Abstract：Electric heavy truck charging and swapping stations are developing rapidly, and battery charging strategies have an important impact on station-side operating costs and user battery swapping experience. How to meet the daily battery swapping needs of electric heavy trucks while minimizing station-side operating costs and shortening user battery swapping waiting time is a key research direction. First, a certain electric heavy truck charging and swapping station is taken as the experimental object, and statistical analysis methods are used to obtain user battery swapping needs at different times of the day. Secondly, a charging strategy optimization control model is proposed with the goal of reducing station-side battery charging costs and life loss costs. Combined with battery swapping demand and time-of-use electricity prices, a genetic algorithm is used to solve the charging rate matrix and charging cut-off voltage of the battery charging compartment at different times of the day. Finally, the effectiveness of the model is verified through experimental examples, which also provides reference for its wide application in actual charging and swapping stations.
王博, 杨克南, 杨迎春, 王少鹏, 韩锦峰. 基于遗传算法的电动重卡充换电站充电策略优化[J]. 电气技术, 2025, 26(3): 36-41.
WANG Bo, YANG Ke’nan, YANG Yingchun, WANG Shaopeng, HAN Jinfeng. Optimization of charging strategy for electric heavy truck charging and swapping stations based on genetic algorithm. Electrical Engineering, 2025, 26(3): 36-41.
2025, Vol. 26 
