Assistant Professor The University of Texas at El Paso El Paso, Texas, United States
Abstract: Due to fundamental differences in their propulsion systems, internal combustion engine (ICE) vehicles and electric vehicles (EVs) exhibit distinct vehicle dynamics. EVs provide rapid acceleration due to electric motors producing peak power across a wider speed range. They also achieve swift deceleration through regenerative braking. While existing microscopic traffic models effectively capture the driving behavior of ICE vehicles, there is a notable lack of modeling frameworks that accurately describe the car-following dynamics of EVs. Developing a comprehensive model for EV driving behavior is essential, especially given their increasing presence on roads and their impact on traffic flow patterns. However, creating an easy-to-use analytical car-following model specifically for EVs is challenging due to their unique performance characteristics and the variability introduced by different EV technologies In this study, we develop an AI-based car-following model for EVs using real-world trajectory data collected from vehicles equipped with adaptive cruise control (ACC). We then conduct comprehensive simulations to examine the macroscopic impacts of EVs, as described by the AI-based model, on traffic flow, comparing these effects to those of ICE vehicles across various EV penetration rates. The numerical results provide valuable insights into how emerging vehicle electrification and automation may influence future traffic conditions, potentially informing traffic management strategies and infrastructure planning.
Learning Objectives:
Attendees can expect to learn the following from this session:
Upon completion, participant will be able to leann the knowledge of automated vehicles.
Upon completion, participant will be able to leann the development of electric vehicles.
Upon completion, participant will be able to leann the development Ai in transportation.