MACHINE LEARNING AND MULTI-SCALE OPTIMIZATION FOR CONTROL AND ENERGY MANAGEMENT IN CONNECTED AND AUTOMATED VEHICLE PROPULSION SYSTEMS

This dissertation presents a multi-scale optimization framework leveraging machine learning (ML) to enhance energy efficiency in connected and automated vehicle (CAV) propulsion systems. As transportation transitions toward hybridization and automation, the integration of vehicle-to-everything (V2X)