New energy vehicle charging station location method based on improved particle swarm optimization algorithm

被引:0
|
作者
Zhang, Liang-Li [1 ]
Ma, Xiao-Feng [2 ]
机构
[1] School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan
[2] Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan
来源
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2024年 / 54卷 / 08期
关键词
genetic algorithm; location of charging station; new energy vehicles; objective function; particle swarm optimization;
D O I
10.13229/j.cnki.jdxbgxb.20230249
中图分类号
学科分类号
摘要
In order to improve the rationality of vehicle charging station layout and reduce resource waste,a new energy vehicle charging station location method based on improved particle swarm optimization algorithm is proposed. Predict the future distribution of electric vehicles, and take the user travel characteristics,traffic density,service radius and other factors as the reference basis for location selection;Taking the shortest distance between the demand point and the charging station as the objective function,set the relevant constraints and establish the location model;Explore the implementation process of classical particle swarm optimization algorithm,and obtain particle velocity and position update formula;Aiming at the problem that the method is easy to fall into local optimum,genetic algorithm is used to improve it;The improved algorithm is used to solve the objective function,set the initial parameters and judgment conditions,increase the particle crossover,mutation and other operations,and improve the quality of particle swarm. When the requirements of iteration times are met,the optimal location of the individual is output,that is,the optimal scheme for the location of the charging station. The experimental results show that the location selected by the proposed method can meet the demand of the objective function,balance the charging demand,and avoid resource waste. © 2024 Editorial Board of Jilin University. All rights reserved.
引用
收藏
页码:2275 / 2281
页数:6
相关论文
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