of millions of consumers worldwide and 36% of U.S. consumers having used ride-hailing services (Mazareanu 2019 ). While most ride-hailing vehicles today are ICEVs (in line with the existing LDV stock), many ride-hailing companies are exploring electrification opportunities (Slowik et al 2019 ). EVs offer a number of potential advantages as high vehicle usage promotes a more favorable business model for recovering the higher EV purchase price by leveraging cheaper fuel costs (Borlaug et al 2020 ). At the same time, long-range vehicles and effective charging solutions are required for ride-hailing companies to transition to EVs (Tu et al 2019 ). Moreover, EVs can mitigate additional fuel use and emissions related to increased travel, mostly due to deadheading, which is estimated to be ∼85% (Henao and Marshall 2019 ). EVs also provide access to restricted areas in some cities (driving some regional goals for ride-hailing electrification). For example, Uber aims for half of its London fleet to be electric by 2021 and 100% electric by 2025 (Slowik et al 2019 ).

Automation trends are also poised to have the potential to disrupt transportation as we know it. The combination of electric and connected automated vehicles (CAVs) is hypothesized to offer natural synergies, including easier integration with CAV sensors and a greater affinity for cheaper fuels aligning with greater travel (Sperling 2018 ). The chief counterargument relates to high power requirements for a heavily instrumented CAV, which would deplete EV batteries quickly and may be accommodated better with PHEV powertrains. Wireless EV charging, both stationary and dynamic, increases the potential synergies enabling autonomous recharging. Also, CAVs may be required to maximize the efficiency of dynamic wireless charging. In fact, without the alignment accuracy enabled by CAVs, in-road dynamic charging may have limited efficacy. The literature on these synergies is relatively sparse, though some studies are beginning to investigate the implications of combining EV and CAV technologies.

Even though the technology is not widely available commercially, several studies are beginning to examine how consumer preferences may be influenced by the combination of connected, automated, and electric vehicles 15 . Thiel et al ( 2020 ), for example, highlight how full EV success may emerge as automated shared vehicles become predominant in a world where the border between public and private transport will cease to exist. Tsouros and Polydoropoulou ( 2020 ) develop a survey combining traditional attributes (e.g. car type and vehicle style) alongside future technology attributes (e.g. fuel type and degree of automation) and estimate preferences using a latent-class structural-regression approach. They find a specific class of consumers, described as technology-savvy, who have a high proclivity for both alternative-fuel vehicle technologies and higher degrees of automation. While the proportion of the population that can be classified as technology-savvy is unclear, Tsouros and Polydoropoulou ( 2020 ) provide early compelling evidence that consumers see explicit value in the combination of EVs and automation. Hardman et al ( 2019 ) provide a complementary perspective of early adopters of automated vehicles based on a survey of existing U.S. EV owners. Similar to the work of Tsouros and Polydoropoulou ( 2020 ), Hardman et al ( 2019 ) find that the type of consumers who would pursue automated vehicles have similar lifestyles, attitudes, and socio-demographic profiles as EV adopters. These include high-income consumers, with high levels of knowledge about technology features, who have positive perceptions of CAV attributes and technology in general, provided that safety concerns are resolved.

Another benefit of the combined technologies is the potential to integrate charging events better with the needs of the electricity grid. Several studies assess the combination of these technologies with new mobility services such as car-sharing systems to optimize VGI. Iacobucci et al ( 2018 ) consider a case study in Tokyo of the ability of connected, automated EVs to be dispatched to respond to both transportation demand and charging to meet demands and constraints of the electricity system. The authors observe the vehicles can take advantage of a variety of different time-of-day pricing structures—leading to a tradeoff between wait times and cost benefits from lower fuel prices. They find that the vehicles in Tokyo can supply on the order of 3.5 MW of charging flexibility per 1000 vehicles, even during times of high mobility demand. Miao et al ( 2019 ) conduct a similar study in a generic region. The authors develop an algorithm that simulates operational behavior of the connected, automated EV technology that includes trip demand and vehicle usage, vehicle relocation, and vehicle charging. Their results indicate that charging behavior is highly sensitive to different levels of charging due to the length of charging—which can affect service provision of trip demand.

The final topic of study considering synergistic opportunities between connected, automated vehicles and EVs focused on emissions benefits. Taiebat et al 2018 explore the environmental impacts of automated vehicles showing net positive environmental impacts at the local vehicle-urban levels due to improved efficiency, but acknowledge that greater vehicle utilization and shifts in travel patterns might to offset some of these benefits. Of course, EVs provide the significant benefit of eliminating tailpipe criteria-pollutant emissions, yielding significant human-health benefits. Regarding GHG emissions, two of the earliest studies on this topic examine the net effect of automation on reducing transport GHG emissions (Brown et al 2014 , Wadud et al 2016 ). Greenblatt and Saxena ( 2015 ) conduct a case-study application of connected and automated vehicles in taxi fleets and find large emissions benefits associated with electrification. They find a decrease of GHG emissions intensities ranging from 87% to 94% below comparable ICEVs in 2014 and 63% to 82% below hybrid electric vehicles (HEVs) in 2030. The total emissions benefit is augmented relative to privately owned vehicles due to the higher travel intensity of taxi vehicles. Following these earlier works, additional case studies examine the hypothetical application of automated and electric fleets. These include two studies in Austin, Texas. Loeb and Kockelman ( 2019 ) examine a variety of scenarios to simulate the operation of different vehicle fleets replacing current-day transportation network companies and taxis. The primary goal of their work is to estimate costs associated with operation. They find that automated EVs are the most profitable and provide the best service among the vehicle-technology options that they examine. Gawron et al ( 2019 ) also perform a case study in Austin, Texas, but focus on the emissions benefits of electrifying an automated taxi fleet. They find that nearly 60% of emissions and energy in a base case CAV fleet can be reduced by electrifying powertrains. These improvements can be pushed up to 87% when coupled with grid decarbonization, dynamic ride-sharing, and various system- and technology-efficiency improvements. These results are consistent with a more generalized study by Stogios et al ( 2019 ), who, in a similar approach simulating fleet behavior, find that emissions from CAVs are most dramatically improved via electrification.

While EVs are a relatively new technology and automated vehicles are not widely available commercially, the implications and potential synergies of electrification and automation operating in conjunction are significant. The studies mentioned in this section are investigating a broad set of impacts when CAVs are coupled with EVs. Future research is necessary to generalize and refine many of these results. However, the potential for transformative changes to transportation emissions is clear.

8.1. Expectations for the future

EVs hold great promise to replace ICEVs for a number of on-road applications. EVs can provide a number of benefits, including addressing reliance on petroleum, improving local air quality, reducing GHG emissions, and improving driving experience. Vehicle electrification aligns with broader electrification and decarbonization trends and integrates synergistically with mobility changes, including urban micro-mobility, automation, and mobility-as-a-service solutions. The effective integration of EVs into power systems presents numerous opportunities for synergistic improvement of the efficiency and economics of electromobility and electric power systems, with EVs capable of supporting power-system planning and operations in several ways. Full exploitation of the synergies between EVs and VRE sources offers a path toward affordable and clean energy and mobility for all, as both technologies promise large-scale deployment in the future. To enable such a future continued technology progress, investments in charging infrastructure (and related building codes), consumer education, effective and secure VGI programs, and regulatory and business models supporting all aspects of vehicle electrification are all critical elements.

The coronavirus pandemic is impacting LDV sales in most countries negatively, and 2020 EV sales are expected to be lower than 2019, marking the first decline in a decade (BloombergNEF 2020 ). However, sales of ICEVs are set to drop even faster and, despite the crisis, EV sales could reach a record share of the overall LDV market in 2020 (Gul et al 2020 ). Despite these short-term setbacks, long-term prospects for EVs remain undiminished (BloombergNEF 2020 ).

Several studies project major roles for EVs in the future, which is reflected in massive investment in vehicle development and commercialization, charging infrastructure, and further technology improvement, especially in batteries and their supply chains. Consumer adoption and acceptance and technology progress form a virtuous self-reinforcing circle of technology-component improvements and cost reductions that can enable widespread adoption. Forecasting the future, including technology adoption, remains a daunting task. Nevertheless, this detailed review paints a positive picture for the future of EVs for on-road transport. The authors remain hopeful that technology, regulatory, societal, behavioral, and business-model barriers can be addressed over time to support a transition toward cleaner, more efficient, and affordable mobility solutions for all.

Acknowledgments

The authors thank Paul Denholm, Elaine Hale, Trieu Mai, Caitlin, Murphy, Bryan Palmintier, and Dan Steinberg for valuable comments on figure 6 , as well as two anonymous reviewers for helpful comments on the paper. This work was co-authored by National Renewable Energy Laboratory (NREL), which is operated by Alliance for Sustainable Energy, LLC, for U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. No funding was received to support this work. The views expressed in this article do not necessarily represent the views of DOE or the U.S. Government. The findings and conclusions in this publication are those of the authors alone and should not be construed to represent any official U.S. Government determination or policy, or the views of any of the institutions associated with this study's authors.

 EVs are defined as vehicles that are powered with an on-board battery that can be charged from an external source of electricity. This definition includes plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs). EVs often are referred to as plug-in electric vehicles (PEVs).

 Transport electrification is confined not only to electric LDVs. Transport electrification includes a wide range of other vehicles, spanning from small vehicles that are used for urban mobility, such as three-wheelers, mopeds, kick-scooters, and e-bikes, to large urban buses and delivery vehicles. In 2019, the number of electric two-wheelers on the road exceeded 300 million and buses approached 0.6 million (IEA 2019 , Business Wire 2020 ), with new deliveries in 2019 close to 100 thousand units (EV Volumes 2020 ).

 Just over 10% of the U.S. heavy-duty truck (Class 7–8) population requires an operating range of 500 miles (805 km) or more, while nearly 80% operate within a 200 mile (322 km) range and around 70% within 100 miles (161 km). Only ∼25% of heavy truck VMT require an operating range of over 500 miles (805 km) (Borlaug et al Forthcoming ).

 As a counterargument, Tesla states that 'all new Tesla cars come standard with advanced hardware capable of providing Autopilot features today, and full self-driving capabilities in the future—through software updates designed to improve functionality over time'.

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Article Contents

1. introduction, 2. paris purposes and the future we made, 3. the problem of unmaking, 4. conclusion: unmaking and is paris possible, conflict of interest statement, bibliography.

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Electric vehicles: the future we made and the problem of unmaking it

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Jamie Morgan, Electric vehicles: the future we made and the problem of unmaking it, Cambridge Journal of Economics , Volume 44, Issue 4, July 2020, Pages 953–977, https://doi.org/10.1093/cje/beaa022

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The uptake of battery electric vehicles (BEVs), subject to bottlenecks, seems to have reached a tipping point in the UK and this mirrors a general trend globally. BEVs are being positioned as one significant strand in the web of policy intended to translate the good intentions of Article 2 of the Conference of the Parties 21 Paris Agreement into reality. Governments and municipalities are anticipating that a widespread shift to BEVs will significantly reduce transport-related carbon emissions and, therefore, augment their nationally determined contributions to emissions reduction within the Paris Agreement. However, matters are more complicated than they may appear. There is a difference between thinking we can just keep relying on human ingenuity to solve problems after they emerge and engaging in fundamental social redesign to prevent the trajectories of harm. BEVs illustrate this. The contribution to emissions reduction per vehicle unit may be less than the public initially perceive since the important issue here is the lifecycle of the BEV and this is in no sense zero-emission. Furthermore, even though one can make the case that BEVs are a superior alternative to the fossil fuel-powered internal combustion engine, the transition to BEVs may actually facilitate exceeding the carbon budget on which the Paris Agreement ultimately rests. Whether in fact it does depends on the nature of the policy that shapes the transition. If the transition is a form of substitution that conforms to rather than shifts against current global scales and trends in private transportation, then it is highly likely that BEVs will be a successful failure. For this not to be the case, then the transition to BEVs must be coordinated with a transformation of the current scales and trends in private transportation. That is, a significant reduction in dependence on and individual ownership of powered vehicles, a radical reimagining of the nature of private conveyance and of public transportation.

According to the UK Society of Motor Manufacturers and Traders (SMMT), the Tesla Model 3 sold 2,685 units in December 2019, making it the 9th best-selling car in the country in that month (by new registrations; in August, a typically slow month for sales, it had been 3rd with 2,082 units sold; Lea, 2019; SMMT, 2019 ). As of early 2020, battery electric vehicles (BEVs) such as the new Hyundai Electric Kona had a two-year waiting list for delivery and the Kia e-Niro a one-year wait. The uptake of electric vehicles, subject to bottlenecks, seems to have reached a tipping point in the UK and this transcends the popularity of any given model. This possible tipping point mirrors a general trend globally (however, see later for quite what this means). At the regional, national and municipal scale, public health and environmentally informed legislation are encouraging vehicle manufacturers to invest heavily in alternative fuel vehicles and, in particular, BEVs and plug-in hybrid vehicles (PHEVs), which are jointly categorised within ‘ultra-low emission vehicles’ (ULEVs). 1 According to a report by Deloitte, more than 20 major cities worldwide announced plans in 2017–18 to ban petrol and diesel cars by 2030 or sooner ( Deloitte, 2018 , p. 5). All the major manufacturers have or are launching BEV models, and so vehicles are becoming available across the status and income spectrum that has in the past determined market segmentation. According to the consultancy Frost & Sullivan (2019) , there were 207 models (143 BEVs, 64 PHEVs) available globally in 2018 compared with 165 in 2017.

In 2018, the UK government published its Road to Zero policy commitment and introduced the Automated and Electric Vehicles Act 2018 , which empowers future governments to regulate regarding the required infrastructure. Road to Zero announced an ‘expectation’ that between 50% and 70% of new cars and vans will be electric by 2030 and the intention to ‘end the sale of new conventional petrol and diesel cars and vans by 2040’, with the ‘ambition’ that by 2050 almost all vehicles on the road will be ‘zero-emission’ at the point of use ( Department for Transport, 2018 ). Progress towards these goals was to be reviewed 2025. 2 However, on 4 February 2020, Prime Minister Boris Johnson announced that in the run-up to Conference of the Parties (COP)26 in Glasgow (now postponed), Britain would bring forward its 2040 goal to 2035. The UK is a member of the Clean Energy Ministerial Campaign (CEM), which launched the EV30@30 initiative in 2017, and its Road to Zero policy commitments broadly align with those of many European countries. 3 Norway has longstanding generous incentives for BEVs ( Holtsmark and Skonhoft, 2014 ) and 31% of all cars sold in 2018 and just under 50% in the first half of 2019 in Norway were BEVs. According to the International Energy Agency (IEA), Norway is the per capita global leader in electric vehicle uptake ( IEA, 2019A ). 4

BEVs, then, are being positioned as one significant strand in the web of policy intended to translate the good intentions of Article 2 of the COP 21 Paris Agreement into reality (see Morgan, 2016 ; IEA, 2019A , pp. 11–2). Clearly, governments and municipalities are anticipating that a widespread shift to electric vehicles will significantly reduce transport-related carbon emissions and, therefore, augment their nationally determined contributions (NDCs) to emissions reduction within the Paris Agreement. And, since the BEV trend is global, the impacts potentially also apply to countries whose relation to Paris is more problematic, including the USA (for Trump and his context, see Gills et al. , 2019 ). However, matters are more complicated than they may appear. Clearly, innovation and technological change are important components in our response to the challenge of climate change. However, there is a difference between thinking we can just keep relying on human ingenuity to solve problems after they emerge and engaging in fundamental social redesign to prevent the trajectories of harm. BEVs illustrate this. In what follows we explore the issues.

The aim of this paper, then, is to argue that it is a mistake to claim, assert or assume that BEVs are necessarily a panacea for the emissions problem. To do so would be an instance of what ecological economists refer to as ‘technocentrism’, as though simply substituting BEVs for existing internal combustion engine (ICE) vehicles was sufficient. The literature on this is, of course, vast, if one consults specialist journals or recent monographs (e.g. Chapman, 2007 ; Bailey and Wilson, 2009 ; Williamson et al. , 2018 ), but remains relatively under-explored in general political economy circles at a time of ‘Climate Emergency’, and so warrants discussion in introductory and indicative fashion, setting out, however incompletely, the range of issues at stake. To be clear, the very fact that there is a range is itself important. BEVs are technology, technologies have social contexts and social contexts include systemic features and related attitudes and behaviours. Technocentrism distracts from appropriate recognition of this. At its worse, technocentrism fails to address and so works to reproduce a counter-productive ecological modernisation: the technological focus facilitates socio-economic trends, which are part of the broader problem rather than solutions to it. In the case of BEVs, key areas to consider and points to make include:

Transport is now one of, if not, the major source of carbon emissions in the UK and in many other countries. Transport emissions stubbornly resist reduction. The UK, like many other countries, exhibits contradictory trends and policy claims regarding future carbon emissions reductions. As such, it is an error to simply assume prior emissions reduction trends will necessarily continue into the future, and the new net-zero goal highlights the short time line and urgency of the problem.

Whilst BEVs are, from an emissions point of view, a superior technology to ICE vehicles, this is less than an ordinary member of the public might think. ‘Embodied emissions’, ‘energy mix’ and ‘life cycle’ analysis all matter.

There is a difference between ‘superior technology’ and ‘superior choice’, the latter must also take account of the scale of and general trend growth in vehicle ownership and use. It is this that creates a meaningful context for what substitution can be reasonably expected to achieve.

A 1:1 substitution of BEVs for ICE vehicles and general growth in the number of vehicles potentially violates the Precautionary Principle. It creates a problem that did not need to exist, e.g. since there is net growth, it involves ‘emission reductions’ within new emissions sources and this is reckless. Inter alia , a host of fallacies and other risks inherent to the socio-economy of BEVs and resource extraction/dependence also apply.

As such, it makes more sense to resist rather than facilitate techno-political lock-in or path-dependence on private transportation and instead to coordinate any transition to BEVs with a more fundamental social redesign of public transport and transport options.

This systematic statement should be kept in mind whilst reading the following. Cumulatively, the points stated facilitate appropriate consideration of the question: What kind of solution are BEVs to what kind of problem? And we return to this in the conclusion. It is also worth bearing in mind, though it is not core to the explicit argument pursued, that an economy is a complex evolving open system and economics has not only struggled to adequately address this in general, it has particularly done so in terms of ecological issues (for relevant critique, see especially the work of Clive Spash and collected, Fullbrook and Morgan, 2019 ). 5 Since we assume limited prior knowledge on the part of the reader, we begin by briefly setting out the road to the current carbon budget problem.

The United Nations Framework Convention on Climate Change (UNFCCC) was created in 1992. Article 2 of the Convention states its goal as, the ‘stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system’ ( UNFCCC, 1992 , p. 4; Gills and Morgan, 2019 ). Emissions are cumulative because emitted CO 2 can stay in the atmosphere for well over one hundred years (other greenhouse gases [GHGs] tend to be of shorter duration). Our climate future is made now. The Intergovernmental Panel on Climate Change (IPCC) collates existent models to produce a forecast range and has typically used atmospheric CO 2 of 450 ppm as a level likely to trigger a 2°C average warming. This has translated into a ‘carbon budget’ restricting total cumulative emissions to the lower end of 3,000+ Gigatonnes of CO 2 (GtCO 2 ). In the last few years, climate scientists have begun to argue that positive feedback loops with adverse warming and other climatological and ecological effects may be underestimated in prior models (see Hansen et al. , 2017 ; Steffen et al. , 2018 ). Such concerns are one reason why Article 2 of the UNFCCC COP 21 Paris Agreement included a goal of at least trying to do better than the 2°C target—restricting warming to 1.5°C. This further restricts the available carbon budget. However, current Paris Agreement country commitments stated as NDCs look set to exceed the 3,000+ target in a matter of a few short years ( UNFCCC, 2015 ; Morgan, 2016 , 2017 ).

Since the industrial revolution began, we have already produced more than 2,000 GtCO 2 . Total annual emissions have increased rather than decreased over the period in which the problem has been recognised. The United Nations Environment Program (UNEP) publishes periodic ‘emissions gap’ reports. Its recent 10-year summary report notes that emissions grew at an average 1.6% per year from 2008 to 2017 and ‘show no signs of peaking’ ( Christensen and Olhoff, 2019 , p. 3). In 2018, the 9th Report stated that annual emissions in 2017 stood at a record of 53.5 Gigatonnes of CO 2 and equivalents (GtCO 2e ) ( UNEP, 2018 , p. xv). This compares to less than 25 GtCO 2 in 2000 and far exceeds on a global basis the level in the Kyoto Protocol benchmark year of 1990. According to the 9th Emissions Gap Report, 184 parties to the Paris Agreement had so far provided NDCs. If these NDCs are achieved, annual emissions in 2030 are projected to still be 53 GtCO 2e . However, if the current ‘implementation deficit’ continues global annual emissions could increase by about 10% to 59 GtCO 2e . This is because current emissions policy is not sufficient to offset the ‘key drivers’ of ‘economic growth and population growth’ ( Christensen and Olhoff, 2019 , p. 3). By sharp contrast, the IPCC Global Warming of 1.5 ° C report states that annual global emissions must fall by 45% from the 2017 figure by 2030 and become net zero by mid-century in order to achieve the Paris target ( IPCC, 2018 ). According to the subsequent 10th Emissions Gap Report, emissions increased yet again to 55.3 GtCO 2e in 2018 and, as a result of this adverse trend, emissions need to fall by 7.6% per year from 2020 to 2030 to achieve the IPCC goal, and this contrasts with less than 4% had reductions begun in 2010 and 15% if they are delayed until 2025 ( UNEP 2019A ). Current emissions trends mean that we will achieve an additional 500 GtCO 2 quickly and imply an average warming of 3 to 4°C over the rest of the century and into the next. We are thus on track for the ‘dangerous anthropogenic interference with the climate system’ that the COP process is intended to prevent ( UNFCCC, 1992 , p. 4). According to the 10th Emissions Gap Report, 78% of all emissions derive from the G-20 nations, and whilst many countries had recognised the need for net zero, only 5 countries of the G-20 had committed to this and none had yet submitted formal strategies. COP 25, December 2019, meanwhile, resulted in no overall progress other than on measurement and finance (for detailed analysis, see Newell and Taylor, 2020 ). As such, the situation is urgent and becoming more so.

Problems, moreover, have already begun to manifest ( UNEP 2019B , 2019B ; IPCC 2019A , 2019B ). Climate change does not respect borders, some countries may be more adversely affected sooner than others, but there is no reason to assume that cumulative effects will be localised. Moreover, there is no reason to assume that they will be manageable based on our current designs for life. In November 2019, several prominent systems and climate scientists published a survey essay in Nature highlighting nine critical climate tipping points that we are either imminently approaching or may have already exceeded ( Lenton et al. , 2018 ). In that same month, more than 11,250 scientists from 153 countries (the Alliance of World Scientists) signed a letter published in BioScience concurring that we now face a genuine existential ‘Climate Emergency’ and warning of ‘ecocide’ if ‘major transformations’ are not forthcoming ( Ripple et al. , 2019 ). We live in incredibly complex interconnected societies based on long supply chains and just in time delivery–few of us (including nations) are self-sufficient. Global human civilisation is extremely vulnerable and the carbon emission problem is only one of several conjoint problems created by our expansionary industrialised-consumption system. Appropriate and timely policy solutions are, therefore, imperative. Cambridge now has a Centre for the Study of Existential Risk and Oxford a Future of Humanity Institute (see also Servigne and Stevens, 2015 ). This is serious research, not millenarian cultishness. The Covid-19 outbreak only serves to underscore the fragility of our systems. As Michael Marmot, Professor of epidemiology has commented, the outbreak reveals not only how political decisions can make systems more vulnerable, but also how governments can, when sufficiently motivated, take immediate and radical action (Harvey, 2020). To reiterate, however, according to both the IPCC and UNEP, emissions must fall drastically. 6

Policy design and implementation are mainly national (domestic). As such, an initial focus on the UK provides a useful point of departure to contextualise what the transition to BEVs might be expected to achieve.

