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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).

Bacevic , J . 2019 . Knowing neoliberalism , Social Epistemology , vol. 33 , no. 4 , 380 – 92

Google Scholar

Bailey , I. and Wilson , G . 2009 . Theorising transitional pathways in response to climate change: technocentrism, ecocentrism, and the carbon economy , Environment and Planning A , vol. 41 , no. 10 , 2324 – 41

CCC. 2019 . Net Zero: The UK’s Contribution to Stopping Global Warming , London , Author

Google Preview

Center for Global Policy Solutions. 2017 . Stick Shift: Autonomous Vehicles, Driving Jobs and the Future of Work , Washington DC , Author

Chapman , L . 2007 . Transportation and climate change: a review , Journal of Transport Geography , vol. 15 , no. 5 , 354 – 67

China Daily. 2018, December 1 . China has 325 million motor vehicles , China Daily

Christensen , J. and Olhoff , A . 2019 . Lessons from a Decade of Emissions Gap Assessments , Nairobi , UNEP

CSTC. 2019 . Clean Growth: Technologies for Meeting the UK’s Emissions Reduction Targets , London , Author

DBEIS. 2018 . Annex: 1990–2016 UK Greenhouse Gas Emissions, Final Figures by end User , London , Author

DECC. 2015 . Different Approaches to Reporting UK Greenhouse Gas Emissions , London , Author

Deloitte. 2018 . Battery Electric Vehicles. New market. New entrants. New challenges , London , Author

Dennis , K. and Urry , J . 2009 . After the Car , Cambridge , Polity

Department for Transport. 2018 . The Road to Zero: Next Steps Towards Cleaner Road Transport and Delivering our Industrial Strategy , London , Author

Department for Transport. 2019 . Vehicle Licensing Statistics: Annual 2018 , London , Author

Dunlap , A . 2019 . Wind, coal and copper: the politics of land grabbing, counterinsurgency and the social engineering of extraction , Globalizations vol. 17 , no. 4 , 661 – 82

Environmental Audit Committee. 2016 . Sustainability in the Department of Transport [Third Report of Sessions 2016–17], London , House of Commons

Faraday Institution. 2019 . UK Electric Vehicle and Battery Production Potential to 2040 , London , Author

Faulkner , P. and Runde , J . 2013 . Technological objects, social positions and the transformational model of social activity , MIS Quarterly , vol. 37 , no. 3 , 803 – 18

Frost & Sullivan. 2019 . Global Electric Vehicle Market Outlook, 2019 , Author , London

Fullbrook , E. and Morgan , J. (eds.). 2019 . Economics and the Ecosystem , London , World Economic Association Books

Georgescu-Roegen , N . 1971 . The Entropy Law and the Economic Process , Cambridge and London , Harvard University Press

Gills , B . 2020 . Deep Restoration: from the Great Implosion to the Great Awakening , Globalizations , vol. 17 , no. 4 , 577 – 9

Gills , B. and Morgan , J . 2019 . Global Climate Emergency: after COP24, climate science, urgency, and the threat to humanity , Globalizations

Gills , B. , Morgan , J. and Patomäki , H . 2019 . President Trump as status dysfunction , Organization , vol. 26 , no. 2 , 291 – 301

Hansen , J. et al.  2017 . Young people’s burden: requirement of negative CO2 emissions , Earth System Dynamics , vol. 8 , 577 – 616

Harvey , F . 2020, March 28 . Tackle climate crisis and poverty with zeal of Covid-19 fight scientists urge , The Guardian

Hirst , D . 2019 . ‘ Electric Vehicles and Infrastructure’ , Briefing Paper no. CBP07480, London , House of Commons Library

Hoekstra , A . 2019 . The underestimated potential of battery electric vehicles to reduce emissions , Joule , vol. 3 , no. 6 , 1412 – 4

Holt , R. , Pressman , S. and Spash , C. (eds.). 2009 . Post Keynesian and Ecological Economics , Cheltenham , Edward Elgar

Holtsmark , B. and Skonhoft , A . 2014 . The Norwegian support and subsidy policy for electric cars. Should it be adopted by other countries? Environmental Science & Policy , vol. 42 , 160 – 8

IEA. 2019A . Global EV Outlook 2019: Scaling up the Transition to Electric Mobility , Paris , Author

IEA. 2019B . World Energy Outlook , Paris , Author

IPCC. 2018 . Global Warming of 1.50C: Summary for Policymakers , Geneva , Author

IPCC. 2019A . IPCC Special Report on Climate Change, Desertification, Land Degradation Sustainable Land Management Food Security and Greenhouse Gas fluxes in Terrestrial Ecosystems , Geneva , Author

IPCC. 2019B . The Ocean and Cryosphere in a Changing Climate , Geneva , Author

Koch , M . 2019 . The state in the transformation to a sustainable postgrowth economy , Environmental Politics , vol. 29 , no. 1 , 115 – 33

Lawson , C . 2012 . Aviation lock-in and emissions trading , Cambridge Journal of Economics , vol. 36 , no. 5 , 1221 – 43

Lawson , C . 2017 . Technology and Isolation , Cambridge , Cambridge University Press

Lawson , T . 2019 . The Nature of Social Reality: Issues in Social Ontology , London , Routledge

Lea , R . 2019, September 6 . Tesla Model 3 enters sales chart at No3 , The Times

Lenton , T. , Rockstrom , J. , Gaffney , O. , Rahmstorf , S. , Richardson , K. , Steffen , W. and Schellnuber , H . 2018 . Climate tipping points too risky to bet against , Nature , vol. 575 , 592 – 5

Manzetti , S. and Mariasiu , F . 2015 . Electric vehicle battery technologies: from present state to future systems , Renewable and Sustainable Energy Reviews , vol. 51 , 1004 – 12

Mattioli , G. , Roberts , C. , Steinberger , J. , and Brown , A . 2020 . The political economy of car dependence: a systems of provision approach , Energy Research & Social Science , vol. 66 , 1 – 18

Morgan , J . 2016 . Paris COP21: power that speaks the truth? Globalizations , vol. 13 , no. 6 , 943 – 51

Morgan , J . 2017 . Piketty and the growth dilemma revisited in the context of ecological economics , Ecological Economics , vol. 136 , 169 – 77

Morgan , J . 2019A . Intervention, policy and responsibility: economics as over-engineered expertise?, pp. 145 – 63 in Dolfsma , W. and Negru , I. (eds.), 2019 The Ethical Formation of Economists , London , Routledge

Morgan , J . 2019B . Will we work in twenty-first century capitalism? A critique of the fourth industrial revolution literature , Economy and Society , vol. 48 , no. 3 , 371 – 98

Morgan , J. and Patomäki , H . 2017 . Contrast explanation in economics: its context, meaning, and potential , Cambridge Journal of Economics , vol. 41 , no. 5 , 1391 – 418

Nasir , A. and Morgan , J . 2018 . The unit root problem: affinities between ergodicity and stationarity, its practical contradictions for central bank policy, and some consideration of alternatives , Journal of Post Keynesian Economics , vol. 41 , no. 3 , 339 – 63

National Audit Office. 2019 . Department of Transport Sustainability Update , London , Author

Newell , P . 2019 . Transformismo or transformation? The global political economy of energy transitions , Review of International Political Economy , vol. 26 , no. 1 , 25 – 48

Newell , P. and Mulvaney , D . 2013 . The political economy of the “just transition” , The Geographical Journal , vol. 179 , no. 2 , 132 – 40

Newell , P. and Taylor , O . 2020 . Fiddling while the planet burns? COP 25 in perspective , Globalizations , vol. 17 , no. 4 , 580 – 92

ONS. 2019 . UK Environmental Accounts: 2019 , London , Author

Patomäki , H . 2019, July 9 . ‘The Climate Movement? What’s Next?’, available at https://patomaki.fi/en/2019/07/the-climate-movement-whats-next/

Petit , S . 2017 . World vehicle population rose 2.6% in 2016 , Ward Intelligence

Regett , A. , Mauch , W. and Wagner , U . 2019 . Carbon Footprint of Electric Vehicles – A Plea for More Objectivity , available at https://www.ffe.de/attachments/article/856/Carbon_footprint_EV_FfE.pdf

Ripple , W. , Wolf , C. , Newsome , T. , Barnbard , P. , Moomaw , W. and 11,258 signatories. 2019 . World Scientists’ warning of a climate emergency , BioScience , vol. 70 , no. 1 , 8 – 12

Romare , M. and Dahlöff , L . 2017 . ‘ The Life Cycle Energy Consumption and Greenhouse gas Emissions From Lithium-Ion Batteries’ , Report No. C243, Stockholm , IVL Swedish Environmental Research Institute

Royal Academy of Engineering. 2010 . Electric Vehicles: Charged with Potential , London , Author

Servigne , P. and Stevens , R . 2015 . Comment tout peut s’efondrer , Paris , Science Humaines , available at https://www.seuil.com/ouvrage/comment-tout-peut-s-effondrer-pablo-servigne/ 9782021223316

Smil , V . 2017A . Energy and Civilization , Boston , MIT Press

Smil , V . 2017B . Energy Transitions , Colorado , Praeger

SMMT. 2019 . UK Electric Car Registrations Surge in August but it’s a Long Road to Zero and Barriers Must be Addressed , Press Release September 5th, London , Author

Spash , C. (ed.). 2017 . Routledge Handbook of Ecological Economics: Nature and Society , New York , Routledge

Spash , C . 2020 . A tale of three paradigms: realising the revolutionary potential of ecological economics , Ecological Economics vol. 169 ,

Spash , C. and Ryan , A . 2012 . Economic schools of thought on the environment: investigating unity and division , Cambridge Journal of Economics , vol. 36 , no. 5 , 1091 – 121

Staffell , I. Green , R. Gross , R. and Green , T . 2019 . How clean is my car? , Electric Insights Quarterly , vol. Q2 , 7 – 10

Statista. 2019A . Number of Motor Vehicles Registered in the United States from 1990 to 2017 (in 1000s) , [data updated June 2019], available at https://www.statista.com/statistics/183505/number-of-vehicles-in-the-united-states-since-1990/

Statista. 2019B . Car “Parc” in China from 2007 to 2018 (millions) , [data updated August 2019], available at https://www.statista.com/statistics/285306/number-of-car-owners-in-china/

Steffen , W. et al.  2018 . Trajectories of the Earth System in the Anthropocene , Proceedings of the National Academy of Sciences of the USA , vol. 115 , 8252 – 9

Taylor , M . 2015 . The Political Ecology of Climate Change Adaptation , London , Routledge/Earthscan

UNEP. 2012 . Global Environmental Outlook Report 5: Environment for the Future We Want , New York , Author

UNEP. 2018 . Emissions Gap Report 2018 , 9th ed., New York , Author

UNEP. 2019A . Emissions Gap Report 2019 , 10th ed., New York , Author .

