Esser Marcel, Orfanoudakis Stavros, Homaee Omid, Vahidinasab Vahid, Vergara Pedro P, Spina Alfio
Institute of Energy Systems, Energy Efficiency and Energy Economics, TU Dortmund University, Dortmund, Germany.
Intelligent Electrical Power Grids (IEPG) Section, Delft University of Technology, Delft, The Netherlands.
Sci Data. 2025 Jul 10;12(1):1192. doi: 10.1038/s41597-025-05524-5.
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.
向电动汽车（EV）的转型给电网集成带来了挑战，尤其是由于对充电基础设施的需求不断增长。为了支持对智能充电策略和双向充电应用的研究，本研究提出了一个开放获取的数据集，其中包含在实验室环境中获得的142个电动汽车充电曲线。该数据集包括静态充电和放电场景以及充电功率随时间变化的动态曲线。这些场景应用于八款商用电动汽车，其中三款支持双向充电。它具有交流和直流充电模式下的测试，并包括以亚秒级间隔的电网和车辆参数的高分辨率时间序列。该数据集通过评估充电效率、无功功率注入、谐波及其对数字电动汽车模型开发的适用性进行了技术验证。该数据集支持模型验证、车辆到电网（V2G）背景下的电网集成模拟、充电基础设施规划和智能充电策略开发等应用。
