A health index-based approach for fuel cell lifetime estimation

Efficient health indicators (HI) and prediction methods are crucial for assessing the remaining useful life (RUL) of fuel cells. However, obtaining HI under dynamic conditions with frequently changing loads is high- ly challenging. Therefore, this study proposes a prediction framework based on dynamic conditions. A method combining complete ensemble empirical mode decomposition with adaptive noise, power spec- tral density, and energy analysis (CPE) is proposed to extract HI under dynamic conditions from the per- spectives of frequency and energy. Furthermore, the time convolution network with adaptive Bayesian optimization (AB-TCN) is introduced to address parameter optimization and prediction challenges. Effec- tive feature parameters of the data are identified using random forest and used to train the AB-TCN. Results show that the extracted HI can effectively determine the end-of-life. The AB-TCN achieves accu- rate RUL estimation with a prediction error of only 6.825% and shows strong adaptability to various pre- diction tasks.