The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 10, 2019

Filed:

Oct. 20, 2015
Applicant:

Hefei University of Technology, Hefei, Anhui, CN;

Inventors:

Yigang He, Anhui, CN;

Chaolong Zhang, Anhui, CN;

Lei Zuo, Anhui, CN;

Sheng Xiang, Anhui, CN;

Baiqiang Yin, Anhui, CN;

Assignee:

HEFEI UNIVERSITY OF TECHNOLOGY, Hefei, Anhui, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01R 31/367 (2019.01); G06F 17/16 (2006.01); G06N 5/04 (2006.01); G06N 3/12 (2006.01); G06N 7/00 (2006.01); G01R 31/392 (2019.01);
U.S. Cl.
CPC ...
G01R 31/367 (2019.01); G06F 17/16 (2013.01); G06N 3/126 (2013.01); G06N 5/04 (2013.01); G06N 7/005 (2013.01); G01R 31/392 (2019.01);
Abstract

A method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, relating to a method for estimating health condition and predicting remaining useful life of lithium battery, includes steps of: (1) obtaining health condition data of each of charge-discharge cycles of the lithium battery by measurement; (2) processing capacity data measured of the lithium battery with wavelet double denoising; (3) calculating a capacity threshold where the lithium battery fails; (4) referring to capacity data and charge-discharge cycle data of the lithium battery, applying a differential evolution algorithm for optimizing a width factor of the relevance vector machine; and (5) predicting the remaining useful life of the lithium battery with the relevance vector machine optimized by the differential evolution algorithm. The method is simple and effective, which can accurately predict remaining useful life of lithium battery.


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