State of Charge Prediction for Lithium-ion Batteries

Fan, Bo; Pu, Jiexin

doi:10.1080/15325008.2013.857368

Electric Power Components and Systems

2014

DOI: 10.1080/15325008.2013.857368

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State of Charge Prediction for Lithium-ion Batteries

Bo Fan

Jiexin Pu

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“…Because the nonlinear electrochemical characteristics of LIBs are easily affected by different factors, such as operating temperature, charge-discharge current, discharge depth, aging status, etc. 3 To master the current operating state of charge (SOC) value of the LIBs, 4 accurately evaluating the current state of health (SOH) 5 and remaining useful life (RUL) 6 status by quantitatively and qualitatively analyzing and calculating the above characteristics is crucial.…”

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confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

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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.

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confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

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An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries

Li,

Wang,

et al. 2023

Ionics

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A Novel Lithium-ion Battery Balancing Strategy Based on Global Best-First and Integrated Imbalance Calculation

Shang

Wang

et al. 2014

International Journal of Electrochemical Science

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State of Charge Prediction for Lithium-ion Batteries

Cited by 5 publications

References 14 publications

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries

A Novel Lithium-ion Battery Balancing Strategy Based on Global Best-First and Integrated Imbalance Calculation

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