  Planning to support widespread transportation electrification depends on
detailed estimates for the electricity demand from electric vehicles in both
uncontrolled and controlled or smart charging scenarios. We present a modeling
approach to rapidly generate charging estimates that include control for
large-scale scenarios with millions of individual drivers. We model
uncontrolled charging demand using statistical representations of real charging
sessions. We model the effect of load modulation control on aggregate charging
profiles with a novel machine learning approach that replaces traditional
optimization approaches. We demonstrate its performance modeling workplace
charging control with multiple electricity rate schedules, achieving small
errors (2.5% to 4.5%), while accelerating computations by more than 4000 times.
We illustrate the methodology by generating scenarios for California's 2030
charging demand including multiple charging segments and controls, with
scenarios run locally in under 50 seconds, and for assisting rate design
modeling the large-scale impact of a new workplace charging rate.
