四川轻化工大学学报（自然科学版）2025，Vol.38Issue(1)：86-93,8.DOI:10.11863/j.suse.2025.01.10
基于精英强化遗传算法的电动汽车充电调度优化
Electric Vehicle Charging Scheduling Optimization Based on Elite Enhanced Genetic Algorithm
摘要
Abstract
In order to solve the problem of how to efficiently schedule charging tasks for electric vehicle(EV)in a large scale,an optimization method for EV charging scheduling based on elite enhanced genetic algorithm(EEGA)has been proposed.Firstly,an adaptive strategy is introduced to dynamically change crossover and mutation probability,which is used to solve the problem that the traditional algorithm is prone to fall into local optimum.Secondly,a new population is formed by the parent and child populations through the elite retention strategy,and the optimal individuals of the new population are searched to find a better solution replacement,which is helpful to improve the algorithm convergence speed.Lastly,the multi-objective vehicle scheduling model is established by making charging scheduling strategy,and entropy-weighted TOPSIS method is used to eliminate the multi-objective dimension.The simulation results show that the charging time,charging cost,charging pile utilization deviation rate and grid load are reduced by about 4.2%,2.3%,4.4%and 6.8%,respectively,compared with the traditional genetic algorithm scheduling.The proposed optimization method provides a near-optimal solution to effectively schedule large-scale EV charging operations.关键词
电动汽车/调度优化/精英强化/遗传算法/熵权TOPSIS法Key words
electric vehicle/scheduling optimization/elite enhancement/genetic algorithm/entropy-weighted TOPSIS method分类
信息技术与安全科学引用本文复制引用
谢涛,谭飞,黄军付,王俊佳,袁超杨..基于精英强化遗传算法的电动汽车充电调度优化[J].四川轻化工大学学报（自然科学版）,2025,38(1):86-93,8.基金项目
国家自然科学基金项目(61902268) （61902268）
四川省科技计划项目(21ZDYF4052 （）
2020YFH0124 （）
2021YFSY0060) （）
四川轻化工大学创新创业训练项目(CX2023198 （）
CX2023193 （）
CX2023195) （）
