Method for predicting remaining service life of lithium battery based on FPCA (functional principal component analysis) and Bayesian updating

A function-type principal component and life prediction technology, which is applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems that affect the accuracy of battery life prediction and cannot give the remaining life distribution of confidence intervals, etc.

Active Publication Date: 2015-07-15
北京恒兴易康科技有限公司
View PDF3 Cites 31 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most current data-driven lithium battery life prediction methods require feature extraction from battery data, and inappropriate feature selection may significantly affect the accuracy of battery life predi

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 remaining service life of lithium battery based on FPCA (functional principal component analysis) and Bayesian updating
  • Method for predicting remaining service life of lithium battery based on FPCA (functional principal component analysis) and Bayesian updating
  • Method for predicting remaining service life of lithium battery based on FPCA (functional principal component analysis) and Bayesian updating

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0047] 1. A method for predicting the remaining life of a lithium battery based on functional principal component analysis and Bayesian updating provided by the present invention, the specific steps are as follows:

[0048] 1.1 FPCA-based lithium-ion battery degradation model

[0049] 1.1.1 Capacity degradation model

[0050] The performance degradation process of lithium-ion batteries can be regarded as a potentially stochastic process. At the same time, the life cycle of the battery can be viewed as a function of time. Firstly, the lithium battery degradation model is established by using the existing capacity data to realize the life prediction of the battery. Since functional principal component analysis (FPCA) is an extension of traditional principal component analysis (PCA), it can provide more stable estimates and avoid the introducti...

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 method for predicting remaining service life of a lithium battery based on FPCA (functional principal component analysis) and Bayesian updating. The method comprises the following steps of utilizing FPCA to build a lithium battery non-parameter degrading model, predicting the remaining service life of the lithium battery based on the degrading model, and analyzing the influence of different amount of modeling data on the predicting accuracy. The method has the advantages that an empirical Bayesian method is provided for updating the lithium battery degrading model in real time; the lithium battery degrading model is corrected in real time by the Bayesian method, so as to obtain the more accurate lithium battery degrading model; the distributing of the remaining service life of the lithium battery is calculated by a parameter bootstrap method, and the confidence interval is calculated.

Description

technical field [0001] The invention relates to the technical field of lithium battery remaining life prediction, in particular to a lithium battery remaining life prediction method based on functional principal component analysis and Bayesian update. Background technique [0002] With the development of lithium-ion batteries, research on life prediction of lithium-ion batteries began in the 1980s. Effective battery life prediction can not only predict potential risks, thus provide effective guidance for battery use, but also reduce related losses caused by battery failure. [0003] There are currently many methods for battery life prediction, most of which are model-based and data-driven prediction methods. Existing model-based lifetime prediction methods can be classified into electrochemical model-based methods, equivalent circuit-based methods, performance-based methods, and analytical model-based methods. The electrochemical model is based on porous electrode theory a...

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
IPC IPC(8): G06F17/50
Inventor 吕琛程玉杰王洋周博
Owner 北京恒兴易康科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap