Presented are intelligent vehicle systems and logic for predictive charge planning and powertrain control of motor vehicles, methods for manufacturing/operating such systems, and electric-drive vehicles with smart charge planning and powertrain control capabilities. Systems and methods of AI-based predictive charge planning for smart electric vehicles use machine-learning (ML) driver models that draws on available traffic, location, and roadway map information to estimate vehicle speed and propulsion torque requirements to derive a total energy consumption for a given trip. Systems and methodsof AI-based predictive powertrain control for smart hybrid vehicles use ML driver models with deep learning techniques to derive a drive cycle profile defined by a preview route with available traffic, geopositional, geospatial, and map data. ML-generated driver models are developed with collected data to replicate driver behavior and predict the drive cycle profile, including predicted vehicle speed, propulsion torque, and accelerator/brake pedal positions for a preview route.
VEHICLES, AND SYSTEMS, AND CONTROL LOGIC FOR PREDICTIVE CHARGE PLANNING AND POWERTRAIN CONTROL