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Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

1
Electronics and Communications Unit and Intelligent Information System Unit, IK4-Tekniker, Calle Iñaki Goenaga 5, 20600 Eibar, Spain
2
Department of Electronics Technology, University of the Basque Country (UPV/EHU), 48080 Bilbao, Spain
*
Author to whom correspondence should be addressed.
Electronics 2018, 7(4), 44; https://doi.org/10.3390/electronics7040044
Received: 9 February 2018 / Revised: 22 March 2018 / Accepted: 28 March 2018 / Published: 29 March 2018
(This article belongs to the Special Issue Energy Harvesting for Smart Sensing System and IoT)
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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. View Full-Text
Keywords: supercapacitor; model; electro-mathematical; machine learning; neuronal networks; low power supercapacitor; model; electro-mathematical; machine learning; neuronal networks; low power
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pozo, B.; Garate, J.I.; Ferreiro, S.; Fernandez, I.; Fernandez de Gorostiza, E. Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications. Electronics 2018, 7, 44.

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