Eco-Driving Optimization of a Signalized Route With Extended Traffic State Information

Literature suggests that driving style and conditions play a major role in vehicle energy consumption. In this sense, this work focuses on vehicle speed planning using information from the environment, through vehicle-to-infrastructure (V2I), and from nearby vehicles, with vehicle-to-vehicle (V2V) information to reduce fuel consumption over a signalized route. By knowing the traffic lights scenario of the route in advance and the current position and speed of the preceding vehicle, the proposed algorithm decides the ego-vehicle speed profile during a given horizon to minimize fuel consumption. The proposed strategy solves the optimal control problem (OCP) in each prediction horizon through dynamic programming (DP) with a simplified model. The scenario and the optimal solution are updated periodically to make up for scenario prediction and modeling uncertainties. Experimental tests were conducted on a test bench to evaluate the fuel consumption of the simulated speed profile when compared to the preceding vehicle. Results show that a reduction of almost 20% in fuel consumption is possible without penalizing travel time while keeping it real-time (RT) feasible.