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A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance

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  • Lin, Mingqiang
  • Yan, Chenhao
  • Wang, Wei
  • Dong, Guangzhong
  • Meng, Jinhao
  • Wu, Ji

Abstract

State-of-health (SOH) estimation of lithium-ion batteries is an important issue in electric vehicle energy management. The complication of the internal electrochemical reaction mechanism and the uncertainty of the external operating conditions pose a significant challenge to SOH estimation. This paper develops a data-driven approach to estimate the SOH of lithium-ion batteries with consideration of the battery's internal resistance, which is used as a bridge to effectively integrate the equivalent circuit model (ECM) and the data-driven method. We try to identify the internal resistance under constant current charging conditions by simplifying the ECM. The poles and offsets are extracted from the differential thermal voltammetry, differential thermal capacity, and incremental capacity curves as thermoelectric coupling features. Then the internal resistance and thermoelectric coupling features are combined as model inputs. An explanation boosting machine (EBM) is used to construct the SOH estimator according to the good fitting performance and interpretability. The model parameters of EBM are optimized by using an ant colony algorithm to improve its robustness. Finally, comparative experiments between features and the model are carried out on the Oxford dataset. The results demonstrate that the mean absolute error of the proposed method is less than 1%.

Suggested Citation

  • Lin, Mingqiang & Yan, Chenhao & Wang, Wei & Dong, Guangzhong & Meng, Jinhao & Wu, Ji, 2023. "A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223010691
    DOI: 10.1016/j.energy.2023.127675
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    References listed on IDEAS

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    6. Zeng, Xiaoyong & Sun, Yaoke & Xia, Xiangyang & Chen, Laien, 2025. "A framework for joint SOC and SOH estimation of lithium-ion battery: Eliminating the dependency on initial states," Applied Energy, Elsevier, vol. 377(PD).
    7. Guo, Wenchao & Yang, Lin & Deng, Zhongwei & Li, Jilin & Bian, Xiaolei, 2023. "Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment," Energy, Elsevier, vol. 281(C).
    8. Zhao, Xinwei & Liu, Yonggui & Xiao, Bin, 2025. "Enhanced prediction for battery aging capacity using an efficient temporal convolutional network," Energy, Elsevier, vol. 320(C).
    9. Jin, Zhao & Li, Xuebin & Qiu, Zhiqiang & Li, Fei & Kong, Erdan & Li, Bo, 2025. "A data-driven framework for lithium-ion battery RUL using LSTM and XGBoost with feature selection via Binary Firefly Algorithm," Energy, Elsevier, vol. 314(C).
    10. Liu, Zimo & Wang, Huirong & Zhou, Xun & Chen, Haoyuan & Duan, Haolei & Liang, Kunfeng & Chen, Bin & Cao, Yong & Wang, Weimin & Yang, Dapeng & Song, Lusheng, 2025. "State of health prediction of lithium-ion batteries based on incremental capacity analysis and adaptive genetic algorithm optimized Elman neural network model," Energy, Elsevier, vol. 335(C).
    11. Tang, Aihua & Huang, Yukun & Xu, Yuchen & Hu, Yuanzhi & Yan, Fuwu & Tan, Yong & Jin, Xin & Yu, Quanqing, 2024. "Data-physics-driven estimation of battery state of charge and capacity," Energy, Elsevier, vol. 294(C).
    12. He, Rui & Peng, Tian & Zhang, Xinyu & Chen, Zhigang & Yao, Junhao & Nazir, Muhammad Shahzad & Zhang, Chu, 2026. "A novel hybrid model for state of health prediction in lithium batteries based on non-stationary transformers optimized by tree-structured Parzen estimator considering health factors," Applied Energy, Elsevier, vol. 402(PC).
    13. Tian, Aina & He, Luyao & Ding, Tao & Dong, Kailang & Wang, Yuqin & Jiang, Jiuchun, 2025. "A generic physics-informed neural network framework for lithium-ion batteries state of health estimation," Energy, Elsevier, vol. 332(C).
    14. Guangyi Yang & Xianglin Wang & Ran Li & Xiaoyu Zhang, 2024. "State of Health Estimation for Lithium-Ion Batteries Based on Transferable Long Short-Term Memory Optimized Using Harris Hawk Algorithm," Sustainability, MDPI, vol. 16(15), pages 1-19, July.
    15. Wang, Fujin & Wu, Ziqian & Zhao, Zhibin & Zhai, Zhi & Wang, Chenxi & Chen, Xuefeng, 2024. "Physical knowledge guided state of health estimation of lithium-ion battery with limited segment data," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    16. Wang, Shunli & Li, Linzhi & Gao, Zhengqing & Li, Huan & Fernandez, Carlos & Blaabjerg, Frede, 2025. "Improved particle swarm - untracked particle filtering for accurate battery energy state estimation with the influence of multi-parameter varying temperature constraints in Inner Mongolia power station," Energy, Elsevier, vol. 341(C).
    17. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    18. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).

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