IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v325y2025ics0360544225015646.html
   My bibliography  Save this article

Prognosability regularized generative adversarial network for battery state of health estimation with limited samples

Author

Listed:
  • Ye, Zhuang
  • Chang, Jiantao
  • Yu, Jianbo

Abstract

State of health (SOH) estimation is essential to improve reliability of battery. However, it is a challenging issue to develop an effective health prognostics model due to the lack of run-to-failure data in the industrial scenarios. In this paper, a prognosability regularized generative adversarial network (ProGAN) is proposed to implement SOH estimation of battery. Firstly, a prognosability regulator is proposed in ProGAN to generate the samples with good trendability over time. Secondly, a multi-level wavelet decomposition long short-term memory (MwLSTM) is proposed to generate samples from low and high frequency components. Finally, an SOH estimator is constructed based on the synthetic samples. Compared to existing generative models, ProGAN introduces a novel prognosability regulator that enhances the trendability of generated samples. Additionally, a MwLSTM is developed by considering both low and high frequency components. Three battery SOH estimation cases are adopted to verify the effectiveness of ProGAN. The experimental results indicate that ProGAN can not only generate the samples with high consistency in space (i.e., data distribution), but also have good trendability over time (i.e., prognosability). Moreover, ProGAN has a better performance in both data augmentation and SOH estimation than other generative models.

Suggested Citation

  • Ye, Zhuang & Chang, Jiantao & Yu, Jianbo, 2025. "Prognosability regularized generative adversarial network for battery state of health estimation with limited samples," Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:energy:v:325:y:2025:i:c:s0360544225015646
    DOI: 10.1016/j.energy.2025.135922
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225015646
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135922?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. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    2. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    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. Kim, Seongyoon & Choi, Yun Young & Choi, Jung-Il, 2022. "Impedance-based capacity estimation for lithium-ion batteries using generative adversarial network," Applied Energy, Elsevier, vol. 308(C).
    5. Li, Fang & Min, Yongjun & Zhang, Ying & Zhang, Yong & Zuo, Hongfu & Bai, Fang, 2024. "State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Jiang, Lidang & Hu, Changyan & Ji, Sibei & Zhao, Hang & Chen, Junxiong & He, Ge, 2025. "Generating comprehensive lithium battery charging data with generative AI," Applied Energy, Elsevier, vol. 377(PC).
    7. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    8. 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).
    9. Ren, Yi & Tang, Ting & Jiang, Fusheng & Xia, Quan & Zhu, Xiayu & Sun, Bo & Yang, Dezhen & Feng, Qiang & Qian, Cheng, 2025. "A novel state of health estimation method for lithium-ion battery pack based on cross generative adversarial networks," Applied Energy, Elsevier, vol. 377(PA).
    10. 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, Zhen & Wang, Zirong & Wu, Wei & Xia, Tangbin & Pan, Ershun, 2024. "A hybrid battery degradation model combining arrhenius equation and neural network for capacity prediction under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    2. Sun, Jing & Wang, Haitao, 2025. "State of health estimation for lithium-ion batteries based on optimal feature subset algorithm," Energy, Elsevier, vol. 322(C).
    3. Chenqiang Luo & Zhendong Zhang & Shunliang Zhu & Yongying Li, 2023. "State-of-Health Prediction of Lithium-Ion Batteries Based on Diffusion Model with Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-14, April.
    4. Zhang, Zongjun & He, Wei & Zhou, Guohui & Li, Hongyu & Cao, You, 2025. "A new interpretable behavior prediction method based on belief rule base with rule reliability measurement," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    5. Wu, Rui & Tian, Jinpeng & Yao, Jiachi & Han, Te & Hu, Chunsheng, 2025. "Confidence-aware quantile Transformer for reliable degradation prediction of battery energy storage systems," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    6. 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).
    7. 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).
    8. Xing, Xueqi & Yan, Tongtong & Xia, Min, 2025. "Early prediction of battery life using an interpretable health indicator with evolutionary computing," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    9. Wu, Jian & Meng, Jinhao & Lin, Mingqiang & Wang, Wei & Wu, Ji & Stroe, Daniel-Ioan, 2024. "Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    10. Liang, Pengfei & Tian, Jiaye & Wang, Suiyan & Yuan, Xiaoming, 2024. "Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    11. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    12. 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).
    13. Liu, Jiale & Wang, Huan, 2024. "A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    14. Deng, Zhongwei & Deng, Hao & Yang, Lin & Cai, Yishan & Zhao, Xiaowei, 2017. "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, Elsevier, vol. 138(C), pages 509-519.
    15. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    16. Chen, Bingyang & Zeng, Xingjie & Zhang, Weishan & Fan, Lulu & Cao, Shaohua & Zhou, Jiehan, 2023. "Knowledge sharing-based multi-block federated learning for few-shot oil layer identification," Energy, Elsevier, vol. 283(C).
    17. 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).
    18. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. 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).
    20. Zhang, Xugang & Gao, Xiyuan & Duan, Linchao & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2025. "A novel method for state of health estimation of lithium-ion batteries based on fractional-order differential voltage-capacity curve," Applied Energy, Elsevier, vol. 377(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:325:y:2025:i:c:s0360544225015646. 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.