Unlock instant, AI-driven research and patent intelligence for your innovation.

A Battery Life Prediction Method Based on gm(1,n) Gray Model

A technology of battery life and prediction method, which is applied based on GM (1, can solve the problems of low prediction accuracy and failure to consider the law of battery capacity change, etc.

Active Publication Date: 2016-10-05
SHANDONG UNIV
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method only establishes the GM(1,1) model of the internal resistance of the battery, and does not consider the changing law of the battery capacity, which has certain limitations and low prediction accuracy

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 Battery Life Prediction Method Based on gm(1,n) Gray Model
  • A Battery Life Prediction Method Based on gm(1,n) Gray Model
  • A Battery Life Prediction Method Based on gm(1,n) Gray Model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0107] The present invention is described in detail below in conjunction with accompanying drawing:

[0108] like figure 1 Shown, a kind of battery life prediction method based on GM (1, N) gray model, comprises the following steps:

[0109] S1. Encode the capacity of the battery along with the number of cycles of the battery to form the system characteristic data sequence C of the battery capacity (0) ;

[0110] S2. Encode the internal resistance value of the battery with the number of battery cycles to form a data sequence R of factors related to the internal resistance of the battery (0) ;

[0111] S3. From system characteristic data sequence C (0) , to obtain the latest 5 capacity data of the battery: c(k-5)~c(k-1); and from the relevant factor data sequence R (0) , to obtain the latest 5 internal resistance data of the battery: r(k-5)~r(k-1), where k is a positive integer, which is the number of cycles of the battery, and the data from the 1st to k-1 times have been ...

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 battery life prediction method based on a GM (1, N) gray model. The capacity of a battery and the inner resistance value of the battery are coded along with the cycle index of the battery, and a system feature data sequence C (0) corresponding to the capacity of the battery and a related factor data sequence R (0) corresponding to the inner resistance of the battery are formed; setting index capacity data and inner resistance data are obtained from the system feature data sequence C (0) corresponding to the capacity of the battery and the related factor data sequence R (0) corresponding to the inner resistance of the battery, and gray processing is carried out on the obtained data; an obtained equal dimension addition gray bivariate first-order time response sequence of the capacity of the battery is recovered to an original number sequence value through regressive generation, and the accuracy of a corresponding prediction model simulation sequence of an original sequence of the capacity of the battery is verified through a mean square error verification method. The battery life prediction method based on the GM (1, N) gray model is simple, easy to implement and good in robustness, and has high practical application value.

Description

technical field [0001] The invention relates to a battery life prediction method based on a GM (1, N) gray model. Background technique [0002] Many factors such as energy crisis, environmental pollution and energy security have once again pushed electric vehicles onto the stage of history, and have become the focus of attention all over the world. As a key component of electric vehicles, vehicle-mounted power batteries are crucial to the power, economy and safety of the vehicle, and are a key factor restricting the scale development of electric vehicles. [0003] Lithium-ion batteries are widely used as power sources in electric vehicles and hybrid electric vehicles due to their outstanding advantages such as high voltage, high energy density, good cycle performance, small self-discharge and no memory effect. [0004] With the increasingly widespread application of lithium-ion batteries, the research on battery life models has gradually become a topic of concern. The life...

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): G01R31/36
Inventor 张承慧商云龙崔纳新
Owner SHANDONG UNIV