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

Lithium ion battery service life forecasting method based on integrated model

A lithium-ion battery and life prediction technology, which is applied in the direction of measuring electricity, measuring devices, and measuring electrical variables, etc., can solve the problems of poor stability and low applicability of lithium-ion battery life prediction, and achieve the effect of improving applicability and stability

Active Publication Date: 2013-09-11
HARBIN INST OF TECH
View PDF7 Cites 32 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention aims to solve the problems of low applicability and poor stability of the existing lithium-ion battery life prediction, thereby providing a lithium-ion battery life prediction method based on an integrated model

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
  • Lithium ion battery service life forecasting method based on integrated model
  • Lithium ion battery service life forecasting method based on integrated model
  • Lithium ion battery service life forecasting method based on integrated model

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0038] The specific embodiment one, based on the integrated model lithium-ion battery life prediction method, it is realized by the following steps:

[0039] Step 1. Preprocess the test data of the battery cycle charge and discharge test, and obtain the equal pressure drop time series as the input data set and the lithium-ion battery remaining capacity sequence as the output data set; divide the original data set into the training data set Train dataset and the test data set Dataset Test dataset;

[0040] Step 2, using the Bagging algorithm to perform secondary resampling on the training data set Train dataset to obtain T new training sets;

[0041] Step 3, establishing a monotone echo state network model, in which the input dimension is L, the reserve pool size is N and the output dimension is M; L, N and M are all positive integers;

[0042] Step 4, initialize the internal connection weights of the monotone echo state network, repeat step 41 to step 43 T times, and obtain T...

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 lithium ion battery service life forecasting method based on an integrated model and relates to a lithium ion battery cycle life forecasting method. The lithium ion battery service life forecasting method is used for solving the problem that the existing lithium ion battery is low in service life forecasting adaptability and poor in stability. The lithium ion battery service life forecasting method includes: performing preprocessing on battery cycle charging and discharging test testing data; adopting a Bagging algorithm to perform secondary resampling on a Train database; building a monotonous echo state network model; initializing inner connection weights of a monotonous echo state network, and repeating for T times to obtain T untrained monotonous echo state network sub-models; setting a first free parameter set and a second free parameter set of the monotonous echo state network model; integrating output RULi of the monotonous echo state network model, adopting the Test database to drive the integrated monotonous echo state network model, and obtaining remaining service life of a lithium ion battery. The lithium ion battery service life forecasting method based on the integrated model is suitable for lithium ion battery service life forecasting.

Description

technical field [0001] The invention relates to a lithium ion battery life prediction method. Background technique [0002] Although the monotonic echo state network can improve the prediction accuracy of the remaining life of lithium-ion batteries, but because the setting of the free parameters of the echo state network lacks the guidance of a strict theoretical system, it is necessary to use the crossover method to obtain the preset prediction accuracy. Verification or expert experience methods get the parameters of the echo state network, but this method is cumbersome and time-consuming. Moreover, due to the opacity inside the neural network, the output is unstable, which will greatly limit the application of the monotonic echo state network. Contents of the invention [0003] The purpose of the invention is to solve the problems of low applicability and poor stability of the existing lithium-ion battery life prediction, thereby providing a lithium-ion battery life pre...

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): G01R31/36
Inventor 刘大同彭宇王红印姗彭喜元
Owner HARBIN INST OF TECH
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