  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.
