Electric vehicle energy consumption prediction for unknown route types using deep neural networks by combining static and dynamic data

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dc.authorid0000-0001-8125-6814
dc.authorid0000-0003-1744-3062
dc.contributor.authorYilmaz, Hilal
dc.contributor.authorYagmahan, Betul
dc.date.accessioned2026-02-08T15:15:08Z
dc.date.available2026-02-08T15:15:08Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractAccurate energy consumption prediction of electric vehicles (EVs) is crucial for drivers considering long trips. All the data should be provided beforehand to determine the energy consumption at the beginning of the trip. Although dynamic vehicle data (vehicle speed, state-of-charge, acceleration, etc.) cannot be known before the trip, factors related to the specified route (route type, elevation, average speed, weather, driving time, etc.) can be used to predict the consumed energy. These factors can be categorized as static and dynamic features, and thus, the question of how to effectively use static and dynamic data arises. This paper investigates the problem of predicting the energy consumption of an EV for a predetermined trip using a deep neural network (DNN) model that effectively uses static features along with dynamic segment features. Furthermore, we address the problem where the route types are unknown in advance. To include more information in the prediction model, we clustered the speed profiles using shape-based clustering with dynamic time warping (DTW) to predict the route type and used the cluster labels as static inputs. Real driving data collected from various drivers of a specific EV were used to train the DNN. The proposed DNN model was compared with the average energy consumption (AEC) model and five machine learning models. The results show that labels obtained from shape-based clustering improved the prediction more than feature-based cluster labels. The prediction errors were minimized with the proposed DNN model, where static features are introduced to the first and second layers twice.
dc.description.sponsorshipTurkish Council of Higher Education (CoHE)
dc.description.sponsorshipHilal Y & imath;lmaz would like to acknowledge the Turkish Council of Higher Education (CoHE) for funding her 100/2000 PhD scholarship program.
dc.identifier.doi10.1016/j.asoc.2024.112336
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85207045250
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2024.112336
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5625
dc.identifier.volume167
dc.identifier.wosWOS:001342767300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectEnergy consumption prediction
dc.subjectElectric vehicles
dc.subjectDeep neural networks
dc.subjectDynamic time warping
dc.subjectTime-series clustering
dc.titleElectric vehicle energy consumption prediction for unknown route types using deep neural networks by combining static and dynamic data
dc.typeArticle

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