The UK is a Kyoto and Paris signatory. It is a member of the European Emissions Trading Scheme (ETS). The UK Climate Change Act 2008 was the world’s first long-term legally binding national framework for targeted statutory reductions in emissions. The Act required the UK to reduce its emissions by at least 80% by 2050 (below the 1990 baseline; this has been broadly in line with subsequent EU policy on the subject). 7 The Act put in place a system of five yearly ‘carbon budgets’ to keep the UK on an emissions reduction pathway to 2050. The subsequent carbon budgets have been produced with input from the Committee on Climate Change (CCC), an independent body created by the 2008 Act to advise the government. In November 2015, the CCC recommended a target of 57% below 1990 levels by the early 2030s (the fifth carbon budget). 8 Following the Paris Agreement’s new target of 1.5°C and the IPCC and UNEP reports late 2018, the CCC published the report Net Zero: The UK’s contribution to stopping global warming ( CCC, 2019 ). 9 The CCC report recognises that Paris creates additional responsibility for the UK to augment and accelerate its targets within the new bottom-up Paris NDC procedure. The CCC recommended an enhanced UK net-zero GHG emissions target (formally defined in terms of long-term and short-term GHGs) by 2050. This included emissions from aviation and shipping and with no use of strategies that offset or swap real emissions. In June 2019, Theresa May, then UK Prime Minister, committed to adopt the recommendation using secondary legislation (absorbed into the 2008 Act—but without the offset commitment). So, the UK is one of the few G-20 countries to, so far, provide a formal commitment on net zero, though as the UNEP notes, a commitment is not itself necessarily indicative of a realisable strategy. The CCC responded to the government announcement:

This is just the first step. The target must now be reinforced by credible UK policies, across government, inspiring a strong response from business, industry and society as a whole. The government has not yet moved formally to include international aviation and shipping within the target , but they have acknowledged that these sectors must be part of the whole economy strategy for net zero. We will assist by providing further analysis of how emissions reductions can be delivered in these sectors through domestic and international frameworks. 10

The development of policy is currently in flux during the Covid-19 lockdown and whilst Brexit reaches some kind of resolution. As noted in the Introduction section, however, May’s replacement, Boris Johnson has signalled his government’s commitment to achieving its statutory commitments. However, this has been met with some scepticism, not least because it has not been clear what new powers administrative bodies would have and over and above this many of the Cabinet are from the far right of the Conservative Party, and are on record as climate change sceptics or have a voting record of opposing environmentally focussed investment, taxes, subsidies and prohibitions (including the new Environment Secretary, George Eustice, formerly of UKIP). The policy may and hopefully will change, becoming more concrete, but it is still instructive to assess context and general trends.

The UK has one of the best records in the world on reducing emissions. However, given full context, this is not necessarily a cause for congratulation or confidence. It would be a mistake to think that emissions reduction exhibits a definite rate that can be projected from the past into the future. 11 This applies both nationally and globally. Some sources of relative reduction that are local or national have different significance on a global basis (they are partial transfers) and overall the closer one approaches net zero the more resistant or difficult it is likely to become to achieve reductions. The CCC has already begun to signal that the UK is now failing to meet its existent budgets. This follows periods of successive emissions reductions. According to the CCC, the UK has reduced its GHG emissions by approximately one-third since 1990. ‘Per capita emissions are now close to the global average at 7–8 tCO 2 e/person, having been over 50% above in 2008’ ( CCC, 2019 , p. 46). Other analyses are even more positive. According to Carbon Brief, emissions have fallen in seven consecutive years from 2013 to 2019 and by 40% compared with the 1990 benchmark. Carbon Brief claim that since 2010 the UK has the fastest rate of emissions reduction of any major economy. However, it concurs with the CCC that future likely reductions are less than the UK’s carbon budgets and that the new net-zero commitment requires: amounting to only an additional 10% reduction over the next decade to 2030. 12

Moreover, all analyses agree that the reduction has mainly been achieved by reducing coal output for use in electricity generation (switching to natural gas) and by relative deindustrialisation as the UK economy has continued to grow—manufacturing is a smaller part of a larger service-based economy. 13 And , the data are based on a production focussed accounting system. The accounting system does not include all emissions sources. It does not include those that the UK ‘imports’ based on consumption. UK consumption-based emissions per year are estimated to be about 70% greater than the production measure (for different methods, see DECC, 2015 ). 14 If consumption is included, the main estimates for falling emissions change to around a 10% reduction since 1990. Moreover, much of this has been achieved by relatively invisible historic transitions as the economy has evolved in lock-step with globalisation. That is, reductions have been ones that did not require the population to confront behaviours as they have developed. No onerous interventions have been imposed, as yet . 15 However, it does not follow that this can continue, since future reductions are likely to be more challenging. The UK cannot deindustrialise again (nor can the global economy, as is, simply deindustrialise in aggregate if final consumption remains the primary goal), and the UK has already mainly switched from coal energy production. Emissions from electricity generation may fall but it also matters what the electricity is being used to power. In any case, future emissions reductions, in general, require more effective changes in other sectors, and this necessarily seems to require everyone to question their socio-economic practices. Transport is a key issue.

As a ‘satellite’ of its National Accounts, the UK Office for National Statistics (ONS) publishes Environmental Accounts and these data are used to measure progress. Much of the data refer to the prior year or earlier. In 2017, UK GHG emissions were reported to be 566 million tonnes CO 2 e (2% less than 2016 and, as already noted about one-third of the 1990 level; ONS, 2019 ). The headline accounts break this down into four categories (for which further subdivisions are produced by various sources) and we can usefully contrast 1990 and recent data ( ONS, 2019 , p. 4):

The Environmental Accounts’ figures indicate some shifting in the relative sources of emissions over the last 30 years. As we have intimated, electricity generation and manufacturing have experienced reduced emissions, though they are far from zero; household and transport, meanwhile, have remained stubbornly high. Moreover, the accounts are also slightly misleading for the uninitiated, since transport refers to the industry and not all transport. Domestic car ownership and use are part of the household sector, and it is the continued dependence on car ownership that provides, along with heating and insulation issues, one of the major sources of the persistently high level of household emissions. The UK Department for Business, Energy and Industrial Strategy (DBEIS) provides differently organised statistics and attributes cars to its transport category and uses a subsequent residential category rather than household category. The Department’s statistical release in 2018 thus attributes a higher 140 MtCO 2 e to transport for 2016, whilst the residential category is a correspondingly lower figure of approximately 106 MtCO 2 e. The 140 MtCO 2 e is just slightly less than the equivalent figure for 1990, although transport achieved a peak of about 156 MtCO 2 e in 2005 ( DBEIS, 2018 , pp. 8–9). As of 2016, transport becomes the largest source of emissions based on DBEIS data (exceeding energy supply) whilst households become the largest in the Environmental Accounts. In any case, looking across both sets of accounts, the important point here is that since 1990 transport as a source of emissions has remained stubbornly high. Transport emissions have been rising as an industrial sector in the Environmental Accounts or relatively consistent and recently rising in its total contribution in the DBEIS data. The CCC Net Zero report draws particular attention to this. Drawing on the DBEIS data, it states that ‘Transport is now the largest source of UK GHG emissions (23% of the total) and saw emissions rise from 2013 to 2017’ ( CCC, 2019 , p. 48). More generally, the report states that despite some progress in terms of the UK carbon budgets, ‘policy success and progress in reducing emissions has been far from universal’ ( CCC, 2019 , p. 48). The report recommends ( CCC, 2019 , pp. 23–6, 34):

A fourfold increase by 2050 in low carbon (renewables) electricity

Developing energy storage (to enhance the use of renewables such as wind)

Energy-efficient buildings and a shift from gas central heating and cooking

Halting the accumulation of biodegradable waste in landfills

Developing carbon capture technology

Reducing agricultural emissions (mainly dairy but also fertiliser use)

Encouraging low or no meat diets

Land management to increase carbon retention/absorption

Rapid transition to electric vehicles and public transport

As we noted in the Introduction section, the UK Department for Transport Road To Zero document stated a goal of ending the sale of conventional diesel- and petrol-powered ICE vehicles by 2040. The CCC suggested improving on this:

Electric vehicles. By 2035 at the latest all new cars and vans should be electric (or use a low-carbon alternative such as hydrogen). If possible, an earlier switchover (e.g. 2030) would be desirable, reducing costs for motorists and improving air quality. This could help position the UK to take advantage of shifts in global markets. The Government must continue to support strengthening of the charging infrastructure, including for drivers without access to off-street parking. ( CCC, 2019 , p. 34)

The UK government’s response to these and other similar suggestions has been to bring the target date forward to 2035 and to propose that the prohibition will also apply to hybrids. However, the whole is set to go out to consultation and no detail has so far (early 2020) been forthcoming. In its 11 March 2020 Budget, the government also committed £1 billion to ‘green transport solutions’, including £500 million to support the rollout of the electric vehicle charging infrastructure, whilst extending the current grant/subsidy scheme for new electric vehicles (albeit at a reduced rate of £3000 from £3500 per new registration). It has also signalled that it may tighten the timeline for sales prohibition further to 2030. 16 As a policy, much of this is, ostensibly at least, positive, but there is a range of issues that need to be considered regarding what is being achieved. The context of transition matters and this may transcend the specifics of current policy.

3.1 BEV transition: life cycles?

The CCC is confident that a transition to electric vehicles can be a constructive contribution to achieving net-zero emissions by mid-century. However, the point is not unequivocal. The previously quoted CCC communique following the UK government’s commitment to implement Net Zero uses the phrase ‘credible UK policies, across government, inspiring a strong response from business, industry and society as a whole’, and the CCC report places an emphasis on BEVs and a transition to public transport. The relative dependence between these two matters (and see Conclusion). BEVs are potentially (almost) zero emissions in use. But they are not zero emissions in practice. Given this, then the substitution of BEVs for current carbon-powered ICEs is potentially problematic, depending on trends in ownership of and use of powered vehicles (private transportation). These points will become clearer as we proceed.

BEVs are not zero emission in context and based on the life cycle. This is for two basic reasons. First, a BEV is a powered vehicle and so the source of power can be from carbon-based energy supply sources (and this varies with the ‘energy mix’ of electricity production in different countries; IEA, 2019A , p. 8). Second, each new vehicle is a material product. Each vehicle is made of metals, plastics, rubber and so forth. Just the cabling in a car can be 60 kg of metals. All the materials must be mined and processed, or synthesised, the parts must be manufactured, transported and assembled, transported again for sale and then delivered. For example, according to the SMMT in 2016, only 12% of cars sold in the UK were built in the UK and 80% of those built in the UK were exported in that year. Some components (such as a steering column) enter and exit the UK multiple times whilst being built and modified and before final assembly. Vehicle manufacture is a global business in terms of procuring materials and a mainly regional (in the international sense) business in terms of component manufacture for assembly and final sales. Power is used throughout this process and many miles are travelled. Moreover, each vehicle must be maintained and serviced thereafter, which compounds this utilisation of resources. BEVs are a subcategory of vehicles and production locations are currently more concentrated than for vehicles in general (Tesla being the extreme). 17 In any case, producing a BEV is an economic activity and it is not environmentally costless. As Georgescu-Roegen (1971) noted long ago and ecologically minded economists continue to highlight (see Spash, 2017 ; Holt et al. , 2009 ), production cannot evade thermodynamic consequences. In terms of BEVs, the primary focus of analysis in this second sense of manufacturing as a source of contributory emissions has been the carbon emissions resulting from battery production. Based on current technology, batteries are heavy (a significant proportion of the weight of the final vehicle) and energy intensive to produce.

Comparative estimates regarding the relative life cycle emissions of BEVs with equivalent fossil fuel-powered vehicles are not new. 18 Over the last decade, the number of life cycle studies has steadily risen as the interest in and uptake of BEVs have increased. Clearly, there is great scope for variation in findings, since the energy mix for electricity supply varies by country and the assumptions applied to manufacturing can vary between studies. At the same time, the general trend over the last decade has been for the energy mix in many countries to include more renewables and for manufacturing to become more energy efficient. This is partly reflected in metrics based on emissions per $GDP, which in conjunction with relative expansion in service sectors are used to establish ‘relative decoupling’. So, given that both the energy mix of power production and the emissions derived from production can improve, then one might expect a general trend of improved emissions claims for BEVs in recent years and this seems to be the case.

For example, if we go back to 2010, the UK Royal Academy of Engineering found that technology would likely favour PHEVs over BEVs in the near future because the current energy mix and state of battery technology indicated that emissions deriving from charging were typically higher for BEVs than an average ordinary car’s fuel consumption—providing a reason to persist with ICE vehicles or, more responsibly, choose hybrids over pure electric ( Royal Academy of Engineering, 2010 ). Using data up to 2013, but drawing on the previous decade, Holtsmark and Skonhoft (2014) come to similar conclusions based on the most advanced BEV market—Norway. Focussing mainly on energy mix (with acknowledgement that a full life cycle needs to be assessed) they are deeply sceptical that BEVs are a significant net reduction in carbon emissions ( Holtsmark and Skonhoft, 2014 , pp. 161, 164). Neither the Academy nor Holtsmark and Skonhoft are merely sceptical. The overall point of the latter was that more needed to be done to accelerate the use of low or no carbon renewables for power infrastructure (a point the CCC continues to make). This, of course, has happened in many places, including the UK. That is, acceleration of the use of renewables, though it is by no means the case government can take direct credit for this in the UK (and there is also evidence on a global level that a transition to clean energy from fossil fuel forms is much slower than some data sources indicate; see Smil, 2017A , 2017B ). 19 In terms of BEVs, however, recent analyses are considerably more optimistic regarding emissions potential per BEV (e.g. Hoekstra, 2019 ; Regett et al. , 2019 ). Research by Staffell et al. (2019) at Imperial for the power corporation, Drax, provides some interesting insights and contemporary metrics.

Staffell et al. split BEVs into three categories based on conjoint battery and vehicle size: a 30–45 kWh battery car, equivalent to a mid-range or standard car; a heavier, longer-range, 90–100 kWh battery car, equivalent to a luxury or SUV model; and a 30–40 kWh battery light van. They observe that a 40-litre tank of petrol releases 90–100 kgCO 2 when burnt and the ‘embodied’ emissions represented by the manufacture of a standard lithium-ion battery are estimated at 75–125 kgCO 2 per kWh. They infer that every kWh of power embodied in the manufacture of a battery is, therefore, approximately equivalent to using a full tank of petrol. For example, a 30 kWh battery embodies thirty 40-litre petrol tank’s worth of emissions. The BEV’s are also a source of emissions based on the energy mix used to charge the battery for use. The in-use emissions for the BEV are a consequence of the energy consumed per km and this depends on the weight of car and efficiency of the battery. 20 They estimate 33 gCO 2 per km for standard BEVs, 44–54 gCO 2 for luxury and SUVs and 40 gCO 2 for vans. In all cases, this is significantly less than an equivalent fossil-fuel vehicle.

The insight that the estimates and comparisons are leading towards is that the battery embodies an ‘upfront carbon cost’ which can be gradually ‘repaid’ by the saving on emissions represented by driving a BEV compared with driving an equivalent fossil fuel-powered vehicle. That is, the environmental value of opting for BEVs increases over time. Moreover, if the energy mix is gradually becoming less carbon based, this effect is likely to improve further. Based on these considerations, Staffell et al. estimate that it may take 2–4 years to repay the embodied emissions in the battery for a standard BEV and 5 to 6 for the luxury or SUV models. Fundamentally, assuming 15 years to be typical for the on-the-road life expectancy of a vehicle, they find lifetime emissions for each BEV category are lower than equivalent fossil-fuel vehicles.

Still, the implication is that BEVs are not zero emission. Moreover, the degree to which this is so is likely to be significantly greater than a focus on the battery alone indicates. Romare and Dahlöff (2017) , assess the life-cycle of battery production (not use), and in regard of the stages of battery production find that the manufacturing stages account for about 50% of the emissions and the mining and processing stages about the same. They infer that there is significant scope for further emissions reductions as manufacturing processes improve and the Drax study seems to confirm this. However, whilst the battery may be the major component, as we have already noted, vehicle manufacture is a major process in terms of all components and in terms of distance travelled in production and distribution. It is also worth noting that the weight of batteries creates strong incentives to opt for lighter materials for other parts of the vehicle. Most current vehicles are steel based. An aluminium vehicle is lighter, but the production of aluminium is more carbon intensive than steel, so there are also further hidden trade-offs that the positive narrative for BEVs must consider. 21

The general point worth emphasising here is that there is basic uncertainty built into the complex evolving process of transition and change. There is a basic ontology issue here familiar in economic critique: there is no simple way to model the changes with confidence, and in broader context confidence in modelling may itself be a problem here when translated into policy, since it invites complacency. 22 That said, the likely direction of travel is towards further improvements in the energy mix and improvements in battery technology. Both these may be incremental or transformational depending on future technologies (fusion for energy mix and organics and solid-state technologies for batteries perhaps). 23 But one must still consider time frames and ultimate context. 24 The context is a carbon budget and the need for radical reductions in emissions by 2030 and net zero by mid-century. Consider: if just the battery of a car requires four years to be paid back then there is no significant difference in the contribution to emissions from the vehicle into the mid 2020s. For larger vehicles, this becomes the later 2020s, and each year of delay in transition for the individual owner is another year closer to 2030. Since transport is (stubbornly) the major source of emissions in the UK and a major source in the world, this is not irrelevant. BEVs can readily be a successful failure in Paris terms. This brings us to the issue of trends in vehicle ownership and substitutions. This also matters for what we mean by transition.

3.2 Substitutions and transformations: successful failure?

There are many ways to consider the problem of transition. Consider the ‘Precautionary Principle’. This is Principle 15 of the 1992 Rio Declaration: ‘In order to protect the environment, the precautionary principle shall be widely applied by the States [UN members] according to their capabilities. Where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures to prevent environmental degradation’ (UNCED). Assuming we can simply depend on unrealised technology potentially violates the Principle. Why is this so? If BEVs are a source of net emissions, then each new vehicle continues to contribute to overall emissions. The current number of vehicles to be replaced, therefore, is a serious consideration, as is any growth trend. Here, social redesign rather than merely adopting new technology is surely more in accordance with the Precautionary Principle. BEVs may be sources of lower emissions than fossil fuel-powered vehicles, but it does not follow that we are constrained to choose between just these two options or that it makes sense to do so in aggregate, given the objective of radical and rapid reduction in emissions. If time is short and numbers of vehicles are large and growing then the implication is that substitution of BEVs should (from a precautionary point of view) occur in a context that is oppositional to this growing trend. That is, the goal should be one of reducing private car ownership and use, and increasing the availability, pervasiveness and use of public transport (and alternatives to private vehicle ownership). This is an issue compounded by the finding that there is an upfront carbon cost from BEVs. Some consideration of current vehicle numbers and trends in the UK and globally serve to reinforce the point.

The UK Department for Transport publishes annual statistics for vehicle licensing. According to the 2019 statistical release for 2018 data, there were 38.2 million licensed vehicles in Britain and 39.4 million including Northern Ireland ( Department for Transport, 2019 ). Vehicles are categorised into cars, light goods vehicles, heavy goods vehicles, motorcycles and buses and coaches. Cars comprised 31.5 million of the total (82%) and the total represented a 1.2% increase in the year 2017. There is, furthermore, a long-term year-on-year trend increase in vehicles since World War II and over the last 20 years that growth (the net change as new vehicles are licensed and old vehicles taken off the road) has averaged 630,000 vehicles per year ( Department for Transport, 2019 , p. 7). This is partly accounted for not only by population growth, and business growth, but also by an increase in the number of vehicles per household. According to the statistical release, 2.9 million new vehicles were registered in 2018, and though this was about 5% fewer than 2017 the figure remained broadly consistent with long-term trends in numbers and still represented growth (contributing to the stated 1.2% increase). 25 Of the total new registrations in 2018, 2.3 million were cars and 360,000 were light goods vehicles. Around 2 million has been typical for cars.

The point to take from these metrics is that numbers are large and context matters. Cars represent 31.5 million emission sources and there are 39.4 million vehicles in the UK. Replacing these 1:1 reproduces an emissions problem. Replacing them in conjunction with an ownership growth trend exacerbates the emissions problem that then has to be resolved. If around 2 million new cars are registered per year then the point at which the BEVs amongst these new registrations can be assumed to begin payback for embodied emissions prior to the point at which they become net sources of reduced (and not zero ) emissions is staggered over future years based on the rate of switching. There are then also net new vehicles. Given there are 31.5 million cars to be replaced over time (plus net growth), there is a high likelihood of significant transport emissions up to and beyond 2030. The problem, of course, is implicit in the Department for Transport policy commitment to end sales of petrol and diesel vehicles by 2035 and ensure all vehicles are zero-emission in use by 2050. Knowingly committing to this ingrained emission problem, given we have already recognised the urgency and challenge of the carbon budget and the ‘stubbornness’ of transport emissions, is not prudent, if alternatives exist . It is producing a problem that need not exist purely because enabling car ownership and use is a line of least resistance in policy terms (it requires the least change in behaviour and thus provokes limited opposition). It is also worth noting that the UK, like most countries, has an ‘integrated’ transport policy. However, the phrasing disguises the relative levels of investment between different modes of transport. Austerity politics may have resulted in declining road quality in the UK but, in general terms, the UK is still committed to heavy investment in and expansion of its road system. 26 This infrastructure investment not only seems ‘economically rational’, but it is also a matter of relative emphasis and ‘lock-in’. The future policy is predicated on the dominance of road use and thus vehicle use.

The crux of the matter here is how we view political expedience. Surely this hinges on the consequences of policy failure. That is, the failure to implement an effective policy given the genuine problem expressed in the goal of 1.5 or 2°C. ‘Alternatives’ may seem unrealistic, but this is a matter of will and policy—of rational social design rather than impossibility. The IPCC and other sources suggest that achieving the Paris goals requires mobilisation of a kind not previously seen outside of wartime. Policy can pivot on this quite quickly, even if perhaps this can seem unlikely in 2020. Climate events may make this necessary and popular pressure and opinion may be transformed. This is currently uncertain. Positions on this may yet move quite quickly.

Lock-in also implies an underlying sociological issue. This is important to consider regarding simply opting for substitution without greater emphasis on reduction. Even if substitution occurs smoothly, it places greater pressure on areas of reduction over which we have less control as societies and involves an orientation that has further potential policy consequences that cannot be readily quantified and which increase the overall uncertainty regarding NDCs. As any modern historian, urban geographer or sociologist will attest, car ownership has been imbricate with the development and design—the configuration—of modern societies, and it has been deeply integrated into identity. Cars are social technologies and philosophers also have much to say about this sociality in general (e.g. Faulkner and Runde, 2013 ; Lawson, 2017 ). Cars are more than merely convenient; they are sources of autonomy and status (e.g. John Urry explored the sociology of ‘automobility’; see, Dennis and Urry, 2009 ). As such, the more that environmental and transport policy validate the car, then the more that the car is normalised through socialisation for the citizen, perhaps leading to citizens being more prepared to countenance locked-in harms (congestion, etc.) prior to change, in turn, making it less likely (sub)urban spaces are redesigned in ways predicated on the absence of (or severe limits to) private transport. The trend in many countries over the car era has been that building roads leads to more car use, which leads to congestion, which leads to more roads (especially in concentrated zones around [sub]urban spaces).

According to the UK Ordnance Survey, Britain has increased its total road surface by 132 square miles over the decade since 2010 (a 9% increase). According to the UK Department for Transport, vehicle traffic increased by 0.8% in 2019 (September to September) to 330.1 billion miles travelled and car travel, as a subset, increased to 258 billion miles (a 1.5% increase). 27 The 11 March 2020 Budget seems to confirm the trend. Whilst it commits around £1 billion to ‘green transport solutions’, this is in the context of a £27 billion announced investment in roads, including upgrading and a proposed 4,000 miles of new road. As the Green Party MP, Caroline Lucas, noted there is a basic disconnect here, since this seems set to increase the UK’s dependence on private transport, when it makes more sense to begin to curtail that dependence, given how significant the UK’s transport emissions are. 28 So, within the various tensions in policy, there seems to be a tendency to facilitate techno-political lock-in or path-dependence on private transportation. As Mattioli et al. (2020) argue, the multiple strands of policy and practice that maintain car dependence contribute to ‘carbon lock-in’. The systemic consequences matter both for the perpetuation of fossil fuel vehicle use in the short term and, given they are not net zero for emissions, powered vehicles in the longer term. Not only does this matter in the UK, but it also matters globally. All the issues stated are reproduced globally. Moreover, in some ways, they are compounded for countries where widespread car ownership is relatively new.

