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

Method for predicting RDR of electric vehicle battery based on big data machine learning

A technology of electric vehicles and prediction methods, applied in machine learning, prediction, data processing applications, etc., can solve problems such as difficult measurement of internal parameters and battery capacity decay

Inactive Publication Date: 2019-11-29
常伟
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Power battery is the power source of electric vehicles. With the increase of charging and discharging times and driving mileage, the capacity of the battery continues to decline. This reaction is a typical dynamic nonlinear electrochemical system. It is difficult to measure internal parameters in online applications. Significant challenges remain in degraded state identification and state estimation

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
  • Method for predicting RDR of electric vehicle battery based on big data machine learning
  • Method for predicting RDR of electric vehicle battery based on big data machine learning
  • Method for predicting RDR of electric vehicle battery based on big data machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0012] The specific implementation of this patent will be described in detail in conjunction with the following figures. It should be pointed out that this specific implementation is only an example of the preferred technical solution of the present invention, and should not be understood as limiting the protection scope of the present invention.

[0013] figure 1 The steps of an electric vehicle battery RDR prediction in the specific implementation manner of this patent are shown. in:

[0014] Step S001 is a data preparation step, acquiring data related to the use of electric vehicle batteries.

[0015] In this step, the data of the battery of the electric vehicle includes the monitoring data of the electric vehicle, and the monitoring data is collected once every ten seconds (possibly other acquisition frequencies according to the actual situation). In different vehicle states of the electric vehicle, such as driving , charging process, will be generated. The monitoring d...

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 relates to a method for predicting RDR of an electric vehicle battery based on big data machine learning. The method is composed of a corresponding application framework, a flow and a calculation model. The method comprises the following steps: firstly, performing data consolidation and cleaning on battery real-time data collected in the running process of an electric vehicle batteryand other operation data of the electric vehicle; carrying out characterization processing on the data; and establishing a model and training a verification algorithm through big data machine learning, wherein a nonlinear hybrid algorithm model, a survival model and a random forest are mainly used for modeling, and results are evaluated and optimized from different angles, so that an electric vehicle battery RDR prediction model is established, maintenance and replacement of a battery are optimized, and the safety index of a vehicle owner is improved, and the balance between system performance and economic benefits is achieved.

Description

technical field [0001] The invention relates to an application analysis method for predicting the RDR of an automobile battery based on big data machine learning. The field of application is to predict the energy use of electric vehicles and evaluate the value of battery packs. Background technique [0002] With the promotion of electric vehicles in China and the application of Internet of Vehicles technology, more and more electric vehicles have entered the consumer market and collected driving data in real time according to the national standard (GBT32960). Power battery is the power source of electric vehicles. With the increase of charging and discharging times and driving mileage, the capacity of the battery continues to decline. This reaction is a typical dynamic nonlinear electrochemical system. It is difficult to measure internal parameters in online applications. Significant challenges remain in degraded state identification and state estimation. [0003] Remaining...

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 Applications(China)
IPC IPC(8): G06N20/00G06Q10/04G01R31/367
CPCG01R31/367G06N20/00G06Q10/04
Inventor 常伟仲旭毛樑储金荻
Owner 常伟
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com