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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


Shunli Wang

Xianyi Jia

Paul Takyi-Aninakwa

Daniel-Ioan Stroe


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.


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:

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
Keywords Battery management systems; Kalman filters; Monte Carlo methods; Lithium-ion battery; State of charge; SOC estimation methods; Modeling methods; Improved PF algorithms
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