A Novel Energy Consumption Prediction Model of Electric Buses Using Real-Time Big Data From Route, Environment, and Vehicle Parameters

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dc.authoridakdağ, ozan/0000-0001-8163-8898
dc.authoridakdag, ozan/0000-0001-8163-8898
dc.authoridAydin, Ahmet Arif/0000-0002-4124-7275
dc.authoridakdag, ozan/0000-0001-8163-8898
dc.authoridakdag, ozan/0000-0001-8163-8898
dc.authorwosidakdağ, ozan/GOH-3170-2022
dc.authorwosidakdag, ozan/KNM-5436-2024
dc.authorwosidAydin, Ahmet Arif/K-6184-2019
dc.authorwosidakdag, ozan/KIC-8784-2024
dc.authorwosidakdag, ozan/KIC-9241-2024
dc.contributor.authorEkici, Yunus Emre
dc.contributor.authorAkdag, Ozan
dc.contributor.authorAydin, Ahmet Arif
dc.contributor.authorKaradag, Teoman
dc.date.accessioned2024-08-04T20:54:46Z
dc.date.available2024-08-04T20:54:46Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractElectric vehicles (EVs) have positive impacts on reducing oil dependence and exhaust emissions. However, the range problem of EVs is a factor that raises concerns for individual users and bus operators. For this reason, studies on increasing the range of the electric buses in public transportation is extremely important to ensure optimum operation. In this study, a novel energy consumption model, MTECM (Malatya Trolleybus Energy Consumption Model), is developed using the multi-parameter linear regression method. The real-time big data was collected on the field of Trolleybus vehicles, which have been operated for 8 years in Malatya / Turkiye. Firstly, by calculating the correlation of the parameters affecting this model, the parameters that are suitable for the purpose of our study are determined and regression analysis is performed on the original Trolleybus dataset. A total of 75.497.472 data are analyzed for this model. The RMSE (Root Mean Square Error) of MTECM is calculated as 0.29996. The trained model is applied to the 10 busiest routes in Malatya in terms of passenger density. The RMSE value on these routes is calculated between 0.30299 and 0.31421. Based on the results, with lower error rates, the proposed novel model is more efficient than other studies in the literature. In addition, energy consumption can be calculated for any route planned to establish an electric bus operation with MTECM. Therefore, according to the consumption obtained, the correct determination and selection of parameters that significantly affect the investment cost such as route, vehicle length, engine power, and battery capacity can be made.en_US
dc.description.sponsorshipResearch Fund of Inonu University [FDK-2023-3215]en_US
dc.description.sponsorshipThis work was supported by the Research Fund of Inonu University under Project FDK-2023-3215en_US
dc.identifier.doi10.1109/ACCESS.2023.3316362
dc.identifier.endpage104322en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85172988646en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage104305en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2023.3316362
dc.identifier.urihttps://hdl.handle.net/11616/101600
dc.identifier.volume11en_US
dc.identifier.wosWOS:001081619500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBig data analysisen_US
dc.subjectelectric vehicleen_US
dc.subjectelectric busen_US
dc.subjectmachine learningen_US
dc.subjectregressionen_US
dc.subjecttrolleybusen_US
dc.titleA Novel Energy Consumption Prediction Model of Electric Buses Using Real-Time Big Data From Route, Environment, and Vehicle Parametersen_US
dc.typeArticleen_US

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