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Connected and Automated Vehicles (CAVs), particularly those with a hybridelectric powertrain, have the potential to significantly improve vehicle energysavings in real-world driving conditions. In particular, the Eco-Drivingproblem seeks to design optimal speed and power usage profiles based onavailable information from connectivity and advanced mapping features tominimize the fuel consumption over an itinerary. This paper presents ahierarchical multi-layer Model Predictive Control (MPC) approach for improvingthe fuel economy of a 48V mild-hybrid powertrain in a connected vehicleenvironment. Approximate Dynamic Programming (ADP) is used to solve theReceding Horizon Optimal Control Problem (RHOCP), where the terminal cost forthe RHOCP is approximated as the base-policy obtained from the long-termoptimization.  The controller was extensively tested virtually (using both deterministic andMonte Carlo simulations) across multiple real-world routes where energy savingsof more than 20\% have been demonstrated. Further, the developed controller wasdeployed and tested at a proving ground in real-time on a test vehicle equippedwith a rapid prototyping embedded controller. Real-time in-vehicle testingconfirmed the energy savings observed in simulation and demonstrated theability of the developed controller to be effective in real-time applications.

Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles using Approximate Dynamic Programming