Real-Time Eco-Driving of a Connected and Automated Fuel Cell Electric Truck Using Approximate Dynamic Programming

Eco-driving of Connected and Automated Vehicles (CAVs), in particular with multiple on-board power sources, has the potential to significantly improve energy savings in real-world driving conditions. The eco-driving problem seeks to design optimal speed and power usage profiles between origin and destination, based upon route information available from connectivity and advanced mapping features. In this work, the eco-driving problem is solved for a fuel cell electric truck over a selected real-world route within the Texas Triangle Region. A study on the effect of availability of look-ahead information, such as grade, is brought up and the results are expressed in terms of hydrogen consumption and travel time. The problem is formulated as an optimal control problem (OCP), and solved as a hierarchical model predictive control (MPC) using Approximate Dynamic Programming (ADP). Different levels of driver aggressiveness have been considered in the virtual simulations performed in SIMULINK and the results have been compared against a heuristic-based baseline controller. The improvements coming from the optimized strategy without look-ahead grade information are approximately 3% to 9% depending on the driver aggressiveness, while a substantial and consistent 8% improvement is provided when leveraging look-ahead data.