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

A Multi-Class Dispatching and Charging Scheme for Autonomous Electric Mobility On-Demand