Open AccessArtificial neural network‐based fault diagnosis in the AC–DC converter of the power supply of series hybrid electric vehicle
Author(s) -
Moosavi Seyed Saeid,
Djerdir Abdesslem,
AitAmirat Youcef,
Arab Khaburi Davood,
N'Diaye Abdoul
Publication year - 2016
Publication title -
iet electrical systems in transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.588
H-Index - 26
ISSN - 2042-9746
DOI - 10.1049/iet-est.2014.0055
Subject(s) - artificial neural network , fault (geology) , engineering , series (stratigraphy) , power (physics) , capacitor , electronic engineering , computer science , electric vehicle , automotive engineering , voltage , electrical engineering , artificial intelligence , paleontology , physics , quantum mechanics , seismology , biology , geology
AC–DC converter switches of the drive train of series hybrid electric vehicles (SHEVs) are generally exposed to the possibility of outbreak open‐phase faults because of troubles with the switching devices. In this framework, the present study proposes an artificial neural network (ANN)‐based method for fault diagnosis after extraction of a new pattern. The new pattern under AC–DC converter failure in view of SHEV application has been used for train‐proposed ANN. To achieve this goal, four different levels of switches fault are considered on the basis of both simulation and experimental results. Ensuring the accuracy and generalisation of the introduced pattern, several parameters have been considered, namely: capacitor size changes, load, and speed variations. The experimental results validate the simulation results thoroughly.