Presented are intelligent vehicle systems and
control logic for predictive charge planning and
powertrain control of electric-drive 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 methods of 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.