  We address the problem of characterising the aggregate flexibility in
populations of electric vehicles (EVs) with uncertain charging requirements.
Building on previous results that provide exact characterisations of the
aggregate flexibility in populations with known charging requirements, in this
paper we extend the aggregation methods so that charging requirements are
uncertain, but sampled from a given distribution. In particular, we construct
\textit{distributionally robust aggregate flexibility sets}, sets of aggregate
charging profiles over which we can provide probabilistic guarantees that
actual realised populations will be able to track. By leveraging measure
concentration results that establish powerful finite sample guarantees, we are
able to give tight bounds on these robust flexibility sets, even in low sample
regimes that are well suited for aggregating small populations of EVs. We
detail explicit methods of calculating these sets and provide numerical results
that validate the theory developed here.
