# Online Charge Scheduling for Electric Vehicles in Autonomous Mobility on Demand Fleets

## TL;DR

This paper proposes an online charge scheduling heuristic for autonomous electric vehicle fleets in mobility-on-demand services, optimizing charging and routing in real-time amid uncertain ride requests.

## Contribution

It introduces a primal-dual based online welfare maximization approach for EV fleet charge scheduling, addressing the challenge of unknown daily ride sequences.

## Key findings

- Competitive ratio analysis shows near-optimal performance
- Numerical results demonstrate effective resource allocation
- Heuristic reduces congestion and improves fleet utilization

## Abstract

In this paper, we study an online charge scheduling strategy for fleets of autonomous-mobility-on-demand electric vechicles (AMoD EVs). We consider the case where vehicles complete trips and then enter a between-ride state throughout the day, with their information becoming available to the fleet operator in an online fashion. In the between-ride state, the vehicles must be scheduled for charging and then routed to their next passenger pick-up locations. Additionally, due to the unknown daily sequences of ride requests, the problem cannot be solved by any offline approach. As such, we study an online welfare maximization heuristic based on primal-dual methods that allocates limited fleet charging resources and rebalances the vehicles while avoiding congestion at charging facilities and pick-up locations. We discuss a competitive ratio result comparing the performance of our online solution to the clairvoyant offline solution and provide numerical results highlighting the performance of our heuristic.