Evaluating the Efficiency of Connected and Automated Buses Platooning in Mixed Traffic Environment

Abstract

Due to the battery capacity limitation of battery electric vehicles (BEVs), the importance of minimizing energy consumption has been increasing in recent years. In the mean time, for improving vehicle energy efficiency, platooning has attracted attention of several automakers. Using the connected and automated vehicles (CAVs) technology, platooning can achieve a longer driving range while preserving a closer distance from the preceding vehicle, resulting in the minimization of the aerodynamic force. However, undesired behaviors of human-driven vehicles (HVs) in the platooning group can prohibit the maximization of the energy efficiency. In this paper, we developed a speed planner based on the model predictive control (MPC) to minimize the total platooning energy consumption, and HVs were programmed to maintain a long enough distance from the preceding vehicle to avoid collision. The simulations were performed to determine how HV influences the efficiencies of the platooning group, which is composed of CAVs and HVs together, in several scenarios including the different positions and numbers of the HVs. Test results show that the CAVs planned by our approach reduces energy consumption by about 4% or more than 4% compared to that of the HVs.

Adaptive Truck Platooning with Drones: A Decentralized Approach for Highway Monitoring

“…Truck platooning [3,8] has been widely studied, aiming to improve efficiency and safety. Initial research focused on Adaptive Cruise Control (ACC) systems for maintaining safe distances between vehicles [9], while newer studies have shifted toward cooperative adaptive cruise control (CACC), exploiting vehicle-to-vehicle (V2V) communication for enabling closer truck spacing, and thereby enhancing performance [10][11][12]. Benefits such as reduced fuel consumption [13] and improved traffic flow [14] have also been highlighted.…”

Deep Q-Network-Enabled Platoon Merging Approach for Autonomous Vehicles

“…Zhao et al proposed an integrated microscopic traffic flow model to describe human-driven vehicle maneuvers under interactions; the drivers plan their headings and accelerations in the predicted future to minimize costs representing undesirable situations (14). Park et al developed an MPC-based speed planner to minimize the total platooning energy consumption, and each human vehicle was programmed to maintain a sufficiently long distance from the preceding vehicle to avoid collision (15). Zhao et al established a behavioral simulation model to represent the overall path dispersion (16).…”