基于改进粒子群算法的新能源汽车充电站选址方法

doi:10.13229/j.cnki.jdxbgxb.20230249

Abstract

Abstract:

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.

Key words: particle swarm optimization, genetic algorithm, new energy vehicles, location of charging station, objective function

CLC Number:

TP391

Liang-li ZHANG,Xiao-feng MA. New energy vehicle charging station location method based on improved particle swarm optimization algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2275-2281.

Figures/Tables 8

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参数名称	参数值
粒子群种群规模/个	50
选择概率	0.3
交叉概率	0.7
变异概率	0.02
最大迭代次数	400
电池容量/kWh	55
电动汽车每公里电耗/（kWh·km^-1）	0.14

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

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