Validating Heavy-Duty Vehicle Models Using a Platooning Scenario 2019-01-1248
Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. Aerodynamic drag can also be reduced thanks to close-gap platooning using information from V2V communications. In order to achieve this goal, the designers of control strategy need to simulate a wide range of driving situations that be able to interact with other vehicles and the infrastructure in a close-loop fashion. RoadRunner is a new MBSE (model-based system engineering) platform based on Autonomie software, which is a collectively provide necessary tools to predict energy consumption for various driving decisions and other characteristics, such as car-following, free-flow, or eco-approach driving, and thereby can help in developing control algorithm.
In the first part of the paper, the control algorithms for adaptive cruise control(ACC) and cooperative ACC (or CACC) inspired by literature are implemented into RoadRunner, for vehicle model simulations of longitudinal movements in the environment considering real route information. In the second part of the paper, we present the validation of 3 heavy-duty truck models on platooning scenario at freeway, based on the test data provided by Lawrence Berkeley Laboratory. RoadRunner builds the Matlab/Simulink diagram of the scenario, including the information flows between truck vehicle models. After the simulation, the results showed that discrepancies in average inter-vehicle gap were within 4% compared to test data, while many of the operational signals, including the fuel consumption, were well matched.
Namdoo Kim, Dominik Karbowski, Aymeric Rousseau