UNEP. 2019B . Global Environmental Outlook Report 6: Healthy Planet Healthy People , New York , Author

UNFCCC. 1992 . United Nations Framework Convention on Climate Change , New York , Author

UNFCCC. 2015 . Adoption of the Paris Agreement and Annex: Paris Agreement , Paris , Author

UNFCCC. 2018 . Katowice Texts , Katowice , Author

Webster , B . 2019, June 6 . Britain “could be held to ransom” on electric cars , The Times

Williamson , K. , Satre-Meloy , A. , Velasco , K. and Green , K . 2018 . Climate Change Needs Behaviour Change: Making the Case for Behavioural Solutions to Reduce Global Warming , Arlington, VA , Centre for Behaviour and the Environment

Zheng , S . 2017, April 19 . China now has over 300 million vehicles… that’s almost America’s total population , South China Morning Post

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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• Published: 23 May 2023

Using machine learning methods to predict electric vehicles penetration in the automotive market

• Shahriar Afandizadeh   ORCID: orcid.org/0000-0001-5137-3673 1 ,
• Diyako Sharifi 1 ,
• Navid Kalantari 2 &
• Hamid Mirzahossein   ORCID: orcid.org/0000-0003-1615-9553 3  

Scientific Reports volume  13 , Article number:  8345 ( 2023 ) Cite this article

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• Engineering
• Environmental social sciences

Electric vehicles (EVs) have been introduced as an alternative to gasoline and diesel cars to reduce greenhouse gas emissions, optimize fossil fuel use, and protect the environment. Predicting EV sales is momentous for stakeholders, including car manufacturers, policymakers, and fuel suppliers. The data used in the modeling process significantly affects the prediction model’s quality. This research’s primary dataset contains monthly sales and registrations of 357 new vehicles in the United States of America from 2014 to 2020. In addition to this data, several web crawlers were used to gather the required information. Vehicles sale were predicted using long short-term memory (LSTM) and Convolutional LSTM (ConvLSTM) models. To enhance LSTM performance, the hybrid model with a new structure called “Hybrid LSTM with two-dimensional Attention and Residual network” has been proposed. Also, all three models are built as Automated Machine Learning models to improve the modeling process. The proposed hybrid model performs better than the other models based on the same evaluation units, including Mean Absolute Percentage Error, Normalized Root Mean Square Error, R-square, slope, and intercept of fitted linear regressions. The proposed hybrid model has been able to predict the share of EVs with an acceptable Mean Absolute Error of 3.5%.

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Introduction.

Emissions of greenhouse gases are increasing rapidly worldwide. According to a United States Environmental Protection Agency report released in 2020, the transportation sector produces about 27% of the entire greenhouse gas emissions in the country, which, compared to other sectors, transportation emits the most greenhouse gases 1 . EVs were introduced as an alternative to gasoline and diesel cars to reduce air pollution and greenhouse gas emissions, optimize the use of natural energy resources and protect the environment. Using electricity generated from renewable energy sources such as wind, water and sunlight for EVs can be one of the most efficient solutions to reduce emissions and climate change 2 . Although much time has passed since the invention of EVs, internal combustion vehicles are still the most popular. EV sales have been on the rise, and in January 2017, the total number of EVs sold worldwide reached two million 3 . Globally, EV sales accounted for 9% of the car market in 2021, a fourfold increase from 2019 4 .

Designing and producing vehicles is time-consuming and requires much investment, so by predicting the number of sales, automobile companies can optimize production, Furthermore, by accurately predicting the penetration of EVs in the market, it is possible to estimate their impact on reducing pollution in the coming years, which is very important from an environmental standpoint. Forecasting the sale of EVs and their penetration into the automotive market has been a significant issue for governments, policymakers, and car manufacturers to plan the production of EVs, set proper policies, and provide sufficient energy and infrastructure.

The main goal of this research is to apply Machine Learning (ML) methods to build an efficient prediction model to estimate the sale of all vehicles in the dataset, the share of EVs in each segment, and determine the main factors that influence the sales of each EV. The effect of a limited number of influencing factors on vehicle sales was examined in previous studies using different models. For this study, a wide range of information was collected, including all factors that previous studies have proven are related to car sales, and it was used in modeling. LSTM and ConvLSTM, powerful Deep Learning (DL) models, have been used for predicting vehicle sales. By combining the two-dimensional Attention model and the Residual network as the proposed hybrid model, it has been tried to improve the performance of the LSTM model. Additionally, using the collected information and the model sensitivity analysis, it was attempted to determine the most influential factors on the sale of each EV.

The literature review of this study includes two general sections. The first section examines ML methods used to predict vehicle sales, and the second section provides an overview of the features used in other methods to predict EV sales.

ML methods in predicting vehicle sales

Several studies have used ML methods to predict the sales of EVs as time-series data. Multiple Linear Regression and Support Vector Machine (SVM) models were compared for predicting vehicle sales using yearly, quarterly and monthly data (the number of new automobile registrations, the number of automobile sales, and economic indicators such as Gross Domestic Product (GDP), Available Personal Income, Consumer Price Index, Interest Rate, Unemployment Rate, Industrial Investment Demand, Petroleum Charge, Private Consumption, and Latent Replacement Demand) in a study by Brühl et al. 5 According to the results, the SVM model had better performance based on the error values (Mean Absolute Error and Mean Absolute Percentage Error), was more interpretable, and gave better results based on quarterly data. In the study of Wang et al. ML techniques were used to predict car sales based on sales quantity, economic indicators, wholesale population, unemployment rate, exchange rate, the prices of vehicles, the oil prices, and the prices of vehicle components. Based on evaluation units (R-square and Mean Squared Error), they evaluated the prediction quality of adaptive network-based fuzzy inference system (ANFIS), Artificial Neural Networks (ANN), and autoregressive integrated moving average models; the results showed that ANFIS performed better than the other models 6 . In another study, Hülsmann et al. compared the performance of linear models, such as Ordinary Least Squares and Quantile Regression, against ML methods like SVM, Decision Tree, k–Nearest Neighbor, and Random Forest for predicting vehicle sales. Based on the monthly data of vehicle sales, new car registrations, and economic indicators (such as GDP, Personal Income and Dow Jones), the Decision Tree of ML methods performed better than the other models based on Mean Absolute Percentage Error (MAPE) 7 .

Moreover, Kitabci et al. analysed the impact of economic policies on vehicle sales in Turkey as a macro-environmental factor by multiple regression and neural network methods. They assessed factors such as the vehicle loan rate presented by the banks, the income of the consumers, the tax deductions made by the government for the automobile, the inflation rate, automobile prices, the euro exchange rate, oil prices, and advertisements spent by the businesses. According to the results, neural networks were more accurate in predicting sales than regression models; some factors, including the euro exchange rate, the rates of vehicle loans offered by banks, and the government's tax deductions, have influenced automobile sales 8 . In another research, Bas et al. applied classification ML methods to predict EV adoption using ride-sourcing factors, underlying sociodemographics, and vehicle characteristics; they examined the contributions of different factors to predict outcomes using a method called “Local Interpretable Model-Agnostic Explanations”. Based on the study’s findings, ML models produced highly accurate predictions regarding EV adoption, and the frequent usage of ride-sourcing, knowledge about EVs, and environmental protection awareness were significant factors in explaining the tendency to adopt EVs 9 . In addition, Zhang et al. applied Singular Spectrum Analysis as a univariate time-series model and the Vector Auto-Regression model (VAR) as a multivariate model for forecasting EV sales. According to the results, the VAR model can significantly improve the prediction accuracy because it considers the effect of economic indicators, such as consumer prices, consumer confidence, producers' prices, fuel and vehicle prices, and Baidu data (An indicator of consumer interest and curiosity in EVs) 10 .

In another study, Kaya et al. 11 used the exchange rate, the GDP, the Consumer Confidence Index, the Consumer Price Index data and a Deep Neural Network model to predict vehicle sales; the results revealed that this ML model predicted sales accurately (based on Mean Squared Error). In another research, Xia et al. introduced the ForeXGBoost model, a vehicle sales prediction system based on large-scale datasets containing comprehensive vehicle information, including brand ID, model, engine power, and displacement. Based on Logarithmic Difference Square Root, MAPE, and running time, the XGBoost model outperforms benchmark algorithms like Linear Regression and Gradient Boosting Decision Trees 12 . Using online survey data and ML methods such as SVM, ANN, Deep Neural Networks, Gradient Boosting Models, and Random Forests, Bas et al. compared different methods for classifying potential EV buyers and identifying the features that affect the adoption of EVs. Results showed that the SVM model outperforms the other algorithms; having only partial information (e.g. only socioeconomic factors) reduces model performance, while synergy across multiple variables increases accuracy 13 . Additionally, Saxena et al. present a study that examines the use of deep learning-based models, including Autoregressive Integration Moving Averages and LSTM models, to predict future directions of vehicle sales. Based on the implementation results, the MAE and the Root Mean Square Error for LSTM-based time series forecasting were reduced, and this model could accurately predict green vehicle sales 14 .

Factors affecting the sale of EVs in other methods

Developing policies requires understanding users' behavior and prioritizing their choices. Therefore, some previous studies used survey data to predict EV demand. To assess the potential demand for EVs, Beggs et al. 15 used survey data and vehicle specifications, such as seat capacity, maximum speed, purchase price, and operating costs. In a similar study, the demand for EVs was estimated based on consumer preferences for vehicle attributes by Calfee et al. 16 The results of this research have shown that the weak performance of EVs limits their demand; however, if EVs become significantly more advanced than other cars or if gasoline becomes scarce, the demand for these vehicles will increase.

Predicting the future demand for EVs is a complex issue. As most studies for new technologies rely on survey data, market share predictions will reflect the share in the survey data, not the actual market share. Consumer opinions and the news published about EVs also influence the sales of these vehicles. Based on Mau et al. 17 research, EV sales are impacted by published information about the penetration rate of EVs, known as the “The neighbor effect”. Electric vehicles' specifications are another factor affecting their sales. According to Balducci et al. 18 study to assess plug-in hybrid EV penetration scenarios in the auto market, fuel economy and reduced motor vehicle emissions are the most important factors when purchasing hybrid EVs, while insufficient engine power, high price, and unreliability are the most important reasons for not purchasing these vehicles. Furthermore, Hess et al. used vehicle specifications such as purchase price, vehicle purchase incentives, Miles Per Gallon (MPG) or equivalent, fuel cost per year, fuel availability, refueling time, driving range, maintenance cost per year, and acceleration to explore consumers' preferences in choosing the type of vehicle and the type of fuel. The results have shown that consumers' choices are adversely affected by factors such as purchase price, operating cost, and vehicle age, whereas their choices are positively affected by factors such as better vehicle acceleration, purchase incentives, driving range, and fuel availability 19 .

