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<p>Ready access to charging facilities is essential if universal use of electric vehicles (EVs) is to be achieved. Hence, governments and EV manufacturers worldwide are actively planning ways to expand EV charging infrastructure. Until full electrification of vehicles takes place, EVs and gasoline vehicles (GVs) will exist on the same road networks, so to effect change it is necessary to consider the route choice behavior of these different user classes. This study develops an optimization model for this public charging station location problem from the governmental perspective. The proposed model adopts a demand-based optimization approach to study the impact of charging station location and route costs on users’ route choice behavior. To explicitly address path overlapping among path alternatives, the C-Logit model is used for determination of route choice probabilities. The proposed model is a mixed-integer nonlinear programming (MINLP) model. As optimal solutions to this model with the optimization software, BARON requires prohibitive amounts of time with large problem sizes, a column generation-based heuristic is proposed and its performance evaluated on the Nguyen-Dupuis, Sioux-Falls, and Fort Worth networks. The results show that for the Nguyen-Dupuis network, the heuristic obtains the same solution as BARON within a similar timeframe, while for the Sioux-Falls and Fort Worth networks, the heuristic achieves a higher-quality solution in a shorter computation time. Sensitivity analysis was also conducted to study the impact of the parameters on solutions. The findings of this study will serve as a reference for governmental decision-making regarding the determination of public charging station locations.</p>

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Public Charging Facility Location Modeling with Route Choice Behavior of Multiple User Classes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月3日

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  • Chen-Yen Lin,
  • Chung-Cheng Lu,
  • Paul M. Schonfeld

摘要

Ready access to charging facilities is essential if universal use of electric vehicles (EVs) is to be achieved. Hence, governments and EV manufacturers worldwide are actively planning ways to expand EV charging infrastructure. Until full electrification of vehicles takes place, EVs and gasoline vehicles (GVs) will exist on the same road networks, so to effect change it is necessary to consider the route choice behavior of these different user classes. This study develops an optimization model for this public charging station location problem from the governmental perspective. The proposed model adopts a demand-based optimization approach to study the impact of charging station location and route costs on users’ route choice behavior. To explicitly address path overlapping among path alternatives, the C-Logit model is used for determination of route choice probabilities. The proposed model is a mixed-integer nonlinear programming (MINLP) model. As optimal solutions to this model with the optimization software, BARON requires prohibitive amounts of time with large problem sizes, a column generation-based heuristic is proposed and its performance evaluated on the Nguyen-Dupuis, Sioux-Falls, and Fort Worth networks. The results show that for the Nguyen-Dupuis network, the heuristic obtains the same solution as BARON within a similar timeframe, while for the Sioux-Falls and Fort Worth networks, the heuristic achieves a higher-quality solution in a shorter computation time. Sensitivity analysis was also conducted to study the impact of the parameters on solutions. The findings of this study will serve as a reference for governmental decision-making regarding the determination of public charging station locations.