A model with high-precision on proton exchange membrane fuel cell performance degradation prediction based on temporal convolutional network-long short-term memory

Abstract

Prediction with high-precision of the performance degradation is crucial for the development of proton exchange membrane fuel cells (PEMFCs) with long lifetime. In this work, a new hybrid neural network model on fuel cell performance degradation prediction (TCN-LSTM) is developed by incorporating the advantages of temporal convolutional network (TCN) and long short-term memory (LSTM). The innovation of TCN-LSTM is that LSTM captures the long-term correlation of data from the high-level features of the original data extracted by TCN. On the available PEMFC datasets (IEEE 2014 PHM Data Challenge), the TCN-LSTM reduces the root mean square error (RMSE) by up to 94% compared with other state-of-the-art algorithms. In addition, the TCN-LSTM is also assessed on 2673 h self-tested fuel cell voltage degradation dataset recorded under dynamic cyclic load conditions with complicated decay mechanism. The RMSE is only 0.00205 even under the 50% training length, which further verifies the excellent prediction performance of TCN-LSTM. It can be concluded that the TCN-LSTM will be a promising fuel cell performance degradation prediction model.