关键词: 电动公交, 充电站选址, 行车计划, 联合优化, 文化基因算法

Abstract:

Under the global low-carbon development trend, battery electric buses (BEBs) have emerged as a crucial component of urban transportation systems due to their environmental benefits. However, their large-scale deployment faces challenges posed by limited driving ranges, demanding coordinated planning of charging infrastructure and operational schedules. Existing studies predominantly treat charging station siting and timetabling as separate optimization problems, overlooking their inherent interdependencies. Furthermore, current approaches focus primarily on single-depot configurations or small-scale networks, resulting in limited generalizability. To address these gaps, this study proposes a spatio-temporal network framework for multi-depot BEB systems, establishing a joint optimization model that integrates charging station siting and timetabling. The model aims to minimize the total system cost while incorporating constraints such as charging station construction, trip sequence continuity, battery charge maintenance, vehicle scheduling, and charging pile-vehicle matching. An enhanced memetic algorithm combining improved genetic operators and local search strategies is developed to solve the proposed model. Case studies on a real-world bus network in Chancheng District, Foshan City, validate the effectiveness of the framework across different problem scales. Results demonstrate that: (1) The proposed algorithm achieves significant total cost reductions compared to traditional genetic algorithms and simulated annealing methods in both small- and large-scale scenarios; (2) Charging station planning indirectly influences the total system cost by affecting timetabling efficiency, thereby justifying the necessity of joint optimization; (3) The framework enriches the theoretical foundation of BEB system optimization and provides practical decision-making references for both transit planners and operators.

Key words: electric buses, charging station siting, vehicle scheduling, joint optimization, memetic algorithm