3.3 The fallacy of composition, problems that need not exist and resource risk

Estimates vary for the global total number of vehicles. According to Wards Intelligence, the global total was 1.32 billion in 2016 ( Petit, 2017 ). Extrapolated estimations imply that the total likely increased to more than 1.5 billion in 2019. In 1976, the figure was 342 million and in 1996, 670 million, so the trend implies an approximate doubling every 20 years, which if it continued would imply a figure approaching 3 billion by end of the 2030s. Clearly, it is problematic to simply extrapolate a linear trend, but it is not unreasonable to assume a general trend of growth. Observed experience is that many ‘developed’ country middle-class households have accommodated more than one car per household. This is classically the case in the USA. In 2017, the USA, with a population of 325.7 million in that year, reported a total of 272.5 million registered vehicles compared with 193 million in 1990 ( Statista, 2019A ). In any case, the world population is still growing, incomes are growing and many countries are far from a position of one car per household. China with a population of 1.3 billion overtook the USA in the total number of registered vehicles around 2016 to 2017, with 300.3 million registered vehicles in March of 2017 (Zheng, 2017). Growth is rapid and the China Traffic Bureau of the Ministry of Public Security reported a total of 325 million registered vehicles, December 2018, an increase of 15.56 million in the year ( China Daily , 2018 ). The People’s Republic is now the world’s largest car market and the number of registered cars increased to 240 million in 2018 ( Statista, 2019B ). India too has rapidly growing car ownership and on a lesser scale this is replicated across the developing world.

For our purposes, two well-known concepts and a further resource dependence risk seem to apply here. First, there is patently a ‘fallacy of composition’ issue. That is, the assumption that many can do what few previously did without changing the conditions or producing different (adverse) consequences than arose when only a few adopted that behaviour or activity. Those consequences are climatological and ecological. It remains the case that we are socialised to desire and appreciate cars and it remains a fact that private transport can be extremely convenient. It can also, given the commentary above, appear hypocritical to be suggesting shifting to a far greater reliance on public transport, since this implicitly involves denying to developing country citizens a facet of modernity enjoyed previously by developed country citizens. But this is a distraction from the underlying collective interest in reduced car ownership and use. It denies the basic premise that a Precautionary Principle applies to all and that societies that are not yet car dependent have the opportunity to avoid a problem, rather than have to manage it via either moving straight to private transport BEVs or a transition from fossil fuel-powered ICEs to BEVs with all that entails in terms of ingrained emissions. Policy may be mainly domestic, but climate change is global and aggregate effects do not respect borders, which brings us to a second concept or risk that may be exacerbated.

Second, a ‘quasi-Jevons’ effect’ may apply. Growth of vehicle use is a problem of resource use and this is a thermodynamic and emissions problem. However, it is, as we have noted, also the case that battery technology and energy mix for BEVs are improving. So, this may involve significant declines in relative cost, which in turn may create a tendency for BEV ownership to accelerate which could exacerbate net growth in numbers of vehicles. Net growth could ironically be to the detriment of emissions savings. Whether this is so, depends, in part, on what kind of overall transport policy countries adopt and whether consumers, corporations and markets are allowed to be the arbiter of which area of transport dominates. It also depends, in part, on what materials are required for future batteries. Current technology implies massive increases in costs based on securing sources of lithium and cobalt as battery demand rises. So even if a Jevons’ effect is avoided, a different issue may apply. Resource procurement is a Precautionary Principle issue since effective BEVs at the kind of numbers necessary to substitute for all vehicles seem to require technological transformation—without it, multiple problems apply whilst emissions remain ingrained.

For example, when the UK CCC announced its 2035 recommendation to accelerate the BEV transition, members of the Security of Supply of Mineral Resources (SSMR) project wrote a research note to the CCC (Webster, 2019). They pointed out that the current total European demand for cobalt is 19,800 tonnes and that producing the batteries to replace 2.3 million cars in the UK (in accordance with contemporary statistics for new registrations) would require 15,600 tonnes. The UK would also need 20,000 tonnes of lithium, which is 45% of the current total European demand. If we replicate this ramping up of demand across Europe and the globe for vehicles, recognising that there are other growing demands for the minerals and metals (including batteries for other purposes) then it seems unlikely that supply can respond, unless dependence on lithium and cobalt (and other constituents) falls sharply as technology changes. Clearly, the problem is also contingent on the uptake of BEVs. Over recent years, there has, in fact, been an oversupply of the main materials for battery production because several of the main mining corporations anticipated that battery demand would take off faster than it actually has. For example, global prices of cobalt, nickel and lithium carbonate have increased significantly over the last decade but have fallen in 2018 to the end of 2019. However, industry analysis indicates that current annual global production is the equivalent of about 10 million standard BEVs based on current technology, and as the previous statistics on global vehicle numbers (see also next section) indicate, this is far less than transition via substitution would seem to require in the next decade. 29

Shortages and price rises, therefore, are if not inevitable, at least likely. Currently, about 60% of the cost of a BEV is the battery and 80% of that 60% (about 50% of the vehicle) is the cost of battery materials. It is, therefore, important to achieve secure supply and stable costs. The further context here is the issue of UK domestic battery capacity. In 2013, the government created the Advanced Propulsion Centre (APC) with a 10 year £500 million investment commitment matched by industry. The APC’s remit is to address supply chain issues for electric vehicles. Not unexpectedly, the APC quickly identified lack of domestic battery production capacity as a major impediment. In response in 2016 another government initiative, Innovate UK set up the Faraday Battery Challenge to encourage domestic capacity and innovation. The Battery Industrialisation Centre was then set up in Coventry, to attract manufacturers in the supply chain for BEVs to locate there, focussed around a centre of research excellence. However, the APC has no control over the global supply and prices of battery materials, the investment and location decisions of battery manufacturers or the necessary infrastructure for BEVs to be a feasible technology. 30 For example, according to the APC, if domestic BEV demand were 500,00 per year by 2025, then the UK would need three ‘gigafactories’. Battery manufacture is currently dominated by LG Chem and Samsung in South Korea, CATL in China and Panasonic in Japan. None of these have current plans to build a gigafactory in the UK. In any case, there is a further problem here which raises a whole set of environmental and ethical issues explored in ecological circles under the general heading ‘extractivism’ (see, e.g. Dunlap, 2019 ). As time goes by, the UK and the world may become dependent on high price supplies of materials drawn from unstable or hostile regimes (the Democratic Republic of Congo, etc.), which is a risk in many ways (and a likely source of Dutch disease—the ‘resource curse’—for unstable regimes). So, not placing a relative emphasis on substituting BEVs for ICEs and not endorsing the current vehicle growth trend (which is different as a suggestion than rejecting BEVs entirely) avoid multiple problems and risks.

It is also worth noting that simple market decisions can have a further collective adverse consequence based on individual consumer preference and reasoning, which may also affect BEVs in the short term. Many current BEVs have smaller or low efficiency batteries and thus short ranges. These favour urban use for short journeys, but most people own cars with a view also to range further afield. As such, it seems likely that until the technology is all long range (and the charging infrastructure is pervasive) many consumers, if the choice exists and income allows, will own BEVs as an additional vehicle, not a replacement vehicle. 31 This may be a short-term issue, given the regulatory changes focussed from 2030 to 2040 in many countries. But, again, from a Paris point of view, taking the IPCC 1.5°C and UNEP Emissions Gap reports into consideration, this matters. This brings us to a final issue. What is the actual take-up of BEVs (and ULEVs)? How rapid is the transition? In the Introduction section, I suggested that the UK had reached a tipping point and that this mirrored a general trend globally. This, however, needs context.

3.4 How many electric vehicles?

The data emerging in recent years and stated in the Introduction section are a step-change, but as a possible tipping point it begins from a low base and BEVs (the least emitting of the low emission vehicles) are a subset, albeit a rapidly expanding one, of ULEVs. According to the UK Department for Transport statistical release for 2018, there were 200,000 ULEVs registered in total, of which 63,992 ULEVs were newly registered in that year ( Department for Transport, 2019 , p. 4). 93% of the total registrations were cars and the total constitutes a 39% increase on the year 2017 total and a 20% increase in the rate of registration—there were just 9,500 ULEVs at the beginning of 2010 (so, about 20 times greater in a decade). However, the 2018 data mean that ULEVs accounted for just 0.5% of all licensed vehicles and were still only 2.1% of all new registrations in that year. Preliminary data available early 2020 indicate continued growth with almost 38,000 new BEV registrations in 2019, a 144% year-on-year increase. As a recent UK House of Commons Briefing Paper notes, however, the government prefers to emphasise the percentage changes in take-up rather than the percentages of the absolute numbers or the absolute numbers themselves ( Hirst, 2019 ). The International Energy Agency (IEA) places the UK in its leading countries list by ULEV and BEV market share (measured by the percentage of total annual registration): Norway dominates, followed by Iceland, Sweden, the Netherlands and then a significant drop-off to a trailing group including China, the USA, Germany, the UK, Japan, France, Canada and South Korea. However, the market share in this trailing group is less than 5% in every case (see appended Figure 1 ). China, given its size (and because of the urgency of its urban air quality problems and its capacity for authoritarian implementation), dominates the raw numbers in terms of total ULEVs and BEVs. All this notwithstanding, the IEA confirms the general point that up-take is accelerating, but the base is low and so achieving total ULEV or BEV coverage is some way off:

The global electric car fleet exceeded 5.1 million in 2018, up by 2 million since 2017, almost doubling the unprecedented amount of new registrations in 2017. The People’s Republic of China… remained the world’s largest electric car market with nearly 1.1 million electric cars sold in 2018 and, with 2.3 million units, it accounted for almost half of the global electric car stock. Europe followed with 1.2 million electric cars and the United States with 1.1 million on the road by the end of 2018 and market growth of 385000 and 361000 electric cars from the previous year. Norway remained the global leader in terms of electric car market share at 46% of its new electric car sales in 2018, more than double the second-largest market share in Iceland at 17% and six-times higher than the third-highest Sweden at 8%. In 2018, electric buses continued to witness dynamic developments, with more than 460000 vehicles on the world’s road, almost 100000 more than in 2017…In freight transport, electric vehicles (EVs) were mostly deployed as light-commercial vehicles (LCVs), which reached 250000 units in 2018, up 80000 from 2017. Medium truck sales were in the range of 1000–2000 in 2018, mostly concentrated in China. ( IEA, 2019A , p. 9)

Over the next few years, it seems likely we will see rapid changes in these metrics. There is a great deal of discussion in policy analysis regarding bottlenecks and impediments and these, of course, are also important (consumer uncertainty, ‘range anxiety’, availability of sufficient infrastructure for charging and so on). 32 However, as everything argued so far indicates regarding transition and trends, underlying the whole is the conditionality of success and the potential for failure, involving avoidable ingrained emission and risks. There is a basic difference between a superior technology and a superior choice since the latter is a socio-economic matter of context: of rates of change, scales and substitutions. Ultimately, this creates deep concerns in terms of achieving the Paris goals. The IEA explores two forecast scenarios for the uptake of ULEVs. Both involve a projection of annual ULEV sales and total stock to 2030 ( IEA, 2019A ). First a ‘New Policies’ Scenario. This takes the current policy commitments of individual countries and extrapolates. By 2030, the scenario projects global ULEV sales at 23 million in that year and a total stock of 130 million. This is considerably less than 30% of all vehicles now and in 2030. Second, the EV30@30 Scenario. This assumes an accelerated commitment that adopts the @30 goals (notably 30% annual sales share for BEVs by 2030; IEA, 2019A , pp. 29–30). By 2030, the scenario projects global ULEV sales at 43 million in that year and a total stock of 250 million. Again, this is less than 30% of all vehicles now and in 2030.

The figures, of course, are highly conditional, but the point is clear, even the best-case scenario currently being anticipated has ULEVs and BEVs as a minority of all vehicles in 2030—and 2030 is a key year for achieving Paris, according to the October 2018 IPCC 1.5°C report. Moreover, it is notable that the projections assume continuous growth in the number of vehicles (and so continuous growth in ICE vehicles) and the major areas of numerical growth in BEVs continue to be China, so some significant part of the anticipated total will be new ingrained emissions that arguably did not need to exist. 33 Again, this is highly conditional but it at least creates questions regarding what is being ‘saved’ when the IEA claims that the New Policies Scenario results in 2.5 million barrels a day less demand for oil in 2030 and the EV30@30 Scenario 4.3 million barrels a day ( IEA, 2019A , p. 7). 34 Less of more is not a saving in an objective sense, if this is a preventable future, and it is not a rational way to set about ‘saving’ the planet. It remains the case, of course, that this is better than nothing, but it is deeply questionable whether in policy terms any of this is the ‘best that can be done’. As stated in the Introduction section, technocentrism distracts from appropriate recognition of this. At its worse, technocentrism fails to address and so works to reproduce a counter-productive ecological modernisation: the technological focus facilitates socio-economic trends, which are part of the broader problem rather than solutions to it. The important inference is that there are multiple reasons to think that greater emphasis on social redesign and less private transport avoids successful failure and is more in accordance with the Precautionary Principle.

I ended the introduction to this essay by stating that we would be exploring the foregrounding question: What kind of solution are BEVs to what kind of problem? It should be clearer now what was meant by this. Ultimately, the balance between private and public transport matters if the Paris goals are to be achieved. Equally clearly, this is not news to the UK CCC or to any serious analyst of electric vehicles and the transport issue for our climatological and ecological future (again, e.g. Chapman, 2007 ; Bailey and Wilson, 2009 ; Williamson et al. , 2018 ; Mattioli et al. , 2020 ). At the same time, the context and issues are not widely understood and the problems are often understated, at least in so far as, discursively, most weight is placed on stating progress in achieving a transition to ULEVs and BEVs. This is technocentric. Despite its general concerns and careful critical stance, the CCC is also partly guilty of this. For example, Ewa Kmietowicz, Transport Team Leader of the CCC Secretariat, refers to the UK Road to Zero strategy as a ‘lost opportunity’, and the CCC identifies a number of shortfalls in the strategy. 35 However, the general thrust of the CCC position is to focus on a rapid transition to BEVs and to overcoming bottlenecks. 36 Broader feasibility is subsumed under general assumptions about continued economic expansion and expansion of the transport system. So, there is more of a situation of complementarity (with caveats) between public and private transport, and the whole becomes an exercise in types of investment within expansionary trends, rather than a more radical recognition of the fundamental problems that we ought to think about avoiding. It is also worth noting that many of the major advocates of BEVs are industry organisations. The UK Society of Motor Manufacturers and Traders, for example, are not unconcerned but they are not impartial either; they have a vested interest in the vehicle industry and its growth. For industry, ULEVs and BEVs are an opportunity before they are a solution to a problem. There are, however, recognitions that a rethink is required. These range from direct activism, such as ‘Rocks in the Gearbox’ (along the lines of Extinction Rebellion), to analysis from establishment think tanks, such as the World Economic Forum 37 , and statements from government oversight committees. For example, the UK Commons Science and Technology Committee (CSTC) not only endorses the CCC 2035 accelerated BEV target but also states more explicitly:

In the long-term, widespread personal vehicle ownership does not appear to be compatible with significant decarbonisation. The Government should not aim to achieve emissions reductions simply by replacing existing vehicles with lower-emissions versions. Alongside the Government’s existing targets and policies, it must develop a strategy to stimulate a low-emissions transport system, with the metrics and targets to match. This should aim to reduce the number of vehicles required, for example by: promoting and improving public transport; reducing its cost relative to private transport; encouraging vehicle usership in place of ownership; and encouraging and supporting increased levels of walking and cycling. ( CSTC, 2019 )

This, as Caroline Lucas suggests, speaks to the need to coordinate public and private transport policy more effectively and clearly, and there is a need for broader informed debate here. In political ecological circles, for example, there is a growing critique of the tensions encapsulated in the concept of an ‘environmental state’ (see Koch, 2019 ). That is the coordination and coherence of environmental imperatives with other policy concerns. State-rescaling and degrowth and postgrowth work highlight the profound problems that are now starting to emerge as states come to terms with the basic mechanisms that have been built into our economies and societies (see also Newell and Mulvaney, 2013 ; Newell, 2019 ). 38 New thinking is required and this extends to the social ontology and theory we use to conceptualise economies (see Spash and Ryan, 2012 ; Lawson, 2012 , 2019 ) and political formations (see Bacevic, 2019 ; Patomäki, 2019. Covid-19 does not change this ( Gills, 2020 ).

In transport terms, there are many specific issues to consider. Some solutions are simple but overlooked because we are always thinking in terms of sophisticated innovations and inventions. However, we do not need to conform to the logics of ‘technological fixes’, that we somehow think will enable the impossible, to perhaps see some scope in ‘fourth industrial revolution’ transformations ( Center for Global Policy Solutions, 2017 ; Morgan, 2019B ). For example, public transport may also extend to a future where no individual owns a range extensive powered vehicle (perhaps just local scooters for the young and mobility scooters for the infirm) and instead a system operates of autonomous fleet vehicles that are coordinated by artificial intelligence with logistics implemented through Smartphone calendar access booking systems—and coordination functions could maximise sharing, where vehicles could also be (given no drivers are involved) adaptable connective pods that chain together to minimise congestion and energy use. This seems like science fiction now, and perhaps a little ridiculous, but a few years ago so did the Smartphone. And the technology already exists in infancy. Such a system could be either state-funded and run or private partnership and franchise, but in either case, it radically redraws the transport environment whilst working in conformity with the geography of living spaces we have already developed. Will is what is required and if the outcome of COP24 ( UNFCCC, 2018 ) and COP25 ( Newell and Taylor, 2020 ) with limited progress towards the Paris goals persists, then it seems likely that emissions will accumulate rapidly in the near future and the likelihood of a serious climate event with socio-economic consequences rises. At that stage, more invasive statutory and regulatory intervention may start to occur as the carbon budget becomes a more urgent target. Prohibitions, transport rationing and various other possibilities may then be on the agenda if we are to unmake the future we are currently writing and, to mix metaphors, avoid a road to nowhere.

None declared

Thanks to two anonymous reviewers for extensive and useful comment—particularly regarding the systematic statement of issues in the Introduction section and for additional useful references. Jamie Morganis Professor of Economic Sociology at Leeds Beckett University, UK. He coedits the Real-World Economics Review with Edward Fullbrook. RWER is the world’s largest subscription based open access economics journal. He has published widely in the fields of economics, political economy, philosophy, sociology, and international politics. His recent books include: Modern Monetary Theory and its Critics (ed. with E. Fullbrook, WEA Books, 2020), Economics and the ecosystem (ed. with E. Fullbrook, WEA Books, 2019); Brexit and the political economy of fragmentation: Things fall apart (ed. with H. Patomäki, Routledge, 2018); Realist responses to post-human society (ed. with I. Al-Amoudi, Routledge, 2018); Trumponomics: Causes and consequences (ed. with E. Fullbrook, College Publications, 2017); What is neoclassical economics? (ed., Routledge, 2015); and Piketty’s capital in the twenty-first century (ed. with E. Fullbrook, College Publications, 2014).

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Global electric car sales and market share, 2013–18.

Source : IEA (2019, p. 10).

ULEV refers to vehicles that emit less than 75 gCO 2 per km. This essentially means BEVs, PHEVs, range-extended (typically an auxiliary fuel tank) electric vehicles, fuel cell (non-plug-in) electric vehicles and hybrid models (non-plug in vehicles with a main fuel tank but whose battery recharges and which drive short distances in electric mode).

Note, there is little sign of legislative and regulatory detail to plans as of early 2020. Furthermore, there is a difference between acknowledging that the uptake of alternatively fuelled vehicles, including BEVs, is growing and drawing the inference that UK government policy (channelled primarily via the Department for Transport) is as effective as it might be (see Environmental Audit Committee, 2016 ; National Audit Office, 2019 and also later discussions).

CEM is coordinated by the IEA and is an initiative lead by Canada and China (but including a steadily growing number of signatory countries). The EV30@30 initiative aims to achieve a 30% annual sales share for BEVs by 2030.

IEA headline statistics include plug-in hybrids so 2018 becomes 46% for Norway (IEA, 2019A, p. 10).

For example, Spash (2020) and Spash and Ryan (2012) . One might also note the work of John O’Neill at Manchester University. Perhaps the most prominent ‘realist’ working on transport and ecology is Petter Naess, at Norwegian University of Life Sciences.

The UNEP 9th Report calls for a 55% reduction by 2030.

The initial rationale in 2008 was that to achieve a maximum limit of 2°C warming global emissions needed to fall from the levels at that time to 20–24 GtCO 2 e with an implied average of 2.1–2.6 t CO 2 per capita on a global basis in 2050. This translated to a 50–60% reduction to the then global total. Since UK emissions were above average per capita, the UK reduction required was estimated at about 80%. Given that emissions then increased and atmospheric ppm has risen the original calculations are now mainly redundant.

For the work of the CCC, see: https://www.theccc.org.uk/about/ .

The report also provides useful context regarding the UN sustainable development goals ( CCC, 2019 : p. 66) and CCC thinking on growth and economics ( CCC, 2019 : pp. 46–7).

https://www.theccc.org.uk/2019/06/11/response-to-government-plan-to-legislate-for-net-zero-emissions-target/ .

And further methodological issues apply in economics (see; Morgan and Patomäki, 2017 ; Nasir and Morgan, 2018 ; Morgan, 2019A ).

For a full analysis, see https://www.carbonbrief.org/analysis-uks-co2-emissions-have-fallen-29-per-cent-over-the-past-decade . The Carbon Brief analysis omits shipping and aviation. As the campaign group Transport and Environment notes UK shipping was responsible for 14.4 MtCO 2 , which is the third highest in Europe (after the Netherlands and Spain) and shipping is exempt from tax on fossil fuels under EU law. See p. 20: https://www.transportenvironment.org/sites/te/files/publications/Study-EU_shippings_climate_record_20191209_final.pdf .

UK coal use for energy supply reduced by approximately 90% from 1990 to 2017 and in 2019 amounted to just 2% of the energy mix and in 2019 the UK went two weeks without using any coal at all for power production (the first time since 1882); 1990 to 2010 natural gas use steadily increased from a near-zero base but has declined since 2010 as use of renewables has grown. Coal use in manufacturing has decreased by 75% from 1990 to 2017 ( ONS, 2019 ). As noted, some assessments place the reduction in total emissions at around 40% based on other metrics and the tabulated figures I provide indicate yet another percentage— all however are trend decreases indicative of a general direction of travel.

‘Embedded emissions’ or the UK carbon footprint is addressed by the UK Department for Environment Food and Rural Affairs (Defra). To be clear, there is a whole set of further issues that one might address in regard of measurement of emissions—how they are attributed and what this means (where created, where induced through demand, which state, what corporation and so different ‘Cartesian’ claims regarding the significance of location are possible), and this is indicative of the conflict over representation and partition of responsibility (so whilst the climate does not care about borders, they have infected measurement and policy). There is no scientifically neutral way to achieve this, merely different sets of criteria with different consequences (I thank an anonymous referee for extended comment on this, see also Taylor, 2015 ; who argues that adaptation politics produces a focus on governance within existing political and economic structures based on borders, etc.).

Congestion charges in London or a plastic bag tax do not meet this threshold.

This is supported, for example, by The Climate Group’s EV100 initiative: a voluntary scheme where corporations commit to making electric the ‘new normal’ of their vehicle fleets by 2030 (recognising that over half of annual new registrations are owned by businesses) https://www.theclimategroup.org/project/ev100 .

