PEMFC Performance Degradation Prediction Based on CNN-BiLSTM with Data Augmentation by an Improved GAN

To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell (PEMFC) performance degradation prediction, this study proposes a data augmentation-based model to predict PEMFC performance degradation. Firstly, an improved generative adversarial network (IGAN) with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples. Then, the IGAN is used to generate data with a distribution analogous to real data, thereby mitigating the insufficiency and imbalance of original PEMFC samples and providing the prediction model with training data rich in feature information. Finally, a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) model is adopted to predict PEMFC performance degradation. Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN, and can fully characterize and enrich the original data’s features. Using the augmented data, the prediction accuracy of the CNN-BiLSTM model is significantly improved, rendering it applicable to tasks of predicting PEMFC performance degradation.