Battery swapping stations (BSS) offer a fast and scalable alternative to conventional electric vehicle (EV) charging, gaining growing policy support worldwide. However, existing BSS control strategies typically rely on heuristics or low-fidelity degradation models, limiting profitability and service level. This paper proposes BSS-MPC: a real-time, degradation-aware Model Predictive Control (MPC) framework for BSS operations to trade off economic incentives from energy market arbitrage and long-term battery degradation effects. BSS-MPC integrates a high-fidelity, physics informed battery aging model that accurately predicts the degradation level and the remaining capacity of battery packs. The resulting multiscale optimization-jointly considering energy arbitrage, swapping logistics, and battery health-is formulated as a mixed-integer optimal control problem and solved with tailored algorithms. Simulation results show that BSS-MPC outperforms rule-based and low-fidelity baselines, achieving lower energy cost, reduced capacity fade, and strict satisfaction of EV swapping demands.
@article{arxiv.2510.07902,
title = {Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage},
author = {Ruochen Li and Zhichao Chen and Zhaoting Zhang and Renjie Guo and Zhankun Sun and Jiwei Yao and Jiaze Ma},
journal= {arXiv preprint arXiv:2510.07902},
year = {2025}
}