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Fault identification of electric energy storage charging pile

Research on fault diagnosis method of power module for charging pile

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, which provides the necessary reference and premise for the rapid realization of fault state identification and on-site maintenance. Firstly, the

Fault Detection of Electric Vehicle Charging Piles Based on Extreme

This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data

A fault state detection method for DC charging pile charging

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 characteristics of the charging module are analyzed (Bayati et al., 2021) based on the electrical structure of the DC charging pile, which provides a basis for fault

Research on the Fault Diagnosis Method of

Preventive maintenance decision model of electric vehicle charging pile

conducted to establish an indicator system for the operation status of charging piles, and a potential fault identification model was constructed. By optimizing the life cycle, the balance problem between optimal maintenance life and optimal opportunity maintenance life has been solved, thus completing preventive maintenance decisions. The

Identification of authenticity of electric energy storage charging piles

Energy management of green charging station integrated with photovoltaics and energy storage system based on electric 1. Introduction With last decade has witnessed a great proliferation of electric vehicles (EVs) and an increasing connection between the transportation network and the electricity network of smart cities [1].Owing to the emerging information technologies [2],

An electric vehicle charging pile fault diagnosis system using

For CPFD implementation, the LightGBM ensemble learning combined with a grid search cross-validation algorithm is designed to build a fault detection model. Related experiments have proven the proposed methods can achieve the highest diagnostic accuracy, which is superior to other popular methods.

Research on the Fault Diagnosis Method of Automotive Charging Pile

5 天之前· In order to improve the situation that the fault data set of electric vehicle charging pile has unbalanced data distribution under each fault and the small amount of data leads to the inconspicuous data features, this paper proposes a method of SAE-MLP model for fault diagnosis of charging pile fault data. This paper firstly utilizes

Frontiers | Machine Learning-based Spatiotemporal Fusion

To address this, we designed a simulated charging pile system and collected fault data at multiple power levels by manually introducing faults. Furthermore, we proposed a fault identification algorithm based on spatiotemporal feature fusion using machine learning.

IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles

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. The

Fault Diagnosis of DC Charging Pile Based on Convolutional

Abstract: Electric vehicle DC charging stations have always been plagued by frequent malfunctions, difficult maintenance, and high repair costs, but traditional fault detection methods are inefficient. Therefore, a diagnostic method is proposed for the operational status of DC charging station charging modules based on wavelet packet

Method of Location and Capacity Determination of

With the popularity of new energy vehicles, a large number of cities began to focus on the installation of electric vehicle charging piles. However, the existing intelligent charging piles have faced problems such as

IoT-Enabled Fault Prediction and Maintenance for Smart Charging

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)

Fault Detection of Electric Vehicle Charging Piles Based on

This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data for common features of the charging pile and establishes a classification prediction frame work that relies on the Extreme Learning Machine (ELM) algorithm

Optimized operation strategy for energy storage charging piles

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity

Research on fault diagnosis method of power module for charging

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

A DC Charging Pile for New Energy Electric Vehicles

New energy electric vehicles will become a rational choice to achieve clean energy alternatives in the transportation field, and the advantages of new energy electric vehicles rely on high energy storage density batteries and efficient and fast charging technology. This paper introduces a DC charging pile for new energy electric vehicles. The DC charging pile

Frontiers | Machine Learning-based Spatiotemporal Fusion

To address this, we designed a simulated charging pile system and collected fault data at multiple power levels by manually introducing faults. Furthermore, we proposed a

Fault diagnosis of DC-DC module of V2G charging pile based on

Simulation results show that the fault diagnosis algorithm based on fuzzy neural network can effectively diagnose faults. Aiming at the problem of fault diagnosis of switching devices in DC/DC module of V2G charging pile, a diagnosis method based on fuzzy neural network is proposed. The method combines fuzzy mathematics with neural network, adopts 4-layer forward network and

Fault Detection of Electric Vehicle Charging Piles Based on

A deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles, and the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault Detection.

[PDF] Fault Detection of Electric Vehicle Charging Pile on Basis of

A fault detection method based on deep learning Convolutional Neural Networks and Long Short-Term Memory and the proposed CNN-LSTM method has the highest accuracy and exhibits

Research Based on Improved CNN-SVM Fault Diagnosis of V2G Charging Pile

With the increasing number of electric vehicles, V2G (vehicle to grid) charging piles which can realize the two-way flow of vehicle and electricity have been put into the market on a large scale, and the fault maintenance of charging piles has gradually become a problem. Aiming at the problems that convolutional neural networks (CNN) are easy to overfit and the

The Design of Electric Vehicle Charging Pile Energy Reversible

and the battery of the electric vehicle can be used as the energy storage element, and the electric energy can be fed back to the power grid to realize the bidirectional flow of the energy. Power factor of the system can be close to 1, and there is a significant effect of energy saving. Keywords Charging Pile, Energy Reversible, Electric

Fault Diagnosis of DC Charging Pile Based on Convolutional

Abstract: Electric vehicle DC charging stations have always been plagued by frequent malfunctions, difficult maintenance, and high repair costs, but traditional fault detection

An electric vehicle charging pile fault diagnosis system using

For CPFD implementation, the LightGBM ensemble learning combined with a grid search cross-validation algorithm is designed to build a fault detection model. Related

[PDF] Fault Detection of Electric Vehicle Charging Pile on Basis

A fault detection method based on deep learning Convolutional Neural Networks and Long Short-Term Memory and the proposed CNN-LSTM method has the highest accuracy and exhibits the best performance in the electric vehicle charging pile diagnosis.

Fault Detection of Electric Vehicle Charging Piles Based on Extreme

A deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles, and the

A fault state detection method for DC charging pile charging

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

Fault Detection of Electric Vehicle Charging Piles Based on

DOI: 10.1109/ICCMC48092.2020.ICCMC-000157 Corpus ID: 216103888; Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm @article{Gao2020FaultDO, title={Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm}, author={Xinming Gao and Gaoteng Yuan and Mengjiao

Fault identification of electric energy storage charging pile [PDF]

6 FAQs about [Fault identification of electric energy storage charging pile]

Which fault detection method is best for electric vehicle charging pile diagnosis?

How accurate is fault detection in DC charging pile?

It is necessary to accurately judge the fault state of the charging module of DC charging pile in order to ensure the safe and reliable operation of DC charging pile. However, the fault signal processing of the fault detection method is poor, resulting in low fault detection accuracy.

What is the error detection procedure of charging pile based on Elm?

What are the possible faults of DC charging pile?

During the operation of DC charging pile, faults are easy to occur, mainly including communication faults, charging gun faults, charging module faults, etc. Among the possible faults of the DC charging post, the charging module failure rate is extremely high.

Can CS-LR predict smart charging pile faults based on classified data?

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.

Why is charging module important in DC charging pile?

Conclusion Charging module is the key to the safe and reliable operation of DC charging pile. The DC charging pile to maintain stable operation state for the charging module fault state identification results, timely development of solution strategies.