With the increasing popularity of battery swapping mode, modeling and optimizing the layout of battery swapping stations (BSS) has become a critical issue affecting the economic profitability of operators. This paper proposes a BSS planning strategy that explicitly considers electric vehicle (EV) user behavior and queuing effects. The proposed method aims to minimize the total construction investment of BSS and introduces a travel chain model to characterize the battery exchange behavior of electric vehicles. Then, to solve the resulting optimization problem, the immune genetic algorithm (IGA) is enhanced with the Levy flight mechanism, thereby improving its ability to escape local optima. Simulation results demonstrate that, compared with conventional methods, the proposed algorithm achieves significantly faster convergence while ensuring solution quality.
 

Electric vehicles,Battery swapping mode,Immune genetic algorithm,Battery swap station planning