Lithium battery state of charge estimation based on adaptive unscented Kalman algorithm

Lithium-ion batteries are widely used, especially in the field of electric vehicles, so the prediction of the battery state of charge is particularly important. Due to the changeable driving state of electric vehicles, the actual working state of lithium-ion batteries is complex, accompanied by vari...

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Main Authors Wang, Shu-Dong, Shen, Ying-dong, Zhang, Quan, Dai, Jun-feng, Deng, Qin-wen
Format Conference Proceeding
LanguageEnglish
Published SPIE 06.02.2024
Online AccessGet full text
ISBN9781510672765
1510672761
ISSN0277-786X
DOI10.1117/12.3015702

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Summary:Lithium-ion batteries are widely used, especially in the field of electric vehicles, so the prediction of the battery state of charge is particularly important. Due to the changeable driving state of electric vehicles, the actual working state of lithium-ion batteries is complex, accompanied by various external and internal factors, making it difficult to accurately estimate the state of charge of lithium-ion batteries. This paper proposes an adaptive unscented Kalman filter algorithm for state-of-charge estimation of stackable lithium batteries, which can effectively solve the problem of inaccurate battery model parameters leading to a decrease in estimation accuracy.
Bibliography:Conference Location: Guilin, China
Conference Date: 2023-08-25|2023-08-27
ISBN:9781510672765
1510672761
ISSN:0277-786X
DOI:10.1117/12.3015702