Supercapacitor Electro-Mathematical And Machine Learning Modelling For Low Power Applications
dc.contributor.author | Pozo Larrocha, Borja | |
dc.contributor.author | Gárate Añibarro, José Ignacio | |
dc.contributor.author | Ferreiro, Susana | |
dc.contributor.author | Fernández, Izaskun | |
dc.contributor.author | Fernández de Gorostiza, Erlantz | |
dc.date.accessioned | 2018-07-05T12:06:32Z | |
dc.date.available | 2018-07-05T12:06:32Z | |
dc.date.issued | 2018-03-29 | |
dc.identifier.citation | Electronics 7 : (2018) // Article ID 44 | es_ES |
dc.identifier.issn | 2079-9292 | |
dc.identifier.uri | http://hdl.handle.net/10810/27924 | |
dc.description.abstract | Low power electronic systems, whenever feasible, use supercapacitors to store energy instead of batteries due to their fast charging capability, low maintenance and low environmental footprint. To decide if supercapacitors are feasible requires characterising their behaviour and performance for the load profiles and conditions of the target. Traditional supercapacitor models are electromechanical, require complex equations and knowledge of the physics and chemical processes involved. Models based on equivalent circuits and mathematical equations are less complex and could provide enough accuracy. The present work uses the latter techniques to characterize supercapacitors. The data required to parametrize the mathematical model is obtained through tests that provide the capacitors charge and discharge profiles under different conditions. The parameters identified are life cycle, voltage, time, temperature, moisture, Equivalent Series Resistance (ESR) and leakage resistance. The accuracy of this electro-mathematical model is improved with a remodelling based on artificial neuronal networks. The experimental data and the results obtained with both models are compared to verify and weigh their accuracy. Results show that the models presented determine the behaviour of supercapacitors with similar accuracy and less complexity than electromechanical ones, thus, helping scaling low power systems for given conditions. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | supercapacitor | es_ES |
dc.subject | model | es_ES |
dc.subject | electro-mathematical | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | neuronal networks | es_ES |
dc.subject | low power | es_ES |
dc.subject | wireless sensor nodes | es_ES |
dc.subject | automotive applications | es_ES |
dc.subject | temperature | es_ES |
dc.subject | capacitors | es_ES |
dc.subject | management | es_ES |
dc.subject | networks | es_ES |
dc.title | Supercapacitor Electro-Mathematical And Machine Learning Modelling For Low Power Applications | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | http://www.mdpi.com/2079-9292/7/4/44 | es_ES |
dc.identifier.doi | 10.3390/electronics7040044 | |
dc.departamentoes | Tecnología electrónica | es_ES |
dc.departamentoeu | Teknologia elektronikoa | es_ES |
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Except where otherwise noted, this item's license is described as © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).