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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Weihua Song & Ranran Liu & Xiaona Jin & Wei Guo, 2025. "SOC Estimation for Lithium-Ion Batteries Based on Weighted Multi-Innovation Sage–Husa Adaptive EKF," Energies, MDPI, vol. 18(16), pages 1-28, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4364-:d:1725824
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    References listed on IDEAS

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    1. Yigang Li & Hongzhong Qi & Xinglei Shi & Qifei Jian & Fengchong Lan & Jiqing Chen, 2024. "A Physics-Based Equivalent Circuit Model and State of Charge Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 17(15), pages 1-31, July.
    2. Da Li & Lu Liu & Chuanxu Yue & Xiaojin Gao & Yunhai Zhu, 2025. "Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range," Energies, MDPI, vol. 18(7), pages 1-19, April.
    3. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    4. Tang, Aihua & Huang, Yukun & Liu, Shangmei & Yu, Quanqing & Shen, Weixiang & Xiong, Rui, 2023. "A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models," Applied Energy, Elsevier, vol. 348(C).
    5. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    6. Lim, KaiChin & Bastawrous, Hany Ayad & Duong, Van-Huan & See, Khay Wai & Zhang, Peng & Dou, Shi Xue, 2016. "Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles," Applied Energy, Elsevier, vol. 169(C), pages 40-48.
    7. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    8. Li Zhang & Min Zheng & Dajun Du & Yihuan Li & Minrui Fei & Yuanjun Guo & Kang Li, 2020. "State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks," Complexity, Hindawi, vol. 2020, pages 1-10, December.
    9. Xiangdong Sun & Jingrun Ji & Biying Ren & Chenxue Xie & Dan Yan, 2019. "Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery," Energies, MDPI, vol. 12(12), pages 1-15, June.
    10. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
    11. Zizhou Lao & Bizhong Xia & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares," Energies, MDPI, vol. 11(6), pages 1-15, May.
    12. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
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