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- W4313563321 abstract "Electric Vehicles (EVs) are on the rise. As users, however, struggle to plan and conveniently integrate EV charging stops into their daily routines, they perceive a limitation of their individual mobility. In the present paper, we aim to reliably and conveniently schedule EV charging stops without limiting users’ mobility. To capture random user behavior, we use graph-based scenario generation and user-individual mobility predictions. Stochastic mixed-integer programming then helps to compute the desired charging schedule for multiple days. For validation, we use a Monte Carlo simulation and comparison with two heuristic strategies. We prove that a sufficient battery reserve for all daily mobility requirements can be ensured. Compared with heuristic strategies, plug-in events, battery calendar aging and energy costs can be significantly reduced." @default.
- W4313563321 created "2023-01-06" @default.
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- W4313563321 date "2022-11-01" @default.
- W4313563321 modified "2023-10-10" @default.
- W4313563321 title "Multi-Day Stochastic Scheduling of Electric Vehicle Charging for Reliability and Convenience" @default.
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- W4313563321 doi "https://doi.org/10.1109/vppc55846.2022.10003402" @default.
- W4313563321 hasPublicationYear "2022" @default.
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