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

摘要/Abstract

摘要：

为提高汽车充电站布局的合理性，减少资源浪费，提出基于改进粒子群算法的新能源汽车充电站选址方法。预测电动汽车未来分布情况，将用户出行特征、交通密度、服务半径等因素作为选址的参考依据；以需求点到充电站间的距离最短为目标函数，设置相关约束条件，建立选址模型；探究经典粒子群算法的实现过程，获取粒子速度与位置更新公式；针对方法容易陷入局部最优问题，使用遗传算法对其加以改进；利用改进后的算法求解目标函数，设置初始参数和判定条件，增加粒子交叉、变异等操作，提高粒子群质量，当满足迭代次数要求时，输出个体最优位置，即充电站选址的最优方案。实验结果表明：本文方法所选的位置符合目标函数要求，令充电需求均衡，避免了资源浪费。

关键词: 粒子群优化, 遗传算法, 新能源汽车, 充电站选址, 目标函数

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

中图分类号:

TP391

张良力,马晓凤. 基于改进粒子群算法的新能源汽车充电站选址方法[J]. 吉林大学学报(工学版), 2024, 54(8): 2275-2281.

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.

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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

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