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

Basit Öğe Kaydı
dc.authoridCEVEN, SULEYMAN/0000-0002-8970-4826
dc.authoridALBAYRAK, AHMET/0000-0002-2166-1102
dc.contributor.authorCeven, Suleyman
dc.contributor.authorAlbayrak, Ahmet
dc.contributor.authorBayir, Raif
dc.date.accessioned2024-09-29T15:55:14Z
dc.date.available2024-09-29T15:55:14Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_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. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipKarabuk University Electric Vehicles Team (KBUELAR); Scientific and Technological Research Council of Turkey (TUBITAK); Karabuk University Scientific Research Projects (BAP) [KBU-BAP-16/1-YL-098]en_US
dc.description.sponsorshipThis study was financially funded by Karabuk University Electric Vehicles Team (KBUELAR), of which The Scientific and Technological Research Council of Turkey (TUBITAK) is the major funder, and Karabuk University Scientific Research Projects (BAP; Project Number: KBU-BAP-16/1-YL-098).en_US
dc.identifier.doi10.1016/j.compeleceng.2020.106577
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85079861742en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2020.106577
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4515
dc.identifier.volume83en_US
dc.identifier.wosWOS:000530032500008en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectric vehiclesen_US
dc.subjectRange estimationen_US
dc.subjectFuzzy logic classifieren_US
dc.subjectCan communicationen_US
dc.titleReal-time range estimation in electric vehicles using fuzzy logic classifieren_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu