  The combination of electric vehicles (EVs) and renewable energy is taking
shape as a potential driver for a future free of fossil fuels. However, the
efficient management of the EV fleet is not exempt from challenges. It calls
for the involvement of all actors directly or indirectly related to the energy
and transportation sectors, ranging from governments, automakers and
transmission system operators, to the ultimate beneficiary of the change: the
end-user. An EV is primarily to be used to satisfy driving needs, and
accordingly charging policies must be designed primarily for this purpose. The
charging models presented in the technical literature, however, overlook the
stochastic nature of driving patterns. Here we introduce an efficient
stochastic dynamic programming model to optimally charge an EV while accounting
for the uncertainty inherent to its use. With this aim in mind, driving
patterns are described by an inhomogeneous Markov model that is fitted using
data collected from the utilization of an EV. We show that the randomness
intrinsic to driving needs has a substantial impact on the charging strategy to
be implemented.
