  The increasing market penetration of electric vehicles (EVs) may pose
significant electricity demand on power systems. This electricity demand is
affected by the inherent uncertainties of EVs' travel behavior that makes
forecasting the daily charging demand (CD) very challenging. In this project,
we use the National House Hold Survey (NHTS) data to form sequences of trips,
and develop machine learning models to predict the parameters of the next trip
of the drivers, including trip start time, end time, and distance. These
parameters are later used to model the temporal charging behavior of EVs. The
simulation results show that the proposed modeling can effectively estimate the
daily CD pattern based on travel behavior of EVs, and simple machine learning
techniques can forecast the travel parameters with acceptable accuracy.
