Method for determining charging demand of electric automobile

A technology for electric vehicles and charging requirements, applied in the direction of electrical digital data processing, instruments, data processing applications, etc., can solve the problems of less consideration of the impact of driving time, neglect of charging load randomness, and difficulty in load prediction, etc., to achieve accurate The effect of the reaction

Inactive Publication Date: 2019-01-01
NANJING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This new technology allows for accurate measurement of how much power each vehicle needs during its journey without having any issues like previous methods or requiring long hours before being charged up again later.

Problems solved by technology

The technical problem addressed by this patented study is how accurately predictive-measuring or calculating power consumption from electrical cars (EV) connections over long periods can be challenged due to their complex spatial distribution and varying patterns of usage that may affect road conditions such as speed limit changes caused by heavy rain or snow coverings.

Method used

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  • Method for determining charging demand of electric automobile
  • Method for determining charging demand of electric automobile
  • Method for determining charging demand of electric automobile

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Embodiment Construction

[0037] A method for determining the charging demand of an electric vehicle according to the present invention comprises the following steps:

[0038] Step 1. Use the Markov chain to describe the change of the state of charge of the power battery during the one-day travel of the electric vehicle user.

[0039] A Markov process is a random process with no aftereffect, and a Markov process with discrete parameters and a discrete state space is called a Markov chain. Markov chain is a model describing a class of stochastic dynamic systems. It means that the state of the system at each time is random, and the state from the current time to the next time is transferred according to a certain probability. The future state is only the same as the current state. The state and its transition probability are related, but have nothing to do with the previous state, that is, there is no aftereffect. Record the current state as S i , the state at the next moment is S j , then the Markov cha

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Abstract

The invention discloses a charging demand calculation method for an electric automobile. The method includes the following steps: 1) describing a changing condition of a state of charge of a power battery of the electric automobile during a one-day trip process of a user through a Markov chain; 2) considering the influence of a time-consuming coefficient corresponding to a traffic index on the driving time and the probability of the trip time of the electric automobile at each time period corresponding to each of destinations during the trip process of the user, and determining a distributioncondition of the charging demand at each time of one day; and 3) obtaining time and spatial distribution of the charging demand of the electric automobile by using a Monte Carlo simulation method. Themethod considers the influence of the traffic time-consuming coefficient on the driving process of the electric automobile, and can more accurately reflect the spatial-temporal distribution of the charging demand.

Description

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Claims

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Application Information

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Owner NANJING UNIV OF SCI & TECH
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