Online Implementation of Optimal Control with Receding Horizon for Eco-Driving of an Electric Vehicle

As connected and automated vehicles (CAVs) are being realized, their optimization has been studied in many areas. CAVs have the potential to improve safety and fuel economy through advanced control and planning with optimization. Implementation of the optimal control and planning is also very important for checking practical impacts in real world. In this study, we developed an optimal controller for CAVs, based on Pontryagin's minimum principle. It was implemented in a model-predictive framework to run it in a closed-loop multi-vehicle simulation with infrastructure. RoadRunner, a closed-loop simulation tool, was used to validate the developed controller. RoadRunner can emulate real-world environments, including road grade, speed limit, intersections, and multiple vehicles from Autonomie, a program that includes various types of vehicles and powertrain models. As a result, Pontryagin's minimum principle based model-predictive controller shows energy savings compared to a baseline controller in the real-world scenarios.