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Research on the state of health estimation of lithium-ion batteries for energy storage based on XGB-AKF method

Authors :
Song Xu
Fang-Lin Zha
Bo-Wen Huang
Bing Yu
Hai-Bo Huang
Ting Zhou
Wen-Qi Mao
Jie-Jun Wu
Jia-Qiang Wei
Shang-Kun Gong
Tao Wan
Xin-Yu Duan
Shang-Feng Xiong
Source :
Frontiers in Energy Research, Vol 10 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

With the advantages of high energy density, long cycle life and high stability, lithium-ion batteries have been used in a large number of fields such as electric vehicles and grid scale energy storage. To ensure the safe and reliable operation of battery systems, it is important to make an accurate and rapid estimation of the state of health (SOH) of Li-ion cells. A Li-ion cell is a complex nonlinear dynamic system. The SOH of a Li-ion can not be measured directly in actual working conditions; it can only be estimated indirectly by external characteristic parameters that reflects the extent of cell aging. It is difficult to ensure the reliability of method based on a single aging feature or model. Therefore, this paper proposes a multi-feature SOH estimation method that combines data-driven XGBoost and a Kalman filter. Firstly, a principal component analysis algorithm to reconstruct multiple battery aging features based on data is used, and an XGBoost online estimation model incorporating multiple features based on the reconstructed feature data is constructed. Finally, the joint optimal estimation of SOH of Li-ion cells by introducing a time-domain Kalman filter based on the real-time correction of the XGBoost model is achieved in this method. The results show that the method improves the accuracy and robustness of the estimation model and achieves a high-precision joint estimation of SOH for Li-ion cells.

Details

Language :
English
ISSN :
2296598X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Energy Research
Publication Type :
Academic Journal
Accession number :
edsdoj.0154504da6c94193a0b62f163fab8d6b
Document Type :
article
Full Text :
https://doi.org/10.3389/fenrg.2022.999676