This paper presents the development of dynamic models for proton exchange membrane fuel cells (PEMFC). The PEMFC control system has an important effect on operation of cell. Traditional controllers could not lead to acceptable responses because of time-change, long-hysteresis, uncertainty, strong-coupling and nonlinear characteristics of PEMFCs, This paper presents a dynamic model for PEMFC system, so an intelligent or adaptive controller is needed. In this paper, a neural network predictive controller have been designed to control the voltage of at the presence of fluctuations of temperature. The results of implementation of this designed NN Predictive controller on a dynamic electrochemical model of a small size 5 KW, PEM fuel cell have been simulated by matlab/SIMULINK.

Issue Section:
Design Innovation Paper
Keywords:
fuel cell dynamic model, PEMFC, neural network, model predictive control
Topics:
Artificial neural networks, Dynamic models, Fuel cells, Predictive control, Proton exchange membrane fuel cells, Control equipment, Temperature

References

1.
Ruan
,
Y.
, Wen, Y. M., Li, P., and
Shen
,
W.
,
2008
, “
Design of Control Methods for a Portable PEMFC PowerSystem
,”
Proceedings of the 7th World Congress on Intelligent Control and Automation
(
WCICA'08
),
Chongqing, China
, June 25–27, pp. 7874–7878.10.1109/WCICA.2008.4594158
2.
Huang
,
Z.
,
2000
,
Research, Development and Application of Proton-Exchange Membrane Fuel Cells
, Metallurgical Industry Press, Beijing, pp.
34
–
144
.
3.
Cirrincione
,
M.
,
Pucci
,
M.
,
Cirrincione
,
G.
, and
Simes
,
M. G.
,
2005
, “
A Neural Non-Linear Predictive Control for PEM-FC
,”
J. Electr. Syst.
,
1–2
, pp.
1
–
18
.
4.
Kim
,
J.
, Lee, S.-M., Srinivasan, S., and Chamberlin, C. E.,
1995
, “
Modeling of Proton Exchange Membrane Fuel Cell Performance With An Empirical Equation
,”
J. Electrochem. Soc.
,
142
(8), pp.
2670
–
2674
.10.1149/1.2050072
Google Scholar
Crossref
Search ADS
 
5.
Larminie
,
J. E.
, and
Dicks
,
A.
,
2000
,
Fuel Cell Systems Explained
,
Wiley
, Chichester, UK.
6.
Corrêa, J. M., Farret, F. A., Canha, L. N., Simões, M. G.,
2004
, “
An Electrochemical-Based Fuel-Cell Model Suitable for Electrical Engineering Automation Approach
,”
IEEE Trans. Ind., Electron.
,
51
(
5
), pp.
1103-1112
.10.1109/TIE.2004.834972
7.
Mann
,
R. F.
,
Amphlett
,
J. C.
,
Hooper
,
M. A. I.
,
Jensen
,
H. M.
,
Peppley
,
B. A.
, and
Roberge
,
P. R.
,
2000
, “
Development and Application of a Generalised Steady-State Electrochemical Model for a PEM Fuel Cell
,”
J. Power Sources
,
86
(1–2), pp.
173
–
180
.10.1016/S0378-7753(99)00484-X
Google Scholar
Crossref
Search ADS
 
8.
Molavi
,
A.
,
Shahini
,
M.
,
Rastgar
,
H.
, and
Ghadimi
,
A.
,
2006
, “
Control of Output Power of PEMFC Based on Calculating Intelligence,” 21st International Power System Conference
, Tehran, Iran.
9.
Mammar
,
K.
, and
Chaker
,
A.
,
2009
, “
Fuzzy Logic-Based Control of Power of PEM Fuel Cell System for Residential Application
,”
Leonardo J. Sci.
,
14
, pp.
147
–
166
.
10.
Hagan
,
M. T.
, and
Menhaj
,
M. B.
,
1994
, “
Training Feedforward Networks With the Marquardt Algorithm
,”
IEEE Trans. Neural Networks
,
5
(
6
), pp.
989
–
993
.10.1109/72.329697
Google Scholar
Crossref
Search ADS
 
11.
Battiti
,
R.
,
1992
,
First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method
,”
Neural Comput.
,
4
(
2
), pp.
141
–
166
.10.1162/neco.1992.4.2.141
Google Scholar
Crossref
Search ADS
 
Copyright © 2013 by ASME
You do not currently have access to this content.