Using dynamic neural networks for battery state of charge estimation in electric vehicles

Vista sencilla de ítem
dc.contributor.authorJiménez Bermejo, David
dc.contributor.authorFraile Ardanuy, Jesús
dc.contributor.authorCastaño Solís, Sandra
dc.contributor.authorMerino, Julia
dc.contributor.authorÁlvaro Hermana, Roberto
dc.date.accessioned2026-05-06T11:08:53Z
dc.date.available2026-05-06T11:08:53Z
dc.date.issued2018
dc.date.updated2026-05-06T11:08:53Z
dc.descriptionPonencia presentada en la 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / 8th International Conference on Sustainable Energy Information Technology (SEIT-2018), celebradas conjuntamente en Oporto, Portugal, entre el 8 y el 11 de mayo de 2018es
dc.description.abstractDue to urban pollution, transport electrification is being currently promoted in different countries. Electric Vehicles (EVs) sales are growing all over the world, but there are still some challenges to be solved before a mass adoption of this type of vehicles occurs. One of the main drawbacks of EVs are their limited range, for that reason an accurate estimation of the state-of-charge (SOC) is required. The main contribution of this work is the design of a Nonlinear Autoregressive with External Input (NARX) artificial neural network to estimate the SOC of an EV using real data extracted from the car during its daily trips. The network is trained using voltage, current and four different battery pack temperatures as input and SOC as output. This network has been tested using 54 different real driving cycles, obtaining highly accurate results, with a mean squared error lower than 1e-6 in all situations.en
dc.identifier.citationJiménez-Bermejo, D., Fraile-Ardanuy, J., Castaño-Solis, S., Merino, J., & Álvaro-Hermana, R. (2018). Using dynamic neural networks for battery state of charge estimation in electric vehicles. Procedia Computer Science, 130, 533-540. https://doi.org/10.1016/J.PROCS.2018.04.077
dc.identifier.doi10.1016/j.procs.2018.04.077
dc.identifier.eissn1877-0509
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5872
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2018 The Authors
dc.subject.otherArtificial neural network
dc.subject.otherBattery pack
dc.subject.otherElectric vehicles
dc.subject.otherState-of-charge
dc.titleUsing dynamic neural networks for battery state of charge estimation in electric vehiclesen
dc.typeconference paper
dcterms.accessRightsopen access
oaire.citation.endPage540
oaire.citation.startPage533
oaire.citation.titleProcedia Computer Science
oaire.citation.volume130
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
jiménez_using_2018.pdf
Tamaño:
1.02 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Ponencias