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

Abstract

Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AE-MoD) 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 multiclass 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.

Multi-Class Management With Sub-Class Service for Autonomous Electric Mobility On-Demand Systems

“…the higher the waiting time will be. Moreover, in previous related work [18] [19], we showed that increasing the number of classes n beyond its strict lower bound introduced in Lemma 2 in [18] will damage the system performance and increase the maximum response time. c , for different decision approaches namely our derived optimal decisions to the following decisions sets: 1) Optimized charging decisions (i.e.…”

“…We proposed to resolve the first limitation, communication/computation delays, by suggesting the exploitation of the new and trendy fog-based networking and computing architectures [20]. The privileges brought by this technique [18], will allow handling instantaneous decision making applications such as AEMoD system operations in a distributed and accelerated way. The fog controller in each service zone is responsible of collecting information about customer requests, vehicle in-flow to the service zone, their state-of-charge (SoC), and the available full-battery charging rates in the service zone.…”

“…In order to solve the second problem, we proposed previously [18], [19] that the fog controller will smartly cope with the available charging capabilities of each service zone, by assigning to each customer a vehicle that has enough charge to serve him without in route charging. In the former solution, arriving vehicles in each service zone are subdivided into different classes in ascending order of their SoC corresponding to the different customer classes.…”

“…None of these papers considered the computational architecture for massive demands on such services, the vehicle electrification, and the influence of charging limitations on its stability. In [18] [19] we proposed a closely related model management model for Amods Systems. We proposed to resolve the first limitation, communication/computation delays, by suggesting the exploitation of the new and trendy fog-based networking and computing architectures [20].…”

Optimal Vehicle Dimensioning for Multi-Class Autonomous Electric Mobility On-Demand Systems

“…In this paper, we address the above vehicle dimensioning problem with bounded response time guarantees for the fogbased multi-class AEMoD management system proposed in [14]. Using a queuing model representing the multi-class charging and dispatching processes of each zone, we first derive the stability conditions and the number of system classes to guarantee the response time bound.…”