Open AccessLi-Ion Battery Fault Diagnosis Dedicated to Electric Vehicles by Neural Network Pattern Recognition
Author(s) -
Imene Djelamda,
Ilhem Bouchareb
Publication year - 2022
Publication title -
mathematical modelling and engineering problems/mathematical modelling of engineering problems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.26
H-Index - 11
eISSN - 2369-0747
pISSN - 2369-0739
DOI - 10.18280/mmep.090118
Subject(s) - matlab , artificial neural network , battery (electricity) , fault (geology) , computer science , process (computing) , electric vehicle , fault detection and isolation , artificial intelligence , pattern recognition (psychology) , power (physics) , physics , quantum mechanics , seismology , actuator , geology , operating system
The most problem in electric vehicles is the detection of faults in the battery; in this paper we discuss a systematic data process for detecting and diagnosing faults in the battery and the application of the method of neural networks for the classification of the various faults of the Li-Ion battery dedicated to the electric vehicle. ; and for that we tried to create a fault classification algorithm using the neural network commands that exist in the MATLAB, we used the MATLAB/Simscape for battery modeling, the latter prepared physical models for use in different fields; and based on this model, we identified the battery parameters and we will apply some faults to classify them with neural networks; creating an algorithm takes a long time but when we use these commands we have to do the classification and the MATLAB gives us the algorithm., These algorithms have shown the efficiency of the application of pattern recognition to the diagnosis.