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  4. Parameter Identification and Model Reduction in the Design of Alkaline Methanol Fuel Cells
 
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2020
Journal Article
Titel

Parameter Identification and Model Reduction in the Design of Alkaline Methanol Fuel Cells

Abstract
Alkaline methanol oxidation is an important electrochemical process in the design of efficient fuel cells. Typically, a system of ordinary differential equations is used to model the kinetics of this process. The fitting of the parameters of the underlying mathematical model is performed on the basis of different types of experiments, characterizing the fuel cell. In this paper, we describe generic methods for creation of a mathematical model of electrochemical kinetics from a given reaction network, as well as for identification of parameters of this model. We also describe methods for model reduction, based on a combination of steady-state and dynamical descriptions of the process. The methods are tested on a range of experiments, including different concentrations of the reagents and different voltage range.
Author(s)
Clees, Tanja
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Klaassen, Bernhard
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Nikitin, Igor
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Nikitina, Lialia
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Pott, Sabine
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Krewer, Ulrike
KIT
Haisch, Theresa
DECHEMA
Kubannek, Fabian
DECHEMA
Zeitschrift
International journal on advances in systems and measurements
Project(s)
MathEnergy
Funder
Bundesministerium für Wirtschaft und Energie BMWi (Deutschland)
Deutsche Forschungsgemeinschaft DFG
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