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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2406.08626 |
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| _version_ | 1866917692296921088 |
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| author | Espin, Jorge Kajiura, Yuichi Zhang, Dong |
| author_facet | Espin, Jorge Kajiura, Yuichi Zhang, Dong |
| contents | Lithium-ion (Li-ion) batteries are ubiquitous in modern energy storage systems, highlighting the critical need to comprehend and optimize their performance. Yet, battery models often exhibit poor parameter identifiability which hinders the development of effective battery management strategies and impacts their overall performance, longevity, and safety. This manuscript explores the integration of Fisher Information (FI) theory with Model Predictive Control (MPC) for battery charging. The study addresses the inherent hurdles in accurately estimating battery model parameters due to nonlinear dynamics and uncertainty. Our proposed method aims to ensure safe battery charging and enhance real-time parameter estimation capabilities by leveraging adaptive control strategies guided by FI metrics. Simulation results underscore the effectiveness of our approach in mitigating parameter identifiability issues, offering promising solutions for improving the control of batteries during safe charging process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safety-Driven Battery Charging: A Fisher Information-guided Adaptive MPC with Real-time Parameter Identification Espin, Jorge Kajiura, Yuichi Zhang, Dong Systems and Control Lithium-ion (Li-ion) batteries are ubiquitous in modern energy storage systems, highlighting the critical need to comprehend and optimize their performance. Yet, battery models often exhibit poor parameter identifiability which hinders the development of effective battery management strategies and impacts their overall performance, longevity, and safety. This manuscript explores the integration of Fisher Information (FI) theory with Model Predictive Control (MPC) for battery charging. The study addresses the inherent hurdles in accurately estimating battery model parameters due to nonlinear dynamics and uncertainty. Our proposed method aims to ensure safe battery charging and enhance real-time parameter estimation capabilities by leveraging adaptive control strategies guided by FI metrics. Simulation results underscore the effectiveness of our approach in mitigating parameter identifiability issues, offering promising solutions for improving the control of batteries during safe charging process. |
| title | Safety-Driven Battery Charging: A Fisher Information-guided Adaptive MPC with Real-time Parameter Identification |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2406.08626 |