Emission Mitigation via Longitudinal Control of Intelligent Vehicles in a Congested Platoon

摘要

Drivers' car-following behavior, coupled with inaccurate perception of ambient conditions, contributes to oscillations and significant emissions in congested highway traffic. The emergence of intelligent vehicles and modern communication technologies provides an opportunity to reduce adverse impacts of human factors and dynamically control car-following vehicles. This article proposes a nonlinear model predictive control (MPC) approach for emission mitigation via longitudinal control of intelligent vehicles in a congested platoon. To relieve the real-time optimization burden, we also propose an instantaneous control model which is essentially a simplified MPC approach with a short and identical prediction and control horizon. The proposed vehicle control strategies are tested using a series of simulations, and results verify that localized and instantaneous control of a few intelligent vehicles could reduce emissions of a platoon of vehicles. The proposed models are also applied to field trajectory data, and results show that the instantaneous emission optimization model significantly reduces emissions without increasing travel time. The effectiveness of the proposed vehicle control on emission mitigation and traffic stabilization is found to increase with the percentage (penetration rate) of intelligent vehicles in the congested vehicle platoon.