  This paper addresses the problem of predicting the energy consumption for the
drivers of Battery electric vehicles (BEVs). Several external factors (e.g.,
weather) are shown to have huge impacts on the energy consumption of a vehicle
besides the vehicle or powertrain dynamics. Thus, it is challenging to take all
of those influencing variables into consideration. The proposed approach is
based on a hybrid model which improves the prediction accuracy of energy
consumption of BEVs. The novelty of this approach is to combine a physics-based
simulation model, which captures the basic vehicle and powertrain dynamics,
with a data-driven model. The latter accounts for other external influencing
factors neglected by the physical simulation model, using machine learning
techniques, such as generalized additive mixed models, random forests and
boosting. The hybrid modeling method is evaluated with a real data set from TUM
and the hybrid models were shown that decrease the average prediction error
from 40% of the pure physics model to 10%.