The sale of EVs is also affected by improving vehicle engine performance and reducing fuel consumption. Using a discrete choice model, Bas et al. investigated EV penetration in the face of new technology for reducing fuel consumption. Results demonstrated a clear tradeoff between the cost of a gasoline-powered system and the fuel savings it provides is perceived by potential purchasers 20 . However, potential EV purchasers are not in this category since their cost–benefit analysis is adverse due to the low cost of electricity 20 . Also, the estimated market shares give a significant share of the market to alternatives that include technology to reduce consumption, due to a more favorable attitude toward environmentally friendly technologies 20 . Additionally, Shafiei et al. analysed the impact of factors such as fuel prices, vehicle attributes, consumer preferences, and social influences on the market share of EVs. The results showed that the combination of high gasoline prices, decreasing EV prices, dropping tax on EVs and eliminating consumer concerns about recharging has the most significant effect on the market share of EVs 21 . Kinski et al. 22 research shows that the information related to searching on the Internet (Google Trends) for vehicles has a positive and significant relationship with car sales.

Based on the previous research, the following two general conclusions were reached:

Firstly, ML and DL methods have been proven to be effective at predicting vehicle sales. Therefore, LSTM and ConvLSTM, powerful DL models, have been used for predicting vehicle sales in this research. Furthermore, a hybrid model was also proposed, and all three models were compared in terms of performance.

Secondly, factors and features that affect EV sales have been identified, and these features have been collected and used in this research.

Methodology

Artificial Intelligence (AI) refers to the ability of machines to perceive, synthesize, and infer information, as opposed to animals and humans displaying intelligence 23 . Machine learning, artificial neural networks, and deep learning are important tools in the development of AI systems and have been shown to perform well in predicting time series data such as vehicle sales. Recurrent neural networks (RNNs) are a type of neural network that remember what they have already processed and can learn from previous iterations 24 . In other words, an RNN is a class of ANNs where connections between nodes form a directed graph along a temporal sequence; this allows it to exhibit temporal dynamic behavior 24 .

Hochreiter and Schmidhuber introduced the LSTM network, a RNN capable of learning long-term dependencies and predicting sequential data with great accuracy 25 . An LSTM is an extension of an RNN, capable of learning patterns from long sequences of source data by retaining a long-term memory 25 . LSTMs improved the forgetfulness of RNNs. An RNN could retain a memory, but only for its immediate past. An LSTM, on the other hand, introduces loops to generate long-term gradients. While going through its loops, it can discover long-term patterns 25 . LSTM is good at storing past information and performing well when faced with vanishing gradient issues. During ANN training, each weight of the neural network receives an update proportional to the partial derivative of the error function. Vanishing gradients occur when gradients become vanishingly small, effectively preventing the weight from changing 26 .

LSTM can tie together three pieces of information at each time step: the current input data, the short-term memory it receives from the previous cell (the hidden state), and the long-term memory from cells farther away (the cell state) 27 . The LSTM unit consists of an input gate a forget gate, an output gate, and a cell state. The input gate determines how much information should be transferred from the current candidate cell state to the current cell state. The forget gate determines how much historical information should be ignored from the previous cell state. The output flow from cells to the rest of the network can be controlled through the output gate. By regulating the flow of information through the three gates, important information over time intervals can be remembered. According to Eqs.  1 – 6 , the LSTM unit process data in cell state and gates 27 . Reference 27 provides more details.

In the above equations, \(f_{t}\) , \(i_{t}\) , and \(o_{t}\) are the forget, input, and output gates, respectively; \(C_{t}\) , \(C_{t - 1}\) , and \(\tilde{C}_{t}\) are the current, previous, and candidate cell state; \(\sigma\) and tanh denotes sigmoid and hyperbolic tangent activation functions, respectively; the interconnected weight matrices for each gate and cell state are \(W_{fh}\) , \(W_{ih}\) , \(W_{oh}\) , \(W_{Ch}\) , respectively; \(W_{fx}\) , \(W_{ix}\) , \(W_{ox}\) , \(W_{Cx}\) represent the input weight matrices in the three gates and the cell state, respectively; \(b_{f}\) , \(b_{i}\) , \(b_{o}\) , \(b_{C}\) represent the respective bias terms; the Hadamard (element product) product of a matrix is denoted by \(\odot\) 27 . According to Fig.  1 , the input layer is an LSTM layer with the same number of neurons as the input data features. In the next step, one or more LSTM layers are set as the hidden layers, and in the final step, a Dense layer with the ReLU activation function is set as the output layer.

Architecture of the LSTM model.

The LSTM model is powerful for handling temporal correlation. In addition, when working with time series data with numerous features, LSTM model performance can be improved by converting the two-dimensional data to a three-dimensional tensor (Fig.  2 illustrates this), connecting states, and applying convolutional operations; this idea was the reason for creating the ConvLSTM model 28 . The ConvLSTM neural network is a fully connected LSTM network with a convolutional structure inside the LSTM cell, which does well in predicting data with temporal correlation. ConvLSTM provides a fully connected extension for data transfer between states and from inputs to states 28 . In other words, ConvLSTM determines the future state of each cell in the grid based on its inputs and neighbours' past states; this can be done by using a convolution operator in the state-to-state and input-to-state transitions 28 . In the ConvLSTM model, data in the input unit, the outputs of each cell, the hidden units, and the gates are arranged as three-dimensional tensors. ConvLSTM has similar parameters as LSTM, and the difference is in how data is transferred and convolutional multiplication is used in calculations, as expressed in Eqs.  7 – 11 28 . Reference 28 provides more details.

Transforming 2-D matrix into 3-D tensor.

In ConvLSTM equations, * indicates the convolution operator, and \(\odot\) indicates the Hadamard product. As shown in Fig.  3 , the input layer is a ConvLSTM layer, the hidden layers are Dense and ConvLSTM layers, and the output layer is a Dense layer with the ReLU activation function.

Architecture of the ConvLSTM model.

Hybrid LSTM with two-dimensional attention and residual network

Time series data have a meaningful temporal relationship. In this research, the data were transformed into three-dimensional tensors with a seven-month time window to maintain the temporal relationship; how to transform a two-dimensional matrix to a three-dimensional tensor is shown in Fig.  2 . As an innovation, the “Two-Dimensional Attention” method has been proposed in this research to determine the importance of each car's feature in a seven-month time frame and to use the weighted data in the modeling process. The two-dimensional attention method assigns weights to each feature in the time window based on how much it influences the model, allowing the features with a more significant impact to receive more attention and reduce the model's complexity. The one-dimensional attention model was proposed for the first time by Bahdanau to address the problem of the limited access of the decoder to the model's input information when the encoder vector has a fixed length in the translation machine 29 .

In the LSTM model architecture, which is shown in Fig.  1 , several LSTM layers are placed inside the hidden layer. When the number of LSTM layers in the hidden layer increases, the primary layers (the layers adjacent to the input layer) have a lesser effect on the output. The primary layers have processed the input data and learned the relationship between the data well, which is why it has been tried to improve this problem by using the Residual network in the proposed hybrid model. Using the Residual Network, the weighted data and outputs of the primary layers have been transferred to the final layers in the proposed hybrid model, as shown in Fig.  4 .

Primary architecture of the hybrid model.

In this study, each input \(x\) is represented by an \(m \times n\) matrix, where m corresponds to the previous months in the window (7), and n represents the number of vehicle features. After entering the data into the first LSTM layer, the processing is done according to Eqs.  1 –6, and the encoded hidden unit ( \(h\) ) with the exact dimensions ( \(m \times n\) ) is entered into the Attention layer. After that, the alignment score is calculated according to Eq. ( 12 ).

In Eq. ( 12 ), \(e_{i, j}\) represents the alignment score, \(W_{a}\) is the attention model’s weight (as a trainable variable), \(h\) is the encoded hidden unit of the primary LSTM layer, \(b_{a}\) is the attention model's bias (as a trainable variable), and the sign "*" denotes the Hadamard product. Since the input data for the attention layer has been encoded by an LSTM layer using tanh nonlinear activation function, tanh has also been used in the attention layer to facilitate data reading during decoding. Each input data element was assigned a degree of attention using Eq. ( 13 ).

Multiplying attention matrix \(\alpha_{i, j}\) by raw data matrix \(x_{i, j}\) yields a weighted data matrix \(W_{i, j}\) based on Eq. ( 14 ). The sign “*” denotes the Hadamard product.

Weighted data \(W_{i, j}\) is then passed through three layers of LSTM as a Residual Network; the output of each layer is combined with the weighted data at the end of the Residual Network and entered into one or more LSTM layers. A Dense layer with the ReLU activation function is the output layer. An overview of the model's architecture is illustrated in Fig.  4 .

Other architectures have also been tried in the hybrid model structure, but they were not more efficient, so only the best architecture has been mentioned.

In this study, EVs are considered as vehicles that use electric motors for propulsion and include all types of EVs. In predicting the sale of vehicles, the number of vehicles in the warehouses is an influential factor, which was not used in this modeling due to a lack of access. Since ML models are based on training, in this study, the models can predict the sales of vehicles that have been on the market for at least 24 months. Emerging vehicles (vehicles that have been on the market for less than 24 months) and cars that have not yet entered the market were not included in the modeling due to insufficient data to train the model. Therefore, the share of EVs in the Automotive Market is expressed as a share in vehicle segments and not as a share of EVs overall.

A wide range of information related to car sales has been used in this research. In the primary dataset, all the data is related to new cars, not used cars. The primary dataset contains monthly information about 357 vehicles, such as brand (or "make" in auto industry lingo, e.g., Benz), model, segmentation, category, shoppers, and sales of different types of cars in the United States from 2014 to 2020. Other information has been extracted based on the cars in this dataset. The data before the outbreak of Covid-19 disease were used since this disease had adverse impacts on the global economy.

As stated in previous studies, vehicle specifications are very effective in car sales prediction models. Vehicle specifications are changed annually. According to Alexa rating 30 and the comprehensiveness of the information presented on the “Thecarconnection” website 31 , vehicle specifications were collected through this website. In order to save time and automate the collection of information due to a large number of vehicles and changes in specifications of vehicles over time, several web crawler have been designed and used in Python programming language to collect vehicle information. Several vehicle specifications of the "CAR-MID/FULL SIZE" segment are shown in Table 1 .

There is similar information collected for gasoline and EVs; for example, the equivalent MPG in EVs. Price, MPG, max mileage, engine power, and warranty are some of the main features taken into account. Other specifications have been divided into the "safety specifications" and the "other specifications" categories. The safety specifications category includes child safety rear door locks, airbags, ABS brakes, daytime running lights, night vision, driver monitoring alerts, collision mitigation braking system, electronic stability control, and side impact beams. All other features (traction control, fog lamps, tire pressure monitoring, parking sensors, parking assist, and backup cameras) have been transferred to the other specifications category.

