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 by close-gap platooning using information from vehicle-to-vehicle communications. In order to achieve these goals, the designers of control strategies need to simulate a wide range of driving situations in which vehicles interact with other vehicles and the infrastructure in a closed-loop fashion. RoadRunner is a new model-based system engineering platform based on Autonomie software, which can collectively provide the necessary tools to predict energy consumption for various driving decisions and scenarios such as car-following, free-flow, or eco-approach driving, and thereby can help in developing control algorithms. In the first part of this paper, control algorithms for adaptive cruise control and cooperative adaptive cruise control inspired by the 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 three heavy-duty truck models under a platooning scenario on a freeway, based on 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.