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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2406.15985 |
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| _version_ | 1866913505061371904 |
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| author | Espin, Jorge Zhang, Dong Toti, Daniele Pozzi, Andrea |
| author_facet | Espin, Jorge Zhang, Dong Toti, Daniele Pozzi, Andrea |
| contents | In the realm of battery charging, several complex aspects demand meticulous attention, including thermal management, capacity degradation, and the need for rapid charging while maintaining safety and battery lifespan. By employing the imitation learning paradigm, this manuscript introduces an innovative solution to confront the inherent challenges often associated with conventional predictive control strategies for constrained battery charging. A significant contribution of this study lies in the adaptation of the Dataset Aggregation (DAGGER) algorithm to address scenarios where battery parameters are uncertain, and internal states are unobservable. Results drawn from a practical battery simulator that incorporates an electrochemical model highlight substantial improvements in battery charging performance, particularly in meeting all safety constraints and outperforming traditional strategies in computational processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15985 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging Espin, Jorge Zhang, Dong Toti, Daniele Pozzi, Andrea Systems and Control Artificial Intelligence In the realm of battery charging, several complex aspects demand meticulous attention, including thermal management, capacity degradation, and the need for rapid charging while maintaining safety and battery lifespan. By employing the imitation learning paradigm, this manuscript introduces an innovative solution to confront the inherent challenges often associated with conventional predictive control strategies for constrained battery charging. A significant contribution of this study lies in the adaptation of the Dataset Aggregation (DAGGER) algorithm to address scenarios where battery parameters are uncertain, and internal states are unobservable. Results drawn from a practical battery simulator that incorporates an electrochemical model highlight substantial improvements in battery charging performance, particularly in meeting all safety constraints and outperforming traditional strategies in computational processing. |
| title | Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2406.15985 |