In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data.
Since the smart charging piles are generally deployed in complex environments and prone to failure, it is significant to perform efficient fault diagnosis and timely maintenance for them.
In case of a fault, the charging pile will display the related fault code on the charging fault record page, the fault light will be on, and the output of the charging pile will be cut off. Faults in charging piles can be reset by swiping the card. After the settlement is completed, faults will be warned and reset, and the charging pile will enter the standby state.
CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The feasibility of the proposed model is illustrated through the case study on fault prediction of real-world smart charging piles.
If a fault is not cleared in a charging pile, it could not work normally after started a second time. After settlement completion, faults are warned and reset, and the charging pile enters a standby state. Only after the fault has been cleared can the charging pile work by restarting.
Instructions for Charging Pile-V1.3.0: Power Output Mode: Can be switched between intelligent mode and priority mode. In intelligent mode, the charging pile power is equally distributed between the two vehicle connectors.
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The invention discloses a method and a system for detecting faults of an energy storage pile, which relate to the technical field of fault detection of an electrochemical energy...
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In order to improve the fault diagnosis accuracy of DC charging pile power devices, a fault diagnosis method based on wavelet packet analysis (WPA) and Elman neural …
In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) …
Through the analysis of different types of faults of the charging module of the DC charging pile, the accuracy and effectiveness of the fault diagnosis method is verified, and its accuracy rate …
Abstract: The fault rate of power module for electric vehicle charging pile is high and it is difficult to identify and locate the fault. A fault diagnosis method based on neural network is proposed, …
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Download Citation | On Dec 18, 2021, Jinfeng Du and others published Research on Fault Diagnosis Method of DC Charging Pile Based on Deep Learning | Find, read and cite all the …
In order to improve the fault diagnosis accuracy of DC charging pile power devices, a fault diagnosis method based on wavelet packet analysis (WPA) and Elman neural …
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fault, charging-pile mechanical fault, charging-pile pro-gram fault, user personal fault, signal fault (oine), pile compatibility fault, charging platform fault, and other faults. Figure 1 presents a …
Abstract: The fault rate of power module for electric vehicle charging pile is high and it is difficult to identify and locate the fault. A fault diagnosis method based on neural network is proposed, …
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Aiming at the fault diagnosis of the charging module of the electric vehicle DC charging pile, a fault diagnosis method based on deep learning is proposed and its accuracy …
In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) …
It is necessary to determine the fault characteristics of the charging module in order to realize the DC charging pile charging module fault state identification, so the fault …
Through the analysis of different types of faults of the charging module of the DC charging pile, the accuracy and effectiveness of the fault diagnosis method is verified, and its accuracy rate …
For the overcharge fault, the authors in ref. conduct several overcharge experiments, then analysed in detail the fault characteristics and the fault mechanism, and proposed a fault diagnosis method based on the voltage …
Aiming at the fault diagnosis of the charging module of the electric vehicle DC charging pile, a fault diagnosis method of the DC charging pile based on deep learning is proposed. First, …