IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p248-d1562493.html
   My bibliography  Save this article

Lithium-Ion Battery Degradation Based on the CNN-Transformer Model

Author

Listed:
  • Yongsheng Shi

    (School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
    School of Engineering, Xi’an International University, Xi’an 710071, China)

  • Leicheng Wang

    (School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China)

  • Na Liao

    (School of Engineering, Xi’an International University, Xi’an 710071, China)

  • Zequan Xu

    (School of Computer Science, South China Normal University, Guangzhou 510631, China)

Abstract

Due to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a massive amount of data, which is disadvantageous for the online monitoring of batteries. This paper proposes a lithium-ion battery degradation research method based on the CNN-Transformer model. By leveraging the efficiency of the CNN model in feature extraction, it reduces the dependency of the Transformer model on data volume, thereby ensuring faster overall model training without a significant loss in model accuracy. This facilitates the online monitoring of battery degradation. The dataset used for training and validation consists of charge–discharge data from 124 lithium iron phosphate batteries. The experimental results include an analysis of the model training results for both single-battery and multiple-battery data, compared with commonly used models such as LSTM and Transformer. Regarding the instability of single-battery data in the CNN-Transformer model, statistical analysis is conducted to analyze the experimental results. The final model results indicate that the root mean square error (RMSE) of capacity predictions for the majority of batteries among the 124 batteries is within 3% of the actual values.

Suggested Citation

  • Yongsheng Shi & Leicheng Wang & Na Liao & Zequan Xu, 2025. "Lithium-Ion Battery Degradation Based on the CNN-Transformer Model," Energies, MDPI, vol. 18(2), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:248-:d:1562493
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/248/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/2/248/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    2. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    3. Zhang, Caiping & Wang, Yubin & Gao, Yang & Wang, Fang & Mu, Biqiang & Zhang, Weige, 2019. "Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method," Applied Energy, Elsevier, vol. 256(C).
    4. Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(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. Yang, Minxing & Sun, Xiaofei & Liu, Rui & Wang, Lingzhi & Zhao, Fei & Mei, Xuesong, 2024. "Predict the lifetime of lithium-ion batteries using early cycles: A review," Applied Energy, Elsevier, vol. 376(PA).
    2. Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
    3. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
    4. Sohn, Suyeon & Byun, Ha-Eun & Lee, Jay H., 2022. "Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation," Applied Energy, Elsevier, vol. 328(C).
    5. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    7. 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).
    8. 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).
    9. Ibraheem, Rasheed & Dechent, Philipp & dos Reis, Gonçalo, 2025. "Path signature-based life prognostics of Li-ion battery using pulse test data," Applied Energy, Elsevier, vol. 378(PA).
    10. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Zhang, Jianping & Zhang, Yinjie & Fu, Jian & Zhao, Dawen & Liu, Ping & Zhang, Zhiwei, 2024. "Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    12. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    13. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    14. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
    15. 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).
    16. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    17. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    18. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    19. Zhaosheng Zhang & Shuo Wang & Ni Lin & Zhenpo Wang & Peng Liu, 2023. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    20. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.

    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:2:p:248-:d:1562493. 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.