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Open AccessArticle

Adaptive Dual Extended Kalman Filter Based on Variational Bayesian Approximation for Joint Estimation of Lithium-Ion Battery State of Charge and Model Parameters

by Jing Hou 1,†, Yan Yang 1,*, He He 2 and Tian Gao 1
1
School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China
2
System Engineering Research Institute of CSSC, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Current address: No. 127, Youyi West Road, Xi’an 710072, China.
Appl. Sci. 2019, 9(9), 1726; https://doi.org/10.3390/app9091726
Received: 4 April 2019 / Revised: 22 April 2019 / Accepted: 23 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)
An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. However, in practical applications, the battery characteristics change with different operating conditions and the measurement noise statistics may vary with time, resulting in nonoptimal and even unreliable estimation of SOC by EKF. To improve the SOC estimation accuracy under uncertain measurement noise statistics, a variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) is proposed in this paper. The variational Bayesian inference is integrated with the dual EKF (DEKF) to jointly estimate the lithium-ion battery parameters and SOC. Meanwhile, the measurement noise variances are simultaneously estimated in the SOC estimation process to compensate for the model uncertainties, so that the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. A constant current discharge test, a pulse current discharge test, and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the DEKF algorithm. The experimental results show that the proposed VB-ADEKF algorithm outperforms the traditional DEKF algorithm in terms of SOC estimation accuracy, convergence rate, and robustness. View Full-Text
Keywords: state of charge (SOC); joint estimation; lithium-ion battery; variational Bayesian approximation; dual extended Kalman filter (DEKF); measurement statistic uncertainty state of charge (SOC); joint estimation; lithium-ion battery; variational Bayesian approximation; dual extended Kalman filter (DEKF); measurement statistic uncertainty
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Hou, J.; Yang, Y.; He, H.; Gao, T. Adaptive Dual Extended Kalman Filter Based on Variational Bayesian Approximation for Joint Estimation of Lithium-Ion Battery State of Charge and Model Parameters. Appl. Sci. 2019, 9, 1726.

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