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Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers

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  • Zhenhong Lin

    (Senior R&D Staff, Oak Ridge National Laboratory, Knoxville, Tennessee 37932)

Abstract

Properly determining the driving range is critical for accurately predicting the sales and social benefits of battery electric vehicles (BEVs). This study proposes a framework for optimizing the driving range by minimizing the sum of battery price, electricity cost, and range limitation cost—referred to as the “range-related cost”—as a measurement of range anxiety. The objective function is linked to policy-relevant parameters, including battery cost and price markup, battery utilization, charging infrastructure availability, vehicle efficiency, electricity and gasoline prices, household vehicle ownership, daily driving patterns, discount rate, and perceived vehicle lifetime. Qualitative discussion of the framework and its empirical application to a sample ( N = 36,664) representing new car drivers in the United States is included. The quantitative results strongly suggest that ranges of less than 100 miles are likely to be more popular in the BEV market for a long period of time. The average optimal range among U.S. drivers is found to be largely inelastic. Still, battery cost reduction significantly drives BEV demand toward longer ranges, whereas improvement in the charging infrastructure is found to significantly drive BEV demand toward shorter ranges. The bias of a single-range assumption and the effects of range optimization and diversification in reducing such biases are both found to be significant.

Suggested Citation

  • Zhenhong Lin, 2014. "Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers," Transportation Science, INFORMS, vol. 48(4), pages 635-650, November.
    • Handle: RePEc:inm:ortrsc:v:48:y:2014:i:4:p:635-650
    DOI: 10.1287/trsc.2013.0516
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    Citations

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    Cited by:

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    2. Guschinsky, Nikolai & Kovalyov, Mikhail Y. & Pesch, Erwin & Rozin, Boris, 2023. "Cost minimizing decisions on equipment and charging schedule for electric buses in a single depot," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
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    4. Liu, Hu-Chen & You, Xiao-Yue & Xue, Yi-Xi & Luan, Xue, 2017. "Exploring critical factors influencing the diffusion of electric vehicles in China: A multi-stakeholder perspective," Research in Transportation Economics, Elsevier, vol. 66(C), pages 46-58.
    5. Gao, Zhiming & Lin, Zhenhong & LaClair, Tim J. & Liu, Changzheng & Li, Jan-Mou & Birky, Alicia K. & Ward, Jacob, 2017. "Battery capacity and recharging needs for electric buses in city transit service," Energy, Elsevier, vol. 122(C), pages 588-600.
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    7. Lieven, Theo, 2015. "Policy measures to promote electric mobility – A global perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 78-93.
    8. Ou, Shiqi & Lin, Zhenhong & He, Xin & Przesmitzki, Steven, 2018. "Estimation of vehicle home parking availability in China and quantification of its potential impacts on plug-in electric vehicle ownership cost," Transport Policy, Elsevier, vol. 68(C), pages 107-117.
    9. Xie, Fei & Lin, Zhenhong, 2021. "Integrated U.S. nationwide corridor charging infrastructure planning for mass electrification of inter-city trips," Applied Energy, Elsevier, vol. 298(C).
    10. Xie, Fei & Liu, Changzheng & Li, Shengyin & Lin, Zhenhong & Huang, Yongxi, 2018. "Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 261-276.
    11. Li, Jiapei & Xie, Chi, 2024. "Identifying and minimizing critical driving range thresholds for electric vehicles in intercity networks," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    12. Ou, Shiqi & Lin, Zhenhong & Qi, Liang & Li, Jie & He, Xin & Przesmitzki, Steven, 2018. "The dual-credit policy: Quantifying the policy impact on plug-in electric vehicle sales and industry profits in China," Energy Policy, Elsevier, vol. 121(C), pages 597-610.
    13. Gao, Yongling & Leng, Mingming & Zhang, Yaping & Liang, Liping, 2022. "Incentivizing the adoption of electric vehicles in city logistics: Pricing, driving range, and usage decisions under time window policies," International Journal of Production Economics, Elsevier, vol. 245(C).
    14. Walter, Antonia & Held, Maximilian & Pareschi, Giacomo & Pengg, Hermann & Madlener, Reinhard, 2020. "Decarbonizing the European Automobile Fleet: Impacts of 1.5 °C-compliant Climate Policies in Germany and Norway," FCN Working Papers 18/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    15. Yan Xing & Alan T. Jenn & Yunshi Wang & Chunyan Li & Shengyang Sun & Xiaohua Ding & Siwen Deng, 2020. "Optimal range of plug-in electric vehicles in Beijing and Shanghai," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(3), pages 441-458, March.
    16. Mark M. Nejad & Lena Mashayekhy & Daniel Grosu & Ratna Babu Chinnam, 2017. "Optimal Routing for Plug-In Hybrid Electric Vehicles," Transportation Science, INFORMS, vol. 51(4), pages 1304-1325, November.
    17. Xie, Fei & Lin, Zhenhong, 2017. "Market-driven automotive industry compliance with fuel economy and greenhouse gas standards: Analysis based on consumer choice," Energy Policy, Elsevier, vol. 108(C), pages 299-311.
    18. Nykvist, Björn & Sprei, Frances & Nilsson, Måns, 2019. "Assessing the progress toward lower priced long range battery electric vehicles," Energy Policy, Elsevier, vol. 124(C), pages 144-155.
    19. Lin, Zhenhong & Ou, Shiqi & Elgowainy, Amgad & Reddi, Krishna & Veenstra, Mike & Verduzco, Laura, 2018. "A method for determining the optimal delivered hydrogen pressure for fuel cell electric vehicles," Applied Energy, Elsevier, vol. 216(C), pages 183-194.

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