The Impact of Driver Behavior on Electric Bus Energy Consumption: Optimizing Driver Performance with Bio-Inspired WUTP Algorithm with Real-Time Big Data

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dc.contributor.authorEkici, Yunus Emre
dc.contributor.authorAkdağ, Ozan
dc.contributor.authorYıldıran, Nisanur
dc.contributor.authorKaradağ, Teoman
dc.date.accessioned2026-04-04T13:19:03Z
dc.date.available2026-04-04T13:19:03Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThe transition toward sustainable urban mobility requires not only technological innovations in electric buses (E-Buses) but also optimization of operational factors such as driver behavior, which significantly influences energy consumption and driving range. This study develops a novel artificial intelligence framework, integrating real-time big data with a bio-inspired Water Uptake and Transport in Plants (WUTP) algorithm, to optimize E-Bus driver performance under real-world conditions. Data were collected from trolleybus-type hybrid electric buses operating in Malatya, Turkey, encompassing nearly 50 milhon observations across diverse seasonal, topographical, and operational contexts. Through preprocessing and correlation-based feature selection, 14 key parameters—including regenerative braking, auxiliary loads (HVAC and static converters), acceleration, and road slope—were identified as critical determinants of energy consumption. The WUTP algorithm, implemented with 60,000 representative data rows, generated optimized driving profiles and weighting coefficients, enabling precise estimation of optimal operational thresholds. Results reveal that maintaining regenerative braking above 77%, moderating accelerator pedal use at approximately 44%, and stabilizing average vehicle speed significantly extend range and reduce energy demand. Comparative evaluation of six drivers demonstrated efficiency disparities exceeding 30%, underscoring the importance oftraining and monitoring systems. The proposed model is distinguished by its dynamic treatment of auxiliary loads, scalability across routes and climates, and applicability for fleet planning, battery sizing, and eco-driving assessment. Overall, this research contributes a robust, adaptable, and scalable framework that enhances operational efficiency, reduces environmental impact, and supports the broader deployment of sustainable E-Bus systems in global transit networks. © (2025), (Society of Automotive Engineers Turkey). All rights reserved.
dc.description.sponsorshipMalatya Metropolitan Municipality Transportation Services; MOTAŞ
dc.identifier.doi10.30939/ijastech.1789079
dc.identifier.endpage617
dc.identifier.issn2587-0963
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105031530265
dc.identifier.scopusqualityQ3
dc.identifier.startpage602
dc.identifier.urihttps://doi.org/10.30939/ijastech.1789079
dc.identifier.urihttps://hdl.handle.net/11616/108122
dc.identifier.volume9
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSociety of Automotive Engineers Turkey
dc.relation.ispartofInternational Journal of Automotive Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.subjectBig data
dc.subjectElectric bus
dc.subjectEnergy consumption
dc.subjectOptimization
dc.titleThe Impact of Driver Behavior on Electric Bus Energy Consumption: Optimizing Driver Performance with Bio-Inspired WUTP Algorithm with Real-Time Big Data
dc.typeArticle

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