IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v342y2026ics0360544225051667.html

Robust state-of-charge estimation for lithium-ion batteries: A high-order lumped disturbance compensation approach with a simple tuning strategy

Author

Listed:
  • Zhang, Shuo
  • Lin, Xijian
  • Wang, Xinghao
  • Xi, Haoda
  • Xiao, Dianxun

Abstract

Accurate state-of-charge (SOC) estimation is essential for ensuring the safe and reliable operation of lithium-ion batteries (LIBs). Effective disturbance compensation is a key factor in achieving robustness under real-world operating conditions. However, conventional SOC estimators often fail to account for the high-order dynamic nature of disturbances, leading to degradation in estimation accuracy. To overcome this limitation, this paper proposes a high-order lumped disturbance observer (HOLDO), which compensates for dynamic disturbances by modeling their successive time derivatives as extended states. System stability is rigorously established using Lyapunov theory, guaranteeing finite-error convergence. To facilitate implementation and enhance practicality, the proposed method employs a simple and efficient tuning strategy that achieves reliable performance with minimal adjustment. The robustness of the HOLDO is thoroughly validated under a variety of disturbance scenarios, and experimental results demonstrate that the proposed approach delivers effective disturbance compensation, achieving high SOC estimation accuracy and consistently yielding lower estimation errors compared with conventional observers.

