Journal
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 35, Issue 21, Pages 12125-12133Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2009.09.071
Keywords
Artificial neural network (ANN); Polymeric electrolyte membrane fuel cell (PEMFC); Backpropagation (BP); Modeling
Categories
Funding
- Istituto di Tecnologie Avanzate per l'Energia (CNR-ITAE)
- CONACYT, Mexico
- Mexican Council for Science and Technology [81728]
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Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs -2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (C) 2009 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.
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