Remaining Useful Life Prediction of PEMFC Based on Multi-feature Fusion

Proton exchange membrane fuel cells (PEMFCs), which directly convert chemical energy to electricity, are widely applied in new energy vehicles and distributed power generation. But real - world factors limit their lifespan, making remaining useful life (RUL) prediction essential. This research presents a deep - learning - based PEMFC RUL prediction approach. First, Pearson correlation analysis picks relevant features from multi - feature data. Then, CEEMDAN denoises the data, and mean/variance statistical features screen IMFs. Next, the INGO algorithm optimizes the CAM for feature fusion. Finally, the IGKSO algorithm optimizes the BiLSTM network for time - series prediction. With R² as the metric, the model reaches 99.86% accuracy. Compared to the traditional LSTM network, it shows marked improvements in iteration speed, stability, and prediction accuracy.