A non-stationary transformer-based remaining useful life prediction method for proton exchange membrane fuel cells

Abstract

The remaining useful life (RUL) is one of the critical factors for proton exchange membrane fuel cells (PEMFCs), as it is hindering the commercialization of PEMFCs in various fields. In this research, a novel deep learning (DL) algorithm, i.e. the Non-stationary Transformer is newly applied to the RUL prediction of a PEMFC stack. For better prediction accuracy, the discrete wavelet transform (DWT) is utilized to denoise the original data before the normalization process. Prediction results of the proposed Non-stationary Transformer-based PEMFC RUL prediction method are analyzed under different lengths of training datasets, under different time steps ahead prediction, and for different PEMFC End of Life (EoL) threshold values and compared to those of other DL-based prediction methods including the long short-term memory (LSTM)-based and echo state network (ESN)-based methods. Results show that the proposed method outperforms the LSTM-based and ESN-based methods for all different cases studied in this research.