The second series of collected data refers to user opinions and news published on reputable websites ranked higher on Alexa 30 . Four websites were examined for this purpose: Autoblog 32 , Auto News 33 , Motor1 34 , and The Car Connection 35 . These websites were crawled using Python web crawlers to save time and collect information automatically. From 2014 to 2020, the daily news published was collected and evaluated for each type of vehicle. The Valence Aware Dictionary and sEntiment Reasoner (VADER) method was used for sentiment analysis of the text. Based on vocabulary analysis, the VADER sentiment analysis method correctly analyzes the sentiment expressed in social media and news texts. Ten independent human raters analyzed over 90,000 ratings in the VADER evaluation, which led to the adoption of 7500 linguistic features that were rated based on their valence scores, which indicate the intensity and polarity of sentiment 36 . For each vehicle, the average monthly score of news and opinions has been calculated based on their daily publication of them.

Another effective source of information about the vehicle market is various economic indicators. Using a Python web crawler, information on several economic indicators affecting the car market has been collected on the Federal Reserve website 37 . Economic indicators include GDP, Consumer Price Index (CPI), Producer Price Index, Consumer Confidence Index, Personal Income Per Capita, Interest Rates on 48-month and 60-month Loans, SP&500, and Dow Jones stock market indicators.

According to Kinski's research, using Google trends in prediction models is beneficial and practical 22 . Three keywords have been selected for Google trend data to evaluate the number of searches for each car from 2014 to 2020 and for the United States of America. The keywords are:

"Make" + "Model"

"Price" + "Make" + "Model"

"Dealer" + "Make"

All cars have the same data collected, and the features collected on a monthly basis for each car are listed in Table 2 . Several different trims were available on the market for some vehicles simultaneously, and some characteristics, such as price and MPG, had multiple values for these vehicles. Due to this, the collected values for these characteristics were divided into three categories: minimum, average, and maximum.

The sales feature has been normalized based on the maximum and minimum values from the training data set. Other features are standardized based on each feature's average and standard deviation in the training set. The input data to models are considered seven-month windows to maintain temporal correlation. For example, in the current month, the last seven months' data are input (X), and the current month's sale is output (Y). In order to achieve this, seven-month data matrices were placed consecutively in the third dimension of a three-dimensional tensor.

Validation and interpretation of results

Since the time series data in this study are monthly, eleven binary columns have been added to the dataset to reflect the effect of each month (in the first month of every year, the column corresponding to the first month is set to 1, and the column for the other months is set to 0). An example of this binary data is shown in Table 3 .

For most vehicles, data includes 79 months (January 2014 through July 2020). According to Fig.  5 , the last 14 months are selected for the testing set as rolling cross-validation. Using cross-validation on a rolling basis is one way to validate the time-series model. Starting with a subset of data for training, forecasting for later data points and then checking the accuracy of the forecasts. The same forecasted data points are included in the next training dataset, and further forecasts are made.

Splitting dataset into training, validation, and testing sets.

The model is cross-validated using 12 forecasting stages, with each stage predicting sales in the next three months. During each prediction stage, the preceding months are divided into training and validation (70% for training and 30% for validation. Then these data are transferred to the model, the model predicts sales in the next three months, then the forecast date is moved forward by one month, and this process has been repeated 12 times. Vehicle sales in the next three months are predicted each time the model runs, assuming most of the vehicle's characteristics remain the same. Due to fluctuation and changes in economic conditions, a three-month time horizon is used for predicting the future.

Overfitting is one of the principal problems in ANN training. The Dropout layers between the neural network layers are one of the best solutions in the ANN to avoid overfitting. During the dropout layer, the number of neurons trained in each layer and those discarded is determined randomly (rather than activating all neurons at once, only a fraction are activated) 38 . TensorFlow's early stopping tool is another basic solution to avoid overfitting. Early stopping works in the following way: during the repetition of training, the validation data is used to calculate the error value, and whenever the validation error value increases throughout several epochs, the model is ready to be stopped, and overfitting is prevented. For all three models, both solutions are used to prevent overfitting. Dimensionality reduction is another way to prevent model overfitting. In this study, Principal Component Analysis was used in several modes to reduce dimensions, but this technique was not used due to the significant decrease in model performance.

In order to improve the modeling process, all three models' hyperparameter values and network architectures were determined by Automated Machine Learning (AutoML). AutoML is the process of automating ML applications. The number of hidden layers, the number of neurons in these layers, and the dropout rate was determined by the Tuners. Several values are introduced to the Tuner for each hyperparameter. The Tuner trains different model versions and selects the best one based on the best result (lowest error or loss) on the validation data. This method sets the hyperparameters to the optimal value, and the model is then applied to a test dataset.

The model's error or loss is calculated using the Mean Absolute Error (MAE) loss function in all three models. Selecting a suitable optimization algorithm for the DL model is essential to reduce the run time and reach the desired result. Adam's optimization algorithm is used for these models, which is a generalized version of stochastic gradient descent. It reduces memory usage, converges faster, and corrects high variance and learning rates 39 .

Comparison of models

With the validation data, hyperparameters are adjusted, and the model is built to predict vehicle sales over the next three months (three months following the last validation date). The model run-time for all vehicles was very long due to the many vehicle types (357). In a random sample of 15 vehicles, different models' states were compared using fixed data, and the results were compared between the three models.

The sale of each vehicle is predicted in 12 stages; each prediction stage includes the prediction for the next three months, respectively, the first month of the prediction, the second month of the prediction, and the third month of the prediction. In total, the first predictions include 12 months, the second predictions include 12 months, and the third predictions include 12 months. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE), the Root Mean Square Error normalized by the change range ( \(NRSME_{range}\) ), and the Root Mean Square Error normalized by the mean value ( \(NRSME_{mean}\) ) according to Eqs.  15 – 18 .

According to the above equations, \(y_{t}\) denotes the actual value at time t, \(\hat{y}_{t}\) denotes the predicted value at time t, \(y_{max}\) denotes the maximum actual value, \(y_{min}\) denotes the minimum actual value, \(y_{mean}\) denotes the average actual value, and T is equal to the total number of predicted samples. The average error values of all vehicles were calculated to compare the results of various models. A weighted average was calculated using the total number of sales of each car per month as a weight for the vehicle according to Eq. ( 19 ) since the numbers of vehicle sales are not on the same scale, and the error rate is more important in vehicles with high sales. A further method of checking the models' performance is to compare the R-square, slope, and intercept of the linear regressions fitted on predicted and observed data for all three models. Table 4 summarizes the evaluation results of the models.

In the proposed hybrid model, the error values are lower, the R-square accuracy is higher, the slope value is closer to 1, and the intercept is closer to 0. At this stage, the proposed hybrid model was recognized as preferable to both the LSTM and ConvLSTM models.

Implementation of the proposed hybrid model to predict the share of EVs

For all vehicles, the proposed hybrid model has been implemented, and 12 points of prediction have been used to determine the sale of all vehicles. Linear regression was fitted on the predicted sales and actual values to evaluate the model's performance, as shown in Table 5 .

Primary data segments vehicles by specifications according to segments like CAR-SMALL_COMPACT, CAR-MID_FULL SIZE, MINIVAN LARGE, and PICKUP LARGE. Each segment consists of similar vehicles in appearance and specifications that compete with one another. Segments that include EVs have been separated to determine the share of EVs. Based on actual and predicted sales, the shares of electric and gasoline vehicles have been compared and evaluated for each month of the test data. For example, the CAR-MID/FULL-SIZE segment includes 28 vehicles (23 gasoline vehicles and five EVs). Figure  6 shows the share of EVs in this segment based on twelve prediction stages (three months per stage), separately for the first, second, and third months of each prediction.

( a ) Share of EVs in CAR-MID/FULL-SIZE based on the first month of each prediction. ( b ). Share of EVs in CAR-MID/FULL-SIZE based on the second month of each prediction. ( c ) Share of EVs in CAR-MID/FULL-SIZE based on the third month of each prediction.

All segments' MAEs for EVs' share forecasting in the forecast's first, second, and third months are shown in Table 6 . The average MAE value of all segments was calculated as 3.2% for the first months, 3.8% for the second months, and 3.5% for the third months. The average value for all segments and all forecast months was calculated at about 3.5%, which shows that the proposed hybrid model performed well.

As part of the model analysis, the segments that included EVs were separated again and ranked by sales within each segment. The rankings were based on actual sales (actual rank) and predicted sales (predicted rank); the actual rank and predicted rank were used for evaluation. Kendall-Tau correlation (Kendall's correlation) is commonly used to check the concordance of two ranked lists; this technique was used to examine the actual and predicted rankings in this study. Kendall's correlation rate for two rating lists \(r_{a}\) and \(r_{b}\) ( \(\tau_{{r_{a} , r_{b} }}\) ) is represented by Eq. ( 20 ) 40 .

In Eq. ( 20 ), \(n_{c}\) represents the number of concordant pairs, \(n_{d}\) represents the number of discordant pairs, and n represents the total number of ranks in each of the rating lists 40 . The maximum number of discordant pairs between two ranking lists equals \(\frac{1}{2} n\left( {n - 1} \right)\) , and Kendall's correlation equals + 1 if all pairs of ranks are concordant and -1 if none are concordant 40 . For all segments, Kendall's correlation values were calculated separately for the first, second, and third prediction months, and the average values are shown in Table 7 . The average Kendall's correlation value of all segments was calculated as 0.76 for the first months, 0.742 for the second months, and 0.75 for the third months. The average Kendall's correlation value for all segments and all forecast months was calculated at about 0.75, which indicates the great performance of the proposed hybrid model in predicting the ranking.

Sensitivity analysis

Sensitivity analysis was performed to determine which features significantly impacted the trained model. Thus, for each vehicle, the pre-trained model that was evaluated in previous stages has once again predicted the number of vehicle sales with new input data, and its outputs have been assessed. All features, except the investigated feature, are valued at their average. For the investigated feature, the five values from the training data (the min value, the first quartile, the second quartile, the third quartile, and the max value) are taken into consideration. Five predictions were made based on these five values, and a range of changes in predicted sales was calculated. The change ranges for all features have been measured, and the four features with the most extensive range have been identified. As an example, during the sensitivity analysis of the BMW I3 for 2020, the following four features had the broadest range of changes: the Consumer Price Index (CPI), the equivalent MPG for EVs, the Google search score for car prices (Google Trends), and the car price. This EV's sensitivity analysis plots are shown in Fig.  7 .

( a ) Sensitivity analysis plot of influential feature 1 for BMW I3. ( b ) Sensitivity analysis plot of influential feature 2 for BMW I3. ( c ) Sensitivity analysis plot of influential feature 3 for BMW I3. ( d ) Sensitivity analysis plot of influential feature 4 for BMW I3.

Based on Eq. ( 21 ), slope values for the four characteristics with the most extensive range of changes are calculated in different parts of the graph, and the results are summarized in Table 8 . For example, the number of sales of this EV has decreased by 8 for every thousand-dollar increase in price when the price is in the range of the minimum value to the first quarter. As the slope is zero percent in the second and third parts of the graph, the price in the first, second, and third quartiles is equal, and when the price is in the third quartile to the maximum price, the number of sales for this EV decrease by 6 for every thousand-dollar increase in price.

