Data-driven model predictive control using road-based disturbance estimations in longitudinal driving of e-bike

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dc.authorid0000-0003-0332-1785
dc.contributor.authorGenc, Mehmet Onur
dc.date.accessioned2026-02-08T15:15:06Z
dc.date.available2026-02-08T15:15:06Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, the electric bike is investigated under the data-driven model predictive control (MPC) model approach to develop road disturbance estimation. The focus of the research study is to develop a new control algorithm proposal for cargo e-bikes subjecting to uncertain road conditions in urban transportation. The developed model is proposed for model predictive driving option in e-bikes to provide optimal motor torque operation against predicted road disturbances. The driving profile is designed to simulate urban driving in different road types by considering pedal usage frequency and pedal load measurement in combined drive. Firstly, a datalogger is designed, and the microcontroller is programmed to measure pedal force independently in all road profiles. Road profiles are divided into two groups, pedal-assisted routes and the full electric driving mode, and all range capacities are recorded to make allover comparisons. Road types consist of asphalt and graded gravel at different slope levels. Pedal load statistical relationship based on each route is also indicated, and the road-based difference is also presented. The obtained data are used for data-driven model development in the MPC control model as measured disturbances. The control model then is integrated into the one-dimensional model to provide road estimation. The design of the developed model provides sensorless road disturbance estimation in urban driving. The proposed control model presents an innovative approach for system designing of micro-e-mobility vehicles in optimum longitudinal vehicle control considering the data-driven MPC method.
dc.description.sponsorshipTrkiye Bilimsel ve Teknolojik Arascedil;timath;rma Kurumu [221N015, TUBITAK 2219]; Bursa Technical University Scientific Research Projects Committee
dc.description.sponsorshipThis study is funded and supported by Bursa Technical University Scientific Research Projects Committee with Project Number 221N015 titled 'Generating Urban Driving Profiles and Determination of Test Procedures for Determining Energy Efficiency of Electric Bicycle Powertrain System,' and TUBITAK 2219 Project titled 'An Experimental Battery Model Development for PAS (Pedal Assist System) of E-Quadricycle under Hill Hybrid Driving Modes at Different Ambient Temperatures.'
dc.identifier.doi10.1007/s40430-024-04778-1
dc.identifier.issn1678-5878
dc.identifier.issn1806-3691
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85187884400
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s40430-024-04778-1
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5592
dc.identifier.volume46
dc.identifier.wosWOS:001181474600004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofJournal of The Brazilian Society of Mechanical Sciences and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectModel predictive control
dc.subjectElectric bike
dc.subjectDriving profile
dc.subjectData-driven modeling
dc.subjectRoad-based disturbance estimations
dc.titleData-driven model predictive control using road-based disturbance estimations in longitudinal driving of e-bike
dc.typeArticle

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