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Data-optimization based SOC-SOH estimation for lithium-ion batteries with current bias compensation

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  • Ye, Min
  • Lian, Gaoqi
  • Li, Wei
  • Xia, Baozhou
  • Zhang, Binrui
  • Li, Yan
  • Wang, Qiao
  • Wei, Meng

Abstract

To achieve high-precision state estimation for lithium-ion batteries under current bias interference, this paper proposes a data-optimized state of charge-state of health (SOC-SOH) estimation method with current bias compensation. First, we detailedly analyze the impact of current bias on the state estimation problem, and demonstrate why Kalman filter fails to operate effectively. Next, an improved parameter identification framework is designed, iteratively refining battery model parameters using high-quality data segments, which are selected via Fisher Information Matrix-based sensitivity analysis, ensuring they provide proper information for parameter estimation. Then, a current bias compensation term is incorporated into the system state equations, and SOH estimation is performed using short-term constant current discharge data. Furthermore, the acquired SOH result are subsequently used for high-precision SOC estimation under complex conditions. Meanwhile, the effectiveness and robustness of the proposed method are validated with relevant experimental data. With different current bias interferences, the final SOH estimation errors for all data segments remain within 1 %, and the Root Mean Square Error and Mean Absolute Error of SOC estimation results under all conditions also remain below 1.5 %. Finally, based on an advanced scalable battery management system, the potential application scheme of the proposed method in real vehicles is discussed.

Suggested Citation

  • Ye, Min & Lian, Gaoqi & Li, Wei & Xia, Baozhou & Zhang, Binrui & Li, Yan & Wang, Qiao & Wei, Meng, 2025. "Data-optimization based SOC-SOH estimation for lithium-ion batteries with current bias compensation," Energy, Elsevier, vol. 321(C).
  • Handle: RePEc:eee:energy:v:321:y:2025:i:c:s0360544225011326
    DOI: 10.1016/j.energy.2025.135490
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    1. Xu, Wenqiang & Wu, Xiaogang & Li, Yalun & Wang, Hewu & Lu, Languang & Ouyang, Minggao, 2023. "A comprehensive review of DC arc faults and their mechanisms, detection, early warning strategies, and protection in battery systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    2. Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
    3. Chen, Siqi & Wei, Xuezhe & Zhu, Zhehui & Wu, Hang & Ou, Yuxin & Zhang, Guangxu & Wang, Xueyuan & Zhu, Jiangong & Feng, Xuning & Dai, Haifeng & Ouyang, Minggao, 2024. "Thermal runaway front propagation characteristics, modeling and judging criteria for multi-jelly roll prismatic lithium-ion battery applications," Renewable Energy, Elsevier, vol. 231(C).
    4. Hou, Jiayang & Xu, Jun & Lin, Chuanping & Jiang, Delong & Mei, Xuesong, 2024. "State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method," Energy, Elsevier, vol. 290(C).
    5. Xiong, Rui & Huang, Jintao & Duan, Yanzhou & Shen, Weixiang, 2022. "Enhanced Lithium-ion battery model considering critical surface charge behavior," Applied Energy, Elsevier, vol. 314(C).
    6. Xiong, Rui & Sun, Xinjie & Meng, Xiangfeng & Shen, Weixiang & Sun, Fengchun, 2024. "Advancing fault diagnosis in next-generation smart battery with multidimensional sensors," Applied Energy, Elsevier, vol. 364(C).
    7. Wu, Chunling & Hu, Wenbo & Meng, Jinhao & Xu, Xianfeng & Huang, Xinrong & Cai, Lei, 2023. "State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment," Energy, Elsevier, vol. 274(C).
    8. Hu, Xiaosong & Deng, Zhongwei & Lin, Xianke & Xie, Yi & Teodorescu, Remus, 2021. "Research directions for next-generation battery management solutions in automotive applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    9. Song, Ziyou & Hofmann, Heath & Lin, Xinfan & Han, Xuebing & Hou, Jun, 2018. "Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study," Applied Energy, Elsevier, vol. 231(C), pages 1307-1318.
    10. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    11. Lian, Gaoqi & Ye, Min & Wang, Qiao & Li, Yan & Xia, Baozhou & Zhang, Jiale & Xu, Xinxin, 2024. "Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments," Energy, Elsevier, vol. 293(C).
    12. Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
    13. Tian, Jiaqiang & Liu, Xinghua & Li, Siqi & Wei, Zhongbao & Zhang, Xu & Xiao, Gaoxi & Wang, Peng, 2023. "Lithium-ion battery health estimation with real-world data for electric vehicles," Energy, Elsevier, vol. 270(C).
    14. Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).
    15. Cai, Hongchang & Tang, Xiaopeng & Lai, Xin & Wang, Yanan & Han, Xuebing & Ouyang, Minggao & Zheng, Yuejiu, 2024. "How battery capacities are correctly estimated considering latent short-circuit faults," Applied Energy, Elsevier, vol. 375(C).
    16. Song, Ziyou & Hou, Jun & Li, Xuefeng & Wu, Xiaogang & Hu, Xiaosong & Hofmann, Heath & Sun, Jing, 2020. "The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection," Energy, Elsevier, vol. 193(C).
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