Abstract
Electric vehicles (EVs) are increasingly used for commuting to the workplace where employees expect charging opportunities. Limited power supply in existing infrastructures prevents charging many EVs concurrently. Smart charging balances scarce charging resources and distributes power by prioritizing EVs. We maximize fair share among EVs by prioritizing for equal chances of reaching a sufficient state of charge by the time of departure. To address uncertain EV availability, we use regression models trained on historical data to predict departures. More sophisticated regression models show higher prediction accuracy. We improve a smart charging heuristic by incorporating these predictions. Simulations show accurate predictions improve EV prioritization and thus fair share.
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