DocumentCode :
2323376
Title :
Notice of Violation of IEEE Publication Principles
A Q-Newton Method Neural Network Model for PEM Fuel Cells
Author :
Hatti, M. ; Tioursi, M. ; Nouibat, W.
Author_Institution :
Univ. des Sci. et de la Technol. d´´Oran, Oran
fYear :
2006
fDate :
9-12 July 2006
Firstpage :
150
Lastpage :
155
Abstract :
Notice of Violation of IEEE Publication Principles

"A Q-Newton Method Neural Network Model for PEM Fuel Cells"
by M. Hatti, M. Tioursi, and W. Nouibat
in the Proceedings of the First International Symposium on Environment Identities and Mediterranean Area, 2006. ISEIMA \´06, July 2006

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper was found to be a near verbatim copy of the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"On-board Fuel Cell Power Supply Modeling on the Basis of Neural Network Methodology"
by S. Jemei, D. Hissel, M.C. Pera, and J.M. Kauffmann,
in Journal of Power Sources 124 (2003), Elsevier, pp 479-486

Proton exchange membranes are one of the most promising fuel cell technologies for transportation and residential applications. Considering these two aims applications, a simulation model of the whole fuel cell system is a major milestone. This would lead to the possibility of optimizing the complete system. In a fuel cell system, there is a strong relationship between available electrical power and actual operating conditions: gas conditioning, membrane hydration state, temperature, current set point . . . Thus, a "minimal behavioural model" of a fuel cell system able to evaluate the output variables and their variations is highly interesting. Artificial neural networks (NN) are a very efficient tool to reach such an aim. In this paper, a proton exchange membrane fuel cell (PEMFC) neural network model is proposed using a Quasi-Newton method- . It is implemented on Matlab/Simulinkreg software. The model uses experimental data found in literature as training specimens; on the condition the system is provided enough hydrogen. Considering the cell operational temperature as inputs, the cell voltage and current density as the outputs and establishing the electric characteristic model of PEMFC according to the different cell temperatures.
Keywords :
mathematics computing; neural nets; proton exchange membrane fuel cells; transportation; Matlab/Simulink; PEM fuel cells; artificial neural networks; electrical power; gas conditioning; membrane hydration state; proton exchange membrane fuel cell; proton exchange membranes; quasiNewton method; residential applications; transportation applications; Proton exchange membrane fuel cell; Quasi-Newton algorithm; nonlinear system modelling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Environment Identities and Mediterranean Area, 2006. ISEIMA '06. First international Symposium on
Conference_Location :
Corte-Ajaccio
Print_ISBN :
1-4244-0231-X
Electronic_ISBN :
1-4244-0232-8
Type :
conf
DOI :
10.1109/ISEIMA.2006.344946
Filename :
4150460
Link To Document :
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