Scholarlink

With the rapid growth of electric vehicle (EV) ownership, highway service area charging facilities face increasing pressure from limited capacity and suboptimal spatial layout, becoming key bottlenecks for long-distance travel. To address this, a capacity planning approach integrating M/M/N queuing theory and a genetic algorithm is proposed. First, an M/M/N model is established based on the spatiotemporal distribution of charging demand to estimate average waiting time and balance system utilization with user satisfaction. Then, a planning model is formulated to minimize the average annual life-cycle cost, subject to constraints on waiting time and service rate. A case study on service areas along the southern Jinan–Qingdao Expressway determines the optimal charger allocation using the genetic algorithm and performs sensitivity analysis on the maximum waiting time. Results demonstrate that the proposed method effectively balances service quality and resource efficiency, offering theoretical support for the rational deployment of highway EV charging infrastructure and the integrated development of transport and energy systems.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Capacity Planning of Electric Vehicle Charging Facilities in Highway Service Areas

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Zihan Wang,
  • Yunpeng Guo,
  • Rongjian Dai,
  • Xiaoteng Zhou,
  • Xu Wang

摘要

With the rapid growth of electric vehicle (EV) ownership, highway service area charging facilities face increasing pressure from limited capacity and suboptimal spatial layout, becoming key bottlenecks for long-distance travel. To address this, a capacity planning approach integrating M/M/N queuing theory and a genetic algorithm is proposed. First, an M/M/N model is established based on the spatiotemporal distribution of charging demand to estimate average waiting time and balance system utilization with user satisfaction. Then, a planning model is formulated to minimize the average annual life-cycle cost, subject to constraints on waiting time and service rate. A case study on service areas along the southern Jinan–Qingdao Expressway determines the optimal charger allocation using the genetic algorithm and performs sensitivity analysis on the maximum waiting time. Results demonstrate that the proposed method effectively balances service quality and resource efficiency, offering theoretical support for the rational deployment of highway EV charging infrastructure and the integrated development of transport and energy systems.