Author
Listed:
- Filipe Menezes
- Sérgio Cunha
- William Assis
- Allan Manito
- Reinaldo Leite
- Thiago Soares
- Hugo Lott
Abstract
Electric energy storage systems have advanced significantly in recent years, driven by the growing expansion of renewable energy sources, the rise of electromobility, and other emerging configurations within the current electrical energy system. Among the various energy storage technologies, supercapacitors have gained considerable attention. Due to their ability to deliver large amounts of power over short periods, supercapacitors can be highly effective in hybrid storage systems, for example, enhancing overall system performance. Therefore, detailed studies on supercapacitors and their electrical circuit models have been developed with the aim of representing them as close as possible to actual physical behavior for numerous applications, such as in the context of Digital Twin (DT), an application that will support the monitoring of the operation and health of the supercapacitor throughout its useful life. The present work aims to estimate optimally some parameters of an electrical circuit model of a supercapacitor, in such a way as to obtain responses with very low errors and, thus, be able to use this computational electrical modeling for the development of a Digital Twin system. For the optimal adjustment of the electrical circuit model parameters, a Genetic Algorithm (GA) is used. The response of the electrical circuit, adjusted by the Genetic Algorithm (GA), is then compared to the response obtained through computer simulation of a supercapacitor using PSIM software, which is a software well validated in such studies. The results demonstrated strong alignment between the response using GA and the response using PSIM. Specifically, the charge and discharge curves of the supercapacitor, obtained through GA adjustment and PSIM simulation, were very similar, showing an error of just 2.2%. Thus, the supercapacitor model adjusted via GA demonstrates a good response to the physical phenomenon in question and can be used to develop a Digital Twin (DT) system, aiding in the operational and health monitoring of the supercapacitor.
Suggested Citation
Filipe Menezes & Sérgio Cunha & William Assis & Allan Manito & Reinaldo Leite & Thiago Soares & Hugo Lott, 2025.
"Genetic algorithm for parameter optimization of supercapacitor model,"
PLOS ONE, Public Library of Science, vol. 20(7), pages 1-19, July.
Handle:
RePEc:plo:pone00:0325645
DOI: 10.1371/journal.pone.0325645
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0325645. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.