Please use this identifier to cite or link to this item: https://repositori.mypolycc.edu.my/jspui/handle/123456789/6891
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dc.contributor.authorHe, Yinglong-
dc.contributor.authorMakridis, Michail A.-
dc.contributor.authorKomnos, Dimitrios-
dc.contributor.authorMarín, Andres L.-
dc.contributor.authorFontaras, Georgios-
dc.contributor.authorCiuffo, Biagio-
dc.contributor.authorMattas, Konstantinos-
dc.date.accessioned2025-10-13T07:56:29Z-
dc.date.available2025-10-13T07:56:29Z-
dc.date.issued2024-07-
dc.identifier.otherDOI : 10.1109/TITS.2024.3378183-
dc.identifier.urihttps://repositori.mypolycc.edu.my/jspui/handle/123456789/6891-
dc.description.abstractHybrid electric vehicles (HEVs) have reached the market share required to meaningfully affect many aspects of the road transport system, including traffic behaviour, energy consumption, and emissions. However, traffic models for hybrids remain insufficiently addressed in microscopic simulation because traditional models ignore vehicle dynamics, and therefore, cannot capture driving differences that exist among the hybrid, conventional, and electric vehicles. This study extends the lightweight microsimulation free-flow acceleration (MFC) model and fills the above gap in the literature by introducing hybrid vehicle dynamics into traffic simulation. First, the methodology underlying the MFC model to reproduce hybrid vehicle dynamics is described, for both charge depleting (CD) and charge sustaining (CS) modes. Then, the experimental setup for model validation and implementation was introduced. The simulations suggest the proposed MFC model can ensure smooth speed and acceleration profiles while converging to the steady state. The results show the MFC model can accurately capture the dynamics of the hybrid vehicle tested on the chassis dynamometer. The MFC model is compared with the Gipps’ model and the intelligent driver model (IDM) regarding their abilities to reproduce driving trajectories of the hybrid vehicle. It was found, in CD mode, the MFC model leads to reductions in both speed and acceleration root mean square errors (RMSEs). In CS mode, the MFC model yields even greater accuracy gains. When predicting the 0-100 km/h acceleration specifications, the MFC model also outperforms the Gipps’ and the IDM, reducing RMSE by 45.8 % and 51.9 %, respectively.ms_IN
dc.language.isoenms_IN
dc.publisherIEEE Accessms_IN
dc.relation.ispartofseriesIEEE Transactions On Intelligent Transportation System;Vol. 25, No. 7-
dc.subjectHybrid electric vehicles (HEVs)ms_IN
dc.subjectMicroscopic traffic simulationms_IN
dc.subjectDriver behaviourms_IN
dc.subjectVehicle dynamicsms_IN
dc.subjectCharge depleting (CD)ms_IN
dc.subjectCharge sustaining (CS)ms_IN
dc.titleINTRODUCING HYBRID VEHICLE DYNAMICS IN MICROSCOPIC TRAFFIC SIMULATIONms_IN
dc.typeArticlems_IN
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