-- Urban driving with connected and automated vehicles (CA Vs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. T o address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24% compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy. Connected and Automated V ehicles (CA Vs) provide benefits in road safety, traffic efficiency, and energy efficiency [1]. Using vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, CA Vs can coordinate with traffic signals and neighboring vehicles to optimize their motion in ways human drivers are incapable of [2]. Prior studies have shown that by optimizing longitudinal behavior using Signal Phase and Timing (SPaT) data from connected traffic lights, a single CA V can adjust its cruising speed to avoid unnecessary stops, yielding substantial energy savings (11.35 % to 16.4%) [3], [4].