  Large-scale integration of electric vehicles (EVs) leads to a tighter
integration between transportation and electric energy systems. In this paper,
we develop a novel integer-clustering approach to model a large number of EVs
that manages vehicle charging and energy at the fleet level yet maintain
individual trip dispatch. The model is then used to develop a spatially and
temporally-resolved decision-making tool for optimally planning and/or
operating EV fleets and charging infrastructure. The tool comprises a two-stage
framework where a tractable disaggregation step follows the integer-clustering
problem to recover an individually feasible solution. Mathematical
relationships between the integer clustering, disaggregation, and individual
formulations are analyzed. We establish theoretical lower and upper bounds on
the true individual formulation which underpins a guaranteed performance of the
proposed method. The optimality accuracy and computational efficiency of the
integer-clustering formulation are also numerically validated on a real-world
case study of Boston's public transit network under extensive test instances.
Substantial speedups with minimal loss in solution quality are demonstrated.
