  Today, there is an ongoing transition to more sustainable transportation, for
which an essential part is the switch from combustion engine vehicles to
battery electric vehicles (BEVs). BEVs have many advantages from a
sustainability perspective, but issues such as limited driving range and long
recharge times slow down the transition from combustion engines. One way to
mitigate these issues is by performing battery thermal preconditioning, which
increases the energy efficiency of the battery. However, to optimally perform
battery thermal preconditioning, the vehicle usage pattern needs to be known,
i.e., how and when the vehicle will be used. This study attempts to predict the
departure time and distance of the first drive each day using online machine
learning models. The online machine learning models are trained and evaluated
on historical driving data collected from a fleet of BEVs during the COVID-19
pandemic. Additionally, the prediction models are extended to quantify the
uncertainty of their predictions, which can be used to decide whether the
prediction should be used or dismissed. Based on our results, the
best-performing prediction models yield an aggregated mean absolute error of
2.75 hours when predicting departure time and 13.37 km when predicting trip
distance.
