Performance Prediction of Proton Exchange Membrane Hydrogen Fuel Cells Using the GRU Model Tongji University

Format:

Book

Conference/Event

Author/Creator:

Yao, Ruimeng, author.

Contributor:

Hou, Yongping

Jiang, Changlong

Li, Wenqi

Pan, Xiangmin

Zhong, Minghui

Conference Name:

WCX SAE World Congress Experience (2022-04-05 : Detroit & Online, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

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. The output of the model is the polarization curve (current-voltage curve) and voltage attenuation curve (time-voltage curve) of the fuel cell. The validity and accuracy of the trained model were verified using the test data of the verification group. The model is evaluated in terms of prediction accuracy and model stability. At the same time, RNN and BP neural networks (BP-NN) are used to establish a prediction model, and the pros and cons of the three algorithms are compared and analyzed

Notes:

Vendor supplied data

Publisher Number:

2022-01-0692

Access Restriction:

Restricted for use by site license