Genetic algorithms in model structure identification for fuel cell polarization curve |
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Author: | Ohenoja, Markku1; Sorsa, Aki1; Leiviskä, Kauko1 |
Organizations: |
1Control Engineering Research Group, University of Oulu, P.O.Box 4300, 90014 Oulu, Finland |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.4 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2018082133861 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2018
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Publish Date: | 2018-08-21 |
Description: |
AbstractEvolutionary optimizers, such as genetic algorithms, have earlier been successfully applied to find the parameter values for the fuel cell polarization curve models. The structure of these, typically semi-empirical, models have evolved during the decades. In this study, the model structures were reviewed and a new model structure was generated. Genetic algorithms were used to determine the optimized model structure with linear model parameters. Four different fuel cells, one with varying operating conditions, were studied. The results show that the model can outperform the semi-empirical model utilized in number of studies without increasing the model complexity. see all
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Series: |
International Conference on Control, Decision and Information Technologies |
ISSN: | 2576-3547 |
ISSN-E: | 2576-3555 |
ISSN-L: | 2576-3547 |
ISBN: | 978-1-5386-5065-3 |
ISBN Print: | 978-1-5386-5066-0 |
Pages: | 1 - 6 |
DOI: | 10.1109/CoDIT.2018.8394829 |
OADOI: | https://oadoi.org/10.1109/CoDIT.2018.8394829 |
Host publication: |
2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), 10-3 April 2018, Thessaloniki, Greece |
Conference: |
International Conference on Control, Decision and Information Technologies |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
215 Chemical engineering 222 Other engineering and technologies |
Subjects: | |
Copyright information: |
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