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An Electric Taxi Charging Station Planning Scheme Based on an Improved Destination Choice Method

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Listed:
  • Ruifeng Shi

    (School of Control & Computer Engineering, North China Electric Power University, Beijing 102206, China
    China Institute of Energy and Transportation Integrated Development, Beijing 102206, China)

  • Jiahua Liu

    (School of Control & Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Zhenhong Liao

    (Guangdong Power Grid Co., Ltd. Zhanjiang Power Supply Bureau, Zhanjiang 524002, China)

  • Li Niu

    (School of Information Resource Management, Renmin University of China, Beijing 100872, China)

  • Eke Ibrahim

    (Department of Electrical and Electronics Engineering, Kırıkkale University, Kirikkale 71451, Turkey)

  • Fang Fu

    (School of Economics & Management, China University of Petroleum, Qingdao 266580, China)

Abstract

The environmental crisis has prompted the development of electric vehicles as a green and environmentally friendly mode of travel. Since a reasonable layout of electric vehicle (EV) charging stations is the prerequisite for developing the EV industry, obtaining an optimal and efficient EV charging station planning scheme is a key issue. Although the Chinese government has carried out a plan to build EV charging piles in residential and working places, it cannot properly fulfill the task of matching the charging needs for public transportation vehicles such as electric taxis (ETs). How to evaluate the performance of fast charging stations (FCSs) and how to help find the optimal ET charging station planning scheme are new challenges. In this paper, an improved destination selection model is proposed to simulate the ET operation system and to help find the optimal ET charging station size with statistical analysis based on the charging need prediction. A numerical case study shows that the proposed method can address ET charging behavior well and can help to statistically determine the size of each ET charging station, which should satisfy the constraints on the preset proportion of the ET charging service requests.

Suggested Citation

  • Ruifeng Shi & Jiahua Liu & Zhenhong Liao & Li Niu & Eke Ibrahim & Fang Fu, 2019. "An Electric Taxi Charging Station Planning Scheme Based on an Improved Destination Choice Method," Energies, MDPI, vol. 12(19), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3781-:d:273775
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    References listed on IDEAS

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    1. Guozhong Liu & Li Kang & Zeyu Luan & Jing Qiu & Fenglei Zheng, 2019. "Charging Station and Power Network Planning for Integrated Electric Vehicles (EVs)," Energies, MDPI, vol. 12(13), pages 1-22, July.
    2. Kong, Weiwei & Luo, Yugong & Feng, Guixuan & Li, Keqiang & Peng, Huei, 2019. "Optimal location planning method of fast charging station for electric vehicles considering operators, drivers, vehicles, traffic flow and power grid," Energy, Elsevier, vol. 186(C).
    3. Schroeder, Andreas & Traber, Thure, 2012. "The economics of fast charging infrastructure for electric vehicles," Energy Policy, Elsevier, vol. 43(C), pages 136-144.
    4. Sun, Bo & Sun, Xu & Tsang, Danny H.K. & Whitt, Ward, 2019. "Optimal battery purchasing and charging strategy at electric vehicle battery swap stations," European Journal of Operational Research, Elsevier, vol. 279(2), pages 524-539.
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    Cited by:

    1. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    2. Qing Li & Xue Li & Zuyu Liu & Yaping Qi, 2022. "Application of Clustering Algorithms in the Location of Electric Taxi Charging Stations," Sustainability, MDPI, vol. 14(13), pages 1-15, June.

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