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We present a customizable online optimization framework for real-time EVsmart charging to be readily implemented at real large-scale chargingfacilities. Notably, due to real-world constraints, we designed our frameworkaround 3 main requirements. First, the smart charging strategy is readilydeployable and customizable for a wide-array of facilities, infrastructure,objectives, and constraints. Second, the online optimization framework can beeasily modified to operate with or without user input for energy requestamounts and/or departure time estimates which allows our framework to beimplemented on standard chargers with 1-way communication or newer chargerswith 2-way communication. Third, our online optimization framework outperformsother real-time strategies (including first-come-first-serve,least-laxity-first, earliest-deadline-first, etc.) in multiple real-world testcases with various objectives. We showcase our framework with two real-worldtest cases with charging session data sourced from SLAC and Google campuses inthe Bay Area.

A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases