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Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach
dc.contributor.author | García-Ordás, María Teresa | |
dc.contributor.author | Marcos del Blanco, David Yeregui | |
dc.contributor.author | Aveleira Mata, Jose Antonio | |
dc.contributor.author | Zayas-Gato, Francisco | |
dc.contributor.author | Jove, Esteban | |
dc.contributor.author | Casteleiro-Roca, José-Luis | |
dc.contributor.author | Quintián, Héctor | |
dc.contributor.author | Calvo-Rolle, José Luis | |
dc.contributor.author | Alaiz Moretón, Héctor | |
dc.date.accessioned | 2024-05-16T11:20:05Z | |
dc.date.issued | 2024-05-09 | |
dc.identifier.citation | María Teresa Ordás, David Yeregui Marcos del Blanco, José Aveleira-Mata, Francisco Zayas-Gato, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, José Luis Calvo-Rolle, Héctor Alaiz-Moreton, Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach, Logic Journal of the IGPL, 2024;, jzae021, https://doi.org/10.1093/jigpal/jzae021 | es_ES |
dc.identifier.issn | 1368-9894 | |
dc.identifier.uri | http://hdl.handle.net/2183/36498 | |
dc.description | This is a pre-copyedited, author-produced version of an article accepted for publication in Logic Journal of the IGPL following peer review. The version of record: María Teresa Ordás, David Yeregui Marcos del Blanco, José Aveleira-Mata, Francisco Zayas-Gato, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, José Luis Calvo-Rolle, Héctor Alaiz-Moreton, Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach, Logic Journal of the IGPL, 2024; jzae021, is available online at: https://doi.org/10.1093/jigpal/jzae021 | es_ES |
dc.description.abstract | [Abstract] Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user’s perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Oxford University Press | es_ES |
dc.relation.uri | https://doi.org/10.1093/jigpal/jzae021 | es_ES |
dc.subject | Battery charge | es_ES |
dc.subject | Hybrid models | es_ES |
dc.subject | k-Means | es_ES |
dc.subject | Agglomerative Clustering | es_ES |
dc.subject | Gaussian mixture | es_ES |
dc.subject | Spectral clustering | es_ES |
dc.title | Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
dc.date.embargoEndDate | 2025-05-09 | es_ES |
dc.date.embargoLift | 2025-05-09 | |
UDC.journalTitle | Logic Journal of the IGPL | es_ES |
dc.identifier.doi | https://doi.org/10.1093/jigpal/jzae021 |
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