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A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Author

Listed:
  • Bolong Yun

    (State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Daniel (Jian) Sun

    (State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    China Institute of Urban Governance, School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Yingjie Zhang

    (Monitoring and Research Center, Shanghai EV Public Data Collection (SHEVDC), Shanghai 201800, China)

  • Siwen Deng

    (Monitoring and Research Center, Shanghai EV Public Data Collection (SHEVDC), Shanghai 201800, China)

  • Jing Xiong

    (China Institute of Urban Governance, School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China)

Abstract

Electric vehicles (EVs) are promising alternatives to replace traditional gasoline vehicles. The relationship between available charging stations and electric vehicles has to be precisely coordinated to facilitate the increasing promotion and usage of EVs. This paper aims to investigate the choice of the charging location with global positioning system (GPS) trajectories of 700 Plug-in Hybrid Electric Vehicle (PHEV) users as well as the charging facility data in Shanghai. First, the recharge accessibility of each PHEV user was investigated, and 9% rely solely on public charging networks. Then, we explored the relationship between fuel consumption and the average distance between charging to analyze the environmental benefits of PHEVs. It was found that 16% PHEVs are similar to EVs, and 9% whose drivers rely solely on public charging stations are similar to internal combustion engine (ICE) vehicles. PHEV users were divided into four types based on the actual recharge access: home and workplace-based user (private + workplace + public), the home-based user (private + public), the workplace-based user (workplace + public), and the public-based user (public). Models were developed to identify and compare the factors that influence PHEV user’s charging location choices (home, workplace, and public stations). The modeling and results interpretation were carried out for all PHEV users, home and workplace-based users, home-based users, and workplace-based users, respectively. The estimation results demonstrated that PHEV users tended to charge at home or workplace rather than public charging stations. Charging price, charging price tariff, the initial state of charge (SOC), dwell time, charging power, the density and size of public charging stations, the total number of public charging, vehicle kilometer travel (VKT) of the current trip and current day are the main predictors when choosing the charging location. Findings of this study may provide new insights into the operational strategies of the public charging station as well as the deployment of public charging facilities in urban cities.

Suggested Citation

  • Bolong Yun & Daniel (Jian) Sun & Yingjie Zhang & Siwen Deng & Jing Xiong, 2019. "A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5761-:d:277551
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    References listed on IDEAS

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    Cited by:

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    2. Zhao, Hui & Hao, Xiang, 2024. "Location decision of electric vehicle charging station based on a novel grey correlation comprehensive evaluation multi-criteria decision method," Energy, Elsevier, vol. 299(C).

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