  Predictive energy management of Connected and Automated Vehicles (CAVs), in
particular those with multiple power sources, has the potential to
significantly improve energy savings in real-world driving conditions. In
particular, the eco-driving problem seeks to design optimal speed and power
usage profiles based upon available information from connectivity and advanced
mapping features to minimize the fuel consumption between two designated
locations.
  In this work, the eco-driving problem is formulated as a three-state receding
horizon optimal control problem and solved via Dynamic Programming (DP). The
optimal solution, in terms of vehicle speed and battery State of Charge (SoC)
trajectories, allows a connected and automated hybrid electric vehicle to
intelligently pass the signalized intersections and minimize fuel consumption
over a prescribed route. To enable real-time implementation, a parallel
architecture of DP is proposed for an NVIDIA GPU with CUDA programming.
Simulation results indicate that the proposed optimal controller delivers more
than 15% fuel economy benefits compared to a baseline control strategy and that
the solver time can be reduced by more than 90% by the parallel implementation
when compared to a serial implementation.