There has been a decrease in car sales due to the increase in the CPI. It is also true that with the increase in the CPI, the final price of the car and the price of auto parts have increased, which has led to a decrease in the desire to buy this car. The second feature is equivalent MPG for EVs, a higher equivalent MPG indicating better performance and less fuel consumption in a fixed distance has led to an increase in sales of this car. The third feature identified is the increase in the car price search score on Google (Google Trend), an indicator that buyers are more curious about this car, contributing to its sales. The fourth specified feature of the car is its price, and its sales have decreased with the increase in its price. As a result of the sensitivity analysis, the manufacturers of this car could use policies such as lowering the price of the car and its parts (CPI and car price), improving the performance of the vehicle's engine (the equivalent MPG), and developing advertisements and introducing the car to the public (Google trend score) to increase sales.

Sensitivity analysis has been conducted for each EV, and the results show different sensitivity for each vehicle. From each segment that includes EVs, one vehicle was selected as a sample, and the results of its sensitivity analysis are shown in Table 9 .

Each EV's sensitivity analysis identifies features that differ from the others, as shown in Table 9 . According to the results of the sensitivity analysis, ten features that were most frequently found in the sensitivity analysis of all the EVs were identified as the most influential features: Shoppers, Min price, CPI, Sales, Google Trends score 3 (Price), Make & model news score, Personal income per capita, Make news score, Interest Rates on 60-month, and Mean options score, respectively.

This study addresses an important topic from a business perspective. Car manufacturers can benefit from this research by understanding their market share and the effect of pricing and vehicle specification on the market share. They can use the results of this study to analyze both their EV market as well as their Non-EV market. Lower down the funnel, car dealers that operate in a highly competitive environment can strategize their sales events, marketing campaigns, and discounts to meet their business goals and target sales. Finally, the model enables the public sector to understand the effect of tax policies on the share of EV vehicles in case they like to promote them.

This study used ML methods to develop a prediction model that estimated the sale of all cars in the dataset, the share of EVs in each segment and identified the main factors affecting each EV's sales. In this research, several web crawlers have been used to collect various data, including factors that previous studies have proven to be associated with EV sales. Vehicles sale were predicted using LSTM, ConvLSTM, and the proposed hybrid model (Hybrid LSTM with two-dimensional Attention and Residual network). Several ML tools have been used to improve the model's training and the modeling process, such as transforming two-dimensional time series data into three-dimensional tensors, Dropout layers, early stopping tools, and AutoML. Because of the variety of car types and the long running time of the models, a random selection of fifteen types of cars was made. All three models are evaluated based on the same evaluation units: the MAPE, NRSME_range, and NRSME_mean, R-square, slope, and intercept of fitted linear regressions have also been assessed. The average error values in the three months of prediction were as follows:

The MAPE value of the proposed hybrid model was 4.5% less than the LSTM model and 14.4% less than the ConvLSTM model.

The NRSME_range value of the hybrid model was 0.11 less than the LSTM model and 0.22 less than the ConvLSTM model.

The NRSME_mean value of the hybrid model was 0.079 less than the LSTM model and 0.169 less than the ConvLSTM model.

As a result of fitting linear regressions to the predicted and actual values, for all three months of predictions, the proposed hybrid model has a higher R-square value, its slope is closer to one, and its intercept is closer to zero, which indicates that the hybrid model performed better than the other two. In comparing the models, it was found that the proposed hybrid model conducted better than other models and was selected to predict the sale of all vehicles in the dataset. Based on the linear regression fitted to the predicted sales and the actual sales of all vehicles, the R-square values for the first, second and third prediction months were 0.912, 0.906, and 0.917.

The predicted sales of all vehicles were used to calculate the predicted share of EVs in each segment and compare them with the actual values. Across all segments and forecasting months, the average MAE value for EV share is about 3.5%, and the hybrid model has accurately predicted the share of EVs across all segments. To further analyze the model results, the cars were ranked according to the number of actual and predicted sales within each segment. The average Kendall's correlation value for all segments and all forecast months was calculated at about 0.75, which indicates the high performance of the proposed hybrid model in predicting the ranking.

The sensitivity analysis was performed to evaluate the model further and identify its most influential features. The results have shown that each EV's sensitivity analysis identifies features that differ from the others. According to the sensitivity analysis of the BMW I3 for 2020, the following four features were most affected: the Consumer Price Index, the equivalent MPG for EVs, the Google search score, and the car price. As a result of the sensitivity analysis, the manufacturers of this car could use policies such as lowering the price of the car and its parts, improving the engine's performance, developing advertisements, and better introducing the car to increase sales (See Appendix Tables A1 to A4.2 , Fig. A1 ).

This research has achieved the following accomplishments:

A wide variety of factors have been collected and used as variables to model the sale of EVs.

LSTM and ConvLSTM, powerful DL models, have been used for predicting vehicle sales. By combining the two-dimensional Attention model and the Residual network, the performance of the LSTM model was enhanced, and the innovative hybrid model performed better than the other two.

EVs differ in terms of the most influential factors for sales depending on the sensitivity analysis results. The ten features that appeared the most in the sensitivity analysis of all EVs were identified as the most influential, including Shoppers, Min price, CPI, Sales, Google Trends score 3 (Price), News score for make and model, Personal income per capita, News score for make, Interest Rates on 60-month, and Mean options score, respectively.

Data availability

The primary dataset was taken from Autometrics, and other data were collected using web crawlers. The data is available from the corresponding author on reasonable request.

https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks (2020).

MacInnis, B. & Krosnick, J. Climate Insights 2020: Electric Vehicles. (2020).

https://theicct.org/the-rise-of-electric-vehicles-the-second-million/ (2020).

https://www.iea.org/fuels-and-technologies/electric-vehicles (2022).

Brühl, B., Hülsmann, M., Borscheid, D., Friedrich, C. M. & Reith, D. in Industrial Conference on Data Mining. 146–160 (Springer).

Wang, F.-K., Chang, K.-K. & Tzeng, C.-W. Using adaptive network-based fuzzy inference system to forecast automobile sales. Expert Syst. Appl. 38 , 10587–10593 (2011).

Article   Google Scholar  

Hülsmann, M., Borscheid, D., Friedrich, C. M. & Reith, D. General sales forecast models for automobile markets and their analysis. Trans. Mach. Learn. Data Min. 5 , 65–86 (2012).

Google Scholar  

Kitapcı, O., Özekicioğlu, H., Kaynar, O. & Taştan, S. The effect of economic policies applied in Turkey to the sale of automobiles: Multiple regression and neural network analysis. Procedia Soc. Behav. Sci. 148 , 653–661 (2014).

Bas, J., Zou, Z. & Cirillo, C. An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption. Transp. Lett. 15 , 30–41 (2023).

Zhang, Y., Zhong, M., Geng, N. & Jiang, Y. Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China. PLoS ONE 12 , e0176729 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Kaya, S. K. & Yildirim, Ö. A prediction model for automobile sales in turkey using deep neural networks. Endüstri Mühendisliği 31 , 57–74 (2020).

Xia, Z. et al. ForeXGBoost: Passenger car sales prediction based on XGBoost. Distrib. Parallel Databases 38 , 713–738 (2020).

Bas, J., Cirillo, C. & Cherchi, E. Classification of potential electric vehicle purchasers: A machine learning approach. Technol. Forecast. Soc. Chang. 168 , 120759 (2021).

Saxena, P., Bahad, P. & Kamal, R. Long short-term memory-RNN based model for multivariate car sales forecasting. Int. J. Adv. Sci. Technol. 29 , 4645–4656 (2020).

Beggs, S., Cardell, S. & Hausman, J. Assessing the potential demand for electric cars. J. Econom. 17 , 1–19 (1981).

Calfee, J. E. Estimating the demand for electric automobiles using fully disaggregated probabilistic choice analysis. Transp. Res. Part B Methodol. 19 , 287–301 (1985).

Mau, P., Eyzaguirre, J., Jaccard, M., Collins-Dodd, C. & Tiedemann, K. The ‘neighbor effect’: Simulating dynamics in consumer preferences for new vehicle technologies. Ecol. Econ. 68 , 504–516 (2008).

Balducci, P. J. Plug-In Hybrid Electric Vehicle Penetration Scenarios. (Pacific Northwest National Lab. (PNNL), Richland, WA (United States) (2008).

Hess, S., Fowler, M., Adler, T. & Bahreinian, A. A joint model for vehicle type and fuel type choice: Evidence from a cross-nested logit study. Transportation 39 , 593–625 (2012).

Bas, J., Zofío, J. L., Cirillo, C., Chen, H. & Rakha, H. A. Policy and industry implications of the potential market penetration of electric vehicles with eco-cooperative adaptive cruise control. Transp. Res. Part A Policy Pract. 164 , 242–256 (2022).

Shafiei, E. et al. An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland. Technol. Forecast. Soc. Chang. 79 , 1638–1653 (2012).

Kinski, A. Google trends as complementary tool for new car sales forecasting: A cross-country comparison along the customer journey , University of Twente, (2016).

https://en.wikipedia.org/wiki/Artificial_intelligence (2023).

https://en.wikipedia.org/wiki/Recurrent_neural_network (2023).

Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9 , 1735–1780 (1997).

Article   CAS   PubMed   Google Scholar  

Basodi, S., Ji, C., Zhang, H. & Pan, Y. Gradient amplification: An efficient way to train deep neural networks. Big Data Min. Anal. 3 , 196–207 (2020).

Wei, X., Zhang, L., Yang, H.-Q., Zhang, L. & Yao, Y.-P. Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks. Geosci. Front. 12 , 453–467 (2021).

Article   ADS   Google Scholar  

Shi, X. et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems 28 (2015).

Bahdanau, D., Cho, K. & Bengio, Y. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

https://www.alexa.com/ (2021).

https://www.thecarconnection.com/ (2021).

https://www.autoblog.com/news/ (2021).

https://www.autonews.com/news (2021).

https://www.motor1.com/news/ (2021).

https://www.thecarconnection.com/news (2021).

Hutto, C. & Gilbert, E. in Proceedings of the international AAAI conference on web and social media. 216–225.

https://fred.stlouisfed.org/ (2021).

Baldi, P. & Sadowski, P. J. Understanding dropout. Advances in neural information processing systems 26 (2013).

Kingma, D. P. & Ba, J. Adam. A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

Bachrach, Y., Herbrich, R. & Porat, E. in International Symposium on String Processing and Information Retrieval. 344–352 (Springer).

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Afandizadeh, S., Sharifi, D., Kalantari, N. et al. Using machine learning methods to predict electric vehicles penetration in the automotive market. Sci Rep 13 , 8345 (2023). https://doi.org/10.1038/s41598-023-35366-3

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Optimal charge scheduling and on-board control of an urban electrified brt fleet considering synthetic representative driving cycles.

