@article{CleesKlaassenNikitinetal.2020,
  author    = {Clees, Tanja and Klaassen, Bernhard and Nikitin, Igor and Nikitina, Lialia and Pott, Sabine and Krewer, Ulrike and Haisch, Theresa and Kubannek, Fabian},
  title     = {Parameter Identification and Model Reduction in the Design of Alkaline Methanol Fuel Cells},
  journal   = {International Journal on Advances in Systems and Measurements},
  volume    = {13},
  number    = {1\&2},
  issn      = {1942-261X},
  url       = {https://www.thinkmind.org/index.php?view=article\&articleid=sysmea_v13_n12_2020_9},
  institution = {Fachbereich Ingenieurwissenschaften und Kommunikation},
  pages     = {94 -- 106},
  year      = {2020},
  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.},
  language  = {en}
}