Until recently Tesla had one main production centre in California. However, it now also has a $5 billion factory in Shanghai and plans for a factory in Berlin. Tesla is currently the world’s largest producer of BEVs (368,000 units in 2019), followed by the Chinese company BYD Auto (195,000 units in 2019). Tesla was founded in July 2003 by Martin Eberhard and Mark Tarpenning in response to General Motors scrapping its EV programme (as unprofitable). Elon Musk joined as a HNWI first-round investor in February 2004 (he put in $6.5 m of the total $7.5 m and became chairman of the Tesla board); Eberhard was initially CEO but was removed and replaced by Musk in 2007 and Tarpenning left in 2008. Tesla floated on the Nasdaq in June 2010 at $17 per share and exceeded $500 per share for the first time in January 2020. Tesla is the USA’s most valuable car manufacturer by market capitalisation (worth more than Ford and GM combined).

The European Commission’s collaborative research forum JEC has been producing ‘well-to-wheels’ analyses of energy efficiency of different engine technologies since the beginning of the century. The USA periodically publishes the findings of its GREET model (the Greenhouse gases Regulated Emissions and Energy use in Transportation model). See https://greet.es.anl.gov .

For example, since 1985 according to Carbon Brief global coal use in power production measured in terawatt hours only reduced in 2009 and 2015 (though it seems likely to do so in 2019); China notably continues to build coal-fired power plants though the rate of growth of use has slowed. (According to the IEA Coal report, 2019, China consumed 3,756 million tonnes of coal in 2018 (a 1% increase) and India 986 million tonnes (a 5% increase). Renewables are a growing part of an expanding global energy system.

https://www.carbonbrief.org/analysis-global-coal-power-set-for-record-fall-in-2019 .

Staffell et al . observe that the British electricity grid produces an average 204 gCO 2 per kWh in 2019 and a standard petrol car emits 120–160 gCO 2 per km.

This is a point made by Richard Smith. There are, of course, alternatives to aluminium. One should also note that manufacturers are responding to consumer preference by increasing the average size of models and this is increasing the weight and resource use. In February 2020, for example, Which Magazine analysed 292 popular car models and found that they were on average 3.4% or 67 kg heavier than older models and this was offsetting some of the efficiency gains for emissions.

And the argument this is leading to is that it makes far greater sense to default to greater dependence on prudential social redesign, rather than optimistic technocentrism, behind which is techno-politics.

For discussion of battery technology and scope for improvement, see Manzetti and Mariasiu (2015) and Faraday Institution (2019) . Currently, most BEVs use lithium-ion phosphate, nickel-manganese cobalt oxide or aluminium oxide batteries. Liquid electrolyte constituents require containment and shielding. Specifically, a battery creates a flow of electrons from the positive electrode (the cathode made of a lithium metal oxide, etc. from the previous list) through a conducting electrolyte medium (lithium salt in an organic solution) to a negative electrode (the anode made typically of carbon, since early experiment with metals tended to produce excess heating and fire). This creates a current. Charging flows to the anode and discharge oxidises the anode which must then be recharged. The batteries are relatively low ‘energy density’ and can be a fire hazard when they heat. Given the chemical constituents, battery disposal is also a significant environmental hazard (see IEA, 2019A: pp. 8, 22–3). A ‘solid-state’ battery uses a specially designed (possibly glass or ceramic) solid medium that allows ions to travel through from one electrode to another. The solid-state technology is in principle higher energy density, much lighter and more durable. The implication is higher kWh batteries with greater range, charging capacity and durability and efficiency. Jeremy Dyson has reportedly invested heavily in solid-state technology and though his proposed own brand BEV is not now going ahead, reports indicate the battery technology investment will continue.

One might also consider hydrogen battery technology. Hydrogen fuel cell technology for vehicles is different than BEV. The vehicle has a tank in the rear for compressed cooled gas, which supplies the cell at the front of the car whilst driving. Refuelling is a rapid pumping process rather than a long wait. The gas has two possible origins: natural gas conversion where ‘steam methane reformation’ separates methane into hydrogen and CO 2 or water electrolysis, where grid AC electricity is converted to DC, which is applied to water and using a membrane splits it into hydrogen and waste oxygen. Currently, over 95% of hydrogen is from the former. Major investors in hydrogen technology are Shell (for natural gas conversion), IMT Power (in partnership with Shell) for water conversion and Toyota whose Mirai model is hydrogen powered.

Though fewer new cars were registered than in previous years, this significant metric for the total number of vehicles is the cumulative number of registrations (taking into account cars no longer registered). There are, however, some underlying issues: uncertainty regarding the status of diesel cars and problems of availability, cost and trust in BEVs seems to be causing many people in the UK to delay buying a new car; the expansion of Uber meanwhile has had a generational and urban effect, reducing car ownership as an aspiration amongst the young.

And re aviation, a new runway at Heathrow between 2026 and 2050.

See: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/852708/provisional-road-traffic-estimates-gb-october-2018-to-september-2019.pdf .

See: https://greenworld.org.uk/article/budget-deeply-disappointing-says-caroline-lucas

For example, global production of cobalt in 2018 was 120,000 tonnes, and production of about 2 million BEVs currently requires around 25,000 tonnes, so 10 million BEVs would require all of the current output. Cobalt traded at more than US$90,000 per ton 2018 but had fallen to around US$30,000 at the end of 2019.

In the UK, the current daily consumption of petrol and diesel for road transport is about 125 million litres or about 45 billion litres per year. So, BEVs are essentially substituting for this scale of energy use, shifting demand to electricity generation. National Grid attempted to model this in 2017. Their forecast (highly contingent obviously) suggests that if all cars sold by 2040 were BEVs and thus the car market was dominated by BEVs by 2050 and if most vehicles were charged at peak times in 2050 then an additional 30 gigawatts of electricity would be required. This is about 50% greater than the current peak winter demand in 2017. This was widely reported in the press. This best/worst case, of course, does not allow for innovative solutions such as off-peak home charging pioneered by Ovo and other niche suppliers. However, even with such solutions, there will still be a net increase in required capacity from the system. This has been estimated at about 10 new Hinckley power stations.

One possible long-term solution currently in development is toughened solar panel devices that can be laid as a road or car park surfaces, enabling contact recharging of the vehicle (in motion or otherwise). There are, however, multiple problems with the technology so far.

For example, analysis from Capital Economics suggests a three-way charging split is likely to develop: home recharging is likely to dominate, followed by an on-route charging model (substituting for current petrol forecourts at roadside) and destination recharging (given charging is slower than filling a fuel tank it makes sense to transform car parks at destinations into charging centres—supermarkets, etc.). They estimate UK demand at 25 million BEV chargers by 2050 of which all but 2.6 million will be home charging. As of early 2020, there were 8,400 filling stations which might be fully converted. Tesco has a reported commitment to install 2,400 charging points. These are issues frequently reported in the press.

This point can also be made in other ways. Not only does the emissions saving relate to net new sources of cars, but the contrast is also in terms of trend changes in the size of vehicle. According to the recent IEA World Energy Outlook report ( IEA, 2019B ), the number of SUVs is increasing and these consume around 25% more fuel than a mid-range car. If current growth trends continue (SUVs are 42% of new sales in China, 30% in India and about 50% in the USA), the IEA projects that the take-up of ICE SUVs will more than offset any marginal gains in emissions from the transition to BEVs.

It is also the case that the projected ‘savings’ from ULEVs are likely inaccurate. Following the EU, most countries adopted (and manufacturers report using) the Worldwide Harmonised Light Vehicle Test Procedure (WLTP). This became mandatory in the UK from September 2018. The WLTP is the new laboratory defined test for car distance-energy metrics. Vehicles are tested at 23°C, but without associated use of A/C or heating. Though claimed to as realistic than its predecessors, it is still basically unrealistic. Temperature range for ULEVs has significant consequences for battery performance and for use of on-board services, so real distance travelled per unit of energy is liable to be less. For similar reasons, ICEs will also travel less distance per litre of fuel so this is not a comparative gain for ICEs, it is likely a comparative loss to all of us if we rely on the figures.

See https://www.theccc.org.uk/2018/07/10/road-to-zero-a-missed-opportunity/ .

See https://www.theccc.org.uk/2018/07/10/governments-road-to-zero-strategy-falls-short-ccc-says/ .

See https://www.weforum.org/agenda/2019/08/shared-avs-could-save-the-world-private-avs-could-ruin-it/ .

For practical network initiatives, see, for example, https://climatestrategies.org .

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The Economics of Electric Vehicles

We examine the private and public economics of electric vehicles (EVs) and discuss when market forces will produce the optimal path of EV adoption. Privately, consumer cost savings from EVs vary. Some experience net benefits from choosing gasoline cars, even after accounting for EV subsidies. Publicly, we survey the literature documenting the external costs and benefits of EVs and highlight several themes for optimal policy design including, 1) promoting regional variation in EV policies that align private incentives with social benefits, 2) pursuing a time-path of policies that reflect changing marginal benefits, and 3) rationalizing electricity and gasoline prices to reflect their social marginal cost. On the extensive margin, purchase incentives should ramp-down as learning-by-doing and network externalities (to the extent that they exist) diminish; on the intensive margin, gasoline should become relatively more expensive over time than electricity (per mile traveled) to reflect cleaner marginal emissions from electricity generation.

The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. We thank Severin Borenstein, Jim Bushnell, Ken Gillingham, Joseph Shapiro, two anonymous referees and many seminar participants for their helpful comments. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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David S. Rapson & Erich Muehlegger, 2023. " The Economics of Electric Vehicles, " Review of Environmental Economics and Policy, vol 17(2), pages 274-294.

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Review and Development of Electric Motor Systems and Electric Powertrains for New Energy Vehicles

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This paper presents a review on the recent research and technical progress of electric motor systems and electric powertrains for new energy vehicles. Through the analysis and comparison of direct current motor, induction motor, and synchronous motor, it is found that permanent magnet synchronous motor has better overall performance; by comparison with converters with Si-based IGBTs, it is found converters with SiC MOSFETs show significantly higher efficiency and increase driving mileage per charge. In addition, the pros and cons of different control strategies and algorithms are demonstrated. Next, by comparing series, parallel, and power split hybrid powertrains, the series–parallel compound hybrid powertrains are found to provide better fuel economy. Different electric powertrains, hybrid powertrains, and range-extended electric systems are also detailed, and their advantages and disadvantages are described. Finally, the technology roadmap over the next 15 years is proposed regarding traction motor, power electronic converter and electric powertrain as well as the key materials and components at each time frame.

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1 Introduction

With the thriving economy, car demand has increased. However, fuel-powered vehicles emit carbon dioxide and nitrogen oxide, causing greenhouse effect on the climate and toxic effect on human health [ 1 , 2 ]. Moreover, a large amount of diesel consumption has caused the global energy crisis. According to statistical data, the increase of two-thirds of the petroleum consumption comes from transportation industries, which is extremely unfavorable to the sustainable development of human society [ 3 , 4 ]. Driven by the global emission reduction targets in the Paris Climate Agreement, new energy vehicles (NEVs) have become an important development direction of the automotive industry [ 5 , 6 ]. While European countries and their car manufacturers have scheduled to limit sales of the fuel vehicle, the NEV Industry Development Plan (2021–2035) has been promulgated by the Chinese government, in which the goals are set for the next 15 years. According to the statistics of the China Association of Automobile Manufacturers (CAAM), the production and sales of NEVs in China reached 1.242 and 1.206 million units, respectively, in 2019[ 7 ], 1.366 and 1.367 million units, respectively, in 2020.

Compared with industry motors, NEV traction motors should be adapted for harsh operating environments. Their operation modes are frequently switched between motoring and generating. Frequent starting and stopping, high rate of acceleration/deceleration, high torque at low speed and high power at vehicle high-speed climbing, high power density, large highly efficient operating area, low vibration and noise, high reliability and high performance-to-price ratio are required by the automotive industry [ 8 , 9 ]. Traction motors and motor power electronic controllers are the core parts for converting the electromechanical energy in NEVs [ 10 , 11 ].

The electric powertrain systems integrated gears, clutch, and other mechanical components with the traction motors and motor controllers are also an indispensable system part of NEVs. The vehicle structure and propulsion are simplified in the e-powertrain dramatically, whose topologies greatly influence the NEV performance [ 12 ].

Therefore, the requirements for the electric drive systems in NEVs mainly include the following aspects: (1) high torque density and good torque control capability for vehicle dynamic performance; (2) reliability and durability for the required vehicle safety and life; (3) high efficiency within operation spectrum [ 13 , 14 ] and high performance-to-cost ratio for the energy economy and the users’ capital investment.

The technologies of traction motors, their power electronic controllers, and electric powertrains are summarized. The advantages and disadvantages of existing technologies and their prospects and development are discussed, providing reference for the researchers and engineers in NEVs powertrain system areas.

2 Development of NEV Traction Motors

2.1 classification and characteristics of nev traction motor.

The traction motors in NEVs mainly include direct current motors (DCMs), induction motors (IMs), permanent magnet motors (PMMs), and switched reluctance motors (SRMs). Among them, PMM is divided into PM DC motor (PMDCM), PM synchronous motor (PMSM), PM brushless DC motor (PM-BLDCM) and PM hybrid excitation motor (PM-HEM) [ 15 ]. To reduce the dependence on PM materials, excitation synchronous motor is also installed onboard vehicles, as shown in Fig.  1 .

Traction motors of NEVs

2.1.1 Direct Current Motor

DCM is used as the traction motor in electric vehicles (EVs) from the late nineteenth century because of its simple speed regulation. However, low efficiency, large mass, and poor reliability due to brushes and commutators make DCMs no longer suitable for high-speed NEVs. They are used only in low-speed EVs, such as carts for logistic cargo moving inside plants, and shuttle bus in scenic areas.

2.1.2 Switched Reluctance Motor

The SRM stator and rotor are composed of silicon steel laminates, and a salient pole structure is adopted. There are no windings, slip rings or PMs on the rotor, and only simple concentrated windings are installed on the stator. The rotor structure enables simple, robust, low cost, and high-speed operation of SRMs. Moreover, its inverter’s reliable topological structure prevents it from short-circuit faults [ 16 ]. High efficiency and simple control are SRMs’ advantages. However, torque fluctuation, noise, and vibration are serious preventing it from applications in NEVs.

2.1.3 Induction Motor

Squirrel-cage IMs are widely used in NEVs. Their stator and rotor are composed of laminated silicon steel sheets, and three-phase windings are inserted inside the stator lamination stack and aluminum or copper bars in the rotor slots with rings at both ends. IMs are characterized as having a simple and strong structure, low cost, high reliability, small torque ripple, low noise, and maintenance-free. IMs can be easily run at high speed over 15,000 rpm with a wide constant power range. However, IMs control circuit is complex, and their efficiency and power density are relatively low compared to PMSMs, leading to its increasingly lower market share globally [ 17 ].

2.1.4 Permanent Magnet Motor

Permanent magnet direct current motor

When field windings and magnetic poles of conventional DCMs are replaced with PMs, a PM-DCM is established. PM-DCMs show higher power density and efficiency, but it needs more maintenance and exhibits low life and torque fluctuation due to the commutator and brush system; these are still the concerns to be solved for EV applications.

• Permanent magnet synchronous motor

In PMSM, its stator with three-phase windings is the same or similar to IM or synchronous motor stator, and PMs replace the excitation winding of traditional synchronous motors. According to the position of PMs on or in the rotor, PMSMs can be divided into surface-mounted PMSM (SPM) and interior embedded type (IPM). Well-designed IPMs are featured by high reluctance torque, high efficiency, high power factor, low heat, simple structure, small package and low noise. With the development of power electronics control strategy, IPMs have become dominant in traction motor applications. In addition, owing to the fully enclosed structure, IPMs, being maintenance-free, show low wind friction losses and low windy noise.

Permanent magnet brushless DC motor

PM-BLDCM is a special PMSM structurally and theoretically, but its windings are concentrated normally and the stator current waveshape is trapezoidal, instead of sinusoidal in SPM. The commutator-brush system is not required. However, the torque ripple and noise appear during electrical commutation, and it is difficult to achieve the maximum speed beyond twice the base speed.

Permanent magnet hybrid excitation motor

By adding excitation windings to PMSM, the motor has both PMs and excitation windings and becomes a hybrid excited motor, which is PM-HEM. This motor has the minimum flux leakage, high flux density in the air gap, high power density, and good torque-speed characteristics. However, its topology and control are relatively complex owing to two separate excitations.

The performance comparison of the aforementioned motors is shown in Table 1 .

In Table 1 , ●, ●●, ●●● represents the low (poor), medium, and high (good) indices, respectively. Thus, PMSM, especially IPM, is the best choice for NEV traction motors [ 18 ].

2.2 Research of NEVs PMSM

A new type of DC saturated hybrid excitation motors was proposed in Ref. [ 19 ]. By introducing additional DC field excitation with step-down DC saturation capability, the magneto resistive effect was constructed in the rear pole Vernier PMM (CP-VPMM). In this topology, the bidirectional flux control of the stator DC excitation reluctance motor and the good torque density in CP-VPMM are combined. An airgap-harmonic-oriented design method was proposed [ 20 ]. The magnetic flux enhancement was adopted, and its characteristics was L d  >  L q , which could be used in sensorless motor controls. By integrating a special design of stator teeth, the air gap length on the q-axis was increased to obtain high reluctance torque, good fault tolerance, and high reliability. Next, a hybrid circuit method to improve the efficiency of wound synchronous traction motors was proposed [ 21 ]. By changing the connection between windings U , V , W and windings X , Y , Z , the efficiency at high speed could be improved. The torque ripples and the coupling between the internal/external magnetic fields of a compound excitation PMM are reduced through finite element analysis in Ref. [ 22 ].

For the PMSM with a complex structure of double saliency, dual stators, and dual air gaps [ 23 ], a multiple sensitive objective optimization method was used to select the key dimensional parameters. Furthermore, the optimized geometrical dimensions were obtained by the response surface optimization method. Next, a six-phase fractional slot concentrated winding PMSM with fault tolerance capability was discussed [ 24 ]. Based on the analysis of magnet electromotive force harmonics, a new pole-slot matching scheme was proposed to reduce the eddy current loss of PMs caused by the concentrated windings and to reduce the number of rotor poles, and consequently the stator core losses.

2.3 Development of the PMSM Technology

The future technologies of traction motors for NEVs focus on the key factors of high efficiency, high speed, high power density, low vibration and noise, better electromagnetic compatibility (EMC) and low cost. In the EV development 2025 roadmap proposed by the US Department of Energy, the EV motors are aimed at achieving high efficiency (97%), high power density (50 kW/L), and low cost (3.3 $/kW). In the “Energy-saving and New Energy Vehicle Technology Roadmap 2.0”, the goals for 2025 are set as a specific power (power-to-mass ratio) of 5.0 kW/kg, power density (power-to-volume ratio) of 35 kW/L, and the peak efficiency of 97% for traction motors. To achieve these goals, global NEV traction motor suppliers and research institutions are collaborating to improve innovation chain and supplier chain, including components and materials.

2.3.1 High Slot Filled Ratio Winding Technologies

By adopting high slot filled ratio windings with flat/rectangular wires or hairpin windings [ 25 ] , the winding heating can be greatly reduced, and the utilization rate of the winding copper materials can be increased by 15%–20%, which is the main method to improve torque density, power density, and efficiency. For example, the power density of 4.6 kW/kg is achieved in GM VOLT motor through hairpin wingdings.

2.3.2 High-Speed Motor Technology

The motor size is proportional to its torque. For a motor with given power requirement, its power equals torque multiplied by speed. By increasing the operation speed, the torque requirement for the motor can be reduced, thereby reducing the motor volume and weight, and its power density increased with the speed. For example, the traction motor speed of 17,900 r/min is used in Tesla Model 3, and the motor speed of 25,000 rpm is aimed to be achieved by 2035 in NEV Technology Roadmap 2.0 of China.

2.3.3 Efficient Thermal Management Technologies

Soil cooling, oil and water combined cooling, and new cooling topologies are used to improve cooling technologies and heat transferring of traction motors, and the power density of the motors is raised consequently.

PMSMs for NEVs have made continuous progress globally in power density, system integration, efficiency, maximum working speed, winding manufacturing process, and cooling technologies. The technical indicators of typical motor products are shown in Table 2 .

3 Research of NEV Motor Control

Nowadays, PMSM requires its control strategies to have fast system dynamic response, high dynamic/static precision, and strong anti-interference ability. However, the PMSM models are nonlinear; with strong coupling, time-varying parameters, multiple variables, and large disturbance, its control algorithms are complex. Therefore, the performance of the motors is affected directly by the control strategies. Typical control strategies include constant voltage/frequency ratio, classical proportion integration differentiation (PID), field-oriented approach, direct torque, sliding mode variable structure, adaptive and intelligent controls.

3.1 Motor Control Technologies

Among the PMSM control technologies, the variable-voltage-variable-frequency (VVVF) control method has absolute advantages in performance through the following three methods: the constant voltages-per-frequency (i.e., V / F  = const) control which is open-loop type and is based on the steady-state motor models, the field-orientation control (FOC) and direct torque control (DTC). The latter two are close-loop types and are based on dynamic motor models. The comparison of the three motor control methods is presented in Table 3 .

3.1.1 Constant V/F Ratio Control

Constant V / F ratio control, also known as constant flux control, can obtain the constant flux by guaranteeing that the stator voltage per frequency maintains constant. The state feedback control was adopted in an N-T coordinate system, and a new sensorless V / F control method for PMSMs was proposed [ 26 ]. When the motor was running at low speed, the T -axis current is used to keep the system at high stability. To operate the motor stable at medium and high frequencies, a velocity stability loop is added, and an active power disturbance component is extracted for compensation [ 27 ].

V / F control is a relatively common method for IMs’ speed control with the advantages of simplicity, effectiveness, and high robustness to parameter variation. However, since it is an open-loop control, the control accuracy, dynamic response, and load capacity of the systems are reduced due to drifts of speed and flux in the V / F open-loop control, which leads to poor startup capability, high torque ripple, and narrow speed range. Therefore, V / F controls are seldom used in vehicular traction motor control.

FOC was proposed by Blaschke in the 1970s. The stator current was decoupled into the torque component and magnetized under the constant rotor flux in the special dq0 coordinate system, and the control of alternating current (AC) motors can be equivalent to that of an unexcited DC motor. FOC can achieve smooth starting, low torque ripple and wide speed range, suitable for high dynamic response of machinery under tough working conditions.

A vector control strategy is proposed based on the motor speed-torque-current diagram [ 28 ]. The power demand and the energy consumption were effectively reduced, and the vehicle driving range was extended. A flux-weakening control strategy was proposed through an estimator with improved uncertainty and disturbance. A flux-weakening adjusting factor to smooth the torque ripple at motor corner speed is introduced [ 29 ]. The robustness at the flux-weakening area is enhanced consequently.

Setting up a motor dynamic model in the two-phase rotating coordinate is the key to a successful FOC, laying basis for high dynamic response under harsh working conditions.

The DTC was proposed by Depenbrock, with the current loop in the FOC system being removed and no complex coordinate transformation required. The two-bit bangbang control is used to generate PWM modulation signals in a two-phase static coordinate. DTC has the advantages of simple structure, fast dynamic response, low sensitivity to parameter perturbation, and strong robustness, therefore suitable for applications requiring rapid dynamic response and wide speed regulation. However, it also has the disadvantages of the current and torque ripples at low speeds and the requirement for high sample frequency. Many scholars combine the space vector pulse width modulation (SVPWM) and DTC to reduce these ripples.

An improved control strategy using the quadratic estimation method (QEM) and the harmonic voltage elimination (HVEM) methods was proposed [ 30 ]. The final voltage vector suppressing harmonic current of the stator was obtained; thus, the fast dynamic response and good steady performance were kept unchanged. A novel multi-machine robust DTC scheme based on the nonlinear model prediction (NMP) method was proposed [ 31 ]. It achieved the acceleration slip regulation (ASR) and anti-lock braking system (ABS) functions of four wheels PMSMs and better driving performance and vehicle stability. A fuzzy model predictive DTC (FMP-DTC) strategy for an IPM of EV is proposed [ 32 ]. The weighting factor adjustment was no more required for optimal switch state selection. The instantaneous torque response, small torque ripple, and accurate speed tracking were achieved. A voltage vector allocation strategy based on a dual-space vector PWM control scheme was proposed [ 33 ]. By selecting the most appropriate mode, the switching frequencies of the two inverters could be balanced and reduced, and the power-sharing in the maximum range could be obtained.

