Data-based fuel-economy optimization of connected automated trucks in traffic

In this paper we perform fuel-economy optimization for a connected automated truck that utilizes motion information from multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Position and velocity data collected from a chain of human-driven vehicles is utilized to design a connected cruise controller that can respond to traffic perturbations while maximizing energy efficiency. The results are compared to those obtained using a high-fidelity truck model and the robustness of the design is validated on multiple data sets. It is shown that optimally utilizing V2V connectivity may lead to 4%-13% fuel economy improvements compared to the best non-connected design.