This paper introduces a novel electricity load time-series prediction model, utilizing a broad learning system to tackle the challenge of low prediction accuracy caused by the unpredictable nature of electricity load
Customer ServiceThe energy storage charging pile achieved energy storage benefits through charging during off-peak periods and discharging during peak periods, with benefits ranging from 558.59 to 2056.71 yuan. At an average demand of 70 % battery capacity, with 50–200 electric vehicles, the cost optimization decreased by 17.7%–24.93 % before and after
Customer ServiceAbstract: In order to accurately predict the power consumption data of charging piles, assist related enterprises to accurately predict the benefits of charging piles and further optimize the
Customer ServiceAn optimal planning model is established to optimize the configuration of charging piles. Simulation results show that the proposed method can decrease both peak-valley difference
Customer ServiceNREL uses expert insight and machine learning to identify accurate and robust models for battery life prediction with the AI-Batt tool. AI-Batt empowers rapid fitting of complex battery degradation trends with a comprehensive set of data
Customer ServiceThis paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality
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) 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
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,...
Customer ServiceThis paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things
Customer ServiceThe wide deployment of charging pile energy storage systems is of great significance to the development of smart grids. Through the demand side management, the effect of stabilizing grid fluctuations can be achieved. Stationary household batteries, together with electric vehicles connected to the grid through charging piles, can not only store electricity, but
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. On this basis, combined with
Customer ServiceAs renewable power and energy storage industries work to optimize utilization and lifecycle value of battery energy storage, life predictive modeling becomes increasingly important. Typically,
Customer ServiceAs renewable power and energy storage industries work to optimize utilization and lifecycle value of battery energy storage, life predictive modeling becomes increasingly important. Typically, end-of-life (EOL) is defined when the battery degrades to a point where only 70-80% of beginning-of-life (BOL) capacity is remaining under nameplate
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,
Customer ServiceBased on this, this paper refers to a new energy storage charging pile system design proposed by Yan [27]. The new energy storage charging pile consists of an AC inlet line, an AC/DC bidirectional converter, a DC/DC bidirectional module, and a coordinated control unit. The system topology is shown in Fig. 2 b. The energy storage charging pile
Customer ServiceIn addition, as concerns over energy security and climate change continue to grow, the importance of sustainable transportation is becoming increasingly prominent [8].To achieve sustainable transportation, the promotion of high-quality and low-carbon infrastructure is essential [9].The Photovoltaic-energy storage-integrated Charging Station (PV-ES-I CS) is a
Customer ServiceAmong the various components of the energy storage converter, the power semiconductor device IGBT is the most vulnerable part [].Junction temperature is the main failure factor of IGBT, accounting for up to 55% [] the existing literature, the research on IGBT life prediction mainly focuses on the converter system with long application time and wide
Customer ServiceThe RUL prediction of various energy storage technologies such as LIB, SC, and FC can be evaluated with suitable data features. Generally, the RUL forecasting of LIB is conducted using the features such as temperature, voltage, current, impedance, and capacity. Similar to LIB, the SC considers voltage, current, temperature, and capacity
Customer ServiceLife prediction of energy storage battery is very important for new energy station. With the increase of using times, energy storage lithium-ion battery will gradually age. Aging of energy storage lithium-ion battery is a long
Customer ServiceLithium-ion batteries have become indispensable power sources across diverse applications, spanning from electric vehicles and renewable energy storage to consumer electronics and industrial systems [5].As their significance continues to grow, accurate prediction of the Remaining Useful Life (RUL) of these batteries assumes paramount importance.
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)
Customer ServiceThis paper introduces a novel electricity load time-series prediction model, utilizing a broad learning system to tackle the challenge of low prediction accuracy caused by the unpredictable nature of electricity load sequences in a specific region of China. First, a correlation analysis with mutual information is utilized to identify the key
Customer ServiceAn optimal planning model is established to optimize the configuration of charging piles. Simulation results show that the proposed method can decrease both peak-valley difference and voltage deviation after the access of EV. This study can accurately forecast charging load demand in residential area, workplace and shopping center, and provide
Customer ServiceMoreover, a coupled PV-energy storage-charging station (PV-ES-CS) is a key development target for energy in the future that can effectively combine the advantages of photovoltaic, energy storage and electric vehicle charging piles, and make full use of them . The photovoltaic and energy storage systems in the station are DC power sources, which can be
Customer ServiceThe energy storage charging pile achieved energy storage benefits through charging during off-peak periods and discharging during peak periods, with benefits ranging from 558.59 to 2056.71 yuan. At an average demand of 70 % battery capacity, with 50–200 electric
Customer ServiceAbstract: In order to accurately predict the power consumption data of charging piles, assist related enterprises to accurately predict the benefits of charging piles and further optimize the relationship between households and transformers, this paper proposes an improved Gate Recurrent Unit (IGRU) prediction model based on spline
Customer ServiceZhang et al. [21] took into account the impact of fast charging protocols on battery life and presented a lithium-ion battery life prediction model based on charging and discharging data. Recent deep learning methodologies demonstrate the ability to handle intricate multi-dimensional datasets, extract effective patterns from datasets and perform feature fusion.
Customer ServiceAs a result, these modeling approaches may fall short to support many real-world applications. On the other hand, while data-driven EV charging demand prediction models can be found in the
Customer ServiceThe RUL prediction of various energy storage technologies such as LIB, SC, and FC can be evaluated with suitable data features. Generally, the RUL forecasting of LIB is conducted
Customer ServiceDesign of Energy Storage Charging Pile Equipment The main function of the control device of the energy storage charging pile is to facilitate the user to charge the electric vehicle and to charge the energy storage battery as far as possible when the electricity price is at the valley period.
The simulation results of this paper show that: (1) Enough output power can be provided to meet the design and use requirements of the energy-storage charging pile; (2) the control guidance circuit can meet the requirements of the charging pile; (3) during the switching process of charging pile connection state, the voltage state changes smoothly.
Due to the urgency of transaction processing of energy storage charging pile equipment, the processing time of the system should reach a millisecond level. 3.3. Overall Design of the System
The main function of the control device of the energy storage charging pile is to facilitate the user to charge the electric vehicle and to charge the energy storage battery as far as possible when the electricity price is at the valley period. In this section, the energy storage charging pile device is designed as a whole.
The new energy storage charging pile system for EV is mainly composed of two parts: a power regulation system and a charge and discharge control system. The power regulation system is the energy transmission link between the power grid, the energy storage battery pack, and the battery pack of the EV.
The data collected by the charging pile mainly include the ambient temperature and humidity, GPS information of the location of the charging pile, charging voltage and current, user information, vehicle battery information, and driving conditions . The network layer is the Internet, the mobile Internet, and the Internet of Things.
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