Open AccessBinary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system
Author(s) -
Xia Kun,
Guo Haotian,
He Sheng,
Yu Wei,
Xu Jingjun,
Dong Hui
Publication year - 2019
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2018.5789
Subject(s) - robustness (evolution) , arc (geometry) , resistor , dc motor , computer science , inverter , battery (electricity) , electric arc , automotive engineering , engineering , voltage , algorithm , power (physics) , electrical engineering , electrode , mechanical engineering , biochemistry , chemistry , physics , quantum mechanics , gene
Direct current (DC) serial arc faults usually occur in the damaged insulation lines or line connections, which will cause serious accidents such as fires and explosions. With the rapid increase of electric vehicles, DC serial arc faults are more and more dangerous to battery system. Therefore, a binary classification model based on machine learning algorithm was proposed to detect DC serial arc faults effectively in this study. It was optimised according to the characteristic signals of the arc to be satisfied with different loads for higher detection accuracy and robustness. In the simulative experiments for the power system electric vehicle, while the loads changing to the motor, the resistor or the inverter, it will all reach a highly successful detection rate, respectively.