  This paper proposes a probabilistic model for uncontrolled charging of
electric vehicles (EVs). EV charging will add significant load to power systems
in the coming years and, due to the convenience of charging at home, this is
likely to occur in residential distribution systems. Estimating the size and
shape of the load will allow necessary reinforcements to be identified. Models
predicting EV charging are usually based on data from travel surveys, or from
small trials. Travel surveys are recorded by hand and typically describe
conventional vehicles, but represent a much larger and more diverse sample of
the population. The model here utilizes both sources: trial data to
parameterize the model, and survey data as the model input. Clustering is used
to identify modes of vehicle use, thus reducing vehicle use to a single
parameter -- which can be incorporated into the model without adding
significant computational burden. Two case studies are included: one
investigating the aggregated charging of 50 vehicles, and one predicting the
increase in after diversity maximum demand for different regions of the UK.
