  In this paper, the problem of electric vehicle (EV) charging at the workplace
is addressed via a two-layer predictive algorithm. We consider a time of use
(TOU) pricing model for energy drawn from the grid and try to minimize the
charging cost incurred by the EV charging station (EVCS) operator via an
economic layer based on dynamic programming (DP) approach. An adaptive
prediction algorithm based on a non-parametric stochastic model computes the
projected EV load demand over the day which helps in the selection of optimal
loading policy for the EVs in the economic layer. The second layer is a
scheduling algorithm designed to share the allocated power limit (obtained from
economic layer) among the charging EVs during each charge cycle. The modeling
and validation is performed using ACN data-set from Caltech. Comparison of the
proposed scheme with a conventional DP algorithm illustrates its effectiveness
in terms of supplying the requested energy despite lacking user input for
departure time.
