Beta-Gaussian iterated posterior linearization filter for lithium-ion battery state-of-charge estimation

Yang, Rui, Garcia-Fernandez, angel F ORCID: 0000-0002-6471-8455 and Lee, Che-Rung (2025) Beta-Gaussian iterated posterior linearization filter for lithium-ion battery state-of-charge estimation JOURNAL OF ENERGY STORAGE, 117. 116097-. ISSN 2352-152X, 2352-1538

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Abstract

In this paper, we introduce the beta-Gaussian Iterated Posterior Linearization Filter (BG-IPLF). This filter is designed to deal with state-space models in which one variable of the state space is constrained to be between zero and one, such as the state of charge (SOC) in lithium-ion battery models. The BG-IPLF propagates a joint beta-Gaussian distribution through the filtering recursion. In each update, the filter performs iterated statistical linear regressions to look for the optimal linearization of the measurement function, accounting for linearization errors. A projection step is performed after each iteration to map the updated moments to the constraints of beta-Gaussian distributions. We conduct simulations and experimental battery test benches to assess the BG-IPLF's performance. This filter outperforms the Extended Kalman Filter, the Unscented Kalman Filter and the standard Iterated Posterior Linearization Filter, demonstrating high accuracy in SOC estimation.

Item Type:	Article
Uncontrolled Keywords:	State of charge (SOC), Kalman filter, Beta-Gaussian distribution, Iterated posterior linearization filter
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Engineering Faculty of Science & Engineering > School of Engineering > Electrical Engineering and Electronics
Depositing User:	Symplectic Admin
Date Deposited:	21 Jan 2026 15:22
Last Modified:	16 Jun 2026 10:46
DOI:	10.1016/j.est.2025.116097
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3196698
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