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Identifying and minimizing critical driving range thresholds for electric vehicles in intercity networks

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  • Li, Jiapei
  • Xie, Chi

Abstract

It is well known that range anxiety faced by electric vehicle (EV) drivers nowadays can be effectively mitigated by either increasing the driving range of vehicles or increasing the distribution density of charging stations. To enact an effective research and development plan, vehicle manufacturers should understand well the relationship between the EV adoption and usage rates and the performance level of battery and energy management technologies. On the other hand, the underlying relationship between EV adoption and usage rates and the number and distribution of charging stations is also critical for charging infrastructure investors in determining their construction plans. This paper is dedicated to analytically identifying two critical driving range thresholds and further reducing these driving range thresholds as much as possible by optimally deploying charging station locations in intercity highway networks. These critical thresholds represent the turning points of individual routing convenience conditions or satisfaction levels for long-distance trips, implying that the inconvenient travel restrictions may be largely overcome if the driving range of EVs goes beyond these thresholds. For this purpose, we develop two integer programming models and dynamic programming algorithms for the driving range threshold identification problems, and extend the modeling framework to accommodate two relevant driving range threshold minimization problems as well, which are solved by a branching algorithm encapsulating the aforementioned dynamic programming procedures. The output of this research offers a set of new analytical tools for EV manufacturers to make strategic decisions on extending the driving range of their products and for charging infrastructure investors to make planning decisions on selecting charging station locations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:soceps:v:93:y:2024:i:c:s0038012124001009
    DOI: 10.1016/j.seps.2024.101901
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    References listed on IDEAS

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    1. Khan, Mobashwir & Kockelman, Kara M., 2012. "Predicting the market potential of plug-in electric vehicles using multiday GPS data," Energy Policy, Elsevier, vol. 46(C), pages 225-233.
    2. Xie, Chi & Wang, Tong-Gen & Pu, Xiaoting & Karoonsoontawong, Ampol, 2017. "Path-constrained traffic assignment: Modeling and computing network impacts of stochastic range anxiety," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 136-157.
    3. Bogdan Ovidiu Varga & Arsen Sagoian & Florin Mariasiu, 2019. "Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges," Energies, MDPI, vol. 12(5), pages 1-19, March.
    4. Zhou, Yue & Wen, Ruoxi & Wang, Hewu & Cai, Hua, 2020. "Optimal battery electric vehicles range: A study considering heterogeneous travel patterns, charging behaviors, and access to charging infrastructure," Energy, Elsevier, vol. 197(C).
    5. Zhenhong Lin, 2014. "Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers," Transportation Science, INFORMS, vol. 48(4), pages 635-650, November.
    6. Liao, Chung-Shou & Lu, Shang-Hung & Shen, Zuo-Jun Max, 2016. "The electric vehicle touring problem," Transportation Research Part B: Methodological, Elsevier, vol. 86(C), pages 163-180.
    7. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    8. Xie, Chi & Travis Waller, S., 2012. "Parametric search and problem decomposition for approximating Pareto-optimal paths," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 1043-1067.
    9. Peterson, Scott B. & Michalek, Jeremy J., 2013. "Cost-effectiveness of plug-in hybrid electric vehicle battery capacity and charging infrastructure investment for reducing US gasoline consumption," Energy Policy, Elsevier, vol. 52(C), pages 429-438.
    10. Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi & Ngoduy, Dong & Watling, David, 2021. "Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    11. Wenig, Jürgen & Sodenkamp, Mariya & Staake, Thorsten, 2019. "Battery versus infrastructure: Tradeoffs between battery capacity and charging infrastructure for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    12. Brumbaugh-Smith, J. & Shier, D., 1989. "An empirical investigation of some bicriterion shortest path algorithms," European Journal of Operational Research, Elsevier, vol. 43(2), pages 216-224, November.
    13. Kınay, Ömer Burak & Gzara, Fatma & Alumur, Sibel A., 2021. "Full cover charging station location problem with routing," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 1-22.
    14. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.
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