Full Text:   <1806>

CLC number: TK01

On-line Access: 

Received: 2006-12-22

Revision Accepted: 2007-04-13

Crosschecked: 0000-00-00

Cited: 6

Clicked: 3189

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.9 P.1505~1509


Nonlinear modelling of a SOFC stack by improved neural networks identification

Author(s):  WU Xiao-juan, ZHU Xin-jian, CAO Guang-yi, TU Heng-yong

Affiliation(s):  Institute of Fuel Cell, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   xj_wu@sjtu.edu.cn

Key Words:  Solid oxide fuel cells (SOFCs), Radial basis function (RBF), Neural networks, Genetic algorithm (GA)

WU Xiao-juan, ZHU Xin-jian, CAO Guang-yi, TU Heng-yong. Nonlinear modelling of a SOFC stack by improved neural networks identification[J]. Journal of Zhejiang University Science A, 2007, 8(9): 1505~1509.

@article{title="Nonlinear modelling of a SOFC stack by improved neural networks identification",
author="WU Xiao-juan, ZHU Xin-jian, CAO Guang-yi, TU Heng-yong",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Nonlinear modelling of a SOFC stack by improved neural networks identification
%A WU Xiao-juan
%A ZHU Xin-jian
%A CAO Guang-yi
%A TU Heng-yong
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 9
%P 1505~1509
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1505

T1 - Nonlinear modelling of a SOFC stack by improved neural networks identification
A1 - WU Xiao-juan
A1 - ZHU Xin-jian
A1 - CAO Guang-yi
A1 - TU Heng-yong
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 9
SP - 1505
EP - 1509
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1505

The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far, most existing models are based on conversion laws, which are too complicated to be applied to design a control system. To facilitate a valid control strategy design, this paper tries to avoid the internal complexities and presents a modelling study of SOFC performance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of modelling, the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations, whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore, it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Ai-Amoudi, A., Zhang, L., 2000. Application of radial basis function network for solar-array modeling and maximum power-point prediction. IEE Proc.-Gener. Transm. Distrib., 147(5):310-316.

[2] Arriagada, J., Olausson, P., Selimovic, A., 2002. Artificial neural network simulator for SOFC performance prediction. J. Power Sources, 112(1):54-60.

[3] Balland, L., Estel, L., Cosmao, J.M., Mouhab, N., 2000. A genetic algorithm with decimal coding for the estimation of kinetic and energetic parameters. Chemometrics and Intelligent Laboratory Systems, 50(1):121-135.

[4] Bove, R., Lunghi, P., Sammes, N.M., 2005. SOFC mathematic model for systems simulations—Part 2: definition of an analytical model. Int. J. Hydrogen Energy, 30(2):189-200.

[5] Calise, F., Dentice d’Accadia, M., Palombo, A., Vanoli, L., 2006. Simulation and exnergy analysis of a hybrid Solid Oxide Fuel Cell (SOFC)-Gas Turbine System. Energy, 31(15):3278-3299.

[6] Chang, C.C., Lin, C.J., 2001. LIBSVM: A Library for Support Vector Machines. Http://www.csie.nut.edu.tw/~cjlin/libsvm

[7] Costamagna, P., Magistri, L., Massardo, A.F., 2001. Design and part-load performance of a hybrid system based on a solid oxide fuel cell reactor and a micro gas turbine. J. Power Sources, 96(2):352-368.

[8] Gao, Y., Shi, L., Yao, P.J., 2000. Study on Multi-Objective Genetic Algorithm. Proceedings of the 3D World Congress on Intelligent Control and Automation. Hefei, China, p.646-650.

[9] Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.

[10] Hall, D.J., Colclaser, R.G., 1999. Transient modeling and simulation of a tubular solid oxide fuel cell. IEEE Transaction on Energy Conversion, 14(3):749-753.

[11] Huo, H.B., Zhu, X.J., Cao, G.Y., 2006. Nonlinear modeling of a SOFC stack based on a least squares support vector machine. J. Power Sources, 160(1):293-298.

[12] Lunghi, P., Ubertini, U., 2001. Solid Oxide Fuel Cells and Regenerated Gas Turbines Hybrid Systems: A Feasible Solution for Future Ultra High Efficiency Power Plants. Proceedings of the Seventh International Symposium on Solid Oxide Fuel Cells (SOFC-VII). Tsukuba, Ibaraki, Japan, p.254-264.

[13] Nehter, P., 2006. Two-dimensional transient model of a cascaded micro-tubular solid oxide fuel cell fed with methane. J. Power Sources, 157(1):325-334.

[14] Recknagle, K.P., Williford, R.E., Chick, L.A., Rector, D.R., 2003. Three-dimensional thermo-fluid electrochemical modeling of planar SOFC stacks. J. Power Sources, 113(1):109-114.

[15] Sjoberg, J., Zhang, Q.H., Ljung, L., 1995. Nonlinear black-box modeling in system identification: A unified overview. Automatica, 31(12):1691-1724.

[16] Warwick, K., 1996. An Introduction to Radial Basis Functions for System Identification: A Comparison with Other Neural Networks Methods. Proceedings of the 35th Conference on Decision and Control. Kobe, Japan, p.464-469.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE