Neural network modeling strategy applied to a multi-stack PEM fuel cell system

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Neural network modeling strategy applied to a multi-stack PEM fuel cell system

da Costa Lopes, Francisco / Kelouwani, Sousso / Boulon, Loic / Agbossou, Kodjo / Marx, Neigel / Ettihir, Khalid

This work proposes applying a modeling methodology based on recurrent neural networks to a multi-stack fuel cell system composed of four Proton Exchange Membrane Fuel Cell (PEMFC) stacks. Even if the stacks have the same rated power and are from the same manufacturer, very often they present different performances (voltage response, efficiency and power curves). In this way, a model able to predict the behavior of each stack is necessary to guarantee an optimized operation of the whole system. Hence, the aforementioned methodology is used to obtain a prediction model for each stack aiming at their final application in a predictive control system. The models are also able to predict the power availability of the multi-stack system, being useful to be employed in the prognostics of the performance of the system in a vehicular application.

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Document information

Title :

Neural network modeling strategy applied to a multi-stack PEM fuel cell system

Contributors:

da Costa Lopes, Francisco (author) / Kelouwani, Sousso (author) / Boulon, Loic (author) / Agbossou, Kodjo (author) / Marx, Neigel (author) / Ettihir, Khalid (author)

Published in:

2016 IEEE Transportation Electrification Conference and Expo (ITEC) ; 1-7

Publication date :

2016-06-01

Size :

385697 byte

ISBN :

978-1-5090-0402-7 , 978-1-5090-0403-4

DOI :

https://doi.org/10.1109/ITEC.2016.7520294

Type of media :

Conference paper