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.
翻译:电动车辆的两个最重要的方面是其效率或可实现的范围。为了实现高效,因此距离甚远,必须避免车动列车超载。因此,车动列车必须保持尽可能轻的重量,同时尽量加以最佳利用。这只有在控制器准确了解车动列车的动态行为的情况下才能实现。控制器的任务是通过控制电动发动机的电流在车轮上达到理想的扭伤。机器学习模型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。为了比较不同的模型方法,在电动车试验台记录了大约4 000万个数据点的数据集。数据集公布在Kagle上,这是一个在线的数据科学家群体。