Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929388911591424 |
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| author | Chowdhury, Myisha A. Al-Wahaibi, Saif S. S. Lu, Qiugang |
| author_facet | Chowdhury, Myisha A. Al-Wahaibi, Saif S. S. Lu, Qiugang |
| contents | Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL-based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this work proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12309 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols Chowdhury, Myisha A. Al-Wahaibi, Saif S. S. Lu, Qiugang Systems and Control Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL-based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this work proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions. |
| title | Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2406.12309 |