Optimization method for location and capacity determination of electric vehicle mobile charging station based on multi-objective hybrid frog jump algorithm

• Published in: Renewables : wind, water, and solar Vol. 12; no. 1; pp. 49 - 17
• Main Authors: Ding, Xiaoyin, Jiang, Zhibo, Fu, Yiheng, Zhang, Huajin
• Format: Journal Article
• Language: English
• Published: London BioMed Central 01.09.2025; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Algorithms; Amphibians; Biomimetics; Collaboration; Construction costs; Coverage rate; Design optimization; Economics and Management; Efficiency; Electric vehicle charging; Electric vehicle charging stations; Electric vehicles; Energy; Energy Policy; Energy Systems; Genetic algorithms; Infrastructure; Integer programming; Linear programming; Mobile charging station; Multi-target mixed frog jump; Multiple objective analysis; Operating costs; Optimal control; Optimization algorithms; Renewable and Green Energy; Roads & highways; Scheduling; Site selection; Site selection and capacity determination; Traffic congestion; Traffic flow; User behavior; User experience; Water Industry/Water Technologies; Site selection and capacity determination; Electric vehicles; Mobile charging station; Coverage rate; Multi-target mixed frog jump
• ISSN: 2731-9237; 2731-9237; 2198-994X
• DOI: 10.1186/s40807-025-00154-2

To effectively reduce the cost between electric vehicle mobile charging station operators and users, and significantly improve the utilization efficiency of charging stations, this article focuses on the design optimization of charging station location and capacity rating. A novel method for determining the location and capacity of electric vehicle mobile charging stations based on multi-objective hybrid frog leaping algorithm has been proposed. Initially, a road network node allocation model is constructed to achieve dynamic optimization of traffic node coverage at charging stations. Subsequently, a multi-objective hybrid frog leaping optimization method is employed to comprehensively consider location, facility costs, and vehicle trajectories to determine the optimal coverage set of charging stations. Simulation experiments have demonstrated that this method significantly reduces the idle rate and congestion level of charging stations by approximately 30% and 25%, respectively. Concurrently, it improves the utilization efficiency and coverage rate of charging stations by about 40% and 35%, respectively. While ensuring the demand for infrastructure land, it reduces operating costs and improves efficiency. Furthermore, by combining the maximum coverage set scheduling method, optimal control of mobile charging station positioning can be achieved. The coverage model-based optimal scheduling and multi-objective hybrid jumping biomimetic optimization strategy can collectively improve system performance and user experience, providing robust support for the construction of electric vehicle charging infrastructure.