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arXiv · 2510.07902

Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage

Ruochen Li·Zhichao Chen·Zhaoting Zhang·Renjie Guo·Zhankun Sun·Jiwei Yao·Jiaze Ma
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Abstract

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.

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Ruochen Li, Zhichao Chen, Zhaoting Zhang, Renjie Guo, Zhankun Sun, Jiwei Yao, Jiaze Ma. 2025-10-09. Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage. https://arxiv.org/abs/2510.07902

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