The number of electric vehicles
in use worldwide is rising every year. Even
with improvements in battery technology giving
longer range to electric vehicles, many owners
still worry about the range of their vehicles.
This worry is termed range anxiety. Obtaining
more precise energy predictions can combat
range anxiety, as they allow the owners of electric
vehicles to more precisely gauge the range of
their vehicles.
In this report, we detail the creation of multiple
machine learning models that predict energy
consumption of electric vehicles by exploiting
contextual information related to trips conducted
by these vehicles.
We base this project on a large dataset of electric
vehicle driving data, which describes trips on the
Danish road network. A number of external data
sources are integrated with the dataset, such as
height data obtained from a height raster of Denmark,
and a speedmap from Vejdirektoratet.
We conclude that a Recurrent Neural Network
architecture is able to successfully exploit the
sequential nature of the context data to improve
significantly upon a baseline model.