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Prediction of Remaining Useful Life for Proton Exchange Membrane Fuel Cell Based on NGO-CNN-BiLSTM Neural Network
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Liang Xu, Yuanyuan Ren, Junfang Li
Automotive Engineer | 2024, (3) : 1 - 7
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Automotive Engineer | 2024, (3): 1-7
• Special Topic on Fuel Cell Vehicle Technology •
Prediction of Remaining Useful Life for Proton Exchange Membrane Fuel Cell Based on NGO-CNN-BiLSTM Neural Network
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Liang Xu, Yuanyuan Ren, Junfang Li
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  • Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Tianjin 300384
Published: 2024-03-15 doi: 10.20104/j.cnki.1674-6546.20230313
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In order to solve the problem of low accuracy in predicting the remaining service life of proton exchange membrane fuel cells, this paper proposed a dynamic fuel cell Remaining Useful Life (RUL) prediction model based on Northern Goshawk Optimization (NGO), Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM) neutral network. Firstly, NGO optimized the learning rate, hidden nodes and regularization coefficient of the CNN-BiLSTM model, and then the CNN-BiLSTM model extracted the features of the input data through the convolutional layer, and input it into the BiLSTM layer for timing modeling and prediction. In addition, wavelet threshold de-noising algorithm was used to smoothen the original data. Pearson correlation coefficient was used to extract model input variables, and NGO-CNN-BiLSTM network power prediction model was built. The simulation and verification results show that this method can effectively improve the prediction accuracy of the remaining service life of fuel cells up to 99.49%, which is higher than that of other comparative models.

Proton Exchange Membrane Fuel Cell (PEMFC)  /  NGO-CNN-BiLSTM network  /  Remaining Useful Life (RUL) predication
Liang Xu, Yuanyuan Ren, Junfang Li. Prediction of Remaining Useful Life for Proton Exchange Membrane Fuel Cell Based on NGO-CNN-BiLSTM Neural Network[J]. Automotive Engineer, 2024 , (3) : 1 -7 . DOI: 10.20104/j.cnki.1674-6546.20230313
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doi: 10.20104/j.cnki.1674-6546.20230313
  • Online Date：2025-11-25
  • Published：2024-03-15
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  • Revised：2023-09-15
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    Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Tianjin 300384
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