High-precision joint estimation of the state of charge and state of energy for new energy electric vehicle lithium-ion batteries based on improved singular value decomposition-adaptive embedded cubature Kalman filtering.

Zhou, Jiani; Wang, Shunli; Cao, Wen; Xie, Yanxin; Fernandez, Carlos

Abstract

Accurate online estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries are essential for efficient and reliable energy management of new energy electric vehicles (EVs). To improve the accuracy and stability of the joint estimation of SOC and SOE of lithium-ion batteries for EVs, based on a dual-polarization (DP) equivalent circuit model and time-varying forgetting factor recursive least squares (TVFFRLS) algorithm for online parameter identification, a joint estimation method based on singular value decomposition with adaptive embedded cubature Kalman filtering (SVD-AECKF) algorithm is proposed. The algorithm adopts the embedded cubature criterion and singular value decomposition method to improve filtering efficiency, accuracy, and numerical stability. Meanwhile, combining the idea of adaptive covariance matching for real-time adaptive updating of system noise to improve joint estimation accuracy. Finally, the results under different initial errors and complex operating conditions show that the SVD-AECKF algorithm improves the convergence time of SOC estimation by at least 26.3% compared to that before optimization. The SOE estimation error is reduced by at least 12.0% compared to that before optimization. This indicates that the SVD-AECKF algorithm has good joint SOC and SOE estimation accuracy, convergence, and stability.

Citation

ZHOU, J., WANG, S., CAO, W., XIE, Y. and FERNANDEZ, C. 2023. High-precision joint estimation of the state of charge and state of energy for new energy electric vehicle lithium-ion batteries based on improved singular value decomposition-adaptive embedded cubature Kalman filtering. Journal of solid state electrochemistry [online], 27(12), pages 3293-3306. Available from: https://doi.org/10.1007/s10008-023-05594-8

• Journal Article Type: Article
• Acceptance Date: Jul 6, 2023
• Online Publication Date: Jul 27, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Sep 24, 2023
• Publicly Available Date: Jul 28, 2024
• Journal: Journal of solid state electrochemistry
• Print ISSN: 1432-8488
• Electronic ISSN: 1433-0768
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 12
• Pages: 3293-3306
• DOI: https://doi.org/10.1007/s10008-023-05594-8
• Keywords: Adaptive embedded cubature Kalman filtering; Lithium-ion batteries; Singular value decomposition; State of charge; State of energy; Time-varying forgetting factor
• Public URL: https://rgu-repository.worktribe.com/output/2035286