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

Shu-Dong Wang; Ying-dong Shen; Quan Zhang; Jun-feng Dai; Qin-wen Deng

doi:10.1117/12.3015702

6 February 2024 Lithium battery state of charge estimation based on adaptive unscented Kalman algorithm

Shu-Dong Wang, Ying-dong Shen, Quan Zhang, Jun-feng Dai, Qin-wen Deng

Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 129794J (2024) https://doi.org/10.1117/12.3015702
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shu-Dong Wang, Ying-dong Shen, Quan Zhang, Jun-feng Dai, and Qin-wen Deng "Lithium battery state of charge estimation based on adaptive unscented Kalman algorithm", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 129794J (6 February 2024); https://doi.org/10.1117/12.3015702

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