DTC control, even though simple, has an excellent dynamic and static performance. However, it has a limitation on the increase of inverter switching frequency. There is no current loop and the current protection should be done directly, so additional measurements to limit currents are needed. The “dead-time effect” is also obvious at low speed, and the change of stator resistance will distort stator current and flux linkage.

3.2 Current Control Strategies (CCS)

The strategies of PM control include i d  = 0, maximum torque per ampere (MTPA), maximum torque per volt (MTPV), flux weakening (FWC), unit power factor (cos  Φ  = 1) controls. The performance comparison of these current control strategies is shown in Table 4 .

3.2.1 i d  = 0 Controls

The advantages of this control strategy are algorithm simplicity, small computations, and no demagnetization effect, which is generally applicable in low-power servo systems. However, its power factor is low. For the interior PMSM, this method does not utilize the reluctance torque of the motor, which reduces the motor torque performance. Thus, it is only used in surface-mounted PM motors (SPM).

3.2.2 MTPA Controls

MTPA strategy makes full use of the reluctance torque of motor, so the maximum torque output is greatly improved. With the same output torque, the stator current of this method is minimum, which reduces copper losses and improves efficiency. However, this control strategy is complex and the parameter robustness is not very high.

3.2.3 MTPV Controls

MTPVs make full use of the voltage limit ellipse and DC bus voltage. The high inverter capacity, the maximum output torque at flux weakening range and the quick system response can be achieved by this method. However, this control algorithm is relatively complex.

3.2.4 FW Controls (FWC)

In this method, the flux of the PM motor is reduced by increasing the d-axis demagnetization current, which guarantees the voltage balance and improves the speed adjusting range. However, this control strategy is sensitive to motor parameter perturbations, which leads to low robustness.

3.2.5 cos Φ = 1 controls

The unit power factor control based on cos  Φ  = 1 makes the power factor equal to 1 by controlling the d -axis and q -axis currents of PMSM simultaneously without reactive power output. This control strategy makes full use of the motor inverter capacity, but its maximum motor torque capacity is reduced.

3.3 Control Algorithms

Besides PID control, many other advanced control algorithms are introduced. The comparison of several control methods is shown in Table 5 .

3.3.1 PID Control

The classic PID control method is stable, reliable, conveniently adjustable, and simply structured, making a good method for linear and stationary objects. However, PMSM is a strong-coupling and nonlinear object, where the parameters change and interact complexly. To improve the motor speed regulation performance, PID control is combined with other control methods, such as adaptive PI, neural network PI, and fuzzy PI controls. However, in terms of motor torque tracking accuracy, response speed, torque ripple suppression, and parameter robustness, the algorithms above are not efficient for achieving excellent dynamic and static performance. Therefore, several other advanced control algorithms are proposed.

3.3.2 Adaptive Control

The adaptive control algorithm handles system uncertainties by adjusting the controller parameters online, thus having strong robustness. Among them, model reference adaptive control is the most common. Its system is composed of a reference model, an adjustable system, and an adaptive mechanism. However, the design of the reference model and adjustable system relies on the precise motor model, which is seriously influenced by the motor parameter perturbations.

3.3.3 H ∞ Control

As a typical robust control (RC) method, the H ∞ control algorithm aims at minimizing the sensitivity of the controller uncertainties to maintain the system control performance. Its robustness and disturbance rejection are both strong, but the solution process is complex.

3.3.4 Active Disturbance Rejection Control (ADRC)

ADRC uses a disturbance observer to estimate the system uncertainties and then introduces the disturbance rejection into the control signals to compensate the uncertainties. ADRC provides a strong disturbance rejection. However, its observer’s design parameters are numerous, the approximation process is delayed, and a certain steady-state error exists, which affects the motor control accuracy.

3.3.5 Model Predictive Control (MPC)

MPC is simple in design and has a fast dynamic response. Its action is based on solving an optimal control problem of open loop in the finite-time domain at every sampling moment. However, this control algorithm is complicated and depends on the motor model parameters.

3.3.6 Neural Network Control (NNC)

The NNC method can achieve a smooth start, small torque ripple, wide speed range, and high robustness with a simple parameter setting, strong self-learning ability, and low motor parameter sensitivity. However, the NNC structure is relatively complex, and online iterative computation leads to poor real-time performance. Thus, it is more suitable for off-line parameter identification.

3.3.7 Fuzzy Logic Control (FLC)

FLC has a simple structure, good robustness, and a small impact on the motor startup. It is well applied in the design of the AC servo motor control system. However, in practical applications, its design relies on experience and expert knowledge.

3.3.8 Sliding Mode Control (SMC)

The SMC algorithm, being invariable to external disturbances, has a simple structure, low sensitivity to the internal parameter perturbations, and high control accuracy. It is suitable for the control of nonlinear uncertain systems, but has a high torque ripple. Chattering, singularity, and mismatched uncertainty limit its applications. Advanced SMC algorithms were proposed to suppress the chattering and even eliminate it by reducing the switching gain and frequency and by smoothing the control signals.

Collaborative optimization for axial flux PMSM control system was proposed [ 34 ]. Fuzzy control improved the torque ripple, and SMC improved the motor dynamic performance, effectively enhancing the range and acceleration performance of EVs. A variable SMC controller based on a speed loop was proposed [ 35 ]. It combined MTPA to control the IPM and obtained significant control reliability and flux-weakening performance.

3.4 PWM Control

Among numerous PWM methods, space vector pulse width modulation (SVPWM), sinusoidal pulse width modulation (SPWM), and six-step voltage (SSV) are the most common. Their performance comparison is shown in Table 6 . For the given DC bus input voltage and the phase output current capability, high DC bus voltage utilization can help the motor output more power at and after the corner speed (i.e., to the flux-weakening range).

3.4.1 SVPWM

SVPWM enables the motor to obtain a circular magnetic field with constant amplitude. Compared with SPWM, 15.47% higher DC bus voltage utilization can be achieved, which allows more power to be outputted at high-speed operation. Low current waveform distortion or a small account of current harmonic components can also be achieved. Furthermore, the rotating magnetic field is closer to the circle, which greatly improves the motor performance. Thus, SVPWM is the dominant modulation in motor control.

SPWM focuses on solving the problem of three-phase symmetrical sinusoidal voltage frequency and voltage regulation from the standpoint of the motor power supply. However, its total harmonic distortion is larger than that in SVPWM, which impacts negatively the control performance. More severely, the amplitude of its fundamental phase voltage can only be 1/2 of the DC bus voltage, which may only be used in the low-speed range before the corner speed.

SSV can adjust the power by controlling the voltage amplitude, flux, and torque, which can provide the highest DC bus voltage utilization ratio, thus beneficial to a greater power output at speeds beyond the motor corner speed. However, its harmonics are rich in the phase current and in the airgap magnetic field, leading to the fifth- and seventh-order harmonics with higher amplitude.

3.5 Power Electronic Devices in Control

The new generation of insulated-gate bipolar transistor (IGBT) chips for NEVs were launched by international component suppliers like Infineon, Fuji, Mitsubishi, and Renesas. For example, Infineon IGBTs are based on an 8-in. or a 12-in. technology platform, while IGBTs are manufactured on a 6-in. or an 8-in. wafer in China. Nevertheless, there are still some gaps in the device performance indicators, key process technology, production quality control, and cost. The IGBT module packaging with vehicle standard (equivalent to imported modules like HP1, HP2, and HP Drive) is close to the international average level in performance and reliability, and their large-scale application in automotive just begin in China.

Compared with traditional silicon devices, wide bandgap (WBG) power devices represented by SiC and GaN show strong advantages in voltage, operating temperature, switching frequency, and switching losses, which makes them more suitable for NEV inverter requiring tolerance to high temperature, high voltage, high frequency, and high power density [ 35 ]. The material property comparison among Si, SiC, and GaN is shown in Fig.  2 [ 36 ].

Property comparison of SiC, Si, and GaN

WBG device applications are explored in electrical drive systems. When the motor controller’s power density exceeds 25 kW/L, the WBG semiconductor can be used for heating reduction due to low conduction loss at a high-frequency switching operation [ 37 ]. A buck converter prototype with a SiC power module was established for continuous operation at high-temperature operation [ 38 ]. The SiC module’s high performance could still be found at its junction temperature of 225 °C. An integrated low-voltage rating of the GaN module was used to set up a three-phase full bridge inverter, resulting in the reduction in not only the inverter weight and volume but also the device resistance and conduction loss [ 39 ]. However, if the WBG device switching frequency was increased to 50–100 kHz, the EMC influence caused by its high d v /d t on the efficiency would be more prominent [ 40 ]. Electromagnetic interference (EMI) levels of SiC and Si devices with similar topology were compared under the same working conditions [ 41 ]. The results showed that the miller effect caused by the parasitic parameters in SiC JFET devices was the main reason of high EMI. An insulated metal substrate was installed on the division of the motor drive inverter with a SiC JFET for restraining the common mode (CM) and EMI [ 42 ]. It was shown that the third-order LCL filter had better performance than the fourth-order of LCL. Stray inductance between power electronics and the converter output was utilized as a filter, combined with an additional RC link, for a high-frequency 100–1 MW inverter with SiC [ 43 ]. The test showed that even for a measured value of 47 kV/μs, the inverter output d v /d t could be limited to 7.5 kV/μs.

To handle the ground-drain current, CM electromagnetic interference (CMEMI) and bearing current in the motor inverter with WBG devices, a new concept of CM voltage cancelation through a balancing inverter topology and a double-winding stator structure was proposed [ 44 ]. In a test based on GaN, although the parasitic capacitance in the case of asymmetric windings limited the CM cancelation, the ground current amplitude could be reduced by 90%, and the conduction CMEMI emission could be reduced by an average of 20 dB without using any filter. In Ref. [ 45 ], a standard driver circuit was adopted by adding a simple coupling circuit, to drive two series-connected SiC metal–oxide–semiconductor field-effect transistors (SiC MOSFETs), and a limiting buffer circuit was used for voltage balancing. It has the advantages of low cost, simple structure, and high reliability.

3.6 Motor Controller Development

High efficiency, high density, and good EMC performance are the development directions of motor controllers. By adopting power electronics integration technology, the weight and volume of the whole controller can be reduced effectively, power density can be increased, and manufacturing cost can be reduced. Power electronics integration technology is mainly divided into three levels: monolithic integration, hybrid integration, and system integration. Hybrid integration schemes are mostly adopted in motor controllers such as Toyota Prius and GM Volt. Module packaging, interconnection, and efficient cooling are the core of power electronics hybrid integration. The comparison among global advanced products is shown in Table 7 .

For SiC motor controllers, full use of the high-temperature tolerance, high efficiency, and high frequency of SiC MOSFET devices is the key to improving the power density and efficiency further. SiC MOSFET inverter was applied in Tesla Model 3, shown in Fig.  3 b. Its SiC motor controller is composed of 24 SiC MOSFET chips grouped in parallel and mounted on a pin–fin radiator to achieve high current output (800Arms). Through the laser welding process, each SiC MOSFET is connected to a copper busbar, which greatly improves the connection reliability. Full SiC inverters are launched for vehicle applications by other companies. It was found by Toyota that under load conditions, the loss of SiC power control unit (PCU) of the prototype vehicle was reduced by 30% compared with IGBT PCU in Fig.  3 f. Double-sided welding and double-sided cooling technology were adopted by Denso to achieve small size and high efficiency of its SiC controller in Fig.  3 a, which was used in Toyota fuel cell vehicles.

Typical SiC motor controllers

Typical full SiC controllers are shown in Fig.  3 .

SiC MOSFET controller was developed by Jing-Jin Electric (JJE) for VW commercial vehicles, whose power density is over 40 kW/L. The hairpin winding motor and SiC controller prototypes were developed for EU passenger car OEM by JJE at the end of 2019. In 2020, 300–600 kW series of SiC MOSFET inverters, shown in Fig.  3 e, were developed by JJE for TRATON group, a VW commercial vehicle division. The SiC inverter, shown in Fig.  3 c, is also embarked onboard of BYD EV-HAN in July 2020.

4 NEV Powertrain Development

4.1 bev powertrain topology.

Battery EV (BEV) powertrain generally includes the motor, power electronics control system, and reducer or transmission. Its configuration depends mainly on the layout of the electric drive system inside the vehicle. Electric drive systems can be categorized into single-motor drive (or lump e-drive), distributed-motor drive, and range-extended drive systems [ 46 ].

4.1.1 Single-Motor Drive System (or Lump e-Drive System)

A single-motor drive system is similar to the scheme of traditional internal combustion engine (ICE) vehicles, but the electrical motor replaces the ICE, and other configurations are modified accordingly. However, this configuration has a large demand for chassis space.

A single-motor drive system can be mounted on the front or rear drives, and its reducer/transmission structures can be divided into three categories, as shown in Fig.  4 .

Single-motor electric drive systems

An EV motor and a high-accuracy vehicle analysis model were proposed in Ref. [ 47 ]. The design space of the motor could be accurately described and the entire electric drive system was optimized. The influence of a non-deterministic modulation scheme on the transmission system was conducted based on the single IPM. The transmission mechanical part was modeled as a dual oscillator, and different inverter modulation schemes (hysteresis controller, PWM method) were applied. The inverter-generated harmonics and switching frequency were taken as the optimization targets. The simulation results showed that the pulse frequency could be reduced in the hysteresis controller. In addition, the inverter-generated partial harmonic energy could compensate for the filter absence. The IPM drive system with five-phase windings was comprehensively compared with IPM systems with three phases [ 48 ]. The proposed five-phase motor has the advantages of low cost, small torque ripple, high power density, good capability of flux weakening, strong fault tolerance, high reliability, and more design freedom.

4.1.2 Distributed Electric Drive System

Multiple motors are distributed to the corresponding vehicle wheels. According to the motor location, it can be categorized into three types: wheel rim, hub type, and combined, as shown in Fig.  5 .

Distributed electric drive systems

Connecting wheels directly to the motors can realize the precise measurement of the wheel torque and rapid response to the driving requirement. The vehicle chassis configuration is simplified, the user available space is expanded, the vehicle weight and the energy consumption are reduced, and the driving range per charge is consequently increased. Therefore, the distributed motor drive system has been recognized as one of the most promising electrified propulsion systems. However, there is no mass production of vehicles with the distributed e-drive systems, except for the wheel side motor drive system used in BYD K9 buses. Now, the research mainly focuses on the control strategies such as torque distribution optimization [ 49 , 50 ].

An algorithm for four-wheel distributed drive control was proposed for a fast, energy-saving, and easy-to-realize torque allocation [ 51 ]. The experimental results showed that the proposed algorithm was reasonable, and the system efficiency was improved significantly in the lateral acceleration range of the entire ramp. The particle swarm optimization algorithm was used for searching the optimal global size of four-wheel-driven EV [ 52 ]. The simulation results showed that the particle swarm optimization method combined with the real-time torque allocation strategy could effectively reduce the size of the main components of the powertrain and reduce energy consumption. Next, a hierarchical control strategy for a four-wheel distributed drive system was proposed to meet the driver's operating instructions and maintain the lateral stability of the vehicle [ 53 ]. The control strategy was divided into two layers. The upper layer realized nonlinear MPC, and the lower layer controlled the wheels through a PID controller. The experimental results showed that the driver's longitudinal and lateral motion commands were performed with a good real-time performance.

Hub motor for passenger vehicles has no global mass production owing to the constraints of cost, reliability, braking safety, and control. The mechanical, electrical, and thermal issues are not well solved. Only the samples or small demonstration prototypes could be seen in the market. As for commercial engineering vehicles like buses and heavy-duty trucks, hub motors are already used owing to relatively unstrung layout space in wheels, low vehicle speed, relatively low sensitivity to unstrung mass increment, which provides low floor, and large space to bus passengers.

4.1.3 Extended Range EV (EREV or REEV) System

An augmented electric drive system differs from single-motor and distributed motor drive systems because it contains an auxiliary power unit (APU). The system configuration is shown in Fig.  6 , which is also classified as plug-in HEV (PHEV). A low-power engine is usually used. Compared with the battery drive system, the battery capacity here can be reduced appropriately, which has a good application prospect in mid-sized vehicles. However, this drive system has a high cost and needs support from ground chargers [ 54 , 55 ].

EREV system

The configuration and performance details of traction motors A and B in the electric drive system of Chevrolet EREV were introduced [ 56 ]. The simulation results showed that the bar-wound winding (hairpin winding) motor had better performance than that of the motors with the strand round wire windings. In addition, the rotor with cavitation at the magnet top was specially designed to improve the spin loss by reducing the flux density harmonics in the motor airgap. Owing to the advantages of the Volt electric drive system and control algorithm, its noise reduction was also significant. A multi-objective optimal energy management method was proposed for APU fuel consumption and battery state of health (SOH) in extended-range electric buses [ 57 ]. The APU fuel consumption and battery SOH were used as optimization targets, and the dynamic program (DP) algorithm was used to solve the multi-objective problem. The simulation results showed that the optimal economic effect could be obtained if the battery pack parameters and the control strategy were set to the minimum without battery replacement. Furthermore, a novel energy management optimization strategy aiming at the APU configuration and control method in EREVs was proposed to solve the power distribution issue of APU and battery [ 58 ]. The model of the APU control system was established, and two strategies for APU power tracking were proposed according to different power change rates under the condition of changing APU dynamic response characteristics and control parameters. The experimental results showed that the APU power change rate under different conditions could significantly affect fuel consumption.

4.2 Hybrid Powertrain

An engine, electric motor(s), and power batteries are combined, and two power sources are matched and optimized for greatly reducing vehicle emissions and fossil energy consumption [ 59 , 60 ]. According to the combination of power sources, HEV powertrains can be classified into three types: serial, parallel, and series–parallel compound.

4.2.1 Series Hybrid Powertrain

The series hybrid power system is shown in Fig.  7 . The engine and the motor are connected in series. The engine does not propel wheels mechanically, and it only drives the generator to generate electricity through burning fuel. The generated electric power is sent to the traction motor, which creates the traction torque to drive the whole vehicle. Simultaneously, the power battery can also supply the electric power to the traction motor to operate the vehicle.

Series hybrid power system

As only the traction motor drives the wheels, the engine is not affected by the driving conditions, which could be set for continuous operation at the most efficient point. However, the energy to drive the vehicle undergoes two conversions: mechanical to electrical and then to mechanical. Thus, the fuel energy utilization rate is relatively low.

In the study of serial hybrid powertrains, the combined optimization of an energy management strategy and driving speed was investigated for minimizing fuel consumption [ 61 ]. An energy management strategy based on the road condition and the driving time was proposed to find the optimal driving speed and energy power allocation for specified driving tasks. A power follower control strategy was combined with the DC side voltage control strategy, and a novel idea for energy management of hybrid EVs was proposed [ 62 ]. Experimental results showed that this series hybrid vehicle had better fuel economy than the single control scheme. Furthermore, a multi-function framework for hybrid powertrains considering the driving conditions was proposed [ 63 ]. Using a diesel-powered traditional vehicle as a hybrid target, a hybrid topology combining the series-connected hybrid and wheel–motor systems was presented. Compared with the traditional vehicles, the acceleration performance and the climbing gradient were improved by 18% and 10%, respectively. For the PM generator in the series hybrid configuration, the hybrid excitation topology was proposed [ 64 ], together with the integrated passive rectifier, replacing the PMSM and the active power electronic converter, which facilitated the constant control of the PM flux linkage. This design was confirmed to provide higher output voltage and power density.

4.2.2 Parallel Hybrid Powertrain

The parallel hybrid power system is shown in Fig.  8 . The engine and the motor shafts are connected in parallel. The vehicle can be driven by the engine and the motor together or by one of them alone. There is no dedicated generator in this configuration, and the power battery pack can only be charged by the traction motor operating in its generating mode. However, the engine working condition is often affected by the vehicle driving cycle, and it cannot always run at the optimal working point. Compared with the series hybrid power system, a more complicated transmission is required.

Parallel hybrid power system

An energy management strategy model based on a deep recursive neural network was proposed for the optimal torque distribution of single-axle parallel hybrid vehicles [ 65 ]. Better performance in terms of fuel economy and accuracy was provided. Further, an adaptive neuro-fuzzy reasoning system was combined with the equivalent power consumption minimization strategy, and a practical adaptive energy management strategy for parallel hybrid buses was proposed [ 66 ]. The results showed that the fuel economy was improved by this control strategy. A torque balance threshold change strategy was proposed for the energy management of parallel hybrid vehicles [ 67 ]. When the engine was active, it runs at constant torque, to ensure its operation at highly efficient points. Further, an electro-hydraulic parallel hybrid powertrain for urban vehicles was introduced [ 68 ]. The power consumption and battery discharge stress of the electric powertrain were reduced by hydraulic system. The vehicle driving range per charge was increased, and the battery life was improved. Aiming at the mode conversion of parallel hybrid vehicles, a control method based on an adaptive double-loop control framework was proposed [ 69 ]. The experimental results showed that the proposed control method could effectively improve the performance of HEVs. In addition, the time of the mode conversion process was shortened and the vehicle turbulence within an acceptable range could be controlled.

4.2.3 Series and Parallel Compound Powertrain

In compound powertrains, the relationship between the generator and the motor can be either series or parallel, as shown in Fig.  9 . The engine can directly output propulsion power through nodes 1, 2, 4 to drive the vehicle together with the motor or to generate electric power through nodes 1, 3 when only the motor drives the vehicle mechanically. Usually, when the vehicle is running at low speed, the driving system works mainly in series. When the vehicle is running stably at high speed, the main operation mode is parallel.

Series and parallel compound powertrain

The optimal matching of all components to the greatest extent can be achieved and the contradiction between the fuel energy utilization rate and the engine’s best working condition can be balanced with the compound hybrid system. Compared with the parallel configuration, this compound is more complex and has higher requirements on the power combination components.

The system efficiency was improved by optimizing the configuration, e.g., by adding gears to components or gearboxes with several transferring ratios [ 70 ]. To solve the working area mismatch between the engine and the motor, a new multi-mode coupling drive system was designed by coupling a distributed drive system with a centralized drive system and adding a clutch [ 71 ]. The results showed that the system efficiency was improved because the engine and motor operating points fall within their effective ranges. The optimal transferring ratio and motor size were determined based on the requirements for fuel economy, acceleration, and maximum speed performance [ 72 ]. The proposed method had the advantage of obtaining a more accurate compact EV powertrain. To solve the power allocation and management in the hybrid configuration, an energy management strategy based on nonlinear MPC was proposed [ 73 ]. Compared with the charge depletion strategy, the fuel economy was improved by 18.86% and 10.36%, through the nonlinear MPC and equivalent power consumption minimization strategies, respectively. Aiming at the PMM performance optimization, the brushless dual-rotor motor with axial magnetic field modulation was investigated [ 74 ], which could be connected to the traditional PMSM, forming a dynamic composite device. The axial tilting moment was analyzed, and the necessary conditions to avoid the axial tilting moment were given.

4.3 Comparison of Electric Drive Systems

4.3.1 comparison of bev drive systems.

The advantages and disadvantages of BEV drive systems are listed in Table 8 .

It can be seen from Table 8 that the three drive systems have their advantages and disadvantages. The main advantages of the centralized motor system are simple to control and relatively low research and development (R&D) cost. However, the low efficiency is its obvious disadvantage. The distributed system has the least mechanical transmission mechanisms and the highest transferring efficiency. However, the latter contains multiple motors and involves a more complex electronic control technology, which has no mass production thus far. For the EREV system, owing to the APU’s existence, the driving distance per charging is increased. However, a lot of space in the system is occupied by the transmission structures, and the working efficiency is not very high.

4.3.2 Hybrid Powertrain Comparison

The advantages and disadvantages of different hybrid powertrains are shown in Table 9 .

The advantages of the series configuration are mainly reflected in the simple structure, relatively easy control, and optimal engine working point. The main parallel configuration advantages are good fuel economy and relatively simple structure. The series–parallel compound configuration combines the characteristics of both series and parallel configurations, so it can adapt to a variety of driving conditions. However, the system is complex and the control is difficult.

