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The growing penetration of electric vehicles (EVs) in urban road networks has brought the problem of EV charging to the forefront. To tackle the EV charging guidance issue in urban road networks, this paper presents an EV guidance strategy based on Mixed Integer Linear Programming (MILP), taking the road network structure of the Dalinghao bay area as the research basis. First, a vehicle-road-network information model is constructed, incorporating the road network structure of the Dalinghao bay area, as well as information regarding local EV fleets and charging station clusters. Second, an EV charging guidance model for urban road networks is formulated using the MILP model, with input data obtained from the vehicle-road-network information model. Finally, practical computational examples are utilized to elaborate on the fundamental characteristics of the proposed method and validate its effectiveness and feasibility. Results from the computational experiments demonstrate that the proposed strategy effectively reduces EV queuing time and improves the utilization balance of charging infrastructure.

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A Coordinated Optimization Strategy for Electric Vehicle Charging Guidance Under Urban Road Network Constraints

  • 会议论文

  • 发表日期：

    2026年

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  • Lingyu Guo,
  • Yang Du,
  • Zhongguang Yang,
  • Yun Zhou

摘要

The growing penetration of electric vehicles (EVs) in urban road networks has brought the problem of EV charging to the forefront. To tackle the EV charging guidance issue in urban road networks, this paper presents an EV guidance strategy based on Mixed Integer Linear Programming (MILP), taking the road network structure of the Dalinghao bay area as the research basis. First, a vehicle-road-network information model is constructed, incorporating the road network structure of the Dalinghao bay area, as well as information regarding local EV fleets and charging station clusters. Second, an EV charging guidance model for urban road networks is formulated using the MILP model, with input data obtained from the vehicle-road-network information model. Finally, practical computational examples are utilized to elaborate on the fundamental characteristics of the proposed method and validate its effectiveness and feasibility. Results from the computational experiments demonstrate that the proposed strategy effectively reduces EV queuing time and improves the utilization balance of charging infrastructure.