Bi-directional long short-term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells

Proton exchange membrane fuel cells (PEMFCs) have zero-emissions and provide power to a variety of devices, such as automobiles and portable equipment. We propose a bi-directional long short-term memory recurrent neural network with an attention mechanism (BILSTM-AT) model to predict the voltage degradation of the PEMFC stack. Random forest regression model is used to extract essential variables as inputs in the model. The prediction interval is derived by using the dropout method. Model parameters are determined by an optimization method. The test data of the two PEMFC stacks are used to compare the proposed model with some existing models. The prediction results show that BILSTM-AT outperforms other models. Moreover, the proposed model with a sliding window method on remaining useful life (RUL) prediction can achieve more accurate results, with a relative error of about 0.09%~0.29%.