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Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system
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DOI：10.1049/iet-pel.2018.5789.png)
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摘要
En 中文
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.
Keyword:
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
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论文数:
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