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Received: 2004-12-10

Revision Accepted: 2005-04-10

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.10 P.1084~1089

10.1631/jzus.2005.A1084


Modelling and control PEMFC using fuzzy neural networks


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

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

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

Key Words:  Proton exchange membrane fuel cell, Adaptive neural-networks fuzzy infer system, Modeling, Neural network


SUN Tao, YAN Si-jia, CAO Guang-yi, ZHU Xin-jian. Modelling and control PEMFC using fuzzy neural networks[J]. Journal of Zhejiang University Science A, 2005, 6(10): 1084~1089.

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author="SUN Tao, YAN Si-jia, CAO Guang-yi, ZHU Xin-jian",
journal="Journal of Zhejiang University Science A",
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number="10",
pages="1084~1089",
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doi="10.1631/jzus.2005.A1084"
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%T Modelling and control PEMFC using fuzzy neural networks
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%A CAO Guang-yi
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%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A1084

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T1 - Modelling and control PEMFC using fuzzy neural networks
A1 - SUN Tao
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A1 - CAO Guang-yi
A1 - ZHU Xin-jian
J0 - Journal of Zhejiang University Science A
VL - 6
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SP - 1084
EP - 1089
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A1084


Abstract: 
Proton exchange membrane generation technology is highly efficient, clean and 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 and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system.

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

Reference

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Open peer comments: Debate/Discuss/Question/Opinion

<1>

ilkim@No address<ilkimozdemir@gmail.com>

2012-08-27 04:49:03

Article seems useful to improve the online control mechanisms with ANFIS.

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