specializing in energy storage, photovoltaic, charging piles, intelligent micro-grid power stations, and related product research and development, production, sales and service. It is a world
AI Customer ServiceIncreased adoption of the electric vehicle (EV) needs the proper charging infrastructure integrated with suitable energy management schemes. However, the available
AI Customer Service1 INTRODUCTION. Concerns regarding oil dependence and environmental quality, stemming from the proliferation of diesel and petrol vehicles, have prompted a search
AI Customer ServiceAdaikkappan and Sathiyamoorthy 6 proposes that a new charging method is designed after considering the constraints of charging time, charging efficiency, charging state,
AI Customer Service(only in DC charging stations), energy metering unit, AC and DC residual current detector, an isolation isolated line voltage sensing across the relay and contactor. Resources TIDA
AI Customer ServiceThis 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
AI Customer ServiceIt is quite simply because the EV charging equipment standard, BS EN 61851-1:2011 stated in Clause 5 (see my highlight): 5 Rating of the supply a.c. voltage. The rated value of the a.c.
AI Customer ServiceAdaikkappan and Sathiyamoorthy 6 proposes that a new charging method is designed after considering the constraints of charging time, charging efficiency, charging state, health state, charging voltage threshold,
AI Customer ServiceIn this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM)
AI Customer ServiceThis 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
AI Customer ServiceCharging pile, "photovoltaic + energy storage + charging" Such a huge charging pile gap, if built into a light storage charging station, will greatly improve the "electric vehicle long-distance
AI Customer ServiceWith 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
AI Customer ServiceThe current research of battery energy storage system (BESS) fault is fragmentary, which is one of the reasons for low accuracy of fault warning and diagnosis in
AI Customer ServiceThrough 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
AI Customer ServiceIt is quite simply because the EV charging equipment standard, BS EN 61851-1:2011 stated in Clause 5 (see my highlight): 5 Rating of the supply a.c. voltage. The rated value of the a.c.
AI Customer ServiceWith the rapid development of DC power supply technology, the operation, maintenance, and fault detection of DC power supply equipment and devices on the user side
AI Customer ServiceIt 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
AI Customer ServiceAiming at the problems that convolutional neural networks (CNN) are easy to overfit and the low localization accuracy in fault diagnosis of V2G charging piles, an improved
AI Customer ServiceThrough 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
AI Customer ServiceSpecifies the general requirements of systems supplied from low-voltage DC sources and energy storage devices not exceeding 60V methods based on waveshape and
AI Customer ServiceAiming at the problems that convolutional neural networks (CNN) are easy to overfit and the low localization accuracy in fault diagnosis of V2G charging piles, an improved fault classification model based on convolutional
AI Customer ServiceAbstract: With the application of the Internet of Things (IoT), smart charging piles, which are important facilities for new energy electric vehicles (NEVs), have become an
AI Customer ServiceThis paper introduces a DC charging pile for new energy electric vehicles. The DC charging pile can expand the charging power through multiple modular charging units in parallel to improve
AI Customer ServiceHowever, few studies have provided a detailed summary of lithium-ion battery energy storage station fault diagnosis methods. GB/T 34131 specifies several methods to
AI Customer ServiceIn this paper, the battery energy storage technology is applied to the traditional EV (electric vehicle) charging piles to build a new EV charging pile with integrated charging,
AI Customer ServiceIn this paper, the battery energy storage technology is applied to the traditional EV (electric vehicle) charging piles to build a new EV charging pile with integrated charging, discharging, and storage; Multisim software is used to build an EV charging model in order to simulate the charge control guidance module.
The failure of the charging pile may be caused by many factors, the most common of which is the external environment and operation and maintenance frequency. Therefore, this paper constructs a potential fault identification model of electric vehicle charging pile from the above two aspects.
However, traditional fault detection methods are still used in charging piles, which makes the detection efficiency low. This paper proposes an error detection procedure of charging pile founded on ELM method.
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.
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.
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