In this paper, we introduce a scalable model for the aggregate electricity demand of a fleet of electric vehicles, which can provide the right balance between model simplicity and accuracy. The model is based on classification of tasks with similar energy consumption characteristics into a finite number of clusters. The aggregator responsible for scheduling the charge of the vehicles has two goals: 1) to provide a hard QoS guarantee to the vehicles at the lowest possible cost; 2) to offer load or generation following services to the wholesale market. In order to achieve these goals, we combine the scalable demand model we propose with two scheduling mechanisms, a near-optimal and a heuristic technique. The performance of the two mechanisms is compared under a realistic setting in our numerical experiments.