2020/03/16 by Sören Hanke, Hanke, Sören, Oliver Wallscheid +3
Computer Science · Engineering · #Computational Physics and Python Applications #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2003.07273
openalex publication_date 2020/03/16 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
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.