Self-Training Enabled Efficient Classification Algorithm: An Application to Charging Pile Risk Assessment

doi:10.1109/ACCESS.2022.3199442

2022-01-01

常晓林 (Xiaolin Chang) *

DOI：10.1109/ACCESS.2022.3199442

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摘要

En 中文

With the continuous development of electric vehicles (EV), large-scale distributed charging piles have been deployed in the wild. Therefore, it is extremely essential to evaluate the risk state of EV charging piles efficiently and effectively. This paper aims to measure the capability of supervised and semi-supervised machine learning techniques in assessing the risk state of EV charging piles. We investigate 8 algorithms, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Self-Training based on SVM (ST-SVM), Self-Training based on RF (ST-RF), Self-Training based on AdaBoost (ST-AdaBoost) and Self-Training based on GBDT (ST-GBDT). We first collect data on normal and abnormal termination of charging services from an actual Internet of Vehicles platform. The dataset consists of 17,773 recordings and 7 features generated from the records, which are used for classification. According to the statistical times of 7 features, 20% of recordings are labeled by knowledgable experts into three classes: low-risk, medium-risk and high-risk. Experimental results indicate that ST-AdaBoost and ST-GBDT show more excellent overall classification performance, compared with the other traditional supervised methods. We also apply ST-GBDT to predict the risk state of the unclassified piles and produce the statistic of piles from different manufacturers.

Keyword:

Support vector machines

Training

Risk management

Classification algorithms

Random forests

Boosting

Safety

Electric vehicles

AdaBoost

charging piles

gradient boosting decision tree