Charging demand prediction method and system of electric vehicles

Abstract

The invention relates to a charging demand prediction method of electric vehicles. The method comprises steps of determining electricity quantity of a cell and expected parking time in the current position of each electric vehicle in a monitoring region, and according to history data, determining charging probability at the current time; calculating charging demand probability of each electric vehicle, and ranking the charging demand probabilities of the electric vehicles so as to form a list of electric vehicles which are possibly to be charged. According to the invention, response time of user charging requests can be remarkably shortened; peak pressure of the system is relieved; and excellent user experience is provided.

Description

technical field [0001] The present invention relates to the technical field of electric vehicles, and more specifically, to a method and system for predicting charging demand of electric vehicles. Background technique [0002] Electric vehicles have been gradually popularized, and charging services suitable for electric vehicles are the focus of attention of those skilled in the art. [0003] In the existing charging services, after the user places an order (request for charging), the system passively dispatches service personnel or service vehicles to provide the charging service. There are some problems in this way: [0004] 1. The response time of all services cannot be guaranteed. When the service personnel are far away from the user's vehicle, the response time of the service will become longer. [0005] 2. There is peak pressure on system scheduling. In some time periods, orders may be relatively concentrated, which will affect the overall operating efficiency of the ...

AI Technical Summary

Problems solved by technology

[0004] 1. The response time of all services cannot be guaranteed. When the service personnel are far away from the user's vehicle, the response time of the service will become longer

[0005] 2. There is peak pressure on system scheduling. In some time periods, orders may be relatively concentrated, which will affect the overall operating efficiency of the system;

[0006] 3. The same user may place orders in different time periods but receive services with different standards (for example, significant differences in response time), which affects user experience

[0007] In addition, for the service mode of mobile charging car, if the driver (service personnel) does not know where the next service order is, he will not know where to drive the charging car and where to park it, but can only passively wait for the system to dispatch , so that the charging resources cannot be fully utilized

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