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

## TL;DR

This paper introduces a fog computing-based multi-class charging and dispatching scheme for autonomous electric mobility on demand, addressing computational and charging delays to improve system responsiveness.

## Contribution

It develops an optimized multi-class scheme and queuing model for vehicle dispatching and charging, enhancing AEMoD system efficiency and stability.

## Key findings

- The proposed model outperforms traditional charging schemes.
- Optimized decisions reduce maximum response time.
- System stability conditions are derived for different city zones.

## 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 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.