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Research Papers

A Hybrid Experimental Model of a Solid Oxide Fuel Cell Stack

[+] Author and Article Information
Xiao-Juan Wu, Xin-Jian Zhu, Guang-Yi Cao, Heng-Yong Tu

Institute of Fuel Cell, Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, P.R.C.

Wan-Qi Hu

Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080, P.R.C.

J. Fuel Cell Sci. Technol 6(1), 011013 (Nov 07, 2008) (4 pages) doi:10.1115/1.2971125 History: Received May 11, 2007; Revised March 05, 2008; Published November 07, 2008

A multivariable hybrid experimental model of a solid oxide fuel cell stack is developed in this paper. The model consists of an improved radial basis function (RBF) neural network model and a pressure-incremental model. The improved RBF model is built to predict the stack voltage with different temperatures and current density. Likewise, the pressure-incremental model is constructed to predict the stack voltage under various hydrogen, oxygen, and water partial pressures. We combine the two models together and make a powerful hybrid multivariable model that can predict the voltage under any current density, temperature, hydrogen, oxygen, and water partial pressure. The validity and accuracy of modeling are tested by simulations, and the simulation results show that it is feasible to build the hybrid multivariable experimental model.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

The hybrid model framework

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Figure 2

The structure of the RBF neural network

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Figure 3

Voltage-current characteristics predicted by the GA-RBF model and experimental data at T=800°C, 900°C, and 1000°C

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