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:
Jul. 28, 2020

Filed:

Jun. 08, 2018
Applicant:

Wuhan University, Hubei, CN;

Inventors:

Yigang He, Hubei, CN;

Yaru Zhang, Hubei, CN;

Hui Zhang, Hubei, CN;

Kaipei Liu, Hubei, CN;

Assignee:

WUHAN UNIVERSITY, Hubei, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01R 31/00 (2006.01); G07C 5/08 (2006.01); G06N 3/04 (2006.01); G01R 31/40 (2020.01); B60K 6/22 (2007.10); B60K 6/46 (2007.10);
U.S. Cl.
CPC ...
G01R 31/007 (2013.01); G01R 31/40 (2013.01); G06N 3/04 (2013.01); G07C 5/0808 (2013.01); B60K 6/22 (2013.01); B60K 6/46 (2013.01); B60Y 2200/92 (2013.01); Y02T 10/6217 (2013.01); Y10S 903/904 (2013.01);
Abstract

A fault diagnosis method for a series hybrid electric vehicle AC/DC (Alternating Current/Direct Current) converter, implementing identifying and diagnosing of an open circuit fault of a power electronic components in an AC/DC converter, and including the following steps: first, establishing a simulation model for a series hybrid electric vehicle AC/DC converter, and selecting a DC bus output current as a fault characteristic; then classifying fault types according to a quantity and locations of faulty power electronic components; next, decomposing the fault characteristic, that is, the DC bus output current by means of fast Fourier transform to different frequency bands, and selecting harmonic ratios of the different frequency bands as fault diagnosing eigenvectors; and finally, identifying the fault types by using a genetic algorithm-based BP (Back Propagation) neural network.


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