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EV Charging Path Distribution Solution Based on Intelligent Network Connection

Author

Listed:
  • Xinxin Wang

    (School of Management, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Qian Xu

    (School of Management, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Xiaopan Shen

    (School of Management, Wuhan University of Science and Technology, Wuhan 430065, China)

Abstract

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper’s model can save the distribution cost of electric vehicles.

Suggested Citation

  • Xinxin Wang & Qian Xu & Xiaopan Shen, 2023. "EV Charging Path Distribution Solution Based on Intelligent Network Connection," Mathematics, MDPI, vol. 11(13), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2879-:d:1180471
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    References listed on IDEAS

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    1. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
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