Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Xia, Kun; Guo, Haotian; He, Sheng; Yu, Wei; Xu, Jingjun; Dong, Hui
• Is Part Of: IET power electronics. Volume 12:Issue 1(2019)
• Page Start: 112
• Page End: 119
• Publication Date: 2019-01-01
• Subjects: DC motors -- power engineering computing -- arcs (electric) -- learning (artificial intelligence) -- fault diagnosis -- pattern classification -- electric vehicles -- battery powered vehicles; binary classification model -- machine learning algorithm -- DC serial arc detection -- electric vehicle battery system -- direct current serial arc faults -- damaged insulation lines -- line connections -- electric vehicles -- DC serial arc faults -- higher detection accuracy -- robustness -- power system electric vehicle; Power electronics -- Periodicals; 621.31705
• DOI: 10.1049/iet-pel.2018.5789
• Languages: English
• ISSNs: 1755-4535

Abstract

Abstract : 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.