Fog-Based Multi-Class Dispatching and Charging 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 (AEMoD) services in big cities are still threatened by two major bottlenecks, namely, the communication/computation and charging delays. In order to target the communication/computation delays, the paper suggests the exploitation of fog-based architectures for localized AEMoD system operations. These emerging architectures are soon to become widely used, allowing for all localized operational decisions to be made with very low latency by fog controllers located close to the end applications (e.g., each city zone for AEMoD systems). As for the charging delays, an optimized multi-class charging and dispatching queuing model, with partial charging option for AEMoD vehicles is developed for each of these zones. The stability conditions of this model and the optimal number of classes are then derived. The decisions on the proportions of each class vehicles to partially/fully charge or directly serve customers are optimized to minimize the maximum and average system response times using convex optimization and Lagrangian analysis. The results show the merits of our proposed model and optimized decision scheme compared to both the always-charge and the equal-split scheme. Furthermore, the comparison of the maximum and average response time minimization results shows a very low variance in performance, which suggests by using the linear programming solution for lower complexity.