  The usability of vehicles is highly dependent on their energy consumption. In
particular, one of the main factors hindering the mass adoption of electric
(EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which
occurs when a driver is uncertain about the availability of energy for a given
trip. To tackle this problem, we propose a machine learning approach for
modeling the battery energy consumption. By reducing predictive uncertainty,
this method can help increase trust in the vehicle's performance and thus boost
its usability. Most related work focuses on physical and/or chemical models of
the battery that affect the energy consumption. We propose a data-driven
approach which relies on real-world datasets including battery related
attributes. Our approach showed an improvement in terms of predictive
uncertainty as well as in accuracy compared to traditional methods.
