华南理工大学学报（自然科学版）2026，Vol.54Issue(3)：79-90,12.DOI:10.12141/j.issn.1000-565X.250098
电动公交充电站选址与行车计划联合优化
Joint Optimization of Electric Bus Charging Station Siting and Vehicle Scheduling
摘要
Abstract
Pure electric buses have become an important component of urban public transportation due to their environmental benefits.However,their widespread adoption is constrained by limited driving range,placing high demands on the planning of charging infrastructure and the formulation of vehicle schedules.Existing research often treats charging station siting and vehicle scheduling as independent problems,overlooking their interdependence.Moreover,most studies focus on single-depot or small-scale scenarios,which cannot adequately address the requirements for coordinated,cross-regional dispatching in large-scale and complex networks.To address these issues,this study constructs an integrated optimization model for electric bus charging station siting and vehicle scheduling.The model is built upon a spatio-temporal network framework designed for a multi-depot electric bus system.The objective is to minimize the total system cost,subject to various constraints including charging station construction,trip connection,state-of-charge(SOC)maintenance,vehicle scheduling,and charger matching.In order to accurately describe the operating cost,the model introduces the time-of-use electricity pricing and accounts for the parallel charging capacity of stations.To effectively solve this high-dimensional,discrete combinatorial optimization problem,an enhanced cultural memetic algorithm is designed.The algorithm incorporates improved genetic operators,introduces local search strategies such as trip-chain relocation and merging,and integrates a hierarchical constraint repair mechanism to ensure solution feasibility.The model and algorithm are validated using a case study based on a partial bus network in Chancheng District,Foshan City.The results demonstrate their effectiveness in handling problems of varying scales.Compared to traditional genetic algorithm and simulated annealing algorithm,the proposed algorithm can achieve better cost reduction in both small and large-scale instances.Sensitivity analysis further reveals that increasing battery capacity and reducing unit energy consumption can significantly reduce the total cost of the system,while the electricity pricing policy,especially off-peak rates,has a decisive influence on the operating cost.The study also confirms that charging station siting indirectly affects total cost by influencing scheduling efficiency,highlighting the necessity of joint optimization.This research enriches the theoretical framework for electric bus charging station siting and vehicle scheduling.The findings provide valuable,simultaneous insights for both the strategic planning and day-to-day operational decision-making of electric bus systems.关键词
电动公交/充电站选址/行车计划/联合优化Key words
electric bus/charging station siting/vehicle scheduling/joint optimization分类
交通工程引用本文复制引用
胡郁葱,黄伟彬,陈俊华,巫威眺..电动公交充电站选址与行车计划联合优化[J].华南理工大学学报（自然科学版）,2026,54(3):79-90,12.基金项目
国家自然科学基金项目(52272310) （52272310）
广东省哲学社会科学规划项目(GD24CGL19) （GD24CGL19）
广东省基础与应用基础研究基金项目(2024A1515010617,2025A1515010544,2025B1515020056)Supported by the National Natural Science Foundation of China(52272310),the Guangdong Philosophy and Social Sciences Planning Foundation(GD24CGL19)and the Guangdong Basic and Applied Basic Research Foundation(2024A1515010617,2025A1515010544,2025B1515020056) （2024A1515010617,2025A1515010544,2025B1515020056）
