Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 7: 21 - 30

DOI: https://doi.org/10.12141/j.issn.1000-565X.240320

Energy, Power & Electrical Engineering

Proton Exchange Membrane Fuel Cell Fault Prediction Method Based on Deep Learning

  • ZUO Bin ,
  • DONG Tianhang ,
  • ZHANG Zehui ,
  • WANG Huajun ,
  • HUO Weiwei ,
  • GONG Wenfeng ,
  • CHENG Junsheng
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  • 1.College of Mechanical and Vehicle Engineering，Hunan University，Changsha 410082，Hunan，China
    2.CEEC Energy Storage Technology （Wuhan） Co. ，Ltd. ，Wuhan 430200，Hubei，China
    3.China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing，Hangzhou Dianzi University，Hangzhou 310018，Zhejiang，China
    4.China Auto Information Technology （Tianjin） Co. ，Ltd. ，Tianjin 300300，China
    5.College of Mechanical and Electrical Engineering，Beijing Information Science & Technology University，Beijing 100192，China
    6.Guangxi Key Laboratory of Ocean Engineering Equipment and Technology，Beibu Gulf University，Qinzhou 535011，Guangxi，China
左彬（1984—），男，博士生，高级工程师，主要从事新能源智能运维研究。E-mail： zuobin123@ceec.net.cn
张泽辉（1994—），男，博士，特聘副研究员，主要从事设备智能健康管理研究。E-mail： zhangtianxia918@163.com

Received date: 2024-06-18

  Online published: 2024-12-24

Supported by

the National Natural Science Foundation of China(52401376);the National Key R & D Program of China(2022YFE0210700);the“Pioneer”and“Leading Goose”R & D Program of Zhejiang Province(2024C03254);the Natural Science Foundation of Zhejiang Province(LTGG24F030004)

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Abstract

Proton exchange membrane fuel cells (PEMFCs) have attracted significant attention in the fields of transportation, marine engineering, and aerospace due to their advantages of pollution-free operation, high efficiency, and low noise. However, reliability issues hinder their large-scale commercialization. To further enhance fuel cell reliability, this paper proposed a fault prediction method based on deep learning. First, for operational monitoring data including voltage, current, humidity, and temperature, feature parameters for fault diagnosis were selected based on fuel cell failure mechanisms. This approach reduces data dimensionality, suppresses redundant information, and improves the computational efficiency of the prediction model. Additionally, pre-processing techniques such as normalization and sliding time windows were employed to eliminate the effects of differing dimensions among monitoring parameters. Then, a fuel cell state prediction model based on the long short-term memory (LSTM) network was constructed. Its inputs were preprocessed multidimensional feature sequences, and its output predicts the fuel cell state for the next T time steps. Finally, the predicted state data was fed into a convolutional neural network (CNN)-based fault identification model to achieve fuel cell fault state prediction. The proposed method was validated using experimental fault data from fuel cell tests, and the results show that the model can predict failures in advance. By virtue of effective data preprocessing, future state prediction via LSTM, and fault recognition through CNN, this deep learning-based approach enables early prediction of operational anomalies in proton exchange membrane fuel cells.

Key words： fuel cell; deep learning; fault prediction; long short-term memory network; convolutional neural network

Cite this article

ZUO Bin , DONG Tianhang , ZHANG Zehui , WANG Huajun , HUO Weiwei , GONG Wenfeng , CHENG Junsheng . Proton Exchange Membrane Fuel Cell Fault Prediction Method Based on Deep Learning[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(7) : 21 -30 . DOI: 10.12141/j.issn.1000-565X.240320

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