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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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    Cited by:

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    3. 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.
    4. Ou, Shiqi & Hao, Xu & Lin, Zhenhong & Wang, Hewu & Bouchard, Jessey & He, Xin & Przesmitzki, Steven & Wu, Zhixin & Zheng, Jihu & Lv, Renzhi & Qi, Liang & LaClair, Tim J., 2019. "Light-duty plug-in electric vehicles in China: An overview on the market and its comparisons to the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 747-761.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Chi Xie & Xing Wu & Stephen Boyles, 2019. "Traffic equilibrium with a continuously distributed bound on travel weights: the rise of range anxiety and mental account," Annals of Operations Research, Springer, vol. 273(1), pages 279-310, February.
    11. 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.
    12. 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).
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. O. Y. Edelenbosch & A. F. Hof & B. Nykvist & B. Girod & D. P. Vuuren, 2018. "Transport electrification: the effect of recent battery cost reduction on future emission scenarios," Climatic Change, Springer, vol. 151(2), pages 95-108, November.

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