IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i6p1463-d1613862.html
   My bibliography  Save this article

Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review

Author

Listed:
  • Chenyuan Liu

    (Xi’an Institute of Optics and Precision Mechanics of CAS, University of Chinese Academy of Sciences, Xi’an 710119, China
    School of Electronic Information, Central South University, Changsha 410004, China
    These authors contributed equally to this work.)

  • Heng Li

    (School of Electronic Information, Central South University, Changsha 410004, China
    These authors contributed equally to this work.)

  • Kexin Li

    (School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yue Wu

    (School of Electronic Information, Central South University, Changsha 410004, China)

  • Baogang Lv

    (Xi’an Institute of Optics and Precision Mechanics of CAS, University of Chinese Academy of Sciences, Xi’an 710119, China)

Abstract

Electric vehicles (EVs) play a crucial role in addressing the energy crisis and mitigating the greenhouse effect. Lithium-ion batteries are the primary energy storage medium for EVs due to their numerous advantages. State of health (SOH) is a critical parameter for managing the health of lithium-ion batteries, and accurate SOH estimation forms the foundation of battery management systems (BMS), ensuring the safe operation of EVs. Data-driven deep learning techniques are attracting significant attention because of their strong ability to model complex nonlinear relationships, which makes them highly suitable for SOH estimation in lithium-ion batteries. This paper provides a comprehensive introduction to the common deep learning techniques used for SOH estimation of lithium-ion batteries, with a focus on model architectures. It systematically reviews the application of various deep learning algorithms in SOH estimation in recent years. Building on this, the paper offers a detailed comparison of these deep learning methods and discusses the current challenges and future directions in this field, with the aim of providing an extensive review of the role of deep learning in SOH estimation.

Suggested Citation

  • Chenyuan Liu & Heng Li & Kexin Li & Yue Wu & Baogang Lv, 2025. "Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review," Energies, MDPI, vol. 18(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1463-:d:1613862
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/6/1463/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/6/1463/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    2. Lin, Mingqiang & Wu, Jian & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, Elsevier, vol. 268(C).
    3. 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).
    4. Qian, Guangjun & Zheng, Yuejiu & Li, Xinyu & Sun, Yuedong & Han, Xuebing & Ouyang, Minggao, 2025. "State of health estimation for lithium-ion batteries using impedance-based simplified timescale information," Applied Energy, Elsevier, vol. 382(C).
    5. Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    6. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Lu, Zhenfeng & Fei, Zicheng & Wang, Benfei & Yang, Fangfang, 2024. "A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve," Energy, Elsevier, vol. 288(C).
    8. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    9. John Guirguis & Ryan Ahmed, 2024. "Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study," Energies, MDPI, vol. 17(14), pages 1-13, July.
    10. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    11. Bockrath, Steffen & Lorentz, Vincent & Pruckner, Marco, 2023. "State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles," Applied Energy, Elsevier, vol. 329(C).
    12. Chen, Biao & Jiang, Haobin & Chen, Xijia & Li, Huanhuan, 2022. "Robust state-of-charge estimation for lithium-ion batteries based on an improved gas-liquid dynamics model," Energy, Elsevier, vol. 238(PC).
    13. Jia, Chenyu & Tian, Yukai & Shi, Yuanhao & Jia, Jianfang & Wen, Jie & Zeng, Jianchao, 2023. "State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer," Energy, Elsevier, vol. 285(C).
    14. Bao, Xinyuan & Chen, Liping & Lopes, António M. & Li, Xin & Xie, Siqiang & Li, Penghua & Chen, YangQuan, 2023. "Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries," Energy, Elsevier, vol. 278(C).
    15. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    16. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    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. 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. Wang, Tong & Wu, Yan & Zhu, Keming & Cen, Jianmeng & Wang, Shaohong & Huang, Yuqi, 2025. "Deep learning and polarization equilibrium based state of health estimation for lithium-ion battery using partial charging data," Energy, Elsevier, vol. 317(C).
    3. 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).
    4. 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).
    5. 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).
    6. Xiong, Ran & Wang, Shunli & Huang, Qi & Yu, Chunmei & Fernandez, Carlos & Xiao, Wei & Jia, Jun & Guerrero, Josep M., 2024. "Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy stor," Energy, Elsevier, vol. 292(C).
    7. 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).
    8. Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
    9. 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.
    10. Kumar, Roushan & Das, Kaushik & Krishna, Anurup, 2024. "Comparative analysis of data-driven electric vehicle battery health models across different operating conditions," Energy, Elsevier, vol. 309(C).
    11. Li, Ziyang & Zhang, Xiangwen & Gao, Wei, 2024. "State of health estimation of lithium-ion battery during fast charging process based on BiLSTM-Transformer," Energy, Elsevier, vol. 311(C).
    12. Huang, Haichi & Bian, Chong & Wu, Mengdan & An, Dong & Yang, Shunkun, 2024. "A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries," Energy, Elsevier, vol. 288(C).
    13. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
    14. 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).
    15. 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).
    16. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    17. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
    18. 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).
    19. Sonthalia, Ankit & Femilda Josephin, J.S. & Varuvel, Edwin Geo & Chinnathambi, Arunachalam & Subramanian, Thiyagarajan & Kiani, Farzad, 2025. "A deep learning multi-feature based fusion model for predicting the state of health of lithium-ion batteries," Energy, Elsevier, vol. 317(C).
    20. Jiang, Fusheng & Ren, Yi & Tang, Ting & Wu, Zeyu & Xia, Quan & Sun, Bo & Yang, Dezhen, 2024. "An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 305(C).

    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:gam:jeners:v:18:y:2025:i:6:p:1463-:d:1613862. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.