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.