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Volume 60 Issue 2
Apr.  2025
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Journal of Southwest Jiaotong University  > 2025  >  60(2): 299-307.
HU Lu, LE Shitong, ZHU Juanxiu. Electric Truck Route Planning Considering Multiple Charging Pile Queues and Time Windows[J]. Journal of Southwest Jiaotong University, 2025, 60(2): 299-307. doi: 10.3969/j.issn.0258-2724.20230084
Citation: HU Lu, LE Shitong, ZHU Juanxiu. Electric Truck Route Planning Considering Multiple Charging Pile Queues and Time Windows[J]. Journal of Southwest Jiaotong University, 2025, 60(2): 299-307. doi: 10.3969/j.issn.0258-2724.20230084
shu

Electric Truck Route Planning Considering Multiple Charging Pile Queues and Time Windows

doi: 10.3969/j.issn.0258-2724.20230084
  • 1.

    School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China

  • 2.

    National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 610031, China

  • 3.

    School of Management, Xihua University, Chengdu 610039, China

  • Received Date: 02 Mar 2023
  • Rev Recd Date: 02 Jul 2023
    • Available Online: 07 Nov 2024
  • Publish Date: 13 Jul 2023
    Abstract

  • In the electric truck route planning problem with time windows, electric trucks may need to queue up when they go to charging stations for charging. To study the impact of different charging station configuration schemes on vehicle route planning and system performance, the queuing model was first built to describe the queuing phenomenon at charging stations. Then, a route optimization model was established by considering the power and flow constraints based on the electric truck route planning problem with time windows, with the queuing model of charging stations embedded into the optimization model. The optimization goals included minimizing vehicle power consumption costs, driver’s wages, penalty costs of time windows, and total costs of all charging piles. To solve the model, a hybrid heuristic algorithm combining mileage saving (C-W) and improved large neighborhood search (LNS) was designed, and the system performance metrics of charging stations were obtained by a recursive algorithm. 18 sets of experimental results show that increasing the number of charging piles simultaneously can control the average queuing time of a vehicle for charging within 1–5 minutes and effectively reduce the total cost by 2.6%–21.0%; increasing the number of charging stations can reduce the queuing time but increase the total cost of the entire route; when the customer time window is short, or the service time is long, the change in the number of charging piles has a more significant impact on the satisfaction of the time window.

     

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