5 Technical Innovation and Development Forecast of the NEV Electric Drive Systems

The technical architecture of the NEV electric drive system is shown in Fig.  10 . It mainly includes powertrains, core sub-assemblies, key materials, components, and basic support.

Technical architecture of the NEV electric drive system

The innovation of the NEV electric drive system technology can be summarized as the overall improvement of the entire supplier chain technology from materials, parts/components, and the motor systems to the powertrains.

5.1 Core material Technology Innovation of the Electric Drive System

In the electric drive system, the development of rare earth PM materials contributes to the PMSM development. The magnet performance characteristics have been significantly improved, such as magnetic energy product, magnetic declination, sinusoidal magnetization, magnet block splitting and bonding, light rare earth element usage, surface coating, and PM measurement. The grade over N50UH Nd-Fe-B PM is mass-produced, and its residual magnetism is close to 90% of the theoretical limit of Nd 2 Fe 14 B compound. However, the coercivity is still lower than 30% of the theoretical limit, so there is much room for improvement.

Heavy rare earth grain boundary diffusion and grain boundary modulation technologies can be adopted for future development, as shown in Fig.  11 . These technologies can greatly reduce the usage of heavy rare earth materials like dysprosium and/or terbium and improve the performance and quality of magnets. Moreover, some mixed PM materials (including ferrite) are used for partially replacing Nd-Fe-B materials by some OEMs like Toyota and GM.

Grain boundary diffusion and grain boundary modulation of the PM

Therefore, besides PM materials, corona guard insulation materials, electromagnetic wires, high-performance magnetic materials, and manufacturing processing have drawn much attention in the electric motor industries. Insulating materials and their insulation structure design, providing high performance, high reliability, high electric and thermal life, high thermal conductivity, corona guard, and oil compatibility, are also paid considerable attention since the high-speed motor and the high-frequency inverter are the development trend.

5.2 Innovation of Power Electronics

In the electric drive system, power electronics plays an important role in the traction motor system and electric powertrain performance. In the future, the NEV industry will focus on the following power electronics research.

The trench technology has become the mainstream in IGBT chips for vehicle applications. It can enhance the ability of electron injection and reduce the switching-on, turning-off, and conduction losses. With the development of groove technology, the refinement of groove plays a key role in improving the overall performance of IGBT chips for EVs.

Rapid thick epitaxial growth and the material inspection technology is drawing much attention for SiC MOSFET chips. Wafer preparation and inspection as well as low-sensitivity and high-density SiC or GaN module packaging are directly related to successful NEV application on the third-generation WBG power semiconductors.

Single-chip functional integration improvement, system complexity reduction, power density increase, and reliability improvement of power electronic chips and packages are focused. IGBTs or SiC MOSFETs and SiC diodes are integrated on the same chip to improve the current density of the power module. Current and temperature sensors are integrated on the electronic power switch chip to detect transient output current and transient junction temperature fluctuations, thus improving the reliability and power density of package modules.

The technologies of copper binding wire, copper terminal direct bonding, belt bonding and flexible connection are adopted to replace the traditional aluminum binding wire technology. The contact area of the silicon wafer is increased for evenly distributing the thermoelectric stress in the power contact part, reducing the peak temperature of the silicon wafer and improving the power cycle life of the module.

Double-side welding, single-side/double-side cooling, and integrating heat dissipation sink can reduce the thermal resistance of the chip, improve the heat dissipation capacity, and improve the power cycle reliability, which has become a new trend of the packaging technologies of the next-generation IGBT and SiC MOSFET module.

5.3 Innovation of the Motor and Powertrain System

In the domestic market of passenger BEV powertrains, the integration products of 3-in-1 and multi-in-1 electric drive assembly are at the same level as those of the global suppliers. Tesla's integrated structure design is relatively advanced and is based on the new electric chassis design and forward research. The specific power of motors exceeds 4–4.6 kW/kg globally. However, there is no first-mover advantage at the time of product launch. To improve the driving efficiency, the SiC MOSFET inverters with discrete devices were commercialized earlier than others, and their highest efficiency of the 3-in-1 electric drive system is approximately 94%. Compared with the Si-based motor drive system, powertrains based SiC provided higher peak and operating efficiencies under the vehicle driving cycles.

In the future, the systematic integration and supplier chain innovation of NEV electric powertrains and their key components should be focused, which include:

Configuration technology of electromechanical coupling assembly;

Sealing (condensation), heat dissipation, and lubrication of highly integrated electromechanical coupling systems;

Key parts and components such as shaft with gears, clutch, planetary gears, and actuators;

Direct drive hub motor, new-type wheel hub electric drive system, and innovative design technology of the brake system;

NVH design, noise suppression, detection, and evaluation technology;

Validation standards and specifications for electromechanical coupling devices.

5.4 2021–2035 Technology Roadmap of Electric Powertrains, Key Components, and Materials

The technology roadmap of electric powertrains, electric motors, power electronics converters, and key components, as well as materials, is described in Ref. [ 75 ]. The future performance parameters of the electric drives, their sub-assembly, parts/components, and materials are predicted and shown in Table 10 in terms of time frame.

6 Conclusions

The current states of the NEV motor systems and powertrain technologies are systematically reviewed; the technological innovations and applications in materials, devices, and powertrains are summarized in details; and different control algorithms are compared. Although the performances of traction motor and powertrain products is dramatically improved, more R&D is required for more innovative technologies, such as motor design optimizations and control algorithms, multi-physical simulation analysis, robustness design, system integration with jointly considered motors, controllers and reducers/transmissions, a next-generation motor system based on SiC devices, PM motors with less or without heavy rare earth PM materials, efficient motor cooling methods, and new material development and applications.

Vehicular dynamic performances, energy saving features, safety, and comfortabilities are mainly summarized from the electric drive systems. The technical roadmaps of electric powertrains, traction motor systems, key components, and materials are summarized in terms of the time frame of 2025, 2030, and 2035, which can be used as a reference by researchers and engineers in the OEMs and NEV industry supplier chains, the government officers, or investors for investing strategies.

Abbreviations

Active disturbance rejection control

Alternating current

Auxiliary power unit

Common mode

Common-mode electromagnetic interference

Direct current

Dynamic program

Direct torque control

Electromagnetic interference

Electric vehicle

Finite element method

Fuzzy logic control

Field orientation control

Jing-Jin Electric

Hybrid electric vehicle

Internal combustion engine

Insulated-gate bipolar transistor

Induction motor

Metal–oxide–semiconductor field-effect transistor

Model predictive control

• New energy vehicle

Neural network control

Plug-in hybrid electric vehicle

Proportion integration differentiation

Permanent magnet

Permanent magnet brushless direct current motor

Permanent magnet motor

Robust control

Sliding mode control

State of health

Switched reluctance motor

Sinusoidal pulse width modulation

Six-step voltage

Space vector pulse width modulation

Voltage per frequency

Wide bandgap

Worldwide light-duty test cycle

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Acknowledgements

We are grateful to Maotong Yang and Guanning Guo from the Institute of Electrical and Power Electronics Engineering at Harbin University of Science and Technology for their help in translating figures and tables as well as partial content of this paper.

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William Cai, Xiaogang Wu & Minghao Zhou

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Cai, W., Wu, X., Zhou, M. et al. Review and Development of Electric Motor Systems and Electric Powertrains for New Energy Vehicles. Automot. Innov. 4 , 3–22 (2021). https://doi.org/10.1007/s42154-021-00139-z

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• Published: 17 January 2023

Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030

• Chengjian Xu   ORCID: orcid.org/0000-0002-2512-5876 1 ,
• Paul Behrens   ORCID: orcid.org/0000-0002-2935-4799 1 ,
• Paul Gasper   ORCID: orcid.org/0000-0001-8834-9458 2 ,
• Kandler Smith   ORCID: orcid.org/0000-0001-7011-0377 2 ,
• Mingming Hu 1 ,
• Arnold Tukker   ORCID: orcid.org/0000-0002-8229-2929 1 , 3 &
• Bernhard Steubing   ORCID: orcid.org/0000-0002-1307-6376 1  

Nature Communications volume  14 , Article number:  119 ( 2023 ) Cite this article

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The energy transition will require a rapid deployment of renewable energy (RE) and electric vehicles (EVs) where other transit modes are unavailable. EV batteries could complement RE generation by providing short-term grid services. However, estimating the market opportunity requires an understanding of many socio-technical parameters and constraints. We quantify the global EV battery capacity available for grid storage using an integrated model incorporating future EV battery deployment, battery degradation, and market participation. We include both in-use and end-of-vehicle-life use phases and find a technical capacity of 32–62 terawatt-hours by 2050. Low participation rates of 12%–43% are needed to provide short-term grid storage demand globally. Participation rates fall below 10% if half of EV batteries at end-of-vehicle-life are used as stationary storage. Short-term grid storage demand could be met as early as 2030 across most regions. Our estimates are generally conservative and offer a lower bound of future opportunities.

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Introduction.

Electrification and the rapid deployment of renewable energy (RE) generation are both critical for a low-carbon energy transition 1 , 2 . They also address many other environmental issues, including air pollution. However, the variability of critical RE technologies, wind and solar, combined with increasing electrification may present a challenge to grid stability and security of supply 1 , 2 . There are several supply-side options for addressing these concerns: energy storage, firm electricity generators (such as nuclear or geothermal generators), long-distance electricity transmission, over-building of RE (resulting in curtailment in periods of lower demand), and power-to-gas 3 (in approximate ascending order of today’s estimated cost). Demand-side management is also vital in shifting and flattening peak demand 4 . Given rapid cost-declines, battery storage is one of the major options for energy storage and can be used in various grid-related applications to improve grid performance. Cost declines in batteries have been the major driver for electric vehicle (EV) cost reductions. Given that many batteries will be produced for light-duty transport these could offer a low-cost and materially-efficient approach for short-term electricity grid storage requirements 5 .

EV batteries can be used while in the vehicle via vehicle-to-grid approaches, or after the end of vehicle life (EoL) (when they are removed and used separately to the chassis in stationary storage). “Smart” vehicle-to-grid charging can facilitate dynamic EV charging and load shifting grid services. EVs can also be used to store electricity and deliver it back to the grid at peak times 6 . These opportunities rely on standards and market arrangements that allow for dynamic energy-pricing and the ability of owners to benefit from the value to the grid. Value to the grid can include deferred or avoided capital expenditure on additional stationary storage, power electronic infrastructure, transmission build-out, and more 6 . When the remaining battery capacity drops to between 70-80% of the original capacity, batteries generally become unsuitable for use in EVs 7 . However, these batteries at vehicle EoL (hereafter termed retired batteries) may still have years of useful life in less demanding stationary energy storage applications and represent substantial value to the grid 8 .

The utilisation of EV batteries could improve the flexibility of supply while reducing the capital costs and material-related emissions associated with additional storage and power-electronic infrastructure. However, the total grid storage capacity of EV batteries depends on different socioeconomic and technical factors such as business models, consumer behaviour (in driving and charging), battery degradation, and more 9 , 10 . Previous global-level studies, including those on vehicle-to-grid capacity 2 , 11 , 12 and retired battery capacity 12 , 13 are informative. However, they rarely consider several important factors that determine storage opportunity, such as non-linear, empirically-based battery degradation and neglect the impact of battery chemistry altogether; 14 , 15 , 16 geographical and/or temporal temperature variance (which impacts battery degradation); and, driving intensity by vehicle type in different countries/regions (which constrains the total capacity available during the day). Additionally, consumer participation in the vehicle-to-grid market and utilisation of retired batteries in the second-use market impact the actual grid storage capacity 10 , both of which are important but rarely quantified.

Here we link three models and databases to assess the global grid storage opportunity of EV batteries by 2050 for both vehicle-to-grid applications and EoL opportunities (see Fig.  1 , Methods, and Supplementary Fig.  1 ). We cover the main EV battery markets (China, India, EU, and US) explicitly, and combine other markets in a Rest of the World region (RoW). We first use a dynamic battery stock model to estimate future battery demand as part of transport fleets per region (Supplementary Fig.  2 ). The model incorporates two EV fleet development scenarios based on the IEA’s (International Energy Agency), stated policy (STEP) and sustainable development (SD) scenarios. The STEP scenario incorporates existing EV policies only, while the SD scenario is compatible with the climate goals of the Paris agreement and sees a larger EV fleet. The scenarios include two battery chemistry sub-scenarios to capture different technological paths: one dominated by lithium nickel cobalt oxides (NCX, with an “X” denoting manganese or aluminum, i.e., NMC/NCA) and another dominated by lithium-ion phosphate or (LFP). Market shares of NCX and LFP batteries are assumed to reach 98% and 2% in the NCX path by 2050, respectively, and 40% and 60% in the LFP path (see Supplementary Fig.  3 for detailed market shares over time).

See legend for use of colours. Square, white boxes indicate model outputs. Please see details for the model framework in the methods section. USDOE US Department of Energy, FASTSim Future Automotive Systems Technology Simulator, NREL National Renewable Energy Laboratory, IEA International Energy Agency, SoH State of Health.

These estimates of future demand are linked to an EV driving and charging behavior model for small, mid, and large-size BEVs (battery electric vehicles) and PHEVs (plug-in hybrid electric vehicles) based on daily driving distance distributions for different regions (Supplementary Figs.  4 – 6 ). EV use behavior, battery chemistry, and temperature in each region are combined with the latest battery degradation data for NCX 14 , 15 , 17 and LFP 16 chemistries to account for region- and chemistry-specific battery degradation (Supplementary Fig.  7 ).

We first analyze the technical capacity for short-term grid storage from vehicle-to-grid and second-use. We then analyze the impact of different factors on the real-world capacity. For example, we analyse in detail the impact of different rates of EV owner participation in vehicle-to-grid markets as well as the impact of different utilisation rates of retired EV batteries in stationary storage (see Fig.  1 and methods for further details). Finally, we compare the technical and real-world short-term storage capacities against scenarios for future storage requirements from the literature.

We focus here on short-term energy storage since this accounts for the majority of the required storage capacity 18 and EV batteries are not well suited for longer-term, seasonal storage due to self-discharging over time. Short-term energy storage demand is typically defined as a typical 4-hour storage system, referring to the ability of a storage system to operate at a capacity where the maximum power delivered from that storage over time can be maintained for 4 hours. For example, the 4-hour storage capacity of batteries that together deliver a maximum of 0.25 GW until depletion will be 1 gigawatt hour 19 (GWh). The short-term storage capacity and power capacity are defined based on a typical 1-time equivalent full charging/discharge cycle per day (amounting to 4 hours of cumulative maximum discharge power per day). This 4-hour threshold is chosen as it is required by some jurisdictions such as the California Public Utilities Commission and New York Independent System Operator 20 , energy system analysts anticipate this threshold as the most important to markets 21 , and is often the length of time used in the literature 22 .

We compare our results against storage requirements reported in the IRENA (International Renewable Energy Agency) Planned Energy and Transforming Energy Scenarios (with a warming of “likely 2.5 °C” and “well below 2 °C” in the second half of this century, respectively) 2 , along with two Storage Lab scenarios (Conservative and Optimistic) 23 . Both Storage Lab scenarios result in a warming of “well below 2 °C” by 2100, but differ in the role for grid storage please see Supplementary Table  1 for more). These scenarios report short-term grid storage demands of 3.4, 9, 8.8, and 19.2 terawatt hours (TWh) for the IRENA Planned Energy, IRENA Transforming Energy, Storage Lab Conservative, and Storage Lab Optimistic scenarios, respectively. When assuming a 4-hour storage period for this capacity, this results in power demand of 850-4800 GW, or, 2500 GW when assuming an average storage capacity demand of 10 TWh.

Total technical capacity

We define technical capacity as the total cumulative available EV battery capacity in use and in second use at a specific time while considering battery degradation and the capacity needed to meet driving demand. Globally, the SD scenario sees a total technical capacity twice that of the STEP scenario due to the larger fleet size (see Supplementary Fig.  8 and Note  1 ). Globally, the LFP scenario sees a slightly higher cumulative capacity than the NCX scenario, due to different battery market shares and the lower degradation of LFP across most countries/regions (see Supplementary Data  1 for a full comparison). Compared to the SD-NCX scenario, The SD-LFP scenario sees 2.6 TWh of higher technical capacity for China, EU, US, and RoW by 2050 compared to the SD-NCX and a 0.05 TWh lower technical capacity for India (see Supplementary Note  2 ). These capacity differences are small compared to the total technical capacity. As shown in Fig.  2 , the SD-LFP scenario has a technical capacity 48% higher by 2030 and 91% higher than the STEP-NCX scenario by 2050 (3.8 TWh and 2.6 TWh in 2030 and 32 TWh and 62 TWh in 2050, respectively).

a STEP-NCX scenario. b SD-NCX scenario. c STEP-LFP scenario. d SD-LFP scenario (see details in Supplementary Table  1 ). IRENA = International Renewable Energy Agency.

Under all scenarios, cumulative vehicle-to-grid and second-use capacity will grow dramatically, by a factor of 13–16 between 2030 and 2050. Putting this cumulative technical capacity into perspective against future demand for grid storage we find that our estimated growth is expected to increase as fast or even faster than short-term grid storage capacity demand in several projections 2 , 23 (Fig.  2 ). Technical vehicle-to-grid capacity or second-use capacity are each, on their own, sufficient to meet the short-term grid storage capacity demand of 3.4-19.2 TWh by 2050. This is also true on a regional basis where technical EV capacity meets regional grid storage capacity demand (see Supplementary Fig.  9 ).

Vehicle-to-grid opportunities and limitations

Examining the vehicle-to-grid opportunity alone, we find that 21%-26% of the global theoretical battery capacity (i.e., on-board EV battery capacity of the entire EV fleet without considering battery degradation) could be available for vehicle-to-grid services by 2050 (Fig.  3a ). The most important limiting factor is the battery capacity required to meet consumer driving demands 24 , 25 . Driving demand can limit the available capacity by 57%-63%. PHEVs, which make up around 11% of the theoretical capacity in 2050, are not considered for vehicle-to-grid as they have a low storage potential due to low capacities. On average, just 5% of the theoretical capacity is lost due to battery degradation by 2050. These losses vary between 7% in India and 4% in RoW due to differences in regional factors such as use conditions and temperature (for full regional results see Supplementary Fig.  10 ). Overall, taking these factors into account yields an estimated technical vehicle-to-grid capacity of 18-30 TWh by 2050 (see Fig.  3 ).

a Technical vehicle-to-grid capacity. Hatched bars indicate the capacity limits due to key factors and blue bars the technical vehicle-to-grid capacity. b Real-world vehicle-to-grid capacity as a function of participation rates. Results are shown for the STEP-NCX and the SD-NCX scenarios with a comparison to the range of storage demand computed by IRENA and Storage Lab models in 2050 (orange shading). Please see Supplementary Fig.  16 for global real-world vehicle-to-grid capacity under STEP-LFP and the SD-LFP scenarios and Supplementary Figs.  17 – 20 for regional real-world vehicle-to-grid capacity.

However, there are other factors that may limit real-world available storage capacity, primarily the vehicle-to-grid participation rate. Not all EV consumers will necessarily participate in the market and the participation rate is defined as the percentage of the technical vehicle to grid capacity connected to the grid, as shown in Fig.  3b . Participation rates of 38% and 20% are required to satisfy short-term storage demands of 10 TWh in 2050 (for STEP-NCX and SD-NCX scenarios, respectively). In practice, it is likely that EVs with high battery capacities and low degradation will be used for providing vehicle-to-grid services since these will provide the highest revenue for EV owners 26 (the full battery capacity distributions by 2050 across countries/regions are available in Supplementary Figs.  11 – 15 ).

Impacts of deploying second-use batteries in stationary storage

Over time EV batteries degrade to the point they cannot be used to power vehicles 27 , generally when the battery’s relative State of Health (SoH) drops below 70%-80% 7 (defined as actual capacity as percentage of original capacity). The relative SoH could fall even lower if a consumer is willing to accept relatively poor battery health and shorter ranges 28 . Given their economic, value, size, and end-of-life regulations, we assume all batteries will be collected 29 . This is reasonable given that today’s lead-acid batteries achieve a near 100% collection rate 30 and modern EV batteries are of much higher economic value.

Once collected, batteries are health tested to determine if they can be used in a less critical second-use application, or if they should be recycled 31 . Given the technical and economic feasibility of retired batteries for second-use 32 , we consider batteries with an SoH of 70% and higher only for second-use (a threshold often used in the literature 32 ). Under this assumption, 74% of retired NCX batteries can be repurposed for second-use globally, while 26% goes to recycling by 2050. Regional differences can be significant due to the impact of temperature on NCX battery degradation (see Supplementary Fig.  21 and Supplementary Data  1 ). In contrast, nearly all LFP retired batteries can be repurposed.

Business models are still developing, and repurposing is highly dependent on the technical specifications and market requirements of second-use applications 33 . Since battery disassembly is costly 32 , battery repurposing will likely happen on the pack level instead of modules and cell level. Repurposing will consist mainly of rebalancing and reconnecting the retired battery packs. There is no strong technical reason to model a capacity difference before and after the repurposing.

Using these assumptions we find that 2.1–4.8 TWh of retired batteries are estimated to become available as annual technical second-use capacity globally in 2050, as shown in Fig.  4a . The cumulative technical second-use capacity is expected to reach 14.8–31.5 TWh by 2050 when assuming second-use batteries have a lifetime of 10-years 34 (Fig.  4b ). The actual second second-use lifespan is uncertain due to uncertainties surrounding the retired battery SoH, use conditions, among other factors. Another uncertainty is the further battery degradation during secondary use, which is difficult to model due to complicated degradation mechanisms of retired batteries 35 . Further research into degradation and second-use life span is required to improve estimates of technical second-use capacity. If the 10TWh global, short-term storage requirements are met with second-use batteries alone, then a 68% utilisation rate of retired batteries would be needed in the STEP-NCX scenario (14.8 TWh technical capacity) and utilisation rate of 32% in the SD-LFP scenario (31.5 TWh technical capacity).

a Average annual additions and cumulative technical capacity of second-use batteries in 2050. Here capacity refers to the technically available capacity considering battery degradation but without considering battery second-use utilisation rate. b Impacts of second-use utilisation rate on cumulative actual second-use capacity and a comparison to storage demand in 2050 (orange shading). See Supplementary Figs.  22 – 25 for regional actual second-use capacity.

Combining vehicle-to-grid participation rate and second-use utilisation rates

The global technical capacity for short-term grid storage of EV batteries grows rapidly in all scenarios. However, the real-world available capacity depends strongly on the vehicle-to-grid participation rate and the second-use utilisation rates. We show the real-world available capacity as a function of these rates in Fig.  5 (for the STEP-NCX scenario, please see Supplementary Figs.  26 – 28 for other scenarios). Participation and utilisation rates of 50% for vehicle-to-grid and second-use, results in a real-world capacity of 25–48 TWh by 2050, far higher than the short-term storage requirements estimated from the literature. Changes in vehicle-to-grid participation rates of 23–96% 36 , 37 by 2050 could influence this real-world capacity by as much as -24% to +21%. When second-use utilisation rates vary from 10%-100%, the real-world capacity varies between -41% and 12%. Taken together, vehicle-to-grid participation rate and second-use utilisation rate could alter the real-world capacity in 2050 by -61% to +32%.

Blue, white, and red colors depict minimum, average, and maximum values. See Supplementary Figs.  26 – 28 for other scenarios.

We could see many different combinations of vehicle-to-grid and second-use to meet the short-term grid storage demands by 2050 (3.4–19.2 TWh). Without any second-use batteries in stationary storage, grids would require vehicle-to-grid participation rates of a modest 12–43%. If we assume that only half of second-use batteries are used on the grid (with others used off-grid, for other EV or storage purposes, etc.), the required participation rate of vehicle-to-grid drops to below 10%.