Suggested Citation

  • Zhang, Shuo & Lin, Xijian & Wang, Xinghao & Xi, Haoda & Xiao, Dianxun, 2026. "Robust state-of-charge estimation for lithium-ion batteries: A high-order lumped disturbance compensation approach with a simple tuning strategy," Energy, Elsevier, vol. 342(C).
  • Handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225051667
    DOI: 10.1016/j.energy.2025.139524
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225051667
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.139524?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Qian, Wei & Li, Wan & Guo, Xiangwei & Wang, Haoyu, 2024. "A switching gain adaptive sliding mode observer for SoC estimation of lithium-ion battery," Energy, Elsevier, vol. 292(C).
    2. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).
    3. 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).
    4. 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.
    5. Pang, Hui & Yan, Xiangping & Jiang, Nan & Fan, Guodong & Du, Jiarong & Lin, Guangyang, 2025. "Towards co-estimation of lithium-ion battery state of charge and state of temperature using a thermal-coupled extended single-particle model," Energy, Elsevier, vol. 326(C).
    6. Ouyang, Tiancheng & Tuo, Xiaoyu & Gong, Yubin & Lu, Xinshu & Deng, Qiaoyang & Zhang, Zhiqiang, 2025. "Lithium-ion battery SOC estimation under data loss and update stagnation," Energy, Elsevier, vol. 330(C).
    7. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    8. Liu, Donglei & Wang, Shunli & Li, Xiaoxia & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2025. "A novel extended Kalman filter-guided long short-term memory algorithm for power lithium-ion battery state of charge estimation at multiple temperatures," Energy, Elsevier, vol. 335(C).
    9. He, Lin & Hu, Xingwen & Yin, Guangwei & Wang, Guoqiang & Shao, Xingguo & Liu, Jichao, 2024. "A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery," Energy, Elsevier, vol. 288(C).
    10. Fan, Xichen & Li, Bangxing & Xie, Zhenjun & Hao, Yuxin & Tang, Qian & Hu, Xiaolin, 2025. "An improved Transformer incorporating fuzzy information entropy and average input strategy for SOC estimation of lithium-ion battery," Energy, Elsevier, vol. 330(C).
    11. 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).
    12. Li, Penghua & Ye, Jiangtao & Hou, Jie & Deng, Zhongwei & Xiang, Sheng, 2025. "State of charge estimation for lithium-ion battery using a multi-feature Mamba network and UKF under mixed operating conditions," Energy, Elsevier, vol. 335(C).
    13. Zhang, Zhongbo & Yu, Wei & Yan, Zhiying & Zhu, Wenbo & Li, Haibing & Liu, Qin & Guan, Quanlong & Tan, Ning, 2025. "State of charge estimation of lithium-ion batteries using a fractional-order multi-dimensional Taylor network with adaptive Kalman filter," Energy, Elsevier, vol. 316(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Luxiao & Duan, Jiandong & Fan, Shaogui & Zhao, Ke, 2024. "An estimated value compensation method for state of charge estimation of lithium battery based on open circuit voltage change rate," Energy, Elsevier, vol. 313(C).
    2. Qi, Wei & Qin, Wenhu & Yun, Zhonghua, 2024. "Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter," Energy, Elsevier, vol. 307(C).
    3. Wu, Xiaoying & Yan, Chong & Wang, Linbing & Dou, Wenwen & Li, Yi & Gao, Guohong & Wang, Jianping & Fan, Yuqian & Tan, Xiaojun, 2025. "Data-driven SOC estimation method for power batteries under driving cycle conditions and a wide temperature range," Energy, Elsevier, vol. 340(C).
    4. Khosravi, Nima & Oubelaid, Adel, 2025. "Deep learning-driven estimation and multi-objective optimization of lithium-ion battery parameters for enhanced EV/HEV performance," Energy, Elsevier, vol. 320(C).
    5. Jin, Zhaorui & Fu, Shiyi & Fan, Hongtao & Tao, Yulin & Dong, Yachao & Wang, Yu & Sun, Yaojie, 2025. "Edge-cloud collaborative method for state of charge estimation of lithium-ion batteries by combining Kalman filter and deep learning," Energy, Elsevier, vol. 332(C).
    6. Zhao, Zhihui & Kou, Farong & Pan, Zhengniu & Chen, Leiming & Yang, Tianxiang, 2024. "Ultra-high-accuracy state-of-charge fusion estimation of lithium-ion batteries using variational mode decomposition," Energy, Elsevier, vol. 309(C).
    7. Shu, Xiong & Li, Yongjing & Wei, Kexiang & Yang, Wenxian & Yang, Bowen & Zhang, Ming, 2025. "Research on the output characteristics and SOC estimation method of lithium-ion batteries over a wide range of operating temperature conditions," Energy, Elsevier, vol. 317(C).
    8. Vahid Behnamgol & Mohammad Asadi & Mohamed A. A. Mohamed & Sumeet S. Aphale & Mona Faraji Niri, 2024. "Comprehensive Review of Lithium-Ion Battery State of Charge Estimation by Sliding Mode Observers," Energies, MDPI, vol. 17(22), pages 1-39, November.
    9. Xiao, Jie & Xiong, Yonglian & Zhu, Yucheng & Zhang, Chao & Yi, Ting & Qian, Xing & Fan, Yongsheng & Hou, Quanhui, 2024. "Multi-innovation adaptive Kalman filter algorithm for estimating the SOC of lithium-ion batteries based on singular value decomposition and Schmidt orthogonal transformation," Energy, Elsevier, vol. 312(C).
    10. Wu, Jiang & Lei, Dong & Liu, Zelong & Zhang, Yan, 2024. "A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 311(C).
    11. 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).
    12. Wang, Yun & Li, Yuhao & Zhang, Ziyang & Yu, Peihua & Li, Yifen & Liu, Bo & Zou, Runmin, 2025. "Bidirectional Mamba network with multi-scale feature fusion and sparse-channel mixture of experts for battery state of charge estimation," Energy, Elsevier, vol. 340(C).
    13. Jiang, Daiyan & Zhang, Yuan & Jin, Yuhong & Li, Siquan & Zhang, Qianqian & Liu, Jingbing & Wang, Hao, 2026. "State of charge estimation and elucidation of sodium-ion batteries based on electrochemical impedance spectroscopy characteristics," Energy, Elsevier, vol. 344(C).
    14. Ma, Liang & Li, Yannan & Zhang, Tieling & Tian, Jinpeng & Guo, Qinghua & Guo, Shanshan & Hu, Chunsheng & Chung, Chi Yung, 2025. "Trustworthy battery state of charge estimation enabled by multi-task deep learning," Energy, Elsevier, vol. 326(C).
    15. Fu, Qiang & Dai, Changlong & Bu, Siqi & Chung, C.Y., 2025. "Integrating power electronics-based energy storages to power systems: A review on dynamic modeling, analysis, and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 213(C).
    16. 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).
    17. Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    18. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    19. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    20. Alessandro Giuliano & Yuandi Wu & John Yawney & Stephen Andrew Gadsden, 2025. "Transformer-Based Transfer Learning for Battery State-of-Health Estimation," Energies, MDPI, vol. 18(20), pages 1-21, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225051667. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.