Dynamic Durability Prediction of Fuel Cells Using Long Short-Term Memory Neural Network

Durability performance prediction is a critical issue in fuel cell research. During the demonstration operation of fuel cell commercial vehicles in China, this issue has attracted more attention. In this article, the long short-term memory neural network (LSTMNN), which is an improved recurrent neural network (RNN), and the demonstration operation data are used to establish the prediction model to predict the durability performance of the fuel cell stack. Then, a model based on a back-propagation neural network (BPNN) is established to be a control group. The demonstration operation data is divided into training group and validation group. The former is used to train the prediction model, and the latter is used to verify the validity and accuracy of the prediction model. The outputs of the prediction model, as the durability performance evaluation indexes of the fuel cell, are the polarization curve (current-voltage curve) and the voltage decay curve (time-voltage curve). Moreover, mean absolute percentage error (MAPE) and relative error are adopted to assess the prediction performance of the model. Ultimately, it is proved that LSTMNN performs better in durability performance prediction of fuel cell stack through dynamic data on the actual road by comparing the prediction results of LSTMNN with that of BPNN.