Performance Evaluation of the Pass at Green Connected Vehicle V2I Application Using Simulation, Dynamometer and Track Testing

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies, such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. As the world of transportation gets more and more connected through these technologies, the need to implement algorithms with V2I capability is amplified. In this paper, an algorithm called Pass at Green (PaG), utilizing V2I to modify the speed profile of a vehicle to decrease fuel consumption has been studied. PaG uses Signal Phase and Timing (SPaT) information acquired from upcoming traffic lights, which are the current phase of the upcoming traffic light and the remaining time that the phase stays active. Then, PaG modifies the speed of the vehicle by accelerating, keeping its speed constant or decelerating to decrease fuel consumption, minimize idling time and reduce the likelihood of catching a red light in an intersection. As presented in this paper, the fuel economy benefit achieved by the PaG algorithm was studied through Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) simulations, as well as Vehicle-in-the-Loop (VIL) testing on both a dynamometer and an actual track. Traffic simulations were run using a commercial microscopic simulator, PTV Vissim, to further test the performance of the PaG model in low, medium, high and no traffic scenarios. Fuel economy performance of the PaG algorithm was then compared to the Intelligent Driver Model (IDM) and manual driving with varying levels of aggressiveness (cautious, normal and aggressive driver behavior). The simulation and experimental results demonstrated that PaG is able to achieve between 1.8% and 38% fuel economy improvement based on the frequency of traffic lights encountered.