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Electrical Engineering and Systems Science > Systems and Control

arXiv:2406.08626 (eess)
[Submitted on 12 Jun 2024]

Title:Safety-Driven Battery Charging: A Fisher Information-guided Adaptive MPC with Real-time Parameter Identification

Authors:Jorge Espin, Yuichi Kajiura, Dong Zhang
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Abstract: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.
Comments: 6 pages, 2 figures, submitted to Modeling, Estimation, and Control Conference (MECC 2024)
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2406.08626 [eess.SY]
  (or arXiv:2406.08626v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2406.08626

Submission history

From: Jorge Esteban Espin [view email]
[v1] Wed, 12 Jun 2024 20:15:29 UTC (1,244 KB)
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