Forward-Looking Traffic-Aware High-Level Decision Control (HLDC) Algorithm for Hybrid Electric-Connected and Automated Vehicles (HE-CAVs)

This article presents a control strategy to improve the overall energy efficiency of connected and automated-hybrid electric vehicles (CA-HEV) in urban driving conditions. A forward-looking, traffic-aware, high-level decision control (HLDC) algorithm is proposed in this article, where both traffic and road information (obtained from surrounding vehicles and municipal traffic management centers through connected vehicle technologies) are utilized. The objective is to dynamically optimize the vehicle speed trajectories to reduce, and potentially eliminate, idling time at red traffic lights. The benefits include reduced unnecessary engine restart, emissions, and an improvement in the overall energy efficiency of the CA-HEV. Utilizing the emerging technologies such as vehicle-to-everything (V2X), available in connected vehicles, which includes vehicle-to-infrastructure (V2I) communication and global positioning system (GPS), the HLDC receives the signal phase and timing (SPaT) information via V2I in order to coordinate the vehicle arrival at green phase of an upcoming traffic light when possible. The optimization also aims to eliminate unnecessary engine restarts in CA-HEV when approaching a red light or a stop sign, which has a significant impact on the overall fuel efficiency of CA-HEV. A complete CA-HEV model in MATLAB/Simulink is constructed to simulate, test, and validate the proposed algorithm. The simulation results show 39.5% fuel consumption savings due to optimization algorithm (HLDC) in addition to approximately 39.14% reduction in the total trip time. The results reflect the elimination of wasted idling fuel consumption at a red light, reduced unnecessary ICE restarts, and better utilization of the electric drive (ED) and energy storage system (ESS) power utilized in HE-CAVs.