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Dynamic Prediction of Proton-Exchange Membrane Fuel Cell Degradation Based on Gated Recurrent Unit and Grey Wolf Optimization

Author

Listed:
  • Xiangdong Wang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Zerong Huang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Daxing Zhang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Haoyu Yuan

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Bingzi Cai

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Hanlin Liu

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Chunsheng Wang

    (School of Automation, Central South University, Changsha 410083, China)

  • Yuan Cao

    (School of Automation, Central South University, Changsha 410083, China)

  • Xinyao Zhou

    (School of Automation, Central South University, Changsha 410083, China)

  • Yaolin Dong

    (School of Automation, Central South University, Changsha 410083, China)

Abstract

This paper addresses the challenge of degradation prediction in proton-exchange membrane fuel cells (PEMFCs). Traditional methods often struggle to balance accuracy and complexity, particularly under dynamic operational conditions. To overcome these limitations, this study proposes a data-driven approach based on the gated recurrent unit (GRU) neural network, optimized by the grey wolf optimizer (GWO). The integration of the GWO automates the hyperparameter tuning process, enhancing the predictive performance of the GRU network. The proposed GWO-GRU method was validated utilizing actual PEMFC data under dynamic load conditions. The results demonstrate that the GWO-GRU method achieves superior accuracy compared to other standard methods. The method offers a practical solution for online PEMFC degradation prediction, providing stable and accurate forecasting for PEMFC systems in dynamic environments.

Suggested Citation

  • Xiangdong Wang & Zerong Huang & Daxing Zhang & Haoyu Yuan & Bingzi Cai & Hanlin Liu & Chunsheng Wang & Yuan Cao & Xinyao Zhou & Yaolin Dong, 2024. "Dynamic Prediction of Proton-Exchange Membrane Fuel Cell Degradation Based on Gated Recurrent Unit and Grey Wolf Optimization," Energies, MDPI, vol. 17(23), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5855-:d:1526968
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    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. Xuan Meng & Jian Mei & Xingwang Tang & Jinhai Jiang & Chuanyu Sun & Kai Song, 2024. "The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model," Energies, MDPI, vol. 17(12), pages 1-13, June.
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