论文标题
数据集说明：识别电动机背后的物理 - 数据驱动的电气行为学习（第I部分）
Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part I)
论文作者
论文摘要
电动汽车最重要的两个方面是它们的效率或可实现的范围。为了实现高效率并因此远距离，至关重要的是避免过度尺寸。因此，必须保持驱动列车尽可能轻巧，同时在最佳范围内使用。仅当控制器准确地知道驱动序列的动态行为时，才能实现这一点。控制器的任务是通过控制电动机的电流来在汽车车轮上实现所需的扭矩。使用机器学习建模技术，可以从测量数据中提取描述行为的准确模型，然后由控制器使用。为了比较不同的建模方法，在电动驱动列车的测试工作台上记录了一个由约4000万个数据点组成的数据集。数据集发表在数据科学家在线社区Kaggle上。
Two of the most important aspects of electric vehicles are their efficiency or achievable range. In order to achieve high efficiency and thus a long range, it is essential to avoid over-dimensioning the drive train. Therefore, the drive train has to be kept as lightweight as possible while at the same time being utilized to the best possible extent. This can only be achieved if the dynamic behavior of the drive train is accurately known by the controller. The task of the controller is to achieve a desired torque at the wheels of the car by controlling the currents of the electric motor. With machine learning modeling techniques, accurate models describing the behavior can be extracted from measurement data and then used by the controller. For the comparison of the different modeling approaches, a data set consisting of about 40 million data points was recorded at a test bench for electric drive trains. The data set is published on Kaggle, an online community of data scientists.
