Optimal Scheduling and Sharing Model for Electric Vehicle Battery Swapping

Electric vehicles (EVs) have become more and more popular over the past decade due to their advantage of reducing carbon emissions and increasing penetration of renewable energy. However, the promotion of EVs is still facing challenges owing to battery material characteristics and inconvenient charging facilities, resulting in range anxiety among drivers. To address these challenges, battery swapping stations (BSSs) have emerged as a promising solution. There has been research on single BSS scheduling, however, multiple BSSs scheduling and battery sharing problems have not been taken into account yet. This paper presents a novel approach to optimize the scheduling and sharing operation of multiple BSSs based on user arrival data, with the aim of minimizing operational costs. The proposed model considers both the number of fully charged batteries to stock and the efficient charging strategies for swapped batteries, while emphasizing the importance of battery sharing mechanisms among BSSs to further reduce costs. The proposed model employs a genetic algorithm to solve the optimization problem efficiently. Simulation results demonstrate the effectiveness of the proposed scheduling and sharing scheme in reducing operational costs and enhancing the efficiency of BSSs.