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dc.contributor.authorJiménez-Bermejo, David
dc.contributor.authorFraile-Ardanuy, Jesús
dc.contributor.authorCastaño-Solis, Sandra
dc.contributor.authorMerino, Julia
dc.contributor.authorAlvaro-Hermana, Roberto
dc.date.accessioned2018-04-27T13:35:29Z
dc.date.available2018-04-27T13:35:29Z
dc.date.issued2018
dc.identifier.citationJiménez-Bermejo, David, Jesús Fraile-Ardanuy, Sandra Castaño-Solis, Julia Merino, and Roberto Álvaro-Hermana. “Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles.” Procedia Computer Science 130 (2018): 533–540. doi:10.1016/j.procs.2018.04.077.en
dc.identifier.issn1877-0509en
dc.identifier.urihttp://hdl.handle.net/11556/539
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 situationsen
dc.description.sponsorshipThis work has been partially financed by the Spanish Ministry of Economy and Competitiveness within the framework of the project DEMS: “Sistema distribuido de gestión de energía en redes eléctricas inteligentes (TEC2015-66126-R)".en
dc.language.isoengen
dc.publisherElsevier B.V.en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleUsing Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehiclesen
dc.typeconferenceObjecten
dc.identifier.doi10.1016/j.procs.2018.04.077en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsArtificial neural networken
dc.subject.keywordsBattery packen
dc.subject.keywordsElectric vehiclesen
dc.subject.keywordsState-of-chargeen
dc.journal.titleProcedia Computer Scienceen
dc.page.final540en
dc.page.initial533en
dc.volume.number130en
dc.conference.title9th International Conference on Ambient Systems, Networks and Technologies, ANT-2018 and the 8th International Conference on Sustainable Energy Information Technology, SEIT 2018, 8-11 May, 2018, Porto, Portugalen


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