A deep learning method based on CNN-BiGRU and attention mechanism for proton exchange membrane fuel cell performance degradation prediction

Abstract

The performance of proton exchange membrane fuel cells (PEMFCs) will gradually deteriorate during long-term operation. Accurate performance degradation prediction is crucial for extending the lifespan and improve the durability of fuel cells. This paper proposes a deep learning method (CNN-BiGRU-AM) that incorporates convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU) and attention mechanism (AM) for fuel cell degradation prediction. In the proposed method, CNN extracts complex features from the input data through convolutional operations, BiGRU models temporal information by considering both forward and reverse directions of the input sequence, and attention mechanism highlights key information in the input data through weight allocation. The proposed method is validated using long-term experimental data from fuel cells under steady-state and quasi-dynamic conditions. The results indicate that the absolute error of the proposed method is less than 1.2 mV for 97.94% of the data samples under steady-state conditions and less than 1.2 mV for 94.82% of the data samples under quasi-dynamic conditions. The prediction accuracy and stability of the proposed method are significantly improved compared to other deep learning prediction methods.