Cooperative Eco-Driving for Mixed Platoons With Heterogeneous Energy at a Signalized Intersection Based on a Mixed Space-Time-State Network

With the ongoing trend of vehicle automation and electrification, the future will present a mixed traffic environment with different automation and energy consumption types. However, current research focuses more on fully connected or homogeneous energy consumption scenarios in eco-driving, ignoring the impact of human-driven vehicles and electric vehicles (EVs) with high energy efficiency and regenerative braking capabilities. This study proposes a cooperative eco-driving framework for mixed vehicle platoons with heterogeneous energy at a signalized intersection. A Mixed Space-Time-State Network is developed to optimize the trajectory of connected autonomous vehicles (CAVs), while predicting and describing the trajectories of human-driven vehicles. Then, a modified dynamic programming algorithm is formulated, which converts the highly nonlinear optimization problem into a state-space search problem. The solving efficiency is further improved by 85% with a feasible state space reduction algorithm, which can meet the real-time computational requirements. The result indicates that cooperation within the mixed platoon can smooth out the trajectory while considering the composition of energy consumption. In addition, the energy efficiency can be improved by 20.39% with a low CAV penetration rate.