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Design Innovation Paper

A Predictive Control Based on Neural Network for Dynamic Model of Proton Exchange Membrane Fuel Cell

[+] Author and Article Information
M. Rezaei

Department of Electrical and Computer Engineering,
Shahid Beheshti University,
G. C., Evin 1983963113, Tehran, Iran
e-mail: m.rezaei@sbu.ac.ir

M. Mohseni

School of Electrical and Computer Engineering,
University College of Engineering,
University of Tehran,
North Kargar Street,
11365-4563 Tehran, Iran
e-mail: m_mohseni@ut.ac.ir

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received September 6, 2012; final manuscript received January 29, 2013; published online May 7, 2013. Assoc. Editor: Whitney Colella.

J. Fuel Cell Sci. Technol 10(3), 035001 (May 07, 2013) (5 pages) Paper No: FC-12-1089; doi: 10.1115/1.4023838 History: Received September 06, 2012; Revised January 29, 2013

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.

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References

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. [CrossRef]
Huang, Z., 2000, Research, Development and Application of Proton-Exchange Membrane Fuel Cells , Metallurgical Industry Press, Beijing, pp. 34–144.
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.
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. [CrossRef]
Larminie, J. E., and Dicks, A., 2000, Fuel Cell Systems Explained, Wiley, Chichester, UK.
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. [CrossRef]
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. [CrossRef]
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.
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.
Hagan, M. T., and Menhaj, M. B., 1994, “Training Feedforward Networks With the Marquardt Algorithm,” IEEE Trans. Neural Networks, 5(6), pp. 989–993. [CrossRef]
Battiti, R., 1992, First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method,” Neural Comput., 4(2), pp. 141–166. [CrossRef]

Figures

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Fig. 1

Schematic diagram of PEMFC mechanism

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Fig. 2

Polarization curve of simulated cell (line), actual polarization curve (nods)

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Fig. 3

Static model of PEMFC

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Fig. 4

Dynamic model of PEMFC

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Fig. 5

Process of NN identification

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Fig. 6

Control of PEMFC voltage

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Fig. 7

1—setpoint reference, 2—NN predictive response

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Fig. 8

NN as an adaptive filter

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Fig. 9

The output voltage of PEMFC in presence of noise and fluctuations: 1—with NN filter, 2—without filter

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