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Received: 2004-02-28

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.5 P.365~370

http://doi.org/10.1631/jzus.2005.A0365


Nonlinear modeling of PEMFC based on neural networks identification


Author(s):  SUN Tao, CAO Guang-yi, ZHU Xin-jian

Affiliation(s):  Fuel Cell Institute, Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China

Corresponding email(s):   xiaosuntao@sjtu.edu.cn, xiaosuntao@126.com

Key Words:  Proton exchange membrane fuel cell, Nonlinear system modeling, LMBP algorithm


SUN Tao, CAO Guang-yi, ZHU Xin-jian. Nonlinear modeling of PEMFC based on neural networks identification[J]. Journal of Zhejiang University Science A, 2005, 6(5): 365~370.

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author="SUN Tao, CAO Guang-yi, ZHU Xin-jian",
journal="Journal of Zhejiang University Science A",
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number="5",
pages="365~370",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0365"
}

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%T Nonlinear modeling of PEMFC based on neural networks identification
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%A ZHU Xin-jian
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%N 5
%P 365~370
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0365

TY - JOUR
T1 - Nonlinear modeling of PEMFC based on neural networks identification
A1 - SUN Tao
A1 - CAO Guang-yi
A1 - ZHU Xin-jian
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 5
SP - 365
EP - 370
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Y1 - 2005
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0365


Abstract: 
The proton exchange membrane generation technology is highly efficient and clean, and is considered as the most hopeful “green” power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model. This paper first simply analyzes the necessity of the PEMFC generation technology, then introduces the generating principle from four aspects: electrode, single cell, stack, system; and then uses the approach and self-study ability of artificial neural network to build the model of nonlinear system, and adapts the Levenberg-Marquardt BP (LMBP) to build the electric characteristic model of PEMFC. The model uses experimental data as training specimens, on the condition the system is provided enough hydrogen. Considering the flow velocity of air (or oxygen) and the cell operational temperature as inputs, the cell voltage and current density as the outputs and establishing the electric characteristic model of PEMFC according to the different cell temperatures. The voltage-current output curves of model has some guidance effect for improving the cell performance, and provide basic data for optimizing cell performance that have practical significance.

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Reference

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