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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).

In recent years, fuel cell vehicles have attracted more attention since the advantages of no environmental pollution and high energy density, however, the cost and durability of fuel cells have been important factors limiting the rapid development of fuel cell vehicles. How to quickly predict the life of fuel cells has always been the emphasis and focus of the industry. Therefore, this paper mainly focuses on two sets of proton exchange membrane hydrogen fuel cell durability test data. In this paper, we establish a fuel cell life prediction model to carry out product prediction research, using Gated Recurrent Unit Neural Network (GRU-NN)—a variant of “Recurrent Neural Networks” (RNN). This article first divides the two sets of fuel cell durability test data into a training group and a verification group and trains the established neural network model with the test data of the training group.

Proton exchange membrane fuel cells are promising in the application of new energy vehicles and other fields. The performance test and analysis are critical components of the fuel cell research. A general procedure for data processing model and performance analysis method of fuel cell stacks were introduced based on a demonstration project of commercial fuel cell buses in Shanghai. To build the data processing model, the fixed node method was used to extract the effective operating data of fuel cell stack from the vehicle database. After data cleaning and conversion, taking fuel cell reversible recession into account, the processed data can be obtained. Data that had been processed and a semi-empirical model were used to accurately fit the polarization curve. Polarization curve is a pivotal approach to describe the performance of fuel cells.