A Predictive control based on Neural Network for Proton Exchange Membrane Fuel Cell

The Proton Exchange Membrane Fuel Cell (PEMFC) control system has an important effect on operation of cell. Traditional controllers couldn-t lead to acceptable responses because of time- change, long- hysteresis, uncertainty, strong- coupling and nonlinear characteristics of PEMFCs, 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.

Authors:

• M. Sedighizadeh
• M. Rezaei
• V. Najmi

Keywords:

• PEMFC
• Neural Network
• Predictive Control..

References:

[1] Yu Ruan, Yumei en, Ping Li , Weidong Shen, "Design of Control
Methods for a Portable PEMFC PowerSystem" Proceedings of the 7th
[2] World Congress on Intelligent Control and Automation
[3] June 25 - 27, 2008, Chongqing, China
[4] Zhuo Huang et al, Research development and application of proton
exchange membrane fuel cell[M], pp.34-144,2000.
[5] M. Cirrincione, M. PucciG, Cirrincione and M. G. Sim├Áes; "Neural
Non-linear Predictive Control for PEM-FC". J. Electrical Systems 1-2
(2005): 1-18
[6] J. Kim, "Modeling of proton exchange membrane fuel cell performance
with anempirical equation," J. Electrochem. Soc., no. 142, pp. 2670-
2674, 1995.
[7] J. E. Larminie and A. Dicks, "Fuel Cell Systems Explained". Chichester,
U.K.:Wiley, 2000, pp. 308-308.
[8] J. M. Corr├¬a, F. A. Farret, L. N. Canha, and Marcelo G. Sim├Áes, "An
Electrochemical-Based Fuel-Cell Model Suitable, for Electrical
EngineeringAutomation Approach", IEEE Trans. Ind., Electr., VOL. 51,
NO. 5, OCTOBER2004.
[9] R. F. Mann, J. C. Amphlett, M. A. I. Hooper, H. M. Jensen, B. A.
Peppley, and P.R. Roberge, "Development and application of a
generalized steady-state electrochemical model for a PEM fuel cell," J.
Power Sources, vol. 86, pp. 173-180, 2000.
[10] A.Molavi, M.Shahini, H.Rastgar, A.GHadimi, "Control of output power
of PEMFC based on calculating intelligence" 21th international power
system conference
[11] Martin T. Hagan and Mohammad B. Menhaj, "Training Feedforward
Networks with the Marquardt Algorithm", IEEE TRANSACTIONS ON
NEURAL NETWORKS, VOL. 5, NO. 6, NOVEMBER 1994.
[12] R. Battiti, "First- and second order methods for learning:
Betweensteepest descent and Newton-s method," Neural Computation,
vol. 4,no. 2, pp. 141-166, 1992.