Notice of Violation of IEEE Publication Principles
A Q-Newton Method Neural Network Model for PEM Fuel Cells

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 4th IEEE International Conference on Industrial Informatics (INDIN), August 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. Jamie, 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 Matl- ab/Simulink^reg 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 neural network model of PEMFC according to the different cell temperatures and different anode and cathode pressures.