A hybrid method combining degradation mechanisms and deep learning for lifetime prediction of proton exchange membrane fuel cells under dynamic load cycle conditions

Abstract

Prognostics and health management (PHM) is an effective method to improve the durability of proton exchange membrane fuel cells (PEMFCs). Accurate lifetime prediction is an essential prerequisite for health management. This paper proposes a hybrid prediction method that combines degradation mechanisms with deep learning neural networks to predict the degradation trends and estimate the remaining useful life (RUL) of PEMFCs under dynamic load cycle conditions. Firstly, the polarization curve model is employed to extract degradation-related parameters and quantify the overvoltage. The relationship between overvoltage and membrane electrode assembly (MEA) degradation is analyzed, revealing that cathode catalyst and membrane are the key components influencing the degradation. Secondly, a comprehensive degradation index (CDI) is developed. A novel method for quantifying the weight coefficients of the CDI is proposed for the first time. The effects of catalyst and membrane degradation on the overall performance degradation are quantified, which are 82.2 % and 17.8 %, respectively. Finally, the long short-term memory (LSTM) and gated recurrent unit (GRU) models are employed to predict the degradation trend. The results show that GRU outperforms LSTM in this study. The maximum RUL estimation error of the proposed hybrid method is 9.50 %, with all errors within the 10 % confidence interval.