Andraž Kravos (Author), Ambrož Kregar (Author), Tomaž Katrašnik (Author)

Abstract

Successful parametrization and re-parametrization of the models used in PEMFC observer applications is instrumental to assure ideal control of the system. To increase ease of parametrisation and accuracy of parameter determination, this paper presents framework of twin analytical ansatzes for modelling PEMFC response in time and frequency domains sharing same set of calibration parameters. Owing to thermodynamically consistent modelling basis of the twin models, which are based on electrochemical model with state-of-the-art extrapolation capabilities and replication of the experimental data with one set of calibration parameters, they are valid in all current density regions. Furthermore, unique sharing of the calibration parameters enables unprecedented enrichment of the dataset that can be used to determine values of model's calibration parameters with higher certainty or enhances identification of individual calibration parameters that are otherwise harder to be uniquely determined. Additionally, proposed hybrid methodology also enables a significant reduction in the measurement time and enable re-parametrization on the fly.

Keywords

fuel cells;proton exchange membrane;modelling;electrochemical impedance spectroscopy;parametrisation in time and frequency domain;diagnostic and control applications;

Data

Language: English
Year of publishing:
Typology: 1.08 - Published Scientific Conference Contribution
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 519.61:621.3(045)
COBISS: 30901507 Link will open in a new window
ISSN: 1938-6737
Views: 1789
Downloads: 379
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Other data

Secondary language: Slovenian
Secondary keywords: gorivne celice;protonske izmenjevalne membrane;modeliranje;elektrokemična impendančna spektroskopija;parametrizacija v časovni in frekvenčni domeni;diagnostične in kontrolne aplikacije;
Type (COBISS): Article
Pages: str. 13-24
Volume: ǂVol. ǂ98
Issue: ǂno. ǂ9
Chronology: 2020
DOI: 10.1149/09809.0013ecst
ID: 12801955