SOC Estimation for Lithium-Ion Batteries Based on Weighted Multi-Innovation Sage–Husa Adaptive EKF

Weihua Song, Ranran Liu (), Xiaona Jin and Wei Guo
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Weihua Song: School of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213000, China
Ranran Liu: School of Automotive and Traffic Engineering, Jiangsu University of Technology, Changzhou 213000, China
Xiaona Jin: School of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213000, China
Wei Guo: School of Automotive and Traffic Engineering, Jiangsu University of Technology, Changzhou 213000, China

Energies, 2025, vol. 18, issue 16, 1-28

Abstract: In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this paper focuses on the second-order RC equivalent circuit model, firstly designs a simple and reliable improved adaptive forgetting factor (IAFF) regulation mechanism, and proposes the improved adaptive forgetting factor recursive least squares (IAFFRLS) algorithm, which not only improves the accuracy of parameter identification, but also exhibits excellent performance in anti-interference. Secondly, based on the identified model, a weighted multi-innovation improved Sage–Husa adaptive extended Kalman filter (WMISAEKF) algorithm is proposed to solve the problem of filter divergence caused by noise covariance updating. It fully utilizes historical innovations to reasonably allocate innovation weights to achieve accurate SOC estimation. Compared with the VFFRLS algorithm and AFFRLS algorithm, the IAFFRLS algorithm reduces the root mean square error (RMSE) by 29.30% and 19.29%, respectively, and the RMSE under noise interference is decreased by 82.37% and 78.59%, respectively. Based on the identified model for SOC estimation, the WMISAEKF algorithm reduces the RMSE by 77.78%, compared to the EKF algorithm. Furthermore, the WMISAEKF algorithm could still converge under different levels of noise interference and incorrect initial SOC values, which proves that the proposed algorithm has good stability and robustness. Simulation results verify that the parameter identification algorithm proposed in this paper demonstrates higher identification accuracy and anti-interference performance. The proposed SOC estimation algorithm has higher estimation accuracy and good robustness, which provides a new practical support for extending battery life.

Keywords: lithium-ion batteries; battery management system; state of charge; improved adaptive forgetting factor; weighted multi-innovation; adaptive extended Kalman filter; parameter identification; anti-interference (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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