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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2007, Vol. 8 Issue (9): 1505-1509    DOI: 10.1631/jzus.2007.A1505
Environmental & Energy Engineering     
Nonlinear modelling of a SOFC stack by improved neural networks identification
WU Xiao-juan, ZHU Xin-jian, CAO Guang-yi, TU Heng-yong
Institute of Fuel Cell, Shanghai Jiao Tong University, Shanghai 200030, China
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Abstract  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.

Key words： Solid oxide fuel cells (SOFCs)      Radial basis function (RBF)      Neural networks      Genetic algorithm (GA)     
Received: 22 December 2006     
CLC:  TK01  
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WU Xiao-juan
ZHU Xin-jian
CAO Guang-yi
TU Heng-yong
Cite this article:

WU Xiao-juan, ZHU Xin-jian, CAO Guang-yi, TU Heng-yong. Nonlinear modelling of a SOFC stack by improved neural networks identification. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2007, 8(9): 1505-1509.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2007.A1505     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2007/V8/I9/1505

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