Design and Implementation of SOC Prediction for a Li-Ion Battery Pack in an Electric Car with an Embedded System

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dc.authoridBAYIR, Raif/0000-0003-3155-8771
dc.contributor.authorSoylu, Emel
dc.contributor.authorSoylu, Tuncay
dc.contributor.authorBayir, Raif
dc.date.accessioned2024-09-29T16:08:05Z
dc.date.available2024-09-29T16:08:05Z
dc.date.issued2017
dc.departmentKarabük Üniversitesien_US
dc.description.abstractLi-Ion batteries are widely preferred in electric vehicles. The charge status of batteries is a critical evaluation issue, and many researchers are studying in this area. State of charge gives information about how much longer the battery can be used and when the charging process will be cut off. Incorrect predictions may cause overcharging or over-discharging of the battery. In this study, a low-cost embedded system is used to determine the state of charge of an electric car. A Li-Ion battery cell is trained using a feed-forward neural network via Matlab/Neural Network Toolbox. The trained cell is adapted to the whole battery pack of the electric car and embedded via Matlab/Simulink to a low-cost microcontroller that proposed a system in real-time. The experimental results indicated that accurate robust estimation results could be obtained by the proposed system.en_US
dc.description.sponsorshipKarabuk University [KBU-BAP-13/2-DR-007]; TUBITAK Efficiency Challenge Electric Vehicleen_US
dc.description.sponsorshipKarabuk University supported this study within the scope of Scientific Research Projects (KBU-BAP-13/2-DR-007). This study is also supported by the TUBITAK Efficiency Challenge Electric Vehicle.en_US
dc.identifier.doi10.3390/e19040146
dc.identifier.issn1099-4300
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85024370326en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/e19040146
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7346
dc.identifier.volume19en_US
dc.identifier.wosWOS:000400579500011en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofEntropyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectembedded systemen_US
dc.subjectLi-Ion batteryen_US
dc.subjectelectricen_US
dc.subjectstate-of-chargeen_US
dc.subjectfeed-forward neural networken_US
dc.subjectbattery monitoring softwareen_US
dc.titleDesign and Implementation of SOC Prediction for a Li-Ion Battery Pack in an Electric Car with an Embedded Systemen_US
dc.typeArticleen_US

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