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Scientific Reports (2024)

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Electric Vehicles

Abstract – This paper gives a mini overview of the recent research and works in the sector of electric vehicles. The paper describes the main part of electric vehicles and their work. The major components are battery, motor, charger, steering, and braking. The paper finally shows basic concepts of electric vehicles with working. There are electric buses in the main focus of this paper. The solar system also can be used for electric busses and this is described in this paper.

Keywords – Electric vehicle, motor, braking system, battery systems, hybrid system

 I.   INTRODUCTION

Electrical vehicle (EV) based on electro mechanical system. No, any internal combustion engine is used for torque development. All the power which is used is electric power as the energy source. In the electric vehicle main power source is electricity which is stored in the battery. The advantage of the highly efficient in power conversion through its proposition system of electric motor. Now in the world, there are several situations in which we cannot tolerate the environment that why we need to reduce carbon emission. In the sector of the automobile, there is a lot of petroleum used and therefore a lot of carbon emissions are occurring in the environment [1]. Electric busses work on the electricity from the power grid and also can work on the solar plates. There is a battery that can be charged and then used. All the car manufacturers have to develop at least one model in hybrid electric vehicles for the reduction in the carbon footprint. France and Japan are the countries who totally close the production of petrol vehicles by 2030. This is one more step toward pollution control and the improvement in electrical vehicles for a clean environment [2]. The table shows the various types of EVs. Image 1 shows the first electric vehicle in 1943-35 (America).

                                                                         Table 1

II.  MAIN COMPONENTS

There are many components in electric vehicles, some main ones are spark ignited, traction system, thermal cooling system, DC/DC converter, power electronics control, battery, motor, storage, braking system etc. [3]. Image 2 shows the main components of the electric busses. There are various parts with different function and working shown in the figure.

DC to DC converter: A full-bridge DC/DC converter is the frequently implemented converter for fuel-cell power conditioning when electrical isolation is needed. Full bridge DC to DC converter is suitable for high-power electrical transmission because switch voltage is not high and current are also not high [3,5].  Electric Motor Generator : Electric vehicle drive system for plug-in hybrid vehicles the integrated motor-generator set provides electric propulsion for the vehicle in its function as a motor, while work as a generator it converts mechanical braking energy into electrical energy, that is college regenerative braking system.  Regenerative Braking system : In the battery-powered electric vehicle system, regenerative braking is the conversion of the electric vehicle's kinetic energy into chemical energy stored in the electric battery system, where it can be used to drive the vehicle.   Recharge Station : In these stations, busses can be charged by an electrical charging system and it will convert electrical supply power into battery storage power. Battery Packs : In this battery packs are arranged and this will provide the power to the motor. Which can be charged by the recharge station. The capacity of the battery is for the 120 km distance which can be charged in 1.5 hours (nearly). Solar Cell : Solar cells can be used in an emergency for electric busses. When there is charging not available cell can work for small distances. It can be mounted on the top of the buses and it will charge through the solar system. There is a lot of space on the bus which can be utilized. The solar cell efficiency formula is shown below.  

III.  The Motor

There are a number of motors available for electric vehicles: DC motors, Induction motors, DC brushless motors, Permanent magnetic synchronous motors and Switched reluctance motors.

1.   2. Induction motor: It’s a very popular Alternate Current motor. It also has a large market share in controlled speed drive applications such as AC, elevator, or escalator. There are various higher power electric vehicles, for more than 10kW. 

5.  Switched reluctance motor: This motor specification is also good for the use of electric and hybrid vehicles. It has variable reluctance that why this machine [14].  IV.  Conclusion

This paper discusses the basics of electric vehicles and the development of electric vehicles, especially electric buses. The electric vehicle is very useful for the reduction of carbon emission and the change of climate. Consumption of petroleum is also reduced due to the use of that. The paper first describes the main components of electric vehicles, it then extends the description of the components with uses.  The paper provides a mini overview of electric vehicles.   Acknowledgment The author acknowledges the support of various references which is very useful for this paper. References [1] Chikhi, F. El Hadri, A. Cadiou, J.C. “ ABS control design based on wheel-slip peak localization”. Proceedings of the Fifth International Workshop on Robot Motion and Control, Publication Date: 23-25 June 2005, pp.73- 77  URL: http://sersc.org/journals/index.php/IJAST/article/download/14920/7565/ [2] Beier, J. et  al, “Integrating on-site Renewable Electricity Generation into a Manufacturing System with Intermittent Battery Storage from Electric Vehicles”, Procedia CIRP, Vol. 48, 2016, pp. 483-488. [3] Rahman, K.M.; Fahimi, B.; Suresh, G.; Rajarathnam, A.V.; Ehsani, M., “Advantages of switched reluctance motor applications to EV and HEV: design and control issues”, IEEE Transactions on Industry Applications, Vol. 36, Issue 1, Jan.-Feb. 2000, pp. 111 – 121. [4] K W E Cheng; “Recent Development on Electric Vehicles”, 3rd International Conference on Power Electronics system and it's application, 2009 [5] M.J. Bradley & Associates. 2013. “Electric Vehicle Grid Integration in the U.S., Europe, and China.” [6] Chan, C.C. (1996), Chau, K.T., Jiang, J.Z., Xia, W., Zhu, M., and Zhang, R., Novel permanent magnet motor drives for electric vehicles. IEEE Transactions on Industrial Electronics, Vol. 43, pp. 331-339. [7] B Smith, M. and J. Castellano. 2015. “Costs Associated with Non-Residential Electric Vehicle Supply Equipment.” [8] C Clint, J., B. Gamboa, B. Henzie, and A. Karasawa. 2015. “Considerations for Corridor Direct Current Fast Charging Infrastructure in California.” [9] Chan, C.C., Jiang, J.Z., Chen, G.H., and Chau, K.T., Computer simulation and analysis of a new polyphase multipole motor drive. IEEE Transactions on Industrial Electronics, Vol. 40, 1993, pp. 570-576. [10] Zhan, Y.J., Chan, C.C., and Chau K.T., A novel sliding-mode observer for indirect position sensing of switched reluctance motor drives, IEEE Transactions on Industrial Electronics, Vol. 46, 1999, pp. 390-397. [11] Nunes, P., M.C. Brito and T. Farias, “Synergies between electric vehicles and solar electricity penetrations in Portugal,” 2013 World Electric Vehicle Symposium and Exhibition, 2013, pp. 1-8. [12] Mwasilu, F. et  al., “Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration”, Renewable and Sustainable Energy Reviews, Vol. 34, 2014, pp. 501-516 [13] Ates, M.N. et al., “In Situ Formed Layered-Layered Metal Oxide as Bifunctional Catalyst for Li-Air Batteries”, Journal of the Electrochemical Society, Vol 163, No. 10, 2016, pp. A2464-A2474 [14] Tuffner, F. and M. Kintner-Meyer, Using Electric Vehicles to Meet Balancing Requirements Associated with Wind Power, U.S. Department of Energy Pacific Northwest National Laboratory, 2011

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Indian Scenario of Electric Vehicles—A Review

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• Niket B. Adhia 9 ,
• Shikhar R. Takke 9 &
• Puskaraj D. Sonawwanay 9  

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Over the past few years, the global automotive sector has benefited greatly from electric vehicles. According to projections from 2020, there were over 12 million electric vehicles in use worldwide. China sold 90% of them, with Europe, the United States, and other nations following. In India, EV development is still in its infancy. In India, 3.8 lakh electric vehicles were sold in the 2019–20 year. Passenger vehicles generate the largest portion of EV sales in India, followed by commercial vehicles and two-wheelers. Numerous government policies and programmes, including FAME-India, NEMMP 2020, and many others, are mandated by the National Automotive Board, National Board for Electric Mobility, and National Council for Electric Mobility. This paper covers a review of Indian Scenario of Electric Vehicles, Statistics and different government policies associated with EV’s, in detail. Electric Vehicles, Indian EV Scenario, Government Policies, FAME-India, EVs in India.

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Ahmad, F., Alam, M. S., & Shahidehpour, M. (2018). Optimal placement of electric, hybrid and plug-in hybrid electric vehicles (xEVs) in Indian power market . IEEE.

Google Scholar  

Gupta, A. K., Kartik, V., & Ramanarayanan, C. P. (2015). Design, development and performance evaluation of a light diesel hybrid electric pickup vehicle using a new parallel hybrid transmission system . IEEE Xplore.

Chhikaraa, R., Garga, R., Chhabrab, S., Karnataka, U., & Agrawala, G. (2021). Factors affecting adoption of electric vehicles in India: An exploratory study. Elsevier, 100 , 103084.

Javkhedkar, S. R., & Ashfa, B. (2021). Future of electric vehicle in India. IRJEdT, 02 (02), 1–7.

Grackova, L., & Oleinikova, I. (2014). Economic motivation for electric vehicles participation in power market . IEEE Xplore.

Sachs, C., Burandt, S., & Mandelj, S. (2016). Assessing the market of light electric vehicles as a potential application for electric in-wheel drives . IEEE.

Cortezón, I. A., Borrull, R. S., López, A. Q., & Roig, V. F. (2014). The impact of fully electric vehicles demands in the spot market . IEEE Xplore.

Warweg, O., & Hartung, P. (2014). Cost-utility analysis to evaluate business cases for electric vehicle market integration . IEEE Xplore.

Vidhi, R., & Shrivastava, P. (2018). A review of electric vehicle lifecycle emissions and policy recommendations to increase EV penetration in India. Energies, 11 , 483 (Multidisciplinary Digital Publishing Institute).

Gupta, S., & Sainai, P. (2018). Electric mobility in India: Potential and policy imperatives. In International conference on recent advances in transport infrastructure (pp. 1–10).

Shalender, K., & Yadav, R. K. (2017). Promoting e-mobility in India: Challenges, framework, and future roadmap. Environment, Development and Sustainability, 28 , 2587–2607.

Bibra, E. M., Connelly, E., Gorner, M., Lowans, C., Paoli, L., Tattini, J., & Teter, J. (2021). Global EV outlook 2021: Accelerating ambitions despite the pandemic, transportation research board . International Energy Agency.

James, A. T., Kumar, G., Pundhir, A., Tiwari, S., Sharma, R., & James, J. (2022). Identification and evaluation of barriers in implementation of electric mobility in India. Research in Transportation Business & Management, 43 , 100757.

IEA. (2021). Global EV outlook 2021 . IEA.

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Adhia, N.B., Takke, S.R., Sonawwanay, P.D. (2024). Indian Scenario of Electric Vehicles—A Review. In: Pawar, P.M., et al. Techno-Societal 2022. ICATSA 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-34648-4_27

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Trends in the electric vehicle industry

• Executive summary
• Electric car sales
• Electric car availability and affordability
• Electric two- and three-wheelers
• Electric light commercial vehicles
• Electric truck and bus sales
• Electric heavy-duty vehicle model availability
• Charging for electric light-duty vehicles
• Charging for electric heavy-duty vehicles
• Battery supply and demand
• Battery prices

Electric vehicle company strategy and market competition

Electric vehicle and battery start-ups.

