Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A method and system for unmanned vehicle power prediction based on parallel neural network

A technology of unmanned vehicles and wavelet neural network, which is applied to vehicle components, vehicle energy storage, and electricity measurement, etc. It can solve the problems of no public and effective technology, poor real-time performance, and the inability to consider the impact of unmanned vehicles' power consumption.

Active Publication Date: 2019-09-27
CENT SOUTH UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] As far as the existing technical conditions are concerned, the calculation method of battery power is generally the SOC estimation algorithm, which has poor real-time performance and cannot consider the impact of environmental changes on the power consumption of unmanned vehicles.
Therefore, how to accurately and real-time estimate the battery power of vehicles in extreme environments is a technical problem faced by unmanned vehicles. So far, there is no effective technology that has been disclosed.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method and system for unmanned vehicle power prediction based on parallel neural network
  • A method and system for unmanned vehicle power prediction based on parallel neural network
  • A method and system for unmanned vehicle power prediction based on parallel neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0096] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0097] Such as figure 1 As shown, an unmanned vehicle power prediction method based on parallel neural network includes the following steps:

[0098] Step 1: Obtain the historical driving data of unmanned vehicles in the rainstorm environment;

[0099] The historical driving data includes rainfall resistance, road water resistance, road slope power loss, vehicle battery temperature, first power consumption rate, second power consumption rate, and remaining power at each moment;

[0100] Wherein, the value of the first power consumption rate collected in a rainstorm environment is subtracted from the second power consumption rate, and the second power consumption rate is collected under no rain when driving on the same road;

[0101] Step 2: Construct a battery power consumption rate fitting model for unmanned vehicles based on parallel wavelet neural ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a power supply prediction method and system of an unmanned vehicle based on a parallel neural network. The method comprises steps of using a forces sensing sensor to measure rainfall resistance of a vehicle body and using a continuous covariance fusion algorithm to carry out data fusion; using a Kinect camera to carry out image acquisition and using a Sobel operator to carry out processing, wherein the obtained gathered water detection images are simple and clear and the detection precision is quite high; using a three-part neural network to carry out real-time prediction on electric quantity of the unmanned vehicle in an extreme rainstorm environment, wherein data of rainfall resistance and gathered water resistance is processed so that an added value of power consumption rate of the vehicle in the rainstorm environment is obtained and transmission data of road slope loss power and cell temperature is processed so that power consumption rate of the vehicle driving in the normal weather is obtained. According to the invention, by calculating variables under the rainstorm environment and variables under the normal weather in a separated manner, data processing speed is improved and timelines of the system is improved.

Description

technical field [0001] The invention belongs to the field of intelligent monitoring, in particular to a method and system for predicting power supply of unmanned vehicles based on a parallel neural network. Background technique [0002] With the rapid development of science and technology, the research and development of unmanned vehicle technology has attracted more and more attention. However, the current unmanned vehicles cannot completely replace the existing vehicles. The reason is that there are still many restrictions. The most important point is the control of the battery power of unmanned vehicles. How to ensure that unmanned vehicles can correctly control the real-time power is the basis for the realization of all vehicle-mounted unmanned driving technologies. [0003] As far as the existing technical conditions are concerned, the calculation method of battery power is generally the SOC estimation algorithm, which has poor real-time performance and cannot consider...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/367
CPCB60L58/10Y02T10/70
Inventor 刘辉龙治豪李燕飞
Owner CENT SOUTH UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products