• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Parameter Identification and Model Reduction in the Design of Alkaline Methanol Fuel Cells
 
  • Details
  • Full
Options
2020
Journal Article
Title

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  orcid-logo
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
Journal
International journal on advances in systems and measurements  
Project(s)
MathEnergy
Funder
Bundesministerium für Wirtschaft und Energie BMWi (Deutschland)  
Deutsche Forschungsgemeinschaft DFG  
Link
Link
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024