Introducing Hybrid Vehicle Dynamics in Microscopic Traffic Simulation

• Published in: IEEE transactions on intelligent transportation systems Vol. 25; no. 7; pp. 7977 - 7986
• Main Authors: He, Yinglong, Mattas, Konstantinos, Makridis, Michail A., Komnos, Dimitrios, Marin, Andres L., Fontaras, Georgios, Ciuffo, Biagio
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; charge depleting (CD); charge sustaining (CS); Computational modeling; Convergence; driver behaviour; Driving; Dynamics; Electric vehicles; Energy consumption; Hybrid electric vehicles; Hybrid electric vehicles (HEVs); Hybrid vehicles; Ice; Mathematical models; microscopic traffic simulation; Microscopy; Simulation; Traffic models; Transportation systems; Vehicle dynamics; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2024.3378183

Hybrid 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.