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Route Guidance Strategies for Electric Vehicles by Considering Stochastic Charging Demands in a Time-Varying Road Network

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  • Yongxing Wang

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Jun Bi

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Chaoru Lu

    (Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7033 Trondheim, Norway)

  • Cong Ding

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Electric vehicles (EVs) are being increasingly adopted because of global concerns about petroleum dependence and greenhouse gas emissions. However, their limited driving range results in increased charging demands with a stochastic characteristic in real-world situations, and the charging demands should be attributed toward charging stations in time-varying road networks. To this end, this study proposes guidance strategies to provide efficient choice for charging stations and corresponding routes, and it includes the time-varying characteristic of road networks in problem formulation. Specifically, we propose two route guidance strategies from different perspectives based on the charging demand information. The first strategy focuses on the effects of the number of EVs on the charging stations’ operation, and the reachable charging stations with the fewest vehicles are selected as the heuristic suggested ones. The other strategy considers the travel cost of individual drivers and selects the charging stations nearest to the destination as heuristic suggested ones. Both strategies ensure that the selected charging stations can be reached in a time-varying road network. In addition, we carry out a simulation analysis to investigate the performance of the proposed route guidance strategies and introduce relevant insights and recommendations for the application of the strategies under various scenarios.

Suggested Citation

  • Yongxing Wang & Jun Bi & Chaoru Lu & Cong Ding, 2020. "Route Guidance Strategies for Electric Vehicles by Considering Stochastic Charging Demands in a Time-Varying Road Network," Energies, MDPI, vol. 13(9), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2287-:d:354274
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    References listed on IDEAS

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    Cited by:

    1. Yu Feng & Xiaochun Lu, 2021. "Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review," Energies, MDPI, vol. 14(24), pages 1-19, December.
    2. 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.
    3. Yin, Wanjun & Jia, Leilei & Ji, Jianbo, 2024. "Energy optimal scheduling strategy considering V2G characteristics of electric vehicle," Energy, Elsevier, vol. 294(C).

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