IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v247y2025ics0960148125006810.html

Future prediction on the remaining useful life of proton exchange membrane fuel using temporal fusion transformer model

Author

Listed:
  • Chou, Jia-Hong
  • Wang, Fu-Kwun

Abstract

Proton exchange membrane fuel cell (PEMFC) is regarded as one of the leading developmental directions of the green power source. Predicting the remaining useful life (RUL) of online PEMFCs is challenging, primarily due to factors such as complex nonlinear degradation patterns and unknown data. To enhance the performance of RUL prediction, we present a deep learning model-based temporal fusion transformer model to predict the future total voltage of a PEMFC stack. The performance of RUL predictions was evaluated using different starting RUL prediction points to compare them with the existing study. The experiments are conducted with different future prediction steps for future RUL prediction to determine the model's prediction step limit. The proposed method achieved relative errors of 0.0148 %, 0.0060 %, 0.0662 %, and 0.0486 % for training lengths 300, 400, 500, and 600, respectively, in the FC1 and 0.0326 % in the FC2 datasets compared to the existing study scenarios. The experimental analysis resulted in relative errors of 0.0738 % for the FC1 dataset, 1.0239 % and 1.6512 % for the two FC2 dataset scenarios.

Suggested Citation

  • Chou, Jia-Hong & Wang, Fu-Kwun, 2025. "Future prediction on the remaining useful life of proton exchange membrane fuel using temporal fusion transformer model," Renewable Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:renene:v:247:y:2025:i:c:s0960148125006810
    DOI: 10.1016/j.renene.2025.123019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125006810
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.123019?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. Benaggoune, Khaled & Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine, 2022. "A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 313(C).
    2. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    3. Tao, Zihan & Zhang, Chu & Xiong, Jinlin & Hu, Haowen & Ji, Jie & Peng, Tian & Nazir, Muhammad Shahzad, 2023. "Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC," Applied Energy, Elsevier, vol. 336(C).
    4. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    5. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    6. Zhang, Caizhi & Zhang, Yuqi & Wang, Lei & Deng, Xiaozhi & Liu, Yang & Zhang, Jiujun, 2023. "A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    7. Hua, Zhiguang & Zheng, Zhixue & Péra, Marie-Cécile & Gao, Fei, 2020. "Remaining useful life prediction of PEMFC systems based on the multi-input echo state network," Applied Energy, Elsevier, vol. 265(C).
    8. Li, Haolong & Chen, Qihong & Zhang, Liyan & Liu, Li & Xiao, Peng, 2023. "Degradation prediction of proton exchange membrane fuel cell based on the multi-inputs Bi-directional long short-term memory," Applied Energy, Elsevier, vol. 344(C).
    9. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    10. Zhang, Zhendong & Wang, Ya-Xiong & He, Hongwen & Sun, Fengchun, 2021. "A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 304(C).
    11. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    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. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    2. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    3. Tian, Lei & Gao, Yan & Yang, Haiyu & Wang, Renkang, 2025. "Multi-scenario long-term degradation prediction of PEMFC based on generative inference informer model," Applied Energy, Elsevier, vol. 377(PA).
    4. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    5. Lv, Jianfeng & Shen, Xiaoning & Gao, Yabin & Liu, Jianxing & Sun, Guanghui, 2024. "The seasonal-trend disentangle based prognostic framework for PEM fuel cells," Renewable Energy, Elsevier, vol. 228(C).
    6. Ko, Taehwan & Kim, Dukyong & Park, Jaewoong & Lee, Seung Hwan, 2025. "Physics-informed neural network for long-term prognostics of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 382(C).
    7. Ma, Yangyang & Li, Songting & Zhou, Shulin & Wang, Xueyuan & Yuan, Hao & Chang, Guofeng & Zhu, Jiangong & Dai, Haifeng & Wei, Xuezhe, 2025. "Performance degradation prediction of proton exchange membrane fuel cells based on CNN-LSTM network with squeeze-and-excitation attention mechanism," Energy, Elsevier, vol. 335(C).
    8. Tianxiang Wang & Hongliang Zhou & Chengwei Zhu, 2022. "A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-17, July.
    9. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    10. Zhang, Tian & Hou, Zhengmeng & Li, Xiaoqin & Chen, Qianjun & Wang, Qichen & Lüddeke, Christian & Wu, Lin & Wu, Xuning & Sun, Wei, 2025. "A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network," Applied Energy, Elsevier, vol. 385(C).
    11. Huang, Ruike & Zhang, Xuexia & Dong, Sidi & Huang, Lei & Liao, Hongbo & Li, Yuan, 2024. "A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation," Renewable Energy, Elsevier, vol. 237(PC).
    12. Wang, Zifei & Tao, Jili & Liu, Zhitao & Feng, Han & Ma, Longhua & Xu, Ming & Su, Hongye, 2024. "A proton exchange membrane fuel cells degradation prediction method based on multi-scale temporal information merging network," Energy, Elsevier, vol. 313(C).
    13. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    14. Liu, Ze & Xu, Sichuan & Zhao, Honghui & Wang, Yupeng, 2022. "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models," Applied Energy, Elsevier, vol. 326(C).
    15. Guo, Bingxin & Zhu, Wenchao & Yang, Wenlong & Yang, Yang & Wei, Peng & You, Li & Xiong, Liangli & Xie, Changjun, 2025. "An interpretable uncertainty quantification framework for proton exchange membrane fuel cell degradation prediction," Renewable Energy, Elsevier, vol. 249(C).
    16. Song, Ke & Huang, Xing & Huang, Pengyu & Sun, Hui & Chen, Yuhui & Huang, Dongya, 2024. "Data-driven health state estimation and remaining useful life prediction of fuel cells," Renewable Energy, Elsevier, vol. 227(C).
    17. Huang, Ruike & Zhang, Xuexia & Dong, Sidi & Huang, Lei & Li, Yuan, 2025. "Degradation prediction of PEM fuel cell using LSTM based on Gini gamma correlation coefficient and improved sand cat swarm optimization under dynamic operating conditions," Applied Energy, Elsevier, vol. 392(C).
    18. Deng, Zhihua & Wang, Haijiang & Liu, Hao & Chen, Qihong & Zhang, Jiashun, 2024. "Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer," Renewable Energy, Elsevier, vol. 234(C).
    19. Yu, Yulong & Zheng, Qiang & Zhang, Tianyi & Li, Zhengyan & Chen, Lei & Tao, Wen-Quan, 2025. "Forecasting the output performance of PEMFCs via a novel deep learning framework considering varying operating conditions and time scales," Applied Energy, Elsevier, vol. 389(C).
    20. Yang, Yang & Yu, Xiaoran & Zhu, Wenchao & Xie, Changjun & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Huang, Liang & Zhang, Ruiming, 2023. "Degradation prediction of proton exchange membrane fuel cells with model uncertainty quantification," Renewable Energy, Elsevier, vol. 219(P2).

    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:renene:v:247:y:2025:i:c:s0960148125006810. 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/renewable-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.