• Vehicle outlook by mode
• Vehicle outlook by region
• The industry outlook
• Light-duty vehicle charging
• Heavy-duty vehicle charging
• Battery demand
• Electricity demand
• Oil displacement
• Well-to-wheel greenhouse gas emissions
• Lifecycle impacts of electric cars

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IEA (2024), Global EV Outlook 2024 , IEA, Paris https://www.iea.org/reports/global-ev-outlook-2024, Licence: CC BY 4.0

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Electric vehicle companies perform well in financial markets, but volatility and competition raise concern.

Since 2019, the stocks of EV companies – including vehicle and battery manufacturers and companies involved in the extraction or processing of battery metals – have consistently outperformed general stock markets, major traditional carmakers, and other segments of clean technology. Return on investment has increased more over the 2019-2023 period for these companies than it has for others, in relative terms. The combined market capitalisation of pure play EV makers boomed from USD 100 billion in 2020 to USD 1 trillion at the end of 2023, with a peak over USD 1.6 trillion at the end of 2021, though this trend was primarily driven by Tesla. The market capitalisation of battery makers and battery metal companies also increased significantly over the same period.

Behind this overall upward trend, however, there has been significant volatility. Supply chain disruptions and battery metal price fluctuations – notably in the wake of Russia’s invasion of Ukraine – as well as increasing competition, price wars among OEMs and expectations of slower relative annual growth as major EV markets mature, and of possible consolidation, are having an important downward impact on investor confidence and EV stocks.

For example, Tesla’s shares were on average 15% lower in 2023 than in 2021‑2022; BYD’s average stock also fell 15% in 2023 relative to 2022; and the combined market capitalisation of pure play EV carmakers fell by nearly 20% on average relative to 2022, while that of major incumbent carmakers remained flat. Many emerging EV players – such as VinFast from Viet Nam, Polestar from Europe, and Canoo, Fisker, Lucid and Nikola from the United States – are missing sales targets and trading low. Fiercer competition and shrinking profits also have an impact upstream, among EV battery makers: in the first weeks of 2024, CATL was trading near a three-year low, with a market capitalisation at its lowest point since the end of 2020. In the first quarter of 2024, the combined market capitalisation of pure play EV players fell below that of major incumbents, even if their financial stock performance remained robust.

As we reported last year in GEVO-2023, companies and investors are exploring new opportunities upstream in EV supply chains, especially as competition intensifies. Carmakers are seeking to secure direct deals with battery makers and companies involved in the mining and processing of critical minerals. Investors including large banks and funds are pouring capital into the metal industry.

In 2023, Stellantis announced a partnership in Argentina to secure projected copper demand, investing USD 155 million . Volkswagen, Glencore and Chrysler each invested USD 100 million in a Special Purpose Acquisition Company operating nickel and copper assets, supported by several global investment banks for an overall USD 1 billion deal. In 2024, Tesla and several Korean battery makers, including LG and SK, met with Chilean government agencies regarding lithium supply, primarily with the aim of supplying the US market with the support of IRA tax credits. AustralianSuper, Australia’s largest pension fund, announced plans to double its exposure to lithium stocks over the next five years, with investments in 2023, such as in Pilbara Minerals, worth AUD 560 million ( USD 370 million ).

As a result of increasing investor appetite and growing EV markets, the valuation of critical mineral companies has increased significantly in the last few years. Over the 2015-2022 period, the market capitalisation of companies involved in the extraction and processing of lithium increased sixfold. The margins for lithium, nickel and copper companies typically outperformed those of the top 100 mining companies over the same period, including relative to gold or iron ore.

However, the picture in 2023 and the first quarter of 2024 is changing. The volatile metal prices seen in the past few years, the increasing competition and pressure to drive down EV and battery prices, and the current overcapacity for several critical minerals (see earlier section on batteries), mean that major mining companies are revisiting growth and performance forecasts. After several years of important cash flows as a result of high prices and increasing volumes, many companies are now starting to struggle to finance both existing and new projects with their own revenues, suggesting external sources will be needed for large-scale capital expenditure.

In Australia, for example, Albemarle, Core Lithium, Liontown Resources and Pilbara Minerals announced project spending reductions, lower dividends, and job cuts in 2024. Albermarle expects capital expenditures to drop by around USD 500 million from USD 2.1 billion in 2023 to USD 1.6‑1.8 billion in 2024, and plans to reduce annual costs by nearly USD 100 million. Pilbara Minerals expects annual exploration spending to be cut by up to AUD 100 million (USD 66 million). Nickel and cobalt projects in Australia have also been delayed or halted, involving companies like BHP, First Quantum Minerals and Wyloo Metals. First Quantum Minerals expects a 30% staff cut as a result of reduced operations. In the United States, Piedmont Lithium Inc. is letting go of 25% of staff . Over the 2024‑2026 period, we could see progressive consolidation of critical mineral extraction and refining projects and businesses around lowest-cost producers.

Global competition is getting tougher, pushing down company margins

Road transport electrification is reshuffling cards in global markets, as carmakers compete fiercely to capture their share of a growing pie. BYD and Tesla remain far ahead of the curve, together accounting for 35% of all electric car sales in 2023. This is more than all the major carmakers outside China combined (just above 30%), and more than all the other Chinese carmakers (just under 30%). BYD and Tesla’s rise as global front-runners has primarily dented the market share of major incumbents, which accounted for 55% of global electric car sales in 2015 but have been falling behind ever since.

In 2022, BYD had already overtaken Tesla as the world’s best-selling EV company when accounting for plug-in hybrid cars. In the second half of 2023, BYD also became the world’s best-selling battery electric car company. Counting both BEV and PHEV models, BYD’s share of global electric car markets was just over 20%. In China, BYD also became the top-selling car company with over 2.4 million new registrations or 11% of the domestic market, ahead of Volkswagen, which had been China’s best-selling brand for over 15 years. BYD’s worldwide sales exceeded 3 million, making it one of the world’s top 10 car sellers. 

Share of global electric car markets by selected carmakers, 2015-2023

Share of global electric car markets by gross margin of selected companies, 2015-2023.

In China, since the end of 2022, greater competition among front-runners has led electric car prices to fall quickly. The price of compact electric cars and SUVs dropped by up to 10% in 2023 relative to 2022. In the first quarter of 2024, Tesla once again slashed prices , by up to 6% or CNY 15 000 for its Models 3 and Y, forcing competitors to follow by squeezing margins. BYD proceeded with a 10-20% cut on model prices, such as a CNY 10 000 reduction on its flagship Qin Plus; XPeng with a CNY 20 000 cut on its G6 series; and the GM-SAIC-Wuling joint venture with a CNY 6 000 cut on its Xingguang sedan.

Tesla has been able to afford such a strategy thus far thanks to a focus on high-end models and greater profits per car sold, around USD 10 000 to USD 15 000 per electric car sold in 2022-2023 against around USD 6 000 for BYD, which also explains why Tesla’s market value in 2023 remained 7 times higher than that of BYD. BYD’s preferred strategy has been to sell cheaper models and protect margins by developing in-house battery supply chains. Other Chinese makers are now actively seeking to catch up by announcing new models. For example, XPeng announced new models for 2024 priced at around CNY 100 000‑150 000, far more affordable than the premium models typically priced in the range of CNY 300 000. However, just one-third of Chinese carmakers (e.g. Li Auto, BYD, Geely) met their sales targets in 2023, while others (e.g. Nio, Xpeng, Leap, Great Wall) fell short for the second straight year.

Competition is also getting tougher outside China. In the United States, Tesla accounted for over 45% of all the battery-electric cars ever sold as of 2023. However, Tesla’s share in new US electric car sales has been shrinking, from over 60% in 2020 to 45% in 2023. Hyundai-Kia overtook GM and Ford in 2023, and now accounts for 8% of US electric car sales, second only to Tesla. Hyundai-Kia plans to start manufacturing operations at a Georgia-based factory in 2024, qualifying for IRA benefits. European carmakers accounted for 25% of US electric car sales, their highest share since 2017, led by Stellantis. About 15% of BMW’s sales were electric, meeting its 2023 sales target , and in the same year the company went on record as saying that a tipping point for ICE cars had been reached, and that EVs would lead future growth. Meanwhile, other US carmakers accounted for 15% of electric car sales, half their share back in the 2015-2017 period, and GM and Ford announced a step back from their earlier sales objectives (see later section on Industry Outlook).

In Europe, the share of electric car sales by local carmakers has been falling since 2015. In 2023, European carmakers accounted for 60% of electric car sales in the region, compared to over 80% in 2015. Volkswagen, Stellantis and BMW aggregated to 45% of European electric car sales in 2023, but competition is getting tougher among front-runners. The share of Stellantis jumped from under 2% in 2015 to nearly 15% in 2023, while Volkswagen’s fell from 27% to 20%. Renault-Nissan-Mitsubishi Alliance accounted for nearly 40% of European electric car sales in 2015, but just 7% in 2023. Tesla’s share has stagnated around 10% between 2015 and 2023, while Chinese carmakers have seen important growth, from 5% in 2015 to just under 15% in 2023.

Global competition is expected to further intensify in the coming years, especially as Chinese carmakers look to export in the context of battery and EV oversupply. Just as for steel and solar PV, China’s subsidised industrial strategy for EVs, which was initiated 15 years ago, has led to significant excess capacity. Capacity utilisation stood below 70% among the top 10 sellers in the first months of 2023. There are also far more EV companies in China than can possibly survive in a competitive market. In 2014 alone, ten years ago, over 80 000 companies registered in China entered the electromobility sector. In 2023, over 80% of electric car sales in China were concentrated in just over 30 companies. In 2024, the Chinese Ministry of Industry and Information Technology noted its concern that too many carmakers remain unprofitable . Nio, for example, recorded net losses of USD 3 billion in 2023.

As shrinking margins push the least profitable out of the race, China’s EV industry is consolidating around a smaller number of robust champions – and they are looking abroad for expansion. Chinese auto exports grew steeply in 2023, up 60% relative to 2022, making China the world’s largest car exporter, ahead of Japan and Germany. Despite high import taxes (e.g.  27% in the United States and 10% in the European Union) and tight regulatory scrutiny (e.g. the anti-subsidy EU investigation on EV imports since October 2023 and possible introduction of new tariffs from July 2024), Chinese OEMs often remain more competitive than incumbents. They could be well-placed to capture market share in the near term, especially in the small car segment. In early 2024, for example, BYD chartered its first cargo ship, with a capacity of 7 000 cars sailing to Europe.

