广西师范大学学报（自然科学版） ›› 2026, Vol. 44 ›› Issue (3): 13-24.doi: 10.16088/j.issn.1001-6600.2025042103

• 物理与电子工程 • 上一篇    下一篇

基于改进粒子群算法的电动出租车快充调度研究

田晟*, 韩江浩, 李乐洋   

  1. 华南理工大学 土木与交通学院,广东 广州 510641
  • 收稿日期:2025-04-21 修回日期:2025-05-20 出版日期:2026-05-05 发布日期:2026-05-13
  • 通讯作者: 田晟(1969—),男,江西九江人,华南理工大学副教授,博士。E-mail: shitian1@scut.edu.cn
  • 基金资助:
    广东省自然科学基金(2020A1515010382,2021A1515011587)

Research on Fast Charging Scheduling of Electric Taxi Based on Improved Particle Swarm Algorithm

TIAN Sheng*, HAN Jianghao, LI Leyang   

  1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou Guangdong 510641, China
  • Received:2025-04-21 Revised:2025-05-20 Online:2026-05-05 Published:2026-05-13

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摘要： 纯电动汽车的普及仍面临充电基础设施布局不均、服务效能低等挑战,且大规模无序充电形成的负荷冲击会导致配电网电压偏移、网损增加。作为纯电动汽车的重要应用类型,电动出租车充电需求频繁且具备调控潜力。本文在快速充电的调度过程中通过时间和空间2个方面考虑个体的调度可行性,并引入快充虚拟负荷实现充电预约机制的动态变化,利用电网负荷曲线情况及快充金钱成本建立多目标优化模型;同时提出基于电量偏差值的补偿机制,考虑充电站利用率平衡及快充时间成本进行空间负荷调度。针对经典粒子群优化(particle swarm optimization, PSO)算法的粒子早熟收敛、容易陷入局部最优解等缺陷,本文结合交叉变异机制提出正态分布权重衰减的遗传粒子群优化(genetic-particle swarm optimization of normal distribution decay inertia weight, NDGAPSO)算法。通过仿真实验从求解质量、收敛性能、运行速度等方面展开评估,证明了NDGAPSO算法的总体性能优于其他改进PSO算法。 最后运用该算法求解快充调度模型,实验证明本文的调度优化研究能够有效兼顾电动出租车充电用户以及电网运营商等多方利益。

关键词: 电动出租车, 快速充电, 充电引导, 粒子群优化, 电网负荷, 多目标优化

Abstract: The popularization of pure electric vehicles still faces the challenges of uneven charging infrastructure layout and low service efficiency, and the load impact formed by large-scale disorderly charging will lead to voltage shift and increased network loss in the distribution network. As an important application type of pure electric vehicles, electric taxi charging demand is frequent and has the potential for regulation. In this paper, we study the scheduling process of fast charging, taking into account the scheduling feasibility of individuals through time and space aspects, introduces the fast charging virtual load to realize the dynamic change of charging reservation mechanism, and establishes a multi-objective optimization model by using the grid load profile situation and the fast charging monetary cost. At the same time, we also investigate the compensation mechanism based on the value of the power deviation, and the spatial load scheduling by taking into consideration of the balance of the utilization rate of the charging station and the fast charging time cost. In view of the defects of the classical Particle Swarm Optimization (PSO) algorithm, such as premature convergence of particles and that it is easy to fall into the local optimal solution, we propose the Genetic-Particle Swarm Optimization of Normal Distribution Decay Inertia Weight (NDGAPSO) by combining with the crossover mutation mechanism. The overall performance of the NDGAPSO algorithm is proved to be better than other improved PSO algorithms through simulation experiments in terms of solution quality, convergence performance, and running speed. Finally, the algorithm is used to solve the fast charging scheduling model, and the experiment proves that the scheduling optimization research in this paper can effectively take into account the interests of electric taxi charging users and power grid operators.

Key words: electric taxi, quick charge, charging guidance, particle swarm optimization, electrical network load, multi-objective optimization

中图分类号:  U491.8;TM73;TP18

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