使用高分辨率数据揭示电池电动汽车的能量动力学
Mohamed Yasko1, Adamou Moussa Issaka2, Fanghao Tian2
1KU Leuven/EnergyVille, Thor Park 8310, Genk, Belgium. mohamed.yasko@kuleuven.be.
Scientific data
|November 29, 2025
概括
本研究引入了一种新方法,用于收集电池电动汽车 (BEV) 的详细能源数据. 高分辨率数据集捕捉了组件的实际能源使用情况,有助于车辆和电网的优化.
科学领域:
- 电气工程 电气工程
- 能源系统 能源系统
- 数据科学数据科学数据科学
背景情况:
- 电池电动汽车 (BEV) 对能源消耗和电网影响至关重要.
- 对车辆内部能源数据 (电池,充电器) 的有限访问妨碍了优化.
- 现有的数据集缺乏分辨率,无法捕捉真正的能量动态.
研究的目的:
- 为了弥补高分辨率BEV能源数据集的缺口.
- 开发用于车辆内部能源消耗的数据生成方法.
- 为优化BEV,充电和电网集成提供详细的见解.
主要方法:
- 开发了一种用于收集内部BEV能源数据的新型数据生成方法.
- 收集了用于充电器,电池和辅助设备的二级分辨率的数据集.
- 在各种操作模式 (充电,驾驶,停车) 和环境条件中捕获的能量动态.
主要成果:
- 创建了一个独特的数据集,其中包含来自BEV的颗粒式能源消耗数据.
- 数据集说明了不同操作条件下的能源动态.
- 提供详细的技术洞察力,了解BEV的能源使用情况.
结论:
- 新的数据集可以优化智能充电策略和运营成本.
- 有助于更好地了解BEV的使用和组件性能.
- 支持开发人工智能模型和精确的电网影响分析.
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