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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

Authors

Jiani Zhou

Shunli Wang

Wen Cao

Yanxin Xie



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

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Copyright Statement
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10008-023-05594-8




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