IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225013271.html
   My bibliography  Save this article

State of health estimation for lithium-ion batteries based on optimal feature subset algorithm

Author

Listed:
  • Sun, Jing
  • Wang, Haitao

Abstract

Currently, most of data-driven lithium-ion battery capacity estimation studies use feature screening by analyzing the level of linear association between features and capacity, leading to the neglect of the hidden non-linear relationship between features and capacity. To overcome this limitation, this study constructs a feature selection evaluation function COMAN, which comprehensively describes the dependency between features and capacity originating in linear and nonlinear aspects. Based on it, the paper further presents an Optimal Feature Subset Algorithm (OFSA), which selects the optimal subset of features that are significantly associated with capacity and have low redundancy with the features. First, 26 features are extracted grounded in various parameters, including voltage, current, temperature, and time. Next, the proposed OFSA is applied to these features to select the optimal feature combination. Finally, CatBoost model was employed to build the connections between the selected features and capacity, with automated hyperparameter tuning performed using the Optuna library. Comparing with existing machine learning algorithms, the root mean square error (RMSE) and mean absolute error (MAE) of the method do not exceed 0.5 %, and the R2 value exceeds 0.9887. The findings from experiments indicate that the proposed method enhances the robustness of battery's SOH.

Suggested Citation

  • Sun, Jing & Wang, Haitao, 2025. "State of health estimation for lithium-ion batteries based on optimal feature subset algorithm," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013271
    DOI: 10.1016/j.energy.2025.135685
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225013271
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135685?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. Zhu, Rui & Duan, Bin & Zhang, Junming & Zhang, Qi & Zhang, Chenghui, 2020. "Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter," Applied Energy, Elsevier, vol. 277(C).
    2. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    3. Sun, Jing & Fan, Chaoqun & Yan, Huiyi, 2024. "SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost," Energy, Elsevier, vol. 306(C).
    4. Zuo, Hongyan & Liang, Jingwei & Zhang, Bin & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2023. "Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction," Energy, Elsevier, vol. 282(C).
    5. Peng, Simin & Chen, Shengdong & Liu, Yong & Yu, Quanqing & Kan, Jiarong & Li, Rui, 2025. "State of power prediction joint fisher optimal segmentation and PO-BP neural network for a parallel battery pack considering cell inconsistency," Applied Energy, Elsevier, vol. 381(C).
    6. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    7. Das, Kaushik & Kumar, Roushan & Krishna, Anurup, 2024. "Analyzing electric vehicle battery health performance using supervised machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    8. Pan, Rui & Liu, Tongshen & Huang, Wei & Wang, Yuxin & Yang, Duo & Chen, Jie, 2023. "State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree," Energy, Elsevier, vol. 285(C).
    9. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. Guo, Yongfang & Yu, Xiangyuan & Wang, Yashuang & Huang, Kai, 2024. "Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    12. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
    13. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
    14. Xu, Huanwei & Wu, Lingfeng & Xiong, Shizhe & Li, Wei & Garg, Akhil & Gao, Liang, 2023. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries," Energy, Elsevier, vol. 276(C).
    15. Son, Seho & Jeong, Siheon & Kwak, Eunji & Kim, Jun-hyeong & Oh, Ki-Yong, 2022. "Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features," Energy, Elsevier, vol. 238(PA).
    16. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Tang, Aihua & Kan, Jiarong & Pecht, Michael, 2024. "SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion," Energy, Elsevier, vol. 308(C).
    17. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    18. Cai, Nian & Que, Xiaoping & Zhang, Xu & Feng, Weiguo & Zhou, Yinghong, 2024. "A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images," Energy, Elsevier, vol. 302(C).
    19. Peng, Simin & Zhang, Daohan & Dai, Guohong & Wang, Lin & Jiang, Yuxia & Zhou, Feng, 2025. "State of charge estimation for LiFePO4 batteries joint by PID observer and improved EKF in various OCV ranges," Applied Energy, Elsevier, vol. 377(PA).
    20. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).
    21. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Yang & Gao, Guoqiang & Chen, Kui & He, Shuhang & Liu, Kai & Xin, Dongli & Luo, Yang & Long, Zhou & Wu, Guangning, 2025. "State-of-health prediction of lithium-ion batteries using feature fusion and a hybrid neural network model," Energy, Elsevier, vol. 319(C).

