High-temporal-resolution dataset of uni-, bidirectional, and dynamic electric vehicle charging profiles
Marcel Esser (Technische Universität Dortmund)
S. Orfanoudakis (TU Delft - Intelligent Electrical Power Grids)
Omid Homaee (Black & White Engineering)
Vahid Vahidinasab (University of Salford)
P.P. Vergara Barrios (TU Delft - Intelligent Electrical Power Grids)
Alfio Spina (Technische Universität Dortmund)
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Abstract
The transition to Electric Vehicles (EVs) introduces challenges for power grid integration, particularly due to the growing demand for charging infrastructure. To support research on smart charging strategies and bidirectional charging applications, this study presents an open-access dataset containing 142 EV charging profiles obtained in a laboratory environment. The dataset includes static charging and discharging scenarios alongside dynamic profiles where the charging power is varied over time. These scenarios are applied to eight commercially available EVs, three of which support bidirectional charging. It features tests in alternating current and direct current charging modes and includes high-resolution time series of grid and vehicle parameters at sub-second intervals. The dataset is technically validated by assessing charging efficiency, reactive power injection, harmonics, and its suitability for development of digital EV models. This dataset supports applications like model validation, grid integration simulations in the context of Vehicle-to-Grid (V2G), charging infrastructure planning, and smart charging strategy development.
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