A Simultaneous Multi-Round Auction Design for Scheduling Multiple Charges of Battery Electric Vehicles on Highways

Highway charging scheduling for battery electric vehicles is a complex research issue depending on the fast charging capacity provided and the information available in coordinating drivers' multiple charges at charging stations. Moreover, user's partially-known preferences and potential dynamic events remain extra challenges in maximizing user's satisfaction, improving the revenue of highway charging stations and utilizing the limited charging capacities. In such separate and simultaneous markets, users are reasonably modelled as the self-interested agents who aim to advance their own benefits but negotiable on their charging schedules. In this paper, we propose a simultaneous multi-round auction to address the highway charging scheduling problem, where users are allowed to hid and compromise on their preferred stops, charging time and energy simultaneously at separate charging stations. The objective is to maximize the total revenue of these stations. In the course of auction, users can gradually figure out how can their charges fit together by adaptively adjusting their bids placed at different stations. As a result, high-quality solutions are obtained and user's privacy can be preserved by progressively eliciting their private preferences as necessary. In addition, we also develop a dynamic scheduling algorithm to address the changes of user's reserved charges and unexpected arrivals of other vehicles. We conduct extensive experiments to validate our approach, the results demonstrate that it can achieve high efficiency with partial private information, as well as a higher revenue with dynamic scheduling algorithm. It can also greatly reduce the total waiting time of users against the first-come-first-serve policy.

Automated summary

This paper proposes a simultaneous multi-round auction model for highway charging scheduling for battery electric vehicles, allowing users to negotiate and maximize the total revenue of charging stations based on their private preferences. Additionally, a dynamic scheduling algorithm is developed to address changes in user's reserved charges and unexpected vehicle arrivals, leading to increased efficiency and revenue, as well as reduced user waiting time.