Modelling of Ultracapacitors Using Recurrent Artificial Neural Network

Adrian Chmielewski , Jakub Możaryn , Piotr Piórkowski , Robert Gumiński , Krzysztof Jakub Bogdziński


This article presents an artificial neural network (ANN) model of the ultracapacitors based on experimental data acquired from laboratory purposely built test stand for dynamic load cycle tests. Because of a nonlinear description of discharging dynamics in subsequent cycles and a coupling of the terminal voltage and temperatures of a ultracapacitor, the recurrent artificial neural network structure (R-ANN) structure is proposed. As a result, it was presented the accuracy analysis based on the statistical quality indices of proposed modeling approach.
Author Adrian Chmielewski (FACME / IV)
Adrian Chmielewski,,
- Institute of Vehicles
, Jakub Możaryn (FM / IACR)
Jakub Możaryn,,
- The Institute of Automatic Control and Robotics
, Piotr Piórkowski (FACME / ICME)
Piotr Piórkowski,,
- The Institute of Construction Machinery Engineering
, Robert Gumiński (FACME / IAE)
Robert Gumiński,,
- Institute of Automotive Engineering
, Krzysztof Jakub Bogdziński (FACME / IV)
Krzysztof Jakub Bogdziński,,
- Institute of Vehicles
Publication size in sheets0.5
Book Szewczyk Roman, Zieliński Cezary, Kaliczyńska Małgorzata (eds.): Automation 2018: Advances in Automation, Robotics and Measurement Techniques, Advances in Intelligent Systems and Computing, vol. 743, 2018, Springer International Publishing, ISBN 978-3-319-77178-6, [978-3-319-77179-3], 795 p., DOI:10.1007/978-3-319-77179-3
Keywords in Englishultracapacitor, test stand, artificial neural networks, recurrent artificial neural network
Languageen angielski
LicensePublisher website (books and chapters only); published final; Uznanie Autorstwa (CC-BY); after publication
Score (nominal)20
Score sourcepublisherList
ScoreMinisterial score = 20.0, 20-10-2019, ChapterFromConference
Publication indicators WoS Citations = 2
Citation count*7 (2019-11-25)
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