Mostrar el registro sencillo del ítem

dc.contributor.authorDriscoll, Lewis
dc.contributor.authorDe la Torre, Sebastián
dc.contributor.authorGomez-Ruiz, Jose Antonio 
dc.date.accessioned2022-06-23T08:50:48Z
dc.date.available2022-06-23T08:50:48Z
dc.date.issued2022-06
dc.identifier.citationLewis Driscoll, Sebastián de la Torre, Jose Antonio Gomez-Ruiz, Feature-based lithium-ion battery state of health estimation with artificial neural networks, Journal of Energy Storage, Volume 50, 2022, 104584, ISSN 2352-152X, https://doi.org/10.1016/j.est.2022.104584es_ES
dc.identifier.urihttps://hdl.handle.net/10630/24469
dc.description.abstractPrecise online lithium-ion battery state of health estimation is critical for the correct operation and management of battery-based energy storage systems such as microgrids and electric vehicles. However, in such applications it is necessary to maintain standard operation and therefore difficult to experimentally determine. Advancements in machine learning techniques and capabilities allow for precise and efficient data-driven predictions. In this paper we propose a simple, yet effective state of health estimation model based on the extraction of features observed from patterns in the voltage, current and temperature profiles during the charging process, which then through artificial neural networks allow for per cycle estimations. We then apply this model to two groups of batteries from the NASA Ames PCoE Battery data set. Results show that the proposed model is capable of estimating the state of health of batteries discharged under varied conditions with resulting coefficients of determination between 0.896 and 0.992 while also employing significantly less input data than other works.es_ES
dc.description.sponsorshipThis work was partially supported by the Junta de Andalucia, Spain under project UMA18-FEDERJA-150 and Gobierno de España, Spain under project RTI2018-093421-B-100. Funding for open access charge: Universidad de Málaga–CBUA .es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subject.otherLithium-ion batterieses_ES
dc.subject.otherState of health Estimationes_ES
dc.subject.otherData-drivenes_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherArtificial neural networkses_ES
dc.titleEstimación del estado de salud de la batería de iones de litio basada en características con redes neuronales artificialeses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.identifier.doihttps://doi.org/10.1016/j.est.2022.104584
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


Ficheros en el ítem

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional