Review: optimized particle filtering strategies for high-accuracy state of charge estimation of LIBs.

Wang, Shunli; Jia, Xianyi; Takyi-Aninakwa, Paul; Stroe, Daniel-Ioan; Fernandez, Carlos

Abstract

Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequently, a systematic and comprehensive analysis of the methods for constructing LIB models is conducted from various aspects such as applicability and accuracy. Finally, the advantages of particle filtering (PF) over the Kalman filter (KF) series algorithm for estimating SOC are summarized, and various improved PF algorithms for estimating the SOC of LIBs are compared and discussed. Additionally, this review provides corresponding suggestions for researchers in the battery field.

Citation

WANG, S., JIA, X., TAKYI-ANINAKWA, P., STROE, D.-I. and FERNANDEZ, C. 2023. Review: optimized particle filtering strategies for high-accuracy state of charge estimation of LIBs. Journal of The Electrochemical Society [online], 170(5), article 050514. Available from: https://doi.org/10.1149/1945-7111/acd148

• Journal Article Type: Review
• Acceptance Date: Apr 14, 2023
• Online Publication Date: May 11, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Jun 5, 2023
• Publicly Available Date: May 12, 2024
• Journal: Journal of the Electrochemical Society
• Print ISSN: 0013-4651
• Electronic ISSN: 1945-7111
• Publisher: Electrochemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 170
• Issue: 5
• Article Number: 050514
• DOI: https://doi.org/10.1149/1945-7111/acd148
• Keywords: Battery management systems; Kalman filters; Monte Carlo methods; Lithium-ion battery; State of charge; SOC estimation methods; Modeling methods; Improved PF algorithms
• Public URL: https://rgu-repository.worktribe.com/output/1977607