A data-driven method with sample entropy and CEEMDAN for short-term performance degradation prediction of dynamic hydrogen fuel cells

Abstract

Short-term degradation prediction plays a crucial role in enhancing the reliability, safety, and cost-effectiveness of hydrogen fuel cells. A data-driven method for short-term degradation prediction is proposed based on Sample Entropy (SE), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and a varying-depth Gated Recurrent Unit (GRU). CEEMDAN decomposes the voltage sequence into Intrinsic Mode Functions (IMFs), which are then reconstructed into three meaningful combined Intrinsic Mode Functions (co-IMFs) using SE. The varying-depth GRU is employed to predict these co-IMFs. Results show that under dynamic conditions, the proposed method achieves an RMSE of 0.0012 for 1-h ahead predictions and 0.0151 for 35-h ahead predictions. Under static conditions, the RMSE for 1-h ahead is 0.0012, and for 35-h ahead prediction, it is 0.0097. Comparative analysis with hybrid models using the same dataset reveals that this novel approach demonstrates superior accuracy and efficiency.