  Despite the significant advances in vehicle automation and electrification,
the next-decade aspirations for massive deployments of autonomous electric
mobility on demand (AEMoD) services are still threatened by two major
bottlenecks, namely the computational and charging delays. This paper proposes
a solution for these two challenges by suggesting the use of fog computing for
AEMoD systems, and developing an optimized multi-class charging and dispatching
scheme for its vehicles. A queuing model representing the proposed multi-class
charging and dispatching scheme is first introduced. The stability conditions
of this model and the number of classes that fit the charging capabilities of
any given city zone are then derived. Decisions on the proportions of each
class vehicles to partially/fully charge, or directly serve customers are then
optimized using a stochastic linear program that minimizes the maximum response
time of the system. Results show the merits of our proposed model and optimized
decision scheme compared to both the always-charge and the equal split schemes.