The required market participation rates depend on EV fleet and battery chemistry scenarios but also are influenced by other factors, such as battery capacity per vehicle. To investigate the impact of our capacity assumptions we investigate a scenario where all BEVs are equipped with a smaller 33kWh battery (instead of 33, 66, and 100 kWh battery per vehicle for small, mid-size, and large BEVs globally, see methods for more details). Even in this extreme case, EV batteries can still meet global, short-term grid storage demand by 2050 with participation rates of 10%-40% in vehicle-to-grid and with half second-use batteries used as stationary storage (see Supplementary Table  4 ).

Previous research has suggested that large EV fleets could exert additional stress on grid stability (e.g., if the majority of EVs are charged at grid peak time) 38 . Our findings reveal a different perspective that EV batteries could promote electricity grid stability via storage solutions from vehicle-to-grid and second-use applications. We estimate a total technical capacity of 32-62 TWh by 2050. This is significantly higher than the 3.4–19.2 TWh required by 2050 in IRENA and Storage lab scenarios.

The real-world capacity depends on participation rates for vehicle-to-grid and utilisation rates for second-use of batteries. Participation rates may vary regionally depending on future market incentives and infrastructure, along with other factors 39 . The STEP-NCX scenario presented in Fig.  5 has the lowest technical capacity (32 TWh compared to 62 TWh in the SD-LFP scenario) which already easily meets requirements at participation rates of 40%–50% for vehicle-to-grid and with around half second-use batteries used as stationary storage. At a regional level, even lower participation rates may still contribute significantly to grid stability. Overall, EV batteries could meet short-term grid storage demand by as early as 2030 if we assume lower storage requirements from the literature and higher levels of participation and utilisation. By 2040–2050 storage demands are met across almost all scenarios and even low participation and utilisation rates.

Harnessing this potential will have critical implications for the energy transition and policymakers should be cognizant of the opportunities. The participation rate of EV users in the vehicle-to-grid market is crucial and the government can play an important role in incentivization. This can include market-based efforts such as micro-payments for services to the grid, or regulations to require the connection of commercial fleets to the network while at depots. Further regulations will be required to ensure the required hardware and software solutions for EV integration. This may include smart controllers for consumers in order to facilitate easy market participation and communication of benefits to EV users 39 . Strong re-use regulations will also be necessary to ensure that batteries are recovered at EOL and easily integrated into the grid 40 . Finally, policymakers and researchers should aim to understand EV user behavior over time in order to tackle the key factors preventing EV users from participating in vehicle-to-grid (which may include concerns surrounding battery degradation).

As we include a broader set of limitations for the total opportunity of EV storage our results are difficult to compare with other literature. Our estimated global EV fleet capacity in 2050 (68-144 TWh) is considerably higher than the estimate from IRENA (7.5-14 TWh) 2 . This is due to the IRENA’s very conservative scenarios on future EV fleet size and battery capacity per vehicle. The IRENA scenario also does not consider the availability of EV fleet capacity for grid services. While a different IEA estimate does not extend beyond 2030 12 it does highlight the importance of including battery degradation in analyses, which we include for our projection to 2050 (Fig.  4 ).

We note several limitations in our approach that could be improved as data availability improves. For example, while we include battery degradation by using state-of-art data, future battery degradation is highly uncertain and depends on further technological breakthroughs both in battery chemistry such as Na-ion, Li-Air, and Li-Sulphur 41 along with developments in battery management systems. Further, while we derived driving behaviour from empirical data, future changes in driving habits are uncertain and dependent on various factors such as EV-related infrastructure. Vehicle chargers increase in power output over time and 50 kW charging and above is already common across many countries 42 . Frequent fast charging could lead to faster degradation, especially in hotter/colder climates 43 . This challenge may be addressed by future technology improvements to battery materials 44 , electrode architectures, and optimized synergy of the cell/module/pack system design 45 . A further limitation is that we compare technical and real-world available vehicle-to-grid capacity with an average 4-hour storage requirement as provided in the scenarios by IRENA and Storage Lab. This omits potential differences in storage requirements at shorter time scales (seconds/minutes). Improved modelling and data can overcome this gap. It is however likely that the technical vehicle-to-grid capacity will be sufficient given low vehicle utilisation rates of just 5% for many regions 46 . Additionally, the development of smart charging infrastructure and grid digitization is likely to provide additional flexibility for matching electricity demand and supply 47 .

A final limitation is that we assume that the rated capacity per vehicle remains the same in the future and that a small number of large BEVs might provide large actual vehicle-to-grid capacity (Fig.  3 ). These capacities may change further in the future due to policy incentives, vehicle design, consumer preferences, charging infrastructure, among other factors. Further, the transportation system could see radical and fundamental changes. A significant and rapid shift away from private car use to mass transit, a move to shared electric vehicles, autonomous driving, and the success of battery swap systems 48 could all alter the available capacity by 2050.

In this study, we build a model framework to combine the EV use model, battery degradation model, and dynamic battery stock model. The model framework combines datasets on the real-world daily driving distance (in the EV use model), battery degradation test datasets (in the battery degradation model), and future EV and battery market data (in the dynamic battery stock model). The framework allows a structured use of diverse data to build a consistent perspective on future battery capacity. Within this model framework, this study provides a more complete understanding of the energy storage capacity available from EV batteries over time in real-world conditions and use. Results reveal a substantial opportunity for EV battery storage to support the stability and flexibility of renewable energy transition, even under modest consumer participation rates. To harness this opportunity, regulations and innovative business models will be needed to incentivize participation.

Model overview

We develop an integrated model to quantify the future EV battery capacity available for grid storage, including both vehicle-to-grid and second-use (see Supplementary Fig.  1 for an overall schematic). The integrated model includes three sub-models:

A dynamic battery stock model 27 to estimate total future EV battery stock and the retired batteries at vehicle EoL. This model considers EV fleet (i.e., battery stock) development and EV lifespan distribution (Supplementary Fig.  2 ), as well as future chemistry development (see Supplementary Fig.  3 for detailed battery market shares by chemistry).

An EV use model which includes behavioral factors such as EV driving cycle and charging behavior (changing power, time, and frequency), based on daily driving distance data for small/mid-size/large BEVs and PHEVs (Supplementary Figs.  4 – 6 ).

A battery degradation model based on the latest battery degradation test data, to estimate battery capacity fading over time under different EV use, battery chemistry, and temperature conditions (Supplementary Fig.  7 ).

Dynamic battery stock model

We build on results and methods from the study 27 where we built a global dynamic battery stock model to quantify the stock and flows of EV batteries. We model future EV fleet development (i.e., battery stock) until 2050. We determine the retired battery availability based on battery stock development and EV lifespan distribution (which is assumed to determine the time when EV batteries are retired). Battery degradation does affect the technical performance (such as driving distance capability) of EVs, thus influencing consumers’ choice of time when EVs come into EoL. Here, for model simplicity, we assume batteries will be retired only when EVs come into EoL. While for EV battery capacity, we use an average capacity of 33, 66, and 100 kWh for small/mid-size/large BEVs, and 21, 10, and 15 kWh for small/mid-size/large PHEVs.

We use two EV fleet scenarios until 2030 from the IEA: the stated policies (STEP) scenario and the sustainable development (SD) scenario. We further extend these two scenarios to 2050 based on a review of EV projections until 2050. We use the EV fleet share across 5 main EV markets (China, India, EU, US, and RoW) from the IEA until 2030, and keep the EV fleet share by countries/regions in 2030-2050 the same as the year 2030 due to lack of reliable data after 2030 (see Supplementary Data  1 for EV fleet scenarios by countries/regions). Further, we include 56 cities in China, 9 cities in India, 32 cities in EU, 53 cities in US, and 9 cities in RoW. We compile future EV sales share among 159 cities globally in STEP scenario and SD scenario based on future EV fleet projections by counties/regions from the IEA 25 and other data sources 49 , 50 (see Supplementary Data  1 ).

We consider battery market shares by chemistry based on the market share projections until 2030 from Avicenne Energy 51 a specialist consulting firm, and potential trends until 2050 from battery technology roadmaps 52 , 53 , 54 and commercial activities 55 , 56 . Current battery technology roadmaps issued by the US 52 , EU 53 , and China 54 focus on the development of high-energy Lithium Nickel Cobalt Manganese Oxide (transition to low cobalt and high nickel content) and Lithium Nickel Cobalt Aluminum-based chemistries. NCM and NCA batteries will likely make up the majority of next-generation EV Lithium-ion batteries. Future battery chemistry is uncertain after 2030. Existing Lithium Iron Phosphate batteries could also dominate the EV market, as indicated by recent commercial activities 55 , 56 . LFP battery manufacturers intend to improve the specific energy of LFP batteries to compete with NCM batteries 45 . Large-scale deployments of LFP may help avoid potential material supply shortage and price spikes associated with NCM and NCA batteries 27 . To encompass these market uncertainties, two battery chemistry scenarios are developed, including an NCX scenario (with X representing Manganese or Aluminum), and an LFP scenario. The market shares of NCX and LFP are assumed to reach 98% and 2% in the NCX path by 2050, and 40% and 60% in the LFP path (see Supplementary Fig.  3 for detailed battery market shares by chemistry in two scenarios).

EV use model

We use the daily driving distance (DDD) of EVs based on data from Spritmonitor.de 24 , an online quality-controlled, crowd-sourced database containing detailed real-world information on distances traveled, fuel consumption, and corresponding costs. It is widely used in the literature, including for estimation of the environmental impacts of vehicles 57 and the CO 2 mitigation potential of EVs 58 . We build historical DDD distributions for small/mid-size/large BEVs/PHEVs models, and explore the EV driving behavior of each EV model based on the corresponding DDD distributions. Please see the DDD distributions of each EV model in Supplementary Data  1 . Note DDDs less than 5 km are excluded.

Further, we compile future DDD in different countries/regions (Supplementary Figs.  29 – 32 ) by assuming the future DDD is proportional to the future energy consumption per vehicle. The future energy consumption per vehicle in different countries/regions is estimated by the total EV fleet energy consumption divided by future EV fleet size in each country/region, which are both projected by the IEA 25 .

By comparing various DDDs in multiples of EV range, we classify 5 DDD classes to formulate driving intensity and charging behavior. These 5 classes divided between 0% of the EV range to 200% of the EV range (i.e., a DDD twice the range of the EV) with intervals of 0–25%, 25–33%, 33–50%, 50–100%, 100–200%. We use the mean DDD of each class for calculations.

We assume two commuting trips between home and working place per day on weekdays and two entertaining trips on weekends for all countries/regions. Each trip distance is half of DDD. According to the required trip distance, we compile the driving cycle of each trip (speed versus time) based on the standard US combined driving cycle (i.e., 55% city driving and 45% highway driving, see details in Supplementary Figs.  5 and 6 , and Supplementary Note  1 ).

Charging behavior may be affected by charging infrastructure, amongst others, on-board EV charger, consumer preferences. We assume an immediate and slow home charging at constant charging power to full charge for all EV sizes and types because home charging is the major charging way (see Supplementary Data  1 ). We assume the home charging power as 1.92, 6.6, 22, and 1.92 kW for small, mid-size, large BEV, and PHEV, respectively 59 . We assume that due to high costs and limited utility no consumers will install higher power charging infrastructure at home. We further anticipate the charging behaviors in terms of changing frequency by comparing the various DDDs in multiples of the EV range. As driving intensity increases, the higher charging frequency is assumed for 5 DDD classes (1x every four days, 1x every three days, 1x every two days, 1x each day, and 2x every day respectively). For example, if the DDD of mid-size BEV (with a 312 km EV range) increases from 75 km to 625 km, and the battery needs to be charged more frequently from 1 time per four days to 2 times per day.

We calculate battery SoC under three EV states: driving, charging, and parked. For the battery SoC during driving, we use FASTSim model 59 , Future Automotive Systems Technology Simulator developed by National Renewable Energy Laboratory (NREL), to calculate EV battery SoC second-by-second. The model inputs include the EV driving cycle, EV configurations, and battery performance parameters (specific energy and battery capacity). We select one representative EV model from the FASTSim model 59 for each EV size and type as EV configuration (Supplementary Table  2 ), and NCM622 as a representative chemistry for all EV types; because it was found that EV configurations and battery performance parameters (such as specific energy) had small effects on the resulting battery SoC simulations. For battery SoC during charging, we assume the battery SoC increases linearly under a constant charging power with a 90% charging efficiency 60 . If an EV is parked, the SoC of the battery is slowly decreasing due to losses caused by battery self-discharging. A typical self-discharging rate of 5% per month is assumed for lithium-ion battery 61 . Self-discharging occurs due to parasitic chemical reactions that consume active lithium and form electrochemically inactive species while lithium-ion batteries are at rest. These parasitic reactions both reduce the SoC of the cell, and also reduce the total amount of lithium available for cycling. The impact of self-discharge on the SoH of NCM and LFP batteries is captured in the battery degradation model we use. Note that for the sake of battery safety, a portion of battery capacity is unusable (15% for BEVs and 30% for PHEVs based on the BatPac model 62 ), therefore we assume the usable SoC range as 5%-90% for BEV battery and 15%-85% for PHEV battery.

The battery temperature depends on the heat generation from chemical reactions inside batteries, amongst others, ambient temperature and environment (such as solar power radiation), battery management system (air or liquid cooling system to control battery temperature). The temperature can also vary from cell to cell, module to module, and component to component in the battery pack. The modelling of battery temperature is complicated and out of scope of this study. Here we use city ambient temperature to represent battery temperature, which is then used to calculate battery degradation. The main justification for this simplification is that the degradation for most consumer vehicles is dominated by calendar aging effects, as light-duty vehicles are only driven for a relatively small fraction of time throughout their life. For higher vehicle utilisation, neglecting battery pack thermal management in the degradation model will generally result in worse battery lifetimes, leading to a conservative estimate of electric vehicle lifetime. As such our modelling suggests a conservative lower bound of the potential for EV batteries to supply short-term storage facilities. Here, we use monthly average temperature of total 159 cities to capture the effects of geographic and temporal temperature variance on battery degradation. The temperature data is collected from 63 , 64 , 65 , 66 , can be found in Supplementary Data  1 .

Battery degradation model

Battery degradation is crucially important for determining EV battery capacity both in use and for second-life applications, but there are still many open research questions surrounding the importance of EV driving habits, charging behavior, and battery chemistries on capacity development 67 . Degradation model approaches include physics-based degradation models 68 as well as machine learning models 69 , 70 though there is no agreed-upon best practice 71 . Here, to balance the complexity and accuracy of the battery degradation model, we develop a semiempirical battery degradation model based on method from 16 . The model considers both calendar life and cycle life aging (Eq. ( 1 )), assuming a square-root dependence on time for calendar life (degradation rates depend on temperature and SoC, see Eq. ( 2 )) and a linear dependence on energy throughput for cycle life (degradation rates depend on temperature, Depth-of-Discharge (DoD), and Current rate (C rate ) see Eq. ( 3 )).

where q is the relative battery degradation, q Loss, Calendar is the relative calendar life degradation, q Loss, Cycling is the relative cycling life degradation, T is temperature, t is time (unit: days), EFC is equivalent full cycles. Note R is the universal gas constant (8.3144598 J mol -1 K -1 ), T ref is the reference temperature (298.15 K), F is Faraday constant (96485 C mol -1 ), k Cal (unit: days 0.5 ), E a (unit: J mol -1 K -1 ), and α (no unit) are fitting parameters for calendar life degradation, and k Cyc (unit: EFC -1 ). A, B, C, D, G, and H (no units) are fitting parameters for cycling life degradation. The value of the anode-to-reference potential, U a (unit: V), is calculated from the storage SoC using the Eqs. ( 4 ) and ( 5 ) 72 .

where x a , which represents the lithiation fraction of the graphite, is a simple linear function of the SoC: 73

where x a, 0 is the lithiation fraction of the graphite at 0% SoC and x a, 100 is the lithiation fraction of the graphite at 100% SoC. x a, 0 equals to 0.0085, and x a, 100 equals to 0.78.

To obtain these fitting parameters, we collect publicly available battery degradation data, including calendar life aging and cycle life aging, for NCM 16 and LFP 14 , 15 , 17 chemistry. These data sets represent state-of-the-art lifetime performance for each chemistry; the LFP cells shown reach between 5000 and 8000 equivalent full cycles before reaching 80% remaining capacity, 4000~5000 equivalent full cycles for NCM cells. This experimental data was then fit with the semiempirical model Eqs. ( 1 ), ( 2 ), and ( 3 ) using a non-linear least squares solver in MATLAB. The NCM model has no C rate dependence, due to lack of data in the aging data set, so the parameters C and D are simply set at 0 and 1. We first fit the calendar fade data with the time-dependent portion of the model (q Loss, Calendar , parameters k Cal , E a , and α); the parameter α is bounded between -1 and 1, with other parameters unbounded. The parameters for the cycling fade (A, B, C, and D) are optimized on the cycling aging data. For both LFP and NCM, the raw cycling fade data was processed prior to optimizing a model based on expert judgement. For LFP, only cells with linear fade trajectories and data for at least 5000 EFCs were used for model optimization. For NCM, only data after 200 EFC at T > 5 °C and data at q < 0.85 at T < 5 °C was used for the optimization of the NCM cycling model parameters. The optimized parameters for the LFP and NCM degradation models are shown in Supplementary Table  3 . Fitting results are shown in Supplementary Fig.  33 and degradation rates are shown in Supplementary Fig.  34 .

Note that we assume NCA battery has the same degradation patterns as NCM battery due to a lack of state-of-the-art open-source data for NCA batteries. Besides cell chemistry, capacity degradation characteristics vary with cell design, manufacturing process, and proprietary additives 67 , 74 , which is out of scope of this study. We use cell degradation patterns to represent battery pack degradation without consideration of cell-to-cell and module-module differences.

For simulation of the degradation under the EV driving loads (battery SoC evolution over time) and during dynamic temperature changes, the degradation model is reformulated to solve for the degradation occurring during consecutive timesteps 15 . We choose a timestep of 1 day for making SoH updates and update the SoC timeseries for each day by the current SoH. At each timestep, the temperature is the average temperature during the simulation month at city from different countries/regions. Average SoC, DoD, C rate , and the number of EFCs is extracted from the SoC timeseries. Average SoC refers to the time-averaged value of SoC. DoD is the difference between the maximum and minimum values of SoC. C rate is calculated using the absolute change of SoC per second, and then taking the average of all C rates greater than 0 during the entire timeseries. The number of EFCs is calculated by summing the changes to SoC over the timeseries. Dependence of the expected degradation rate on current SoH is incorporated by calculating a ‘virtual time’ 15 . The virtual time is found by inverting the calendar degradation equation to solve for time:

The degradation change ∆q during any given timestep Δt is then calculated by the following equation:

For cycling fade, the virtual EFC does not need to be calculated, as the degradation rate is constant with respect to the change of EFC during any given timestep. This reformulation of the degradation model captures the path-dependent degradation observed in real-world battery use. See Supplementary Note  2 for modelled battery degradation for NCM and LFP.

Available capacity from EV batteries

Vehicle EoL does not necessarily correspond to battery EoL. With technological improvements in battery reliability and durability, many batteries in EoL vehicles may still have years of useful life at the end of vehicle end of life. Vehicle battery EoL is usually as defined the time at which remaining battery capacity is between 70 and 80% of the original capacity 7 . We assume an EV lifespan distribution, used in our previous work 27 , to account for EoL of EV. In our modelling approach, the vehicle lifespan distribution determines when batteries are not used in EVs any more (i.e., retired batteries). Retired batteries may have quite different capacity under different use conditions. When vehicles reach EoL due to consumer choices or other issues before the battery pack reaches 70% relative capacity, retired batteries will still have over 70% relative SoH and are assumed to be used in a second-life application. When battery pack reaches 70% relative SoH before a vehicle reaches its EoL, we assume that batteries may be still be used in EVs for low distances-driving. Retired batteries from such vehicles will have lower than 70% relative SoH and are assumed to be recycled rather than for a second-use. We assume any battery with a relative SoH lower than 60% is recycled and removed from potential grid storage capacity 75 . However, even batteries with a relative SoH of 60–70% have a limited economic value and can have relatively high safety risks. (methods) 32 .

We define technical vehicle-to-grid capacity as the availability of EV battery stock capacity for vehicle-to-grid application, considering the capacity reserved for EV driving, the capacity of PHEVs that will not participate in vehicle-to-grid due to low capacity, and capacity fade due to battery degradation. We further define the actual vehicle-to-grid capacity as the availability of technical vehicle-to-grid capacity for the grid under different consumer participation rates in the vehicle-to-grid business. Results focus on investigating under which participation rate can actual vehicle-to-grid capacity meet grid storage demand.

The technical second-use capacity is defined as the retired batteries capacity that can be repurposed (i.e., retired batteries with over 70% relative SoH). We further investigate actual second-use capacity under different utilisation rates (i.e., not all retired batteries will be deployed in second-use). The results are intended to determine the required utilisation rate for the second-use battery to meet grid storage demand.

We investigate the real-world capacity as a function of both vehicle-to-grid participation rate and second-use utilisation rates. We further analyze the market participation rates and utilisation rates that are required to meet short-term grid storage demand globally.

Impact of battery capacity assumptions

The model is highly influenced by the battery capacity per vehicle. Therefore, we conduct a sensitivity analysis of battery capacity per vehicle by assuming all BEVs are small BEVs equipped with a battery with a capacity of 33 kWh. This assumption is based on three arguments: first, small BEVs could provide most of the daily driving demand for consumers, even though they have a lower driving range than large BEVs equipped with a high-capacity battery. Second, the development of widespread EV charging infrastructure, including fast charging technology, could help to overcome the range anxiety of small BEV owners. Third, the increasing use of small BEVs would reduce demand for batteries and materials, along with lowering embodied GHG emissions of those batteries.

Data availability

The datasets, including EV fleet size by country, EV sales share by cities, and battery chemistry share, are all deposited in an Excel file ( https://doi.org/10.6084/m9.figshare.21542472.v1 ). These raw data are used for the dynamic battery stock model for quantifying future battery flows. Please see the dynamic battery stock model from this link ( https://doi.org/10.6084/m9.figshare.13042001.v4 ). City ambient temperature and its effects on battery degradation are also deposited in the Excel file, while the code for estimating battery degradation, which is under privacy and license, is available upon reasonable request.

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Acknowledgements

C.X. acknowledges the China scholarship for financial support as well as Haixiang Lin and Gjalt Huppes for their useful recommendations to the study. P.G. and K.S. are supported by the National Renewable Energy Laboratory which is operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy under Contract No. DE-AC36-08GO28308, and acknowledge support from the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the U.S. Department of Energy. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

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C.X. designed and conducted the research with valuable inputs from B.S., P.B., A.T., M. H., as well as P. G. and K. S. C.X. wrote the manuscript with the help of P.B., A.T., B.S., P. G., and other authors. P.B., A.T., and B.S. contribute significantly to the structure of research results and scientific writing of this research. P. G. and K. S. developed the battery degradation model, and further provided technical inputs on how to integrate the degradation model into the analysis of the results.

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Xu, C., Behrens, P., Gasper, P. et al. Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030. Nat Commun 14 , 119 (2023). https://doi.org/10.1038/s41467-022-35393-0

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Here's everything you don't know about electric vehicles

Depending on the type of charging equipment you’re using, powering up your electric vehicle can take anywhere from half an hour to longer than overnight. Image:  Unsplash/ Chuttersnap

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Stay up to date:, automotive and new mobility.

• The first electric car was developed in the early 1900s in Iowa, but was far from the electric cars of today.
• However, since then they have become critical components for a more sustainable society, despite having to surpass engineering challenges.
• The range of these vehicles is still being questioned by consumers today, despite many electric cars having a range of around 250 miles per charge.

If you think electric cars are a new concept, you're not alone. Until recently, EV’s drew interest mainly from a niche group of enthusiasts.

Gasoline-powered cars and trucks are usually considered to be the ‘traditional’ types of those vehicles, but electric vehicles were being developed right around the same time, according to a U.S. Department of Energy (DOE) post . Although Karl Benz is credited with inventing the first gasoline-powered automobile in Germany in the mid-1880s, the first crude electric vehicle — an electric carriage — was invented in Scotland around 1832. Other early forms of electric vehicles popped up in the ensuing decades.

Within 50 years, the first “successful electric car” was developed in Iowa, although the vehicles were still a far cry from modern-day electric vehicle technology, according to the DOE.

