Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach

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dc.contributor.authorEkici, Yunus Emre
dc.contributor.authorKaradag, Teoman
dc.contributor.authorAkdag, Ozan
dc.contributor.authorAydin, Ahmet Arif
dc.contributor.authorTekin, Hueseyin Ozan
dc.date.accessioned2026-06-19T06:39:55Z
dc.date.available2026-06-19T06:39:55Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractElectrical and mechanical failures in electric vehicles (EVs) during passenger operation cause significant operational losses and elevated energy consumption, amplifying range anxiety. To address this issue, we utilized 250,000 rows of real-time data from electric trolleybuses operating in T & uuml;rkiye to develop a robust artificial intelligence (AI)-based optimization model for failure mitigation. Initially, Tri layered Neural Network (TNN) was employed to create a predictive function for electrical and mechanical failures, followed by comparative analyses across six optimization algorithms widely adopted in failure prediction studies. Among these, the Developed Honey Badger Algorithm with AI Approach (DHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). The DHBA incorporates a Dynamic Fitness-Distance Balance (DFDB) mechanism and a novel spiral motion feature to enhance search precision, leading to the DHBA-FPM (Developed-Honey Badger Algorithm - Failure Prediction Model). The final DHBA-FPM model was applied to the 10 highest-density bus routes in T & uuml;rkiye to predict and optimize failures. Results indicate that applying the DHBA-FPM model across these routes yielded a 3.96 % average range increase in EVs, extending the total range by approximately 79,200 km annually. It can be concluded that the model could prevent the release of 238.7 tons/year of CO2, NO, and NO2 emissions through its potential to improve both the operational efficiency and sustainability of EVs in public transit networks.
dc.description.sponsorshipResearch Fund of Inonu University [FDK-2023-3215, FDP-2021-2678, FBG-2021-2283]
dc.description.sponsorshipThis study is supported by the Research Fund of Inonu University with Project Number: FDK-2023-3215, FDP-2021-2678 and FBG-2021-2283.
dc.identifier.doi10.1016/j.icte.2025.03.009
dc.identifier.endpage558
dc.identifier.issn2405-9595
dc.identifier.issue3
dc.identifier.orcid0000-0001-7791-0473
dc.identifier.orcid0000-0001-8163-8898
dc.identifier.orcid0000-0002-4124-7275
dc.identifier.scopus2-s2.0-105002802922
dc.identifier.scopusqualityQ1
dc.identifier.startpage547
dc.identifier.urihttps://doi.org/10.1016/j.icte.2025.03.009
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5850
dc.identifier.volume11
dc.identifier.wosWOS:001518608900014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofIct Express
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectElectric Vehicles
dc.subjectMechanical And Electrical Failure
dc.subjectRange Anxiety
dc.subjectOptimization
dc.titleEnhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach
dc.typeArticle

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