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Reserving Charging Decision-Making Model and Route Plan for Electric Vehicles Considering Information of Traffic and Charging Station

Author

Listed:
  • Haoming Liu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Wenqian Yin

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Xiaoling Yuan

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Man Niu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

With the advance of battery energy technology, electric vehicles (EV) are catching more and more attention. One of the influencing factors of electric vehicles large-scale application is the availability of charging stations and convenience of charging. It is important to investigate how to make reserving charging strategies and ensure electric vehicles are charged with shorter time and lower charging expense whenever charging request is proposed. This paper proposes a reserving charging decision-making model for electric vehicles that move to certain destinations and need charging services in consideration of traffic conditions and available charging resources at the charging stations. Besides, the interactive mechanism is described to show how the reserving charging system works, as well as the rolling records-based credit mechanism where extra charges from EV is considered to hedge default behavior. With the objectives of minimizing driving time and minimizing charging expenses, an optimization model with two objective functions is formulated. Then the optimizations are solved by a K shortest paths algorithm based on a weighted directed graph, where the time and distance factors are respectively treated as weights of corresponding edges of transportation networks. Case studies show the effectiveness and validity of the proposed route plan and reserving charging decision-making model.

Suggested Citation

  • Haoming Liu & Wenqian Yin & Xiaoling Yuan & Man Niu, 2018. "Reserving Charging Decision-Making Model and Route Plan for Electric Vehicles Considering Information of Traffic and Charging Station," Sustainability, MDPI, vol. 10(5), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1324-:d:143115
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    References listed on IDEAS

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    1. Montoya, Alejandro & Guéret, Christelle & Mendoza, Jorge E. & Villegas, Juan G., 2017. "The electric vehicle routing problem with nonlinear charging function," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 87-110.
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    Cited by:

    1. Li Zhang & Ke Gong & Maozeng Xu, 2019. "Congestion Control in Charging Stations Allocation with Q-Learning," Sustainability, MDPI, vol. 11(14), pages 1-11, July.
    2. Anders F. Jensen & Thomas K. Rasmussen & Carlo G. Prato, 2020. "A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    3. Witt Andreas, 2023. "Determination of the Number of Required Charging Stations on a German Motorway Based on Real Traffic Data and Discrete Event-Based Simulation," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 1-11, January.
    4. Junpeng Cai & Dewang Chen & Shixiong Jiang & Weijing Pan, 2020. "Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles," Sustainability, MDPI, vol. 12(18), pages 1-20, September.

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