Key parameters of the modeled supercapacitor.
<div><p>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 t...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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| _version_ | 1852018394100924416 |
|---|---|
| author | Filipe Menezes (13205305) |
| author2 | Sérgio Cunha (21736375) William Assis (21736378) Allan Manito (21736381) Reinaldo Leite (21736384) Thiago Soares (6148289) Hugo Lott (21736387) |
| author2_role | author author author author author author |
| author_facet | Filipe Menezes (13205305) Sérgio Cunha (21736375) William Assis (21736378) Allan Manito (21736381) Reinaldo Leite (21736384) Thiago Soares (6148289) Hugo Lott (21736387) |
| author_role | author |
| dc.creator.none.fl_str_mv | Filipe Menezes (13205305) Sérgio Cunha (21736375) William Assis (21736378) Allan Manito (21736381) Reinaldo Leite (21736384) Thiago Soares (6148289) Hugo Lott (21736387) |
| dc.date.none.fl_str_mv | 2025-07-17T17:38:48Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0325645.t006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Key_parameters_of_the_modeled_supercapacitor_/29593111 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Genetics Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified software well validated renewable energy sources present work aims hybrid storage systems gained considerable attention emerging configurations within deliver large amounts computational electrical modeling actual physical behavior electrical circuit models electrical circuit model response using psim response using ga digital twin system electrical circuit digital twin psim simulation physical phenomenon good response useful life short periods recent years parameter optimization optimal adjustment obtain responses numerous applications low errors highly effective growing expansion genetic algorithm ga adjustment ga ), estimate optimally discharge curves computer simulation advanced significantly |
| dc.title.none.fl_str_mv | Key parameters of the modeled supercapacitor. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_dbf5a00894e563766a683109c19f8b19 |
| identifier_str_mv | 10.1371/journal.pone.0325645.t006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29593111 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Key parameters of the modeled supercapacitor.Filipe Menezes (13205305)Sérgio Cunha (21736375)William Assis (21736378)Allan Manito (21736381)Reinaldo Leite (21736384)Thiago Soares (6148289)Hugo Lott (21736387)MedicineGeneticsBiotechnologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsoftware well validatedrenewable energy sourcespresent work aimshybrid storage systemsgained considerable attentionemerging configurations withindeliver large amountscomputational electrical modelingactual physical behaviorelectrical circuit modelselectrical circuit modelresponse using psimresponse using gadigital twin systemelectrical circuitdigital twinpsim simulationphysical phenomenongood responseuseful lifeshort periodsrecent yearsparameter optimizationoptimal adjustmentobtain responsesnumerous applicationslow errorshighly effectivegrowing expansiongenetic algorithmga adjustmentga ),estimate optimallydischarge curvescomputer simulationadvanced significantly<div><p>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.</p></div>2025-07-17T17:38:48ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0325645.t006https://figshare.com/articles/dataset/Key_parameters_of_the_modeled_supercapacitor_/29593111CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295931112025-07-17T17:38:48Z |
| spellingShingle | Key parameters of the modeled supercapacitor. Filipe Menezes (13205305) Medicine Genetics Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified software well validated renewable energy sources present work aims hybrid storage systems gained considerable attention emerging configurations within deliver large amounts computational electrical modeling actual physical behavior electrical circuit models electrical circuit model response using psim response using ga digital twin system electrical circuit digital twin psim simulation physical phenomenon good response useful life short periods recent years parameter optimization optimal adjustment obtain responses numerous applications low errors highly effective growing expansion genetic algorithm ga adjustment ga ), estimate optimally discharge curves computer simulation advanced significantly |
| status_str | publishedVersion |
| title | Key parameters of the modeled supercapacitor. |
| title_full | Key parameters of the modeled supercapacitor. |
| title_fullStr | Key parameters of the modeled supercapacitor. |
| title_full_unstemmed | Key parameters of the modeled supercapacitor. |
| title_short | Key parameters of the modeled supercapacitor. |
| title_sort | Key parameters of the modeled supercapacitor. |
| topic | Medicine Genetics Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified software well validated renewable energy sources present work aims hybrid storage systems gained considerable attention emerging configurations within deliver large amounts computational electrical modeling actual physical behavior electrical circuit models electrical circuit model response using psim response using ga digital twin system electrical circuit digital twin psim simulation physical phenomenon good response useful life short periods recent years parameter optimization optimal adjustment obtain responses numerous applications low errors highly effective growing expansion genetic algorithm ga adjustment ga ), estimate optimally discharge curves computer simulation advanced significantly |