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CLC number: TM911.4

On-line Access: 2011-04-11

Received: 2009-11-08

Revision Accepted: 2010-08-14

Crosschecked: 2011-02-17

Cited: 2

Clicked: 8420

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.4 P.338-344


Proton exchange membrane fuel cell voltage-tracking using artificial neural networks

Author(s):  Seyed Mehdi Rakhtala, Reza Ghaderi, Abolzal Ranjbar Noei

Affiliation(s):  Faculty of Electrical and Computer Engineering, Babol Industrial University, P. O. Box 47144-484, Babol, Iran

Corresponding email(s):   mhj_g@yahoo.com

Key Words:  Feed forward control, Neural network, Proton exchange membrane (PEM) fuel cell, Terminal voltage tracking

Seyed Mehdi Rakhtala, Reza Ghaderi, Abolzal Ranjbar Noei. Proton exchange membrane fuel cell voltage-tracking using artificial neural networks[J]. Journal of Zhejiang University Science C, 2011, 12(4): 338-344.

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%T Proton exchange membrane fuel cell voltage-tracking using artificial neural networks
%A Seyed Mehdi Rakhtala
%A Reza Ghaderi
%A Abolzal Ranjbar Noei
%J Journal of Zhejiang University SCIENCE C
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%P 338-344
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910683

T1 - Proton exchange membrane fuel cell voltage-tracking using artificial neural networks
A1 - Seyed Mehdi Rakhtala
A1 - Reza Ghaderi
A1 - Abolzal Ranjbar Noei
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 4
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EP - 344
%@ 1869-1951
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910683

Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells. The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and back pressures degrade system performance and lead to problems in controlling tuning parameters including temperature, pressure, and flow rate. To overcome this problem, fast and delay-free systems are necessary for predicting control signals. In this paper, we propose a neural network model to control the stack terminal voltage as a proper constant and improve system performance. This is done through an input air pressure control signal. The proposed artificial neural network was constructed based on a back propagation network. A fuel cell nonlinear model, with and without feed forward control, was investigated and compared under random current variations. Simulation results showed that applying neural network feed forward control can successfully improve system performance in tracking output voltage. Also, less energy consumption and simpler control systems are the other advantages of the proposed control algorithm.

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


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