Real-time range estimation in electric vehicles using fuzzy logic classifier

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dc.contributor.authorÇeven, Süleyman
dc.contributor.authorAlbayrak, Ahmet
dc.contributor.authorBayır, Raif
dc.date.accessioned2020-04-30T13:33:13Z
dc.date.available2020-04-30T13:33:13Z
dc.date.issued2020
dc.departmentDÜ, Düzce Meslek Yüksekokulu, Elektronik ve Otomasyon Bölümüen_US
dc.description.abstractNowadays, many scientists and companies in the automotive sector in the world are undertaking many important studies on electric vehicle technologies. For the electric vehicle to function as desired, the subsystems of the vehicle must be monitored and the parameters related to the vehicle must be kept in the most efficient range. Efficient use of these systems in electric vehicle will increase the vehicle range, as well as ensure the long life of the components used in the vehicle subsystems. Today, problem areas such as calculating the range of electric vehicles and battery state of charge have not yet been sufficiently standardized. The aim of this study is to make a range estimation in electric vehicle with fuzzy logic classifier which has been successfully applied in various problem areas. The fuzzy logic classifier is designed for range estimation, which is one of the most important research areas of electric vehicles today. In the Mamdani type fuzzy logic approach, dynamic vehicle parameters are taken into consideration. The fuzzy logic classifier considers the battery parameters of the vehicle and the power consumed instantly. In the prediction system, the power spent on the vehicle and the battery charge status are selected as inputs. The developed system was evaluated with three different test scenarios on the same track. These tests were conducted with no load (driver only), half load (driver + one person) and fully load (driver + three persons). The fuzzy logic classifier system determines in real-time how far electric vehicle can travel. © 2020 Elsevier Ltden_US
dc.description.sponsorshipKBÜ-BAP-16/1-YL-098 Türkiye Bilimsel ve Teknolojik AraÅŸtirma Kurumuen_US
dc.description.sponsorshipThis study was financially funded by Karabük University Electric Vehicles Team (KBUELAR), of which The Scientific and Technological Research Council of Turkey (TÜBİTAK) is the major funder, and Karabük University Scientific Research Projects (BAP; Project Number: KBÜ-BAP-16/1-YL-098).en_US
dc.identifier.doi10.1016/j.compeleceng.2020.106577en_US
dc.identifier.issn0045-7906
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.compeleceng.2020.106577
dc.identifier.urihttps://hdl.handle.net/20.500.12684/580
dc.identifier.volume83en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers and Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCan communication; Electric vehicles; Fuzzy logic classifier; Range estimationen_US
dc.titleReal-time range estimation in electric vehicles using fuzzy logic classifieren_US
dc.typeArticleen_US

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