    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. Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    2. Li, Yang & Gao, Guoqiang & Chen, Kui & He, Shuhang & Liu, Kai & Xin, Dongli & Luo, Yang & Long, Zhou & Wu, Guangning, 2025. "State-of-health prediction of lithium-ion batteries using feature fusion and a hybrid neural network model," Energy, Elsevier, vol. 319(C).
    3. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).
    4. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).
    5. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    6. Tao, Junjie & Wang, Shunli & Cao, Wen & Fernandez, Carlos & Blaabjerg, Frede & Cheng, Liangwei, 2025. "An innovative multitask learning - Long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current condi," Energy, Elsevier, vol. 314(C).
    7. Tao, Junjie & Wang, Shunli & Cao, Wen & Cui, Yixiu & Fernandez, Carlos & Guerrero, Josep M., 2024. "Innovative multiscale fusion – Antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 312(C).
    8. Mu, Guixiang & Wei, Qingguo & Xu, Yonghong & Li, Jian & Zhang, Hongguang & Yang, Fubin & Zhang, Jian & Li, Qi, 2025. "State of health estimation of lithium-ion batteries based on feature optimization and data-driven models," Energy, Elsevier, vol. 316(C).
    9. Li, Xiaopeng & Zhao, Minghang & Zhong, Shisheng & Li, Junfu & Fu, Song & Yan, Zhiqi, 2024. "BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator," Energy, Elsevier, vol. 313(C).
    10. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    11. Zhao, Jingyuan & Wang, Zhenghong & Wu, Yuyan & Burke, Andrew F., 2025. "Predictive pretrained transformer (PPT) for real-time battery health diagnostics," Applied Energy, Elsevier, vol. 377(PD).
    12. Sun, Jing & Fan, Chaoqun & Yan, Huiyi, 2024. "SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost," Energy, Elsevier, vol. 306(C).
    13. Ji, Shanling & Zhang, Zhisheng & Stein, Helge S. & Zhu, Jianxiong, 2025. "Flexible health prognosis of battery nonlinear aging using temporal transfer learning," Applied Energy, Elsevier, vol. 377(PD).
    14. Sun, Rongli & Chen, Junsheng & Li, Benchuan & Piao, Changhao, 2025. "State of health estimation for Lithium-ion batteries based on novel feature extraction and BiGRU-Attention model," Energy, Elsevier, vol. 319(C).
    15. Wu, Ji & Wang, Jieming & Lin, Mingqiang & Meng, Jinhao, 2025. "Retired battery capacity screening based on deep learning with embedded feature smoothing under massive imbalanced data," Energy, Elsevier, vol. 318(C).
    16. 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).
    17. Meng-Xiang Yan & Zhi-Hui Deng & Lianfeng Lai & Yong-Hong Xu & Liang Tong & Hong-Guang Zhang & Yi-Yang Li & Ming-Hui Gong & Guo-Ju Liu, 2025. "A Sustainable SOH Prediction Model for Lithium-Ion Batteries Based on CPO-ELM-ABKDE with Uncertainty Quantification," Sustainability, MDPI, vol. 17(11), pages 1-28, June.
    18. Zhao, Haichuan & Meng, Jinhao & Peng, Qiao, 2025. "Early perception of Lithium-ion battery degradation trajectory with graphical features and deep learning," Applied Energy, Elsevier, vol. 381(C).
    19. Ni, Yulong & Song, Kai & Pei, Lei & Li, Xiaoyu & Wang, Tiansi & Zhang, He & Zhu, Chunbo & Xu, Jianing, 2025. "State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model," Applied Energy, Elsevier, vol. 385(C).
    20. Chen, Bingyang & Zeng, Xingjie & Liu, Chao & Xu, Yafei & Cao, Heling, 2025. "Health management of power batteries in low temperatures based on Adaptive Transfer Enformer framework," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).

    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:322:y:2025:i:c:s0360544225013271. 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.