Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles

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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.contributor.institutionTecnalia Research & Innovation
dc.date.issued2018
dc.descriptionPublisher Copyright: © 2018 The Authors. Published by Elsevier B.V.
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.statusPeer reviewed
dc.format.extent8
dc.format.extent1030496
dc.identifier.citationJiménez-Bermejo , D , Fraile-Ardanuy , J , Castaño-Solis , S , Merino , J & Alvaro-Hermana , R 2018 , ' Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles ' , Procedia Computer Science , vol. 130 , pp. 533-540 . https://doi.org/10.1016/j.procs.2018.04.077
dc.identifier.doi10.1016/j.procs.2018.04.077
dc.identifier.issn1877-0509
dc.identifier.otherresearchoutputwizard: 11556/539
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85051217310&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofProcedia Computer Science
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsArtificial neural network
dc.subject.keywordsBattery pack
dc.subject.keywordsElectric vehicles
dc.subject.keywordsState-of-charge
dc.subject.keywordsArtificial neural network
dc.subject.keywordsBattery pack
dc.subject.keywordsElectric vehicles
dc.subject.keywordsState-of-charge
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsSDG 11 - Sustainable Cities and Communities
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.subject.keywordsFunding Info
dc.subject.keywordsThis 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)".
dc.subject.keywordsThis 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)".
dc.titleUsing Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehiclesen
dc.typeconference output
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