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.
Skip Nav Destination
Article navigation
June 2013
This article was originally published in
Journal of Fuel Cell Science and Technology
Design Innovations
A Predictive Control Based on Neural Network for Dynamic Model of Proton Exchange Membrane Fuel Cell
M. Rezaei,
M. Rezaei
Department of Electrical and Computer Engineering,
e-mail: m.rezaei@sbu.ac.ir
Shahid Beheshti University
,G. C., Evin 1983963113, Tehran
, Iran
e-mail: m.rezaei@sbu.ac.ir
Search for other works by this author on:
M. Mohseni
M. Mohseni
School of Electrical and Computer Engineering,
University College of Engineering,
e-mail: m_mohseni@ut.ac.ir
University College of Engineering,
University of Tehran
,North Kargar Street
,11365-4563 Tehran
, Iran
e-mail: m_mohseni@ut.ac.ir
Search for other works by this author on:
M. Rezaei
Department of Electrical and Computer Engineering,
e-mail: m.rezaei@sbu.ac.ir
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,
e-mail: m_mohseni@ut.ac.ir
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. Jun 2013, 10(3): 035001 (5 pages)
Published Online: May 7, 2013
Article history
Received:
September 6, 2012
Revision Received:
January 29, 2013
Citation
Rezaei, M., and Mohseni, M. (May 7, 2013). "A Predictive Control Based on Neural Network for Dynamic Model of Proton Exchange Membrane Fuel Cell." ASME. J. Fuel Cell Sci. Technol. June 2013; 10(3): 035001. https://doi.org/10.1115/1.4023838
Download citation file:
Get Email Alerts
Cited By
Analytical Modeling of Water Droplet Behavior at the Gas Channel Corner for Proton Exchange Membrane Fuel Cells
J. Electrochem. En. Conv. Stor (February 2025)
Lithium-ion battery capacity prediction method based on improved extreme learning machine
J. Electrochem. En. Conv. Stor
Composite structural battery: A review
J. Electrochem. En. Conv. Stor
Related Articles
Use of Optimal Controls to Mitigate Competing Performance Objectives in a Polymer Electrolyte Membrane Fuel Cell System
J. Fuel Cell Sci. Technol (August,2009)
Dynamic Test and Real-time Control Platform of Anode Recirculation for PEM Fuel Cell Systems
J. Fuel Cell Sci. Technol (August,2006)
Thermal and Air Management of an Open Cathode Proton Exchange Membrane Fuel Cell Using Sliding Mode Control
J. Electrochem. En. Conv. Stor (May,2024)
PEM Fuel Cell Dynamic Model With Phase Change Effect
J. Fuel Cell Sci. Technol (November,2005)
Related Proceedings Papers
Related Chapters
Design of Hopfield Neural Network Controller for an Inchworm Miniature Robot Locomotion
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
A Semi-Adaptive Fractional Order PID Control Strategy for a Certain Gun Control Equipment
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Designing an Artificial Muscle Based on PID Controller and Tuned by Neural Network with a NN Identification of the Plant
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16