Time-Domain Circuit Modelling for Hybrid Supercapacitors

Fabio Corti, Michelangelo-Santo Gulino, Maurizio Laschi, Gabriele Maria Lozito, Luca Pugi, Alberto Reatti and Dario Vangi
Additional contact information
Fabio Corti: Dipartimento di Ingegneria, Università di Perugia, Via G. Duranti 67, 06125 Perugia, Italy
Michelangelo-Santo Gulino: Dipartimento di Ingegneria Industriale, Università di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Maurizio Laschi: Dipartimento di Ingegneria Industriale, Università di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Gabriele Maria Lozito: Dipartimento di Ingegneria dell’Informazione, Università di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Luca Pugi: Dipartimento di Ingegneria Industriale, Università di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Alberto Reatti: Dipartimento di Ingegneria dell’Informazione, Università di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Dario Vangi: Dipartimento di Ingegneria Industriale, Università di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy

Energies, 2021, vol. 14, issue 20, 1-16

Abstract: Classic circuit modeling for supercapacitors is limited in representing the strongly non-linear behavior of the hybrid supercapacitor technology. In this work, two novel modeling techniques suitable to represent the time-domain electrical behavior of a hybrid supercapacitor are presented. The first technique enhances a well-affirmed circuit model by introducing specific non-linearities. The second technique models the device through a black-box approach with a neural network. Both the modeling techniques are validated experimentally using a workbench to acquire data from a real hybrid supercapacitor. The proposed models, suitable for different supercapacitor technologies, achieve higher accuracy and generalization capabilities compared to those already presented in the literature. Both modeling techniques allow for an accurate representation of both short-time domain and steady-state simulations, providing a valuable asset in electrical designs featuring supercapacitors.

Keywords: supercapacitors; equivalent circuit models; time-domain; model identification; neural networks; genetic algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/20/6837/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/20/6837/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:20:p:6837-:d:659745

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().