Modeling car-following behavior of electric adaptive cruise control vehicles using experimental testbed data

Automated vehicles (AVs) have the potential to revolutionize the transportation industry. While extensive research has been conducted to explore the benefits of AVs on traffic flow, commercially available adaptive cruise control (ACC) vehicles with advanced driver assistance features have been shown adverse effects on traffic flow. As vehicle automation advances, electric vehicles (EVs) equipped with ACC are emerging as an alternative to traditional internal combustion engine (ICE) vehicles. However, there is still a limited understanding of the differences in vehicle dynamics between EV-ACC and ICE-ACC vehicles. This study utilizes microscopic car-following models to describe the vehicle dynamics of EV-ACC vehicles. The model parameters are calibrated based on an experiment conducted with commercially available EV-ACC vehicles. The calibration results indicate that the optimal velocity relative velocity (OVRV) model outperforms the intelligent driver model (IDM) in most gap settings by up to 37%, suggesting that the OVRV model can effectively capture the driving behavior of EV-ACC vehicles. However, simulations of a string of vehicles imply that the IDM is more accurate in capturing amplifications due to velocity disturbances. Therefore, the development of higher-fidelity microscopic car-following models specifically for EV-ACC vehicles is necessary.