Deployment Optimization of Battery Swapping Stations Accounting for Taxis’ Dynamic Energy Demand

Current research on battery swapping stations (BSSs) deployment problems overlooks the influence of BSS layouts on the daily performance of battery swapping taxis (BSTs). This study aims to propose a BSS deployment optimization model to guarantee taxi transport capacity. To this end, the dynamic swapping demands of BSTs are predicted by modeling battery swapping behavior, reconstructing trip chain, and simulating BSS operation. Furthermore, BSSs’ deployment is optimized to minimize the swapping loss time. Using the daily trajectory data of 9862 taxis in Tianjin, we set up six scenarios to elucidate the trade-off among the layout and service level, the transport capacity, and the environmental efficiency. The optimal layout ensures 98.1% of the taxi transport capacity and reduces carbon emissions by 44.09%. The employed behavioral model enhances the network equilibrium from 0.33 to 0.067. Finally, the roles of driving range and charging speed in attracting and assigning energy demand are revealed.