A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter.

Jiang, Cong; Wang, Shunli; Wu, Bin; Fernandez, Carlos; Xiong, Xin; Coffie-Ken, James

Abstract

The control strategy of electric vehicles mainly depends on the power battery state-of-charge estimation. One of the most important issues is the power lithium-ion battery state-of-charge (SOC) estimation. Compare with the extended Kalman filter algorithm, this paper proposed a novel adaptive square root extended Kalman filter together with the Thevenin equivalent circuit model which can solve the problem of filtering divergence caused by computer rounding errors. It uses Sage-Husa adaptive filter to update the noise variable, and performs square root decomposition on the covariance matrix to ensure its non-negative definiteness. Moreover, a multi-scale dual Kalman filter algorithm is used for joint estimation of SOC and capacity; the forgetting factor recursive least-square method is used for parameter identification. To verify the feasibility of the algorithm under complicated operating conditions, different types of dynamic working conditions are performed on the ternary lithium-ion battery. The proposed algorithm has robust and accurate SOC estimation results and can eliminate computer rounding errors to improve adaptability compared to the conventional extended Kalman filter algorithm.

Citation

JIANG, C., WANG, S., WU, B., FERNANDEZ, C., XIONG, X. and COFFIE-KEN, J. 2021. A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter. Energy [online], 219, article ID 119603. Available from: https://doi.org/10.1016/j.energy.2020.119603

• Journal Article Type: Article
• Acceptance Date: Dec 12, 2020
• Online Publication Date: Dec 15, 2020
• Publication Date: Mar 15, 2021
• Deposit Date: Dec 18, 2020
• Publicly Available Date: Dec 16, 2021
• Journal: Energy
• Print ISSN: 0360-5442
• Electronic ISSN: 1873-6785
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 219
• Article Number: 119603
• DOI: https://doi.org/10.1016/j.energy.2020.119603
• Keywords: State-of-charge; Extended Kalman filter; Adaptive; Square root; Power lithium-ion battery
• Public URL: https://rgu-repository.worktribe.com/output/1017292