Long short-term memory deep learning model for predicting the dynamic performance of automotive PEMFC system

Abstract

As a high efficiency hydrogen-to-power device, proton exchange membrane fuel cell (PEMFC) attracts much attention, especially for the automotive applications. Real-time prediction of output voltage and area specific resistance (ASR) via the on-board model is critical to monitor the health state of the automotive PEMFC stack. In this study, we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset, and a long short-term memory (LSTM) deep learning model is developed to predict the dynamic performance of PEMFC. The results show that the developed LSTM deep learning model has much better performance than other models. A sensitivity analysis on the input features is performed, and three insensitive features are removed, that could slightly improve the prediction accuracy and significantly reduce the data volume. The neural structure, sequence duration, and sampling frequency are optimized. We find that the optimal sequence data duration for predicting ASR is 5 s or 20 s, and that for predicting output voltage is 40 s. The sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz, which slightly affects the prediction accuracy, but obviously reduces the data volume and computation amount.