Potential Energy Saving of V2V-Connected Vehicles in Large-Scale Traffic

Abstract

Most studies evaluating the energy efficiency of connected and automated vehicles (CAVs) in car-following scenarios have considered a few preceding vehicles communicating with the controlled CAVs. However, considering rapidly evolving technologies in CAVs, extended vehicle-to-vehicle (V2V) connectivity over large-scale traffic needs to be considered in estimating CAVs’ energy benefits. This paper investigates the potential energy saving of V2V-connected vehicles in large-scale downstream traffic by adopting a human driver model generating stable car-following trajectories for many consecutive vehicles. The energy-efficient driving of a CAV is demonstrated based on an optimal controller minimizing the longitudinal acceleration by forecasting an immediately preceding vehicle's trajectory over a fixed prediction horizon. Various traffic scenarios are considered by applying different simulation parameters, including the distribution of vehicle time gaps, the number of connected vehicles, and prediction horizon lengths. Furthermore, a comprehensive analysis is conducted to discover the relationships between the parameters of interest and system performance, including prediction and control. Our findings from the parameter study are validated by evaluating the realistic energy consumption of a vehicle in a simulation platform operating high-fidelity powertrain models.