Application of Computer Big Data Technology in the Safety Intelligent Monitoring of High Voltage Charging of Electric Vehicles

Pan, Lili; Chen, Bin; Yin, Xiao; Yan, Wensen; Hao, Yang; Hu, Jin‐Song; Yao, Yan

doi:10.1109/icicas53977.2021.00083

Cited by 1 publication

(1 citation statement)

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“…As mentioned above, we focus on high voltage interlock fault and low SOC fault. When it is detected that the connection of the high voltage circuit is abnormally disconnected or damaged, the Battery Management System (BMS) will reduce the power through the vehicle controller or cut off the high voltage power in time, and report the high voltage interlock fault [56]. Common factors that cause high voltage interlock fault include open circuit caused by failure of interlock switch, open circuit caused by terminal withdrawal, short circuit of interlock terminal to ground, internal fault of power battery etc.…”

Section: Experimental Verification and Analysismentioning

confidence: 99%

STLRF‐Stack: A fault prediction model for pure electric vehicles based on a high dimensional imbalanced dataset

Sheng

Zhang

et al. 2022

IET Intelligent Trans Sys

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Fault prediction is an important part of the safe operation and maintenance of pure electric vehicles, and it is crucial to predict faults efficiently and accurately. To address the problems that the pure electric vehicle fault prediction model has less than ideal prediction effect and classification bias to most classes due to unbalanced data set categories and high dimensionality of features, this paper firstly obtains a low dimensional balanced dataset based on hybrid sampling and joint feature selection. Then the classification ability of the model is improved through ensemble learning. Finally, a new hybrid model STLRF‐Stack is proposed to realize efficient fault prediction of pure electric vehicles. This paper is validated on a real operating dataset of pure electric vehicles. The experimental results show that the prediction effect of the hybrid model proposed in this paper is better than that of the single model and related literature. In addition, this model can successfully predict the fault 5 min before the actual fault of electric vehicles occurs, which provides the drivers with the opportunity of accident prevention and early treatment. Therefore, the model has specific feasibility and practical application value.

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Section: Experimental Verification and Analysismentioning

confidence: 99%