Measurement-based assessment and optimization of electric bus energy consumption under thermal variability using long-term field data

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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, Huseyin Ozan
dc.date.accessioned2026-06-19T06:39:47Z
dc.date.available2026-06-19T06:39:47Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThis study investigates the effect of ambient temperature on the energy consumption of overhead battery-hybrid electric buses, also known as trolleybuses. The analysis uses real field data. The data were collected from 22 hybrid buses over 24 months. Each bus was monitored during daily passenger service. The onboard black-box units recorded the main operating signals at 1 Hz. This allowed second-by-second changes in speed, braking, passenger load, and energy use to be examined under real route conditions. The study is not based on standard driving cycles or simulation data. It uses measured data from daily bus operation. The dataset includes energy consumption, regenerative braking, vehicle speed, passenger load, road gradient, auxiliary power demand, and ambient temperature. Before the analysis, missing, corrupted, and inconsistent records were removed. This step was used to improve the reliability of the measurement dataset. In this study, outside temperature is not treated as a direct electric motor parameter. It is considered a factor that affects total energy use through battery behavior and heating, ventilation, and air conditioning demand. Different prediction methods were tested on the processed data. These methods include Decision Trees, Ensemble Learning, Gaussian Process Regression, Support Vector Machines, and Three-Layer Neural Networks. The Three-Layer Neural Network optimized with the Modified Tunicate Swarm Algorithm achieved the best predictive performance. According to the analysis results, the model aimed to provide a practical basis for seasonal energy forecasting and route-based fleet planning by evaluating the sensitivity of energy consumption over a wide temperature range.
dc.identifier.doi10.1016/j.measurement.2026.121795
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-105038851913
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2026.121795
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5793
dc.identifier.volume280
dc.identifier.wosWOS:001770469200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofMeasurement
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectElectric Bus Energy Measurement
dc.subjectCanbus Data
dc.subjectField Measurements
dc.subjectAmbient Temperature
dc.subjectRegenerative Braking
dc.subjectData Reliability
dc.subjectEnergy Consumption
dc.titleMeasurement-based assessment and optimization of electric bus energy consumption under thermal variability using long-term field data
dc.typeArticle

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