European and US carmakers are under growing pressure as Chinese carmakers start to export at scale, and are adjusting corporate strategy accordingly. In February 2024, for example, Ford and GM expressed that they could even be open to partnering to compete against Chinese carmakers, and BYD in particular. Ford estimates losses on EV investments of up to USD 5.5 billion in 2023, and expects even tougher future competition from more affordable Tesla models and Chinese carmakers. Another example comes from Stellantis, which – considering the growing importance of Chinese pure play EV manufacturers on the global market – recently became an important shareholder of China’s Leapmotor brand.

Venture capital investments in electric vehicle start-ups dropped in 2023, following the global trend

Venture capital (VC) funding to EV start-ups has boomed in the past decade. Financial investors such as banks and VC or private equity funds see in EV start-ups a potential for significant future returns. Many companies – including major incumbent carmakers – also use corporate VC to fund start-ups to develop new technology, or to acquire concepts developed by new entrants. Whereas in the past century, most carmakers typically developed ICE technology and manufacturing through in-house R&D, investing in start-ups has now become a notable trend. This allows incumbents to bolster their own position and maintain a competitive edge in quickly evolving markets and regulatory environments.

In 2023, however, global VC investments in clean energy start-ups fell considerably relative to 2022, and EVs and batteries were no exception. Early-stage investments (i.e. seed and series A, referring to the first rounds of financing and the earlier stages of development) in start-ups developing EV and battery technologies dropped 20% to USD 1.4 billion in 2023. Meanwhile, growth-stage investments (i.e. series B and growth equity, which refer to the later rounds of financing as start-ups increase activity) dropped 35% to USD 10.1 billion.

Several important factors are contributing to this downward trend. As competition intensifies in EV and battery markets, and as incumbents ramp up their own investments and manufacturing plans, barriers to entry for new actors get higher, and so do investor perceptions of risk. The front-running start-ups that raised funds over the 2015-2020 period are now maturing and transitioning to other sources of capital, leaving fewer alternatives for newcomers – early-stage VC for electric carmakers dropped radically in 2023. Geopolitical tensions, supply chain disruptions, high energy prices, and rising inflation and interest rates limit the availability of higher-risk capital. We also observe a cooldown following the post-Covid-19 boom of 2021-2022, which was fuelled by investment restraint during the pandemic and the expectation of significant economic recovery packages afterwards.

Despite the drop, investments remain far greater than in 2019, prior to the Covid-19 pandemic. Start-ups developing EV charging technology attracted over USD 400 million in early-stage VC, and battery and battery component makers USD 260 million, together accounting for half of early-stage VC. There was also a surge for two- and three-wheeler start-ups, which raised USD 200 million in early-stage VC in 2023, up from below USD 100 million in previous years. While early-stage funding for electric carmakers has dried up, and in 2023 fell far short of the 2018-2022 rounds, investors continue to show interest in upstream and downstream segments of EV supply chains, as well as other EV types, especially if these can scale quickly.

Notable deals in 2023 included Germany-based Jolt Energy’s first round of VC funding, which raised USD 160 million . The company seeks to bring its fast-charging technology to urban areas of Europe and the United States, and claims to provide 100 km of driving range in just 5 minutes. Similarly, German EV charging start-up Numbat raised USD 75 million in series A funding through the European Infrastructure Fund, as well as another USD 75 million in loans. Indian start-up Charge Zone also raised nearly USD 55 million to develop nearly 300 charging stations.

Chinese electric truck maker DeepWay raised USD 110 million in series A funding to start mass production, after having raised USD 70 million in 2022 to fund R&D and early-stage manufacturing. In the 2/3W space, Benin-based start-up Spiro raised USD 60 million , Indonesian Maka Motors raised over USD 37 million and Volta Indonesia USD 35 million , Brazilian Vammo raised USD 30 million, and Indian Simple Energy India raised USD 20 million, illustrating the importance of this segment in EMDEs. In India, Evera, a provider of all-electric cab services and management, raised USD 7 million to expand operations in Delhi, and Zyngo, an operator of electric last-mile delivery fleets, raised USD 5 million .

At the growth stage, battery technology developers attracted USD 5 billion, and electric carmakers USD 2.5 billion, with significant support from public investors, indicating interest from governments to help new entrants ramp up manufacturing capacity or accelerate expansion and deployment. However, growth-stage investor appetite for electric trucks and EV charging dried up relative to previous years.

Notable deals include French battery maker Verkor, which raised USD 900 million in growth equity from investors including public-private EIT InnoEnergy and BPI France, and major incumbent carmaker Renault. The company also received a nearly USD 700 million grant from the French Government and another USD 650 million in debt from the European Investment Bank to develop a 16 GWh gigafactory project in Dunkirk. To support its global expansion, Swedish battery maker Northvolt raised USD 400 million from Ontario’s asset manager, USD 1.2 billion and then another USD 150 million in debt from public Canadian investors and private banks, a USD 740 million grant from the German Government, and a USD 5 billion loan from the European Investment Bank and Nordic Investment Bank. Chinese lithium-ion battery maker Hithium also raised over USD 600 million.

Chinese electric carmakers continued raising money in 2023, such as Hozon and Rox Motor , which each raised around USD 1 billion. Like other Chinese new entrants, Hozon Automobile is looking to expand in overseas markets as well as domestically, and could go public in Hong Kong. Rox Motor launched its first model in 2023, an electric SUV priced under USD 50 000. Premium electric carmaker Avatr raised USD 400 million , after having raised a similar amount in series A funding in 2022.

In the electric taxi fleet space, India-based BluSmart, a start-up providing ride-hailing services, raised two rounds of USD 42 million and USD 24 million in growth equity in 2023, and another USD 25 million in 2024. However, there has been fierce competition with larger taxi fleet apps and operators in the past few years in EMDEs. In 2023, for example, electric ride-sharing app Beat, owned by Free Now, withdrew from Latin America.

Early-stage venture capital investments in batteries by technology, 2010-2023

Growth-stage venture capital investments in batteries by technology, 2010-2023, early-stage venture capital investments in electric mobility by technology, 2010-2023, growth-stage venture capital investments in electric mobility by technology, 2010-2023, leading electric vehicle start-ups and venture capital investments are geographically concentrated.

Similarly to for other clean energy technologies, start-ups for EVs are predominantly headquartered in the United States, Europe or China. There is, however, significant variability across different EV technology areas. Over the 2018-2023 period, in cumulative terms, about 70% of the VC investments in start-ups developing electric cars were made in China, 20% in the United States and 10% in Europe. Meanwhile, about 95% of the VC investments in electric trucks, buses, and commercial vehicles were made in the United States.

India, the only EMDE other than China that has a significant share in global VC markets, accounted for 70% of the investments in start-ups developing electric 2/3Ws. Over the 2018-2023 period, Indian EV start-ups raised USD 2.7 billion, of which over 70% was for electric 2/3Ws. Policy support has contributed to building investor appetite for the EV sector, and the high investment levels seen in 2022 and 2023 will warrant further examination in 2024 and beyond if the FAME subsidies are reduced or phased out. The investment potential in India’s EV sector is estimated at around USD 200 billion, suggesting there are still considerable opportunities ahead for Indian entrepreneurs and start-ups.

With regards to batteries, start-ups based in the United States attracted most VC investments – from 30% to over 80% of global investments over the 2018-2023 period. Investments in battery makers, however, were split evenly among major VC markets, with 30% each for China, Europe and the United States. It is noticeable that battery VC remains extremely limited in historic battery-producing countries such as Japan and Korea, where technology innovation through start-ups is generally less common.

Share of total cumulative venture capital investment in electric mobility technology areas by country or region, 2018-2023

Investors look to new battery chemistries, recycling, and critical mineral extraction and refining technologies.

As VC investors look for new opportunities across EV supply chains, novel battery concepts and critical mineral extraction and refining are gaining momentum. Over the 2018-2020 period, cumulative early-stage VC flowing to battery and component makers stood around USD 430 million, nearly 75% of which was for lithium-based battery chemistries. Over the 2021-2023 period, cumulative investments for batteries not only more than tripled, to nearly USD 1.4 billion, they also diversified. Lithium chemistries accounted for just 60% of the total, while the share of emerging concepts such as metal-hydrogen, redox-flow, solid state and sodium-ion batteries rose, growing from less than 15% to more than 25%.

Not all the start-ups active in the battery space today will eventually serve EV supply chains. In fact, many companies in our sample have so far announced that long-duration power storage is their primary target market. However, there are important spillover opportunities between those two closely related technology fields. For example, sodium-ion batteries can be used for both grid-level storage and EVs, and learnings in one sector can inform technology development in the other.

Notable early-stage VC deals in novel battery chemistries in 2023 included rounds by Chinese WeView ( USD 90 million ), United States-based Noon Energy ( USD 30 million ) and XL Batteries ( USD 10 million ), and Singapore’s VFlow Tech ( USD 10 million ), all of which are developing redox-flow batteries. Peak Energy, based in Colorado, was established in 2023 and raised USD 10 million in seed money to develop sodium-ion batteries. Similarly, Inlyte Energy raised USD 8 million in seed funding, after having received at least USD 250 000 in grants from the US Department of Energy (DoE) Advanced Research Projects Agency–Energy ( ARPA-E ) in 2022.

In the battery recycling space, notable deals in 2023 included a USD 540 million round of growth equity funding to US-based Ascend Elements, a start-up that also received two grants from the DoE totalling USD 480 million in 2022. The company, which raised another USD 160 million in 2024, aims to build North America’s first cathode precursor manufacturing facility using black mass, the typical output of lithium-ion battery recycling. Chinese Tianneng New Materials raised a series A of USD 140 million , and French MECAWARE USD 40 million .

As reported last year, critical minerals VC is also expanding, standing just under USD 400 million in 2023, or nearly twice as much as in 2021 or 2022. This technology field attracted under USD 75 million in cumulative VC over the 2018‑2020 period. Cobalt extraction attracted nearly USD 200 million in 2023, just like in 2022. Lithium extraction and refining attracted USD 135 million, much higher than the dip of 2022 but still below the record year of 2021. We now observe deals for nickel extraction, suggesting investors are also looking for opportunities beyond cobalt and lithium.

In 2023, several governments have also launched critical raw material funds. For example, France committed EUR 500 million to the EUR 2 billion private equity Critical Metals Fund managed by InfraVia, as part of France 2030. Similarly, EIT InnoEnergy launched the European Battery Alliance’s Strategic Battery Raw Material Fund , targeting investments of EUR 500 million. Other announcements and projects are mentioned in the later section on Industry Outlook in this report.

Notable critical mineral deals in 2023 included a series A of USD 50 million by Canada-based Summit Nanotech to scale its more sustainable lithium extraction technology, after it received grants from public investors such as Sustainable Development Technology Canada and Natural Resources Canada. In the United States, Kobold Metals raised USD 195 million in growth equity to expand cobalt extraction, Energy Exploration Technologies raised USD 50 million for direct lithium extraction from GM Ventures, and Atlas Materials raised USD 27 million in seed money to develop nickel extraction technologies.

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