Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 22, Issue 10, Pages 6646-6653Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.2988648
Keywords
Predictive models; Uncertainty; Schedules; Employment; Optimal scheduling; Load modeling; Electric vehicle charging; Smart charging; machine learning; uncertainty; electric vehicles
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Funding
- German Federal Ministry for Economic Affairs and Energy through the TRADE EVs Project
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Studies show that using regression models for prediction can improve the prioritization of electric vehicle charging, achieving fair charging shares. Optimizing smart charging helps balance charging resources and improve the accuracy of precise predictions.
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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