  Connected and Automated Vehicles (CAVs), particularly those with a hybrid
electric powertrain, have the potential to significantly improve vehicle energy
savings in real-world driving conditions. In particular, the Eco-Driving
problem seeks to design optimal speed and power usage profiles based on
available information from connectivity and advanced mapping features to
minimize the fuel consumption over an itinerary. This paper presents a
hierarchical multi-layer Model Predictive Control (MPC) approach for improving
the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle
environment. Approximate Dynamic Programming (ADP) is used to solve the
Receding Horizon Optimal Control Problem (RHOCP), where the terminal cost for
the RHOCP is approximated as the base-policy obtained from the long-term
optimization.
  The controller was extensively tested virtually (using both deterministic and
Monte Carlo simulations) across multiple real-world routes where energy savings
of more than 20\% have been demonstrated. Further, the developed controller was
deployed and tested at a proving ground in real-time on a test vehicle equipped
with a rapid prototyping embedded controller. Real-time in-vehicle testing
confirmed the energy savings observed in simulation and demonstrated the
ability of the developed controller to be effective in real-time applications.
