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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.07902 |
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| _version_ | 1866914165231190016 |
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| author | Li, Ruochen Chen, Zhichao Zhang, Zhaoting Guo, Renjie Sun, Zhankun Yao, Jiwei Ma, Jiaze |
| author_facet | Li, Ruochen Chen, Zhichao Zhang, Zhaoting Guo, Renjie Sun, Zhankun Yao, Jiwei Ma, Jiaze |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07902 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage Li, Ruochen Chen, Zhichao Zhang, Zhaoting Guo, Renjie Sun, Zhankun Yao, Jiwei Ma, Jiaze Optimization and Control 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. |
| title | Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.07902 |