A Multi-Class Dispatching and Charging Scheme for Autonomous Electric Mobility On-Demand
For operators of large-scale AEMoD fleets, this work provides a practical method to mitigate delays, though the improvements are incremental over existing approaches.
This paper addresses computational and charging delays in autonomous electric mobility on demand (AEMoD) systems by proposing a fog computing-based multi-class charging and dispatching scheme. The optimized scheme reduces maximum response time by up to 30% compared to baseline schemes.
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.