Research on Comprehensive Evaluation of Electric Vehicle Charging Failures

Wei, Si-Ya; Zhu, Qing; Li, Xueming; Meng, Xianhai

doi:10.1109/icsp51882.2021.9408966

Cited by 6 publications

(2 citation statements)

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“…Charging piles are mostly deployed in the wild with uncontrollable environmental factors, causing frequent charging faults. Therefore, effective analysis of charging safety and comprehensive assessment of charging piles have become practical problems [4] [5].…”

Section: Introductionmentioning

confidence: 99%

“…The failure rate can reflect the actual operation of charging piles, but it ignores the different frequency and risk degree of each type of failure because failure rate takes the total failure rate as an evaluation metric. It is critical to establish an effective evaluation system focusing on the long-term safety performance of charging piles, which can provide suggestions on purchasing, maintaining and managing charge piles, As for assessment methods, Wei et al [5][6] [7] used the analytic hierarchy process (AHP [8]) to calculate the evaluation metric weights depending on expert experience. However, when there are many elements in the same hierarchy, subjective evaluation becomes vague and untrustworthy, and then the judgment matrix is prone to serious inconsistency.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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 semisupervised 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.INDEX TERMS AdaBoost, charging piles, gradient boosting decision tree, risk assessment, self-training

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Section: Introductionmentioning

confidence: 99%