A design method of EV charging security early warning model

Li, Pengcheng; Zhao, Yuanliang; Yang, Jing; Xu, Rui; Qiu-yan, Zhang; Ying, Liyun

doi:10.23919/chicc.2017.8028843

Cited by 3 publications

(7 citation statements)

References 5 publications

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“…# Fault Times Fault Rate # Effective Service Times Fault Times = + (7) To further compare the overall state of different manufacturers, we calculate the average effective service times and fault times for every manufacturer. And the fault rate of each charging pile is calculated by equation (7). Then the average fault rate of charging piles for every manufacturer can be obtained.…”

Section: E Comparing Statistics Of Manufacturersmentioning

confidence: 99%

“…Furthermore, the raw data on internal factors is hard to obtain for the providers. Li et al [7] took the failure rate of charging piles into account when establishing the integrated safety-assessment-index system. 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.…”

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: E Comparing Statistics Of Manufacturersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…However, the above methods based on the TTR only considered the one-to-one relationship between input and output, which significantly increased the computational cost and complicated the algorithm structure, because the TTR needed to calculate the operating state parameters of the vehicle in real time. 11,12 In recent years, the artificial neural network has been applied in many fields, including intelligent video surveillance, automatic driving, and motion recognition. [13][14][15] Additionally, many remarkable achievements have been made in topology optimization of material structure, effective performance prediction, and multiscale composite design using the artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Warning model of new energy vehicle under improving time-to-rollover with neural network

Chao

Zhang

Wang

et al. 2022

Measurement and Control

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The probability of electric vehicle rollover accident can be effectively reduced by shortening the prediction time interval and improving the prediction accuracy. Based on a multilayer neural network, an improved time-to-rollover method is presented in this paper. Firstly, the force model of vehicle rollover is established and analyzed where the structure and mass of a battery box have an important influence on the occurrence of rollover. Then, the rollover indexes considering hyperparameters are divided into five categories, and the multi-layer neural network is used to simplify the algorithm structure of the time to rollover, and quickly calculate the operating state parameters with a variation step size in real time. Finally, the influence of the hyperparameters on the prediction results of neural network is studied, and higher efficiency is obtained by comparing with traditional methods.

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“…In Bouabana et al [6], a testing device was developed that could analyze a new charging station by means of a power test and a security test. Li et al [7] designed an early warning model for EV charging security to decrease the safety-related accidents involving EV charging. Kim et al [8] studied the methodological evaluation and testing standards for on-board chargers and provided some suggestions for the test method.…”

Section: Introductionmentioning

confidence: 99%

Performance Status Evaluation of an Electric Vehicle Charging Infrastructure Based on the Fuzzy Comprehensive Evaluation Method

Xiao

Zhao

et al. 2019

WEVJ

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Performance status evaluation is essential for the safe running of electric vehicle (EV) charging infrastructure. With the development of the EV industry, the EV charging infrastructure industry has advanced considerably. Safe and reliable operation of the charging infrastructure is important for the development of EVs. As such, we propose a comprehensive evaluation method to assess the performance condition of an EV charging infrastructure. First, based on the analysis of the existing EV charging principles, we established an evaluation index system for EV charging infrastructure. Second, the subjective weight, objective weight, and comprehensive weight of the index system were determined through analytic hierarchy processes (AHP) and the entropy weight method. Then, we used fuzzy comprehensive evaluation to appraise the performance of the charging infrastructure through expert investigation. Finally, based on the actual data from an EV charger, the performance conditions of the EV charging infrastructure were evaluated to demonstrate the feasibility of the method and the reliability of the index system.

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A design method of EV charging security early warning model

Cited by 3 publications

References 5 publications

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

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

Warning model of new energy vehicle under improving time-to-rollover with neural network

Performance Status Evaluation of an Electric Vehicle Charging Infrastructure Based on the Fuzzy Comprehensive Evaluation Method

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