Mougenot, M. (2011) Elaboration et optimisation d’électrodes de piles PEMFC à très faible taux de platine par pulvérisation plasma. Ph.D. Thesis, Université d’Orléans, Orléans.

has been cited by the following article:

• TITLE: Mechanistic Model versus Artificial Neural Network Model of a Single-Cell PEMFC

  AUTHORS: Brigitte Grondin-Perez, Sébastien Roche, Carole Lebreton, Michel Benne, Cédric Damour, Jean-Jacques Amangoua Kadjo

  KEYWORDS: Mechanistic Model, Artificial Neural Network Model, Proton Exchange Membrane Fuel Cell, Real-Time Experiment

  JOURNAL NAME: Engineering, Vol.6 No.8, July 11, 2014

  ABSTRACT: Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.