Multi-agent unmanned electric vehicle battery replacement scheduling method based on Internet of Vehicles

Abstract

According to a multi-agent electric vehicle battery replacement scheduling method based on the Internet of Vehicles, a vehicle-road cooperative service is deployed on an MEC platform, and man-vehicle-road cooperative interaction is realized by means of a Uu interface or a PC5 interface and communication modes such as VANET, 4G or 5G; according to a map around an electric vehicle with a battery replacement service requirement, a roadside unit divides a battery replacement station cluster with a high potential cooperation matching degree into a whole and gathers the battery replacement station cluster into a battery replacement area, and the battery replacement area with the maximum service capability probability shares a plurality of electric vehicles with the battery replacement service requirement at the same time; the service rate of each battery swap station is taken as an assessment target to mainly assess the self-service capability, self-service quality and sitting information ofeach battery swap station node and the current self-state of the electric vehicle with the battery swap requirement; and the optimal joint action of global electric vehicles is provided, so that theoverall service balance of each battery swap station is maintained, and the long-term performance of the Internet of Vehicles is improved. According to the invention, the electric vehicle can exchangepower as soon as possible, and each power exchange station can maintain service balance.

Description

technical field [0001] The invention relates to a multi-agent unmanned electric vehicle battery replacement scheduling method based on the Internet of Vehicles, and belongs to the technical field of electric vehicle battery replacement scheduling. Background technique [0002] Communication between the vehicle and the roadside unit (Vehicle to Roadside Unit, V2R) is maintained, and the Internet of Vehicles can obtain road traffic information in real time, reduce network delay, and improve network transmission capacity. In addition, MEC (Multi-access Edge Computing, multi-access edge computing technology) provides efficient computing functions and an IT service environment at the edge of the network. This environment features ultra-low latency and high bandwidth, enabling real-time access to wireless network information. [0003] In particular, by deploying roadside units in roadside units (Roadside Units, RSUs), the computing load of intelligent transportation systems is tr...

AI Technical Summary

Problems solved by technology

[0007] However, the focus of research in the prior art is: when multiple power exchange stations can provide power exchange services for a certain user, how to provide the user with an optimal solution to reduce the waiting for future power exchange; How to provide the user with a battery replacement plan with the shortest waiting time when the battery replacement service cannot be provided for the user; how to balance the phenomenon of idleness and overload when the battery replacement resources are fully utilized, and effectively improve the utilization efficiency of the battery replacement station

[0008] In particular, although the existing research on the selection of battery swapping stations has considered the coordinated scheduling of moving electric vehicles and swapping stations, they have not considered the following problems: when the demand for battery swapping increases to a certain threshold due to insufficient battery reserves, A single battery swap station accepts too many battery swap demands and the waiting time for battery swaps is too long; through reasonable and effective centralized allocation of battery resources, the collaboration between swap stations is promoted; the competitive relationship between swap stations is balanced, Reasonable allocation of replacement resources

In particular, in this technical field, no technical solution aimed at directly optimizing the service rate of the power station has been retrieved so far, and there is no system that can realize self-learning, self-improvement, and continuous improvement of decision-making accuracy and reliability

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