CLC number: TM911
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 0
Clicked: 6572
WANG Rui-min, CAO Guang-yi, ZHU Xin-jian. New hybrid model of proton exchange membrane fuel cell[J]. Journal of Zhejiang University Science A, 2007, 8(5): 741-747.
@article{title="New hybrid model of proton exchange membrane fuel cell",
author="WANG Rui-min, CAO Guang-yi, ZHU Xin-jian",
journal="Journal of Zhejiang University Science A",
volume="8",
number="5",
pages="741-747",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0741"
}
%0 Journal Article
%T New hybrid model of proton exchange membrane fuel cell
%A WANG Rui-min
%A CAO Guang-yi
%A ZHU Xin-jian
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 5
%P 741-747
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0741
TY - JOUR
T1 - New hybrid model of proton exchange membrane fuel cell
A1 - WANG Rui-min
A1 - CAO Guang-yi
A1 - ZHU Xin-jian
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 5
SP - 741
EP - 747
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A0741
Abstract: Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box component. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.
[1] Ahmed, M.A., Istvan, E., 2005. Online optimal management of PEM fuel cells using neural networks. IEEE Transactions on Power Delivery, 20(2):1051-1059.
[2] Al-Baghdadi, M.A.R.S., 2005. Modelling of proton exchange membrane fuel cell performance based on semi-empirical equations. Renewable Energy, 30(10):1587-1599.
[3] Biyikoglu, A., 2005. Review of proton exchange membrane fuel cell models. International Journal of Hydrogen Energy, 30(11):1181-1212.
[4] Correa, J.M., Farret, F.A., Popov, V.A., Simoes, M.G., 2005. Sensitivity analysis of the modelling parameters used in simulation of proton exchange membrane fuel cells. IEEE Transactions on Energy Conversion, 20(1):211-218.
[5] Costamagna, P., 2001. Transport phenomena in polymer membrane fuel cells. Chem. Eng. Sci., 56(2):323-332.
[6] Dutta, S., Shimpalee, S., Van, Z.J., 2000. Three-dimensional numerical simulation of straight channel PEM fuel cells. J. Appl. Electrochem., 30(2):135-146.
[7] Fazil, M.S., Serhat, Y., 2005. Modelling Transients of a Proton Electrolyte Membrane Fuel Cell. Proceedings International Hydrogen Energy Congress and Exhibition IHEC.
[8] Furrer, D., Thaler, S., 2005. Neural-network modeling. Advanced Materials and Processes, 11(163):42-46.
[9] Heinzel, A., Nolte, R.K., Le, D.H., Zedda, M., 1998. Membrane fuel cells—Concepts and system design. Electrochimica Acta, 43(24):3817-3820.
[10] Jemeï, S., Hissel, D., Péra, M.C., Kauffmann, J.M., 2003. On-board fuel cell power supply modeling on the basis of neural network methodology. Journal of Power Sources, 124(2):479-486.
[11] Lee, W.Y., Park, G.G., Yang, T.H., Yoon, Y.G., Kim, C.S., 2004. Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks. International Journal of Hydrogen Energy, 29(9):961-966.
[12] Marr, C., Li, X.G., 1998. An engineering model of proton exchange membrane fuel cell performance. ASME Proceedings on Energy Sources Technology, 50(3):190-200.
[13] Mehta, V., Cooper, J.S., 2003. Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1):32-53.
[14] Ogaji, S.O.T., Singh, R., Pilidis, P., Diacakis, M., 2006. Modelling fuel cell performance using artificial intelligence. Journal of Power Sources, 154(1):192-197.
[15] Ou, S.D., Achenie, L.E.K., 2005. A hybrid neural network model for PEM fuel cells. Journal of Power Sources, 140(2):319-330.
[16] Pathapati, P.R., Xue, X., Tang, J., 2005. A new dynamic model for predicting transient phenomena in a PEM fuel cell system. Renewable Energy, 30(1):1-22.
[17] Pukrushpan, J.T., Peng, H., Stefanopoulou, A.G., 2005. Control-oriented Modeling and Analysis for Automotive Fuel Cell Systems. IFAC, Control Engineering Practice.
[18] Shan, Y.Y., Choe, S.Y., 2005. A high dynamic PEM fuel cell model with temperature effects. Journal of Power Sources, 145(1):30-39.
[19] Tian, Y.D., Zhu, X.J., Cao, G.Y., 2005. Proton exchange membrane fuel cells modeling based on artificial networks. Journal of University of Science and Technology Beijing, 12:72-77.
[20] Yuan, H.C., Xiong, F.L., Huai, X.Y., 2003. A method for estimating the number of hidden neurons in feed-forward neural networks based on information entropy. Computers and Electronics in Agriculture, 40(1-3):57-64.
Open peer comments: Debate/Discuss/Question/Opinion
<1>