Electric Vehicles (EVs) play a crucial role in carbon emission reduction and energy structure adjustment. The EV battery swapping mode, characterized by short energy replenishment time and strong grid interaction capabilities, has developed rapidly in recent years. Research on EV battery swapping recommendations and optimization of charging/discharging strategies for swapping stations has gradually become a hotspot, but existing studies focus on optimizing single issues without considering the deep coupling relationship between swapping recommendations and station operations under an aggregated system. For the aggregation of battery swapping station (BSS), this paper proposes a multi-time-scale scheduling model. On a large time scale, the model aims to minimize the operational costs of the BSSs by optimizing charging/discharging strategies and battery swapping demand allocation. On a small time scale, Lyapunov optimization is employed to recommend suitable swapping stations and driving routes for EV users in real time. Additionally, considering scenarios where EVs reject system recommendations, the model achieves real-time adjustments through rolling optimization by promptly updating the status of the swapping station cluster, thereby improving the economic efficiency of the aggregated system while meeting battery swapping demands. Based on the real operational data of NIO’s BSSs, the case study analysis shows that under the proposed strategy, the operational cost of the aggregated battery swap station system is reduced by 37.11%.
Xu et al. (2026) studied this question.