“By 1900, electric cars were at their heyday, accounting for around a third of all vehicles on the road,” explained the DOE, noting that “strong sales” continued for the next decade. But technological advancements and an American desire to travel outside of electrified cities played a part in propelling the popularity of gasoline-powered vehicles past electric vehicles, a preference that stuck for decades.

What are the types of electric vehicles?

Hybrid electric vehicles (hevs).

Hybrid electric vehicles utilize both internal combustion engines and vehicle batteries that power electric motors. Drivers can fill up their gas tank and go, but benefit from a 'regenerative braking' system that charges the internal battery, storing the energy created otherwise lost by braking the vehicle. An HEV’s battery can’t be charged with a plug like other kinds of electric vehicles.

“In an HEV, the extra power provided by the electric motor may allow for a smaller combustion engine,” according to the DOE’s Alternative Fuels Data Center. “The battery can also power auxiliary loads and reduce engine idling when the vehicle is stopped.”

“Together, these features result in better fuel economy without sacrificing performance,” the center continued in a post .

Although such vehicles have electric batteries, some don’t consider them to be full electric vehicles.

“Generally, since hybrids don't have a plug, we don't consider them to be electric vehicles,” explained Amalia Siegel, a program manager at Efficiency Maine, a quasi-state energy efficiency agency. “Although hybrids are a great choice [and] they have great fuel economy, those are sort of in a separate category.”

Plug-in hybrid electric vehicles (PHEVs)

Plug-in hybrid electric vehicles (PHEVs), similar to their non-plug-in cousins, use both conventional internal combustion engines and batteries for their power sources. The primary difference, however, is that while PHEVs also can charge their batteries with regenerative braking, they can also power up through the electrical grid with a plug.

Battery Electric Vehicles (BEVs)

Like PHEVs, battery electric vehicles (BEVs), also known as all-electric vehicles, are able to connect to the grid with a plug and charge up with the same electricity running through our homes and businesses. BEVs can also top up their batteries through regenerative braking.

However, BEVs don’t have any combustion engines that require gasoline, a design feature that means there are no direct fossil fuel-related emissions from the vehicle while driving.

Are electric vehicles better for the environment?

While some potential electric vehicle drivers may not care too much about whether their car is better or worse for the environment, many people first hear about the vehicles in the context of environmental protection and climate crisis mitigation.

Some critics of electric vehicles note that the “greenness” of an electric vehicle’s charge is dependent on how clean the power is in the grid it’s plugged into. In New England, for example, the regional grid operator primarily generates electricity through natural gas, according to a U.S. Energy Information Administration database . And Texas , which has its own electricity grid, natural gas and coal are the first- and second-most utilized energy source for power production.

Nevertheless, “the thing about electric vehicles is that they're just more efficient than gasoline vehicles, regardless of where the energy is coming from,” explained Siegel. “So there are some places in the country that have a very strong renewable energy mix, and where the electricity is very green to begin with, but even in states… that have more coal in the mix or some other fossil fuels, it still ends up being that the greenhouse gas emissions are still lower” stemming from the use of an electric vehicle.

However, there are other environmental considerations beyond transportation efficiency and greenhouse gas emissions, even if the emissions-heavy transportation sector is still a large concern.

Critics and event supporters of electric vehicle development point to the necessity and procurement of certain metals, such as lithium and cobalt, that are integral to the production of electric vehicle batteries. Mining for these materials is controversial because while the substances are essential to the transportation electrification evolution, environmental damage and human labor exploitation persist.

Interest has grown in seafloor mining as developers seek more sources of cobalt and nickel, but there is limited collective knowledge on what the long-term impact of excavating the ocean floors and disrupting the ecosystem and its aquatic life may be.

Back onshore, mining for critical battery materials is no less contentious. Over half of the world’s known deposits of cobalt , for example, can be found in the ground beneath the Democratic Republic of Congo, whose cobalt mining industry is rife with corruption and human rights concerns, according to a recent New York Times investigation .

Have you read?

What are countries doing to encourage the transition to electric vehicles, the innovation that could speed up the rollout of electric vehicles, 5 innovators making the electric vehicle battery more sustainable, how long does it take to charge an electric vehicle.

Depending on the type of charging equipment you’re using, powering up your electric vehicle can take anywhere from half an hour to longer than overnight. Older electric vehicle models may not have plugs for more-advanced rapid chargers.

According to an Efficiency Maine post , level 1 chargers are the slowest type, taking sometimes up to 15 hours to fully charge a battery. These cords are typically included with the vehicle at purchase or lease. Level 2 chargers, which Efficiency Maine noted are the most common type to be installed in homes, usually add around 14 to 35 miles of range in an hour of plug-in time. Subject to the size of the battery, a full charge can take as little as three hours to as long as 10 hours, the agency explained.

Level 3 chargers provide the most-rapid power-ups for electric vehicles and are usually found at public locations like rest stops. According to South Korean car manufacturer Kia , “it tends to take longer to charge at a lower temperature, particularly when using a rapid charger,” in addition to other charging speed limitations like battery size and charging equipment speed.

Of course, not everyone has their own garage at home to plug into. Many people live in apartment or condo buildings where their utility bills aren’t tied to the power being used in the garages. In those cases, Siegel said, electric vehicle drivers looking to power their cars up at home should be proactive with their landlords or building managers.

“I think the important thing is to give them a sense of roughly how much electricity you'll be using, because maybe some landlords don't really have a sense of that,” Siegel explained. “It's going to be different in different situations,” she added, noting that some buildings might actually be able to directly charge your apartment or condo for the electricity used. But either way, striking up a deal ahead of time about when you will charge and how much you will compensate your property manager will save you a headache later on.

Of course, there are publicly available chargers that anyone can tap into. Two resources for locating publicly available chargers include the DOE Alternative Fuels Data Center’s filterable map and Plugshare, a free website and phone app that bills itself as “the most accurate and complete public charging map worldwide, with stations from every major network in North America and Europe.”

How much does it cost to charge an electric vehicle?

Even when gasoline prices aren’t high, many drivers dream of a cheaper way to fuel up. Siegel said the best way to see the cost difference between a combustion engine and an electric vehicle is not to compare the cost to fill a gas tank with the cost of charging up a vehicle battery. Instead, she said it’s better to compare the cost of traveling 100 miles in either vehicle to gauge savings.

“If you’re just using electricity from your home, you could expect to pay about half as much to drive an EV than to drive a gasoline car,” said Siegel, noting that she recently calculated that it would cost between $4.80 - $5.00 for a resident of Maine to charge their electric vehicle for a 100-mile drive, compared to around $9.00 - $10.00 to drive 100 miles in a gasoline-powered vehicle.

For her calculation, she assumed an electric vehicle with an efficiency of 30 kWh per 100 miles, like the Nissan LEAF, and a gasoline-powered car with an efficiency of 30 miles per gallon. Siegel also assumed gasoline cost $3.00 per gallon.

Outside of the home, public charging stations aren’t likely to be more expensive than charging at home but may require you to make a free account, Siegel said. However, “it's no different than signing up for a frequent shopper card at your local grocery store,” she added.

What are electric vehicle tax credits?

There are two primary ways that governments are looking to incentivize electric vehicle purchases: rebates and tax credits.

According to the Alternative Fuels Data Center, federal electric vehicle tax credits can apply to eligible vehicles acquired after December 31, 2009. Federal credits range from $2,500 to $7,500. You still need the money to pay for the sticker price at the lot, but you’ll get a certain amount back on your next tax filing, depending on how heavy the vehicle is and its battery capacity.

However, the major infrastructure bill currently making its way through the U.S. Congress may increase the upper limit of the tax credit. According to CNET , some electric vehicle models may be eligible for up to $12,500 in federal tax credits, if the bill becomes law.

An important consideration for those looking to take advantage of the federal tax credit (as currently codified) is that if you don’t owe more than $7,500 on your taxes, you can’t receive the maximum $7,500 in tax credits, Siegel explained.

“You can only claim that up to the amount of taxes you have,” she said. “So if you only have $5,000 worth of tax liability, you're not going to be able to get the full amount of the tax credit.”

Of course, states can implement their own rebate or tax credit programs that can provide additional savings for potential electric vehicle drivers.

“In Maine and a lot of other states that I know about, you can absolutely stack them because [the federal tax credit and state-specific rebate or tax programs are] completely different,” said Siegel.

To learn whether your state has additional credits, it may be best to reach out to environmental, efficiency or energy organizations, nonprofits or utilities in your state to ask for the latest information on rebate or tax credit programs for which you qualify.

Some Pros and Cons of Electric Vehicles

In addition to the already stated positives and negatives of electric vehicles, potential electric vehicle purchasers may wonder about other practical driving and maintenance concerns.

Lower maintenance costs

No engine? No problem. While you still need to go to the mechanic, “What the research shows is, generally speaking, electric vehicles result in lower operating and maintenance costs over a given year," said Nicholas Ucci, commissioner of the Rhode Island Office of Energy Resources. “You don't have a lot of the same ‘moving pieces’ that you do in a gasoline engine, like oil changes, for example, so that can reduce… the total cost to a consumer and operating a vehicle like this.”

Ucci added that, of course, there are some components that will still need to be replaced, like windshield wipers and tires, just as frequently as a gasoline-powered vehicle would require.

However, not all mechanics may be trained up on servicing an electric vehicle. Certain parts require specific training, according to a recent SF Weekly article . That has led to efforts to attract new mechanics to the field who can specialize in electric vehicles or service both electric and gasoline vehicles.

In general, Ucci said, “When you go to a mechanic, you always want to know what their qualifications are, so if you do have an electric vehicle, I think it's important to ask the mechanic if they're certified to work on the type of vehicle you own, if there are additional certifications that are needed [in your area]."

Range anxiety

Some potential electric vehicle drivers and owners worry that they’ll be driving and get stuck without a charge, unable to get to their actual destination. That range anxiety used to be more common with older models that couldn’t take you as far on a single charge.

“For most modern electric vehicles, [you can drive] about 250 miles or more before needing another charge,” explained Siegel, adding that most people only drive up to 30 miles in a day. “So if you know how far you drive on a typical day, you can get a sense of what your needs are going to be” in terms of selecting the type of electric vehicle for you.

If you’re regularly taking rides over 200 miles in a single day, you’ll likely want a hybrid electric vehicle or plug-in hybrid electric vehicle to minimize any anxiety. But “for most people, the range of an average, new electric vehicle is going to be plenty for their driving needs,” Siegel said.

What’s the future for electric vehicles?

As concern for the environment and climate has grown, so too has the desire to return to the electric vehicle. By the end of 2020, around 11 million electric vehicles were registered with governments around the globe.

That may not sound like many — and it’s not, considering that the planet surpassed 1 billion cars alone back in 2010 — electric cars, buses, vans and trucks are expected to account for 7% of road transportation by the end of this decade, assuming government policies as of May 2021 are followed. For example, U.S. President Joe Biden signed an executive order in August 2021 setting a goal to have half of all new vehicles sold in the country be zero-emission models by 2030. Some estimates even suggest that by 2035, every single new car or truck sold in the U.S. could be an electric vehicle.

Some areas of the U.S. seem more keen than others to adopt the personal transportation technology. For example, a recent poll commissioned by two clean energy companies found that a majority of registered Massachusetts voters said they were likely to buy an electric vehicle within five years.

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29 May 2024  ·  Zongbao Zhang , Jiao Hao , Wenmeng Zhao , Yan Liu , Yaohui Huang , XinHang Luo · Edit social preview

The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal variations in usage patterns. To address these challenges, we propose a Multiscale Spatio-Temporal Enhanced Model (MSTEM) for effective load forecasting at EVCS. MSTEM incorporates a multiscale graph neural network to discern hierarchical nonlinear temporal dependencies across various time scales. Besides, it also integrates a recurrent learning component and a residual fusion mechanism, enhancing its capability to accurately capture spatial and temporal variations in charging patterns. The effectiveness of the proposed MSTEM has been validated through comparative analysis with six baseline models using three evaluation metrics. The case studies utilize real-world datasets for both fast and slow charging loads at EVCS in Perth, UK. The experimental results demonstrate the superiority of MSTEM in short-term continuous load forecasting for EVCS.

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Energy & Environmental Science

Multiscale correlative imaging reveals sequential and heterogeneous degradations in fast-charging batteries.

Fast-charging lithium-ion batteries provide a promising solution to addressing the range anxiety of electric vehicles (EVs) but they face challenges in durability and safety. Kinetically-driven lithium-plating on the anode is widely considered as a major bottleneck and has dominated the research attention so far. Here we develop lengthscale-bridging, multimodal SEM-Raman-NanoSIMs techniques to understand the complex chemical-structural-mechanical interplays within the fast-charge batteries. Statistical understanding from multiple-particle analysis at the electrode-level is obtained for the first time. Electrolyte depletion is found to be the first domino to fall (even when the capacity fade is still trivial), which triggers a wide range of anode failures including the lithium plating and byproducts accumulation. The cathode remains relatively healthy early on but does exhibit increasing heterogeneity in lithium concentration, particle fracture behaviors, and lattice-structure disorder as the batteries cycle. These complex electro-chemo-mechanical interplays trigger self-amplifying “vicious circles” and cause severe electrode distortion and even rupture inside the batteries, which become hidden safety threats. This work provides new perspectives and useful insights to developing better fast-charging batteries.

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D. Meng, Z. Xue, G. Chen, D. Zhou, Y. He, Z. Ma, Y. Liu and L. Li, Energy Environ. Sci. , 2024, Accepted Manuscript , DOI: 10.1039/D4EE01497A

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Energy transition: will electric vehicle sales ever catch up? | The Excerpt

On Sunday’s episode of The Excerpt podcast: Sales for electric vehicles in the U.S. and worldwide will reach their highest levels ever this year. But a big sticking point remains. Even with tax incentives and rebates, the price for EVs is still thousands of dollars higher than their gas-guzzling cousins. The Biden administration set 2030 as the goal for when half of all new vehicles sold would be electric powered. The question is, can we get there? Duncan Wood, vice president for strategy & new initiatives at the Wilson Center, a non-profit thinktank, joins The Excerpt to talk about the possibilities and what it will take for this growing market to mature.

Hit play on the player below to hear the podcast and follow along with the transcript beneath it.  This transcript was automatically generated, and then edited for clarity in its current form. There may be some differences between the audio and the text.

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Dana Taylor:

Hello and welcome to The Excerpt. I'm Dana Taylor. Today is Sunday, May 26th, 2024. Electric vehicles are in demand. Sales here and in markets around the world will reach their highest levels ever this year, but a big sticking point remains. Even with tax incentives and rebates, the price for EVs is still thousands of dollars higher than their gas guzzling cousins.

The Biden administration set 2030 as the goal for when half of all new vehicles sold would be electric powered. The question is, can we get there? Our guest today is Duncan Wood, the Vice President for Strategy and New Initiatives at the Wilson Center, a non-profit think tank. Thanks for joining us, Duncan.

Duncan Wood:

Great pleasure to be with you, Dana.

EVs have always embodied the dream of a carbon-free future, but there seem to be speed bumps on the way to getting there. What do you see as the biggest obstacles here in the US right now?

I think there's a number of issues that are facing the manufacturers, the OEMs, as we call them, for EVs. First of all, we see the incredible lead that the Chinese auto industry has developed over the past 10 years or so in producing, manufacturing electric vehicles and really getting them up to a world-class quality. Secondly, I think that we see the challenges that are being faced by the manufacturers here in North America when it comes to securing access to the electric vehicle battery materials that they need.

And by that I'm talking about the classic critical minerals of lithium, cobalt, nickel, and manganese, as well as the rare earth elements that are so important for the permanent magnets that we find in EV engines. And that's a real challenge that we've been engaged in trying to unravel for the past four years now and we're making progress. And of course, the third challenge that we see is in terms of consumer take up. Because whilst we're selling record numbers of EVs, a lot of analysts are saying that this is disappointing.

There's not nearly as many sales as we were hoping for by this point, and that it's going to be a struggle to reach that goal of 2030. And that consumer take up side of things involves a number of factors. One is cost, absolutely. The other one is what's classically known as range anxiety. And the third one is, is this electric vehicle going to be reliable in the long term for me?

Well, you mentioned China, how are other countries making the transition to EVs?

So China's an extraordinary example, which has sunk a lot of money into this quest to electrify transportation. But at the same time, of course, they've been producing a lot of the classic internal combustion engine vehicles or ICE vehicles as we see them. And China has engaged in very unfair trading practices internationally, essentially selling their vehicles at very low prices, if not lower than cost, then at the price that we would recognize as being dumping on global markets. Now, they have had an incredible headstart.

But when you look across the Atlantic to Europe, the Europeans have been seeing a higher take up for EVs as well. And a lot of that has less to do with providing incentives for consumers, but more penalties for those who continue to drive internal combustion engine vehicles. And that's to do with the very high price of gasoline. It's to do with the taxes that are on those vehicles. And in some cases, it's to do with environmental charges that are placed on those vehicles when they circulate in city centers.

And so we're seeing a very varied approach around the world. Here in the United States, of course, we've seen an approach which focuses on really essentially dumping a huge amount of money into the market in the form of tax credits for people to encourage them to buy electric vehicles, as well as production credits for the manufacturers. And those hundreds of billions of dollars that were authorized through the Inflation Reduction Act a couple of years ago, they're moving the market.

I often describe this actually as a seismic shift in the way that we look at the EV supply chain. It's moving the market, but it hasn't succeeded yet in really pushing us to the tipping point where a majority of consumers want to buy EVs.

I want to go back to some of the hurdles in this market shift to electric vehicles. What are the key barriers standing in the way of Americans being able to buy EVs?

So I think that there are two big barriers towards creating a reliable and resilient supply chain for American or North American EVs. And I say North American because we have to recognize that vehicles produced here in the United States really do rely upon a North American supply chain involving Canada and Mexico. But the big problems that I see are, number one, we're playing catch up.

We didn't get into the game early enough, which means that we haven't really advanced in terms of battery technologies and in terms of the vehicles that are being sold at a consumer friendly price. Of course, we all know the brands that are out there that cost many tens of thousands of dollars that would really class as luxury vehicles, but we've had a really tough time bringing the price point down for EVs so that they're accessible to a majority of consumers.

The second big challenge, which is connected to that, is to do with the supply chain itself. And the fact is is the Chinese have been out there for over a decade securing access to those critical minerals that I mentioned earlier on, not just in terms of buying up the production in places like South America and in Africa, Indonesia, Australia, et cetera, but also in terms of the processing and refining of those materials, the majority of which today either takes place in China or in third countries in facilities which are owned by Chinese companies.

So we really do see this monopoly that exists right now on the part of the Chinese in terms of the critical minerals and battery technologies. We're catching up fast, and I do believe that American innovation will be an extraordinarily powerful tool in allowing us not only to catch up, but eventually to leapfrog over the Chinese. But at this point in time, we really are playing that game of catch up.

You've written about a global race between the world's car makers. The Biden administration recently announced tariffs on EVs and other imports from China to help American car manufacturers. How might this impact prices and availability here in the US?

First of all, I think we have to recognize that the option of placing such a high tariff on vehicles coming in from China would not really have been considered by a US administration 10 years ago. We were still then in very much a free trade mindset. But it's fascinating to see that both the Biden administration and the Trump administration, which came before it, was much more willing to use tariffs to try to shift the balance of trade.

The second point that I'll say here is that as you're trying to achieve multiple goals as a government, and in the case of the Biden administration, there is the one of electrification of transport, which really is about the decarbonization of transportation for climate goals, at the same time as you're trying to achieve your geopolitical goals in terms of the strategic competition with China, at the same time as you're trying to increase the competitiveness of US businesses and making sure that investment and jobs either stay here in America or come here to America.

And then of course, you layer on top of that the electoral cycle, the whims of democracy as it were, and you begin to see that the policy options available to you are actually quite limited. And so what we've seen now is that partly because we're in election season, that there is this willingness on the part of politicians to talk more openly about the use of tariffs and to really promise that these will come into play as a way to protect US drug North American manufacturers and really to drive that success on their part. But the problem is is that as you correctly identify, the consumer is going to end up paying the price.

Duncan, there's been some talk about profit disincentives for car companies, specifically the fact that with gas-powered vehicles, there's a much bigger profit margin than with EVs. How much of a factor is that?

I think that it's a major factor, and we're trying to overcome that by the production tax credits that are included in legislation like the Inflation Reduction Act. We're trying to lower the cost of doing business here in the United States, and I think that those will help ultimately. But as we are in this phase of trying to catch up to the Chinese, I think that it's very, very challenging to be able to lower that cost profile in the manufacturing process.

Now, one thing that we do have in our favor is this integrated North American manufacturing platform that I mentioned earlier on. There are some things that could be done cheaper in the United States, some things that could be done cheaper in either Canada or Mexico. And allowing for that specialization across the three countries of North America is a big competitive advantage that we have to recognize here in the United States.

So I'm confident actually that US or North American manufacturers will actually catch up and be able to increase their profit margins on EVs by lowering the costs without having to raise the prices of those EVs. And I think that in particular, as we see more resilient supply chains, not just for the technology, but for those critical minerals that are needed in EVs, then we'll begin to see greater price stability allowing for greater certainty in the market, which will allow in turn companies to actually become more profitable from the EV side of their business.

The rebates and tax write-offs that you mentioned, they've been touted as big incentives that maybe aren't working as well as originally hoped. What are your thoughts on that and how policy shifts might help boost incentives for consumers?

So I think that number one, there is still a great deal of concern on the part of the consumer, not just about the initial sticker shock of an EV or the ticket price there, but really about am I going to be able to charge my vehicle? And I have to say that the progress that's been made over the past couple of years with regards to building out the charging network for EVs across the United States is really disappointing. The money has been there, but it takes a long time to get these projects up and running.

So although for the past two years we haven't seen a huge amount of progress, I think that now it's beginning to kick in. So over the next couple of years, let's say the next two to five years, we'll see a lot more charging stations being installed across the United States, primarily in urban areas. But in some work that we're about to publish here at the Wilson Center, we're also talking about importance of building out the charging network in rural areas to encourage take-up outside of cities.

Changing building codes so that apartment buildings, condominiums have enough capacity for the residents to charge their electric vehicles in their parking spots underneath the building. It's a process of changing the mindset. And to change the mindset, we're going to have to actually spend the money and make the progress in building out that charging network.

Google recently announced that its Map app will start sharing charging station information to help EV drivers navigate longer trips. Between finding charging stations on your route to the need to download apps on your phone to locate places to charge your car, do you see the system evolving soon?

Yes. I think that the application of technology to the EV conundrum is going to be critically important. It's not just identifying where the EV charging point is, it's whether that EV charging point is actually in service. One problem that we've observed over the past couple of years, and you may have seen this yourself, is that as you drive around and you're looking for a charging point, often that charging point is damaged or is out of service.

So we need to not only think about the installation of those charging points, we need to think about their maintenance. And a lot of this comes down to questions of human capital and the talent that's available. We've got to think about training our workforce here, not just to build the charging stations, but to maintain them and make sure they're in good working order.

And then finally, what to you is the most surprising part about why the transition to EVs has been so difficult?

Call me naive, but when I saw the Inflation Reduction Act sail through Congress, there was this enormous sense that we were on the verge of something very, very big just because of the scale of the money that was involved and the commitment of the US government to make that happen. And so there was that moment of enormous optimism that in fact, this was going to be possible.

And I think the big surprise for me is how slow it's been to put a lot of that money into play in the market. It's there, it's available, but these things take a great deal of time. As incredibly powerful as the US government is, the enormous resources that it has at its disposal, and that incredible moment of bipartisan cooperation that got the Inflation Reduction Act through Congress, they're still dealing with the speed of government question.

Duncan, thank you so much for joining me on The Excerpt.

Thank you so much, Dana.

Thanks for our senior producers, Shannon Rae Green and Bradley Glanzrock, for their production assistance. Our executive producer is Laura Beatty. Let us know what you think of this episode by sending a note to [email protected]. Thanks for listening. I'm Dana Taylor. Taylor Wilson will be back tomorrow morning with another episode of The Excerpt.

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