Improved Multi-Objective Strategy Diversity Chaotic Particle Swarm Optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior

作     者：Zhao, Shuyi Ma, Chenshuo Cao, Zhiao 

作者机构：Northeastern Univ Sydney Smart Technol Coll Qinhuangdao 066004 Peoples R China Taylors Univ Sch Accounting & Finance Selangor 47500 Malaysia Natl Univ Def Technol Sch Elect Sci Changsha 410073 Peoples R China Guangdong Commun Polytech Sch Intelligent Transportat Engn Guangzhou 510800 Peoples R China 

出 版 物：《ENERGIES》 (Energies)

年 卷 期：2025年第18卷第3期

页      面：690-690页

核心收录：

基　　金：the Support Research Funds of Northeastern University at Qinhuangdao Support Research Funds of Northeastern University at Qinhuangdao 

主　　题：electric vehicles orderly charging and discharging tent chaotic sequence perturbation particle swarm optimization algorithm multi-objective optimization 

摘      要：With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns elite particle, general particle, and learning particle roles to the particles and assigns strategies for maintaining search, developing search, and learning search, respectively. In order to avoid falling into local optima, chaotic sequence perturbations are added during each iteration process avoiding premature falling into local optima. Finally, case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results show that the proposed algorithm is capable of achieving up to 23% microgrid load reduction and up to 20% improvement in convergence speed compared to other algorithms. It is superior to other algorithms in solving the problem of orderly charging and discharging of electric vehicles and has strong usability and feasibility.