Neural network model for a commercial PEM fuel cell system

被引：87
|
作者：
Saenrung, Anucha [1 ]
Abtahi, Amir
Zilouchian, Ali
机构：
[1] Florida Atlantic Univ, Dept Elect Engn, Boca Raton, FL 33431 USA
[2] Florida Atlantic Univ, Dept Mech Engn, Boca Raton, FL 33431 USA
来源：
JOURNAL OF POWER SOURCES | 2007年 / 172卷 / 02期
关键词：
artificial neural network (ANN); neural network; proton exchange membrane fuel cell (PEMFC); back-propagation (BP); radial basis function (RBF) network; modeling;
D O I：
10.1016/j.jpowsour.2007.05.039
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Performance prediction of a commercial proton exchange membrane (PEM) fuel cell system by using artificial neural networks (ANNs) is investigated. Two artificial neural networks including the back-propagation (BP) and radial basis function (RBF) networks are constructed, tested and compared. Experimental data as well as preprocess data are utilized to determine the accuracy and speed of several prediction algorithms. The performance of the BP network is investigated by varying error goals, number of neurons, number of layers and training algorithms. The prediction performance of RBF network is also presented. The simulation results have shown that both the BP and RBF networks can successfully predict the stack voltage and current of a commercial PEM fuel cell system. Speed and accuracy of the prediction algorithms are quite satisfactory for the real-time control of this particular application. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码：749 / 759
页数：11
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