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Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety

Author

Listed:
  • Huasheng Liu

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Yu Li

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Chongyu Zhang

    (Vivo Mobile Communication Co., Ltd., Shenzhen 518101, China)

  • Jin Li

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Xiaowen Li

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Yuqi Zhao

    (College of Transportation, Jilin University, Changchun 130022, China)

Abstract

Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their limited range. This paper selects electric vehicle intercity medium- and long-distance travel as the research object, and takes the classical flow-capturing location problem as the theoretical basis for the expressway network or national highway network. This paper also considers the driver’s charging choice behavior and range anxiety, studies the electric vehicle charging station location problem, establishes the charging station location model, and uses the Tabu search algorithm to solve the problem. Finally, the effectiveness of the model and algorithm is verified by empirical analysis. The results show that the charging station location model considering the driver’s charging choice behavior and range anxiety performs better.

Suggested Citation

  • Huasheng Liu & Yu Li & Chongyu Zhang & Jin Li & Xiaowen Li & Yuqi Zhao, 2022. "Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4213-:d:785359
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    References listed on IDEAS

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    Cited by:

    1. Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    2. Jiang, Ziyue & Han, Jingzuo & Li, Yetong & Chen, Xinyu & Peng, Tianduo & Xiong, Jianliang & Shu, Zhan, 2023. "Charging station layout planning for electric vehicles based on power system flexibility requirements," Energy, Elsevier, vol. 283(C).
    3. Maria Elena Bruni, 2022. "MDPI Sustainability: Special Issue: “Women’s Special Issue Series: Sustainable Energy”," Sustainability, MDPI, vol. 14(8), pages 1-2, April.

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