An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries.

Liang, Yawen; Wang, Shunli; Fan, Yongcun; Takyi-Aninakwa, Paul; Xie, Yanxin; Fernandez, Carlos

Abstract

Accurate state-of-charge (SOC) estimation is essential for fully utilizing the battery performance of electric vehicles. Considering the demand for algorithms with the advantages of simplicity, fewer calculations, good stability, and high accuracy in practical applications, this paper proposes a novel error covariance correction-adaptive extended Kalman filter (ECC-AEKF) for accurate and robust SOC estimation. In this paper, the maximum likelihood function of the probability density function of the error series (conditional on the priori covariance) is calculated by mathematical derivation to obtain a new priori error covariance, which is used to obtain a more appropriate Kalman gain. The ECC-AEKF can minimize the estimation error and reduces the effect of process noise characteristics and inappropriate error covariances on priori estimates. Meanwhile, a piecewise forgetting factor recursive least square (PFFRLS) is presented for model parameter identification. The PFFRLS using error feedback for real-time adaptively adjusts the forgetting degree of data based on the principle of integral separation. Furthermore, a comparative analysis of SOC estimation in PFFRLS-ECC-AEKF and commonly used methods is presented for validation of the performance of the proposed method under different temperatures and operating conditions. The results prove that the PFFRLS-ECC-AEKF achieves higher accuracy with less computation time than other methods.

Citation

LIANG, Y., WANG, S., FAN, Y., TAKYI-ANINAKWA, P., XIE, Y. and FERNANDEZ, C. 2023. An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries. Journal of energy storage [online], 68, article 107629. Available from: https://doi.org/10.1016/j.est.2023.107629

• Journal Article Type: Article
• Acceptance Date: May 3, 2023
• Online Publication Date: May 19, 2023
• Publication Date: Sep 15, 2023
• Deposit Date: May 15, 2023
• Publicly Available Date: May 20, 2024
• Journal: Journal of energy storage
• Electronic ISSN: 2352-152X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 68
• Article Number: 107629
• DOI: https://doi.org/10.1016/j.est.2023.107629
• Keywords: State-of-charge; Adaptive extended Kalman filter; Lithium-ion batteries; Piecewise forgetting factor; Priori error covariance correction
• Public URL: https://rgu-repository.worktribe.com/output/1965670