Electric Vehicle Charging Piles Research Articles

Preventive maintenance decision model of electric vehicle charging pile based on mutation operator and life cycle optimization

AbstractThis paper proposes a preventive maintenance decision model for electric vehicle charging stations based on mutation operators and lifecycle optimization to address the impact of potential faults on maintenance effectiveness. By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was 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 experimental results show that the accuracy of this method in preventive maintenance decision‐making for electric vehicle charging piles can reach 98%, with an average preventive maintenance decision‐making time of 1.6 s for load piles. At the same time, the risk probability value and load loss value are effectively controlled. This study has good application prospects in improving the preventive maintenance effect of electric vehicle charging piles.

Visual research on data distribution of electric vehicle charging piles in China

With the popularity of electric vehicles, the demand for charging piles is also increasing. It is of great significance that the distribution of electric vehicle charging piles is studied for urban planning, traffic management and energy policy. This paper discusses the distribution of charging piles in China using visual charts, analyzes in detail the growth in the number of charging piles of different companies, the characteristics of geographical distribution, and the types of charging pile. Meanwhile, the differences in the distribution of charging piles in different types of cities are discussed using Shanghai and Nanchang as examples. Through the analysis of these data, the charging pile network structure can be further optimized, a valuable reference basis is provided for urban planning and the development of electric vehicles.

Research Review on Multi-Port Energy Routers Adapted to Renewable Energy Access

With the continuous development of renewable energy technologies, both domestically and internationally, the focus of energy research has gradually shifted towards renewable energy directions such as distributed photovoltaics and wind power. The penetration rate of renewable energy generation is constantly increasing, at the same time, the elements in the grid are becoming increasingly complex, and large-scale energy storage, as well as a variety of electricity loads such as electric vehicle charging piles and data centers are gradually appearing. Therefore, traditional distribution methods of the power grid are difficult to ensure the stable operation of the power system and cannot achieve efficient integration of renewable energy. Consequently, some scholars have proposed the concept of an energy internet. Compared to traditional power grids, the energy internet employs more comprehensive power electronics and communication technologies, enabling the interconnection of various new and traditional energy sources, and effectively integrating renewable energy. As the core device in the energy internet, the energy router plays a role in energy transformation and distribution, facilitating multi-information interconnection and multi-energy exchange within the energy internet. At the level of distribution network, the energy router can realize the efficient access of various forms of energy and the flexible control and management, which is of great significance for the optimal operation of distribution network. Against this backdrop, this paper reviews the development and current research status of energy routers, systematically analyzes the typical topologies and related control technologies of multi-port energy routers and summarizes and forecasts key issues and future development trends, aiming to provide thoughts and reference for subsequent related research.

Challenges and countermeasures in planning, building, and managing electric vehicle charging piles

The widespread adoption of new energy vehicles, particularly electric vehicles (EVs), has created a significant demand for charging infrastructure globally. China, a key player in the EV market, has made substantial advancements in charging pile technology and infrastructure development. However, several critical challenges threaten the sustainability and efficiency of the EV charging ecosystem. This paper identifies and analyzes these challenges, including insufficient planning and construction of charging piles, increased demand for electric energy affecting power grids, high construction costs of fast-charging infrastructure, regional disparities in investment returns, and operational management issues. Moreover, it explores potential strategies to address these challenges. Proposed strategies include optimized planning for charging pile construction, the creation of integrated vehicle-charging-pile platforms, the development of distributed energy systems using blockchain technology, promoting recycling and reutilization of waste charging infrastructure, continuous financial subsidies, and enhanced follow-up operation supervision. Addressing these challenges through comprehensive strategies is essential for China and other nations to establish robust and sustainable EV charging infrastructure, ensuring it meets the growing demand for new energy vehicles in the years ahead. This effort will contribute to a cleaner and more sustainable future for the transportation sector.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This 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 caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

Location Selection of Electric Vehicle Charging Stations Through Employing the Spherical Fuzzy CoCoSo and CRITIC Technique

Energy conservation and emission reduction are important policies vigorously promoted in China. With the continuous popularization of the concept of green transportation, electric vehicles have become a green transportation tool with good development prospects, greatly reducing the pressure on the environment and resources caused by rapid economic growth. The development status of electric vehicles has a significant impact on urban energy security, environmental protection, and sustainable development in China. With the widespread application of new energy vehicles, charging piles have become an important auxiliary infrastructure necessary for the development of electric vehicles. They have significant social and economic benefits, so it is imperative to build electric vehicle charging piles. There are many factors to consider in the scientific layout of electric vehicle charging stations, and the location selection problem of electric vehicle charging stations is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Combined Compromise Solution (CoCoSo) technique and CRITIC technique have been utilized to deal with MAGDM issues. Spherical fuzzy sets (SFSs) can uncover the uncertainty and fuzziness in MAGDM more effectively and deeply. In this paper, on basis of CoCoSo technique, a novel spherical fuzzy number CoCoSo (SFN-CoCoSo) technique based on spherical fuzzy number cosine similarity measure (SFNCSM) and spherical fuzzy number Euclidean distance (SFNED) is conducted for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the CRITIC technique is extended to SFSs to acquire the attribute weights based on the SFNCSM and SFNED. Finally, the SFN-CoCoSo technique is utilized for location selection problem of electric vehicle charging stations to prove practicability of the developed technique and compare the SFN-CoCoSo technique with existing techniques to further demonstrate its superiority.

Optimizing the configuration of electric vehicle charging piles in public parking lots based on a multi-agent model

With the rapid development of electric vehicles, how to improve the charging efficiency of electric vehicles has become a challenge. The Chinese government has made great efforts to build charging piles. At present, the most popular construction mode is to build charging piles on a fixed proportion of spaces in existing parking lots. The proportions of charging piles recommended by the government, which is known as a one-size-fits-all strategy. Challenges have arisen with the growth of electric vehicles, such as how to keep hot charging piles from overheating. This paper mainly simulates the actual demand and optimizes the configuration of charging piles to reduce the uneven spatial distribution of charging demand, to improve the overall utilization efficiency of regional charging stations. In particular, based on agent-based simulation technology, a multi-agent system of the road network, vehicles, and charging stations is constructed to simulate the charging behavior in the mixed scenario of electric vehicles and traditional fuel vehicles. The optimization model aims to design the configuration of charging piles to minimize the sum of electric vehicle queueing time, gasoline vehicle queueing time, and vehicle transfer time to idle parking lots. The model is solved by the genetic algorithm. This paper takes the Wulin Square business district in Hangzhou as a real-world example. The simulation results show that by optimizing the number of charging piles, the objective function is reduced by 17.1% compared with the initial number of charging piles, which effectively improves the operation efficiency of the parking system. To this end, this paper predicts the electric vehicle charging demand in the city and optimizes the configuration of charging facilities, which is conducive to maximizing social benefits in the transportation system and has good practical engineering application value.

Analysis of energy storage capacity optimization of distribution network based on quadratic planning algorithm under large scale DG access

Abstract The intermittent, random and uncontrollable characteristics of renewable energy generation bring many challenges to the actual operation of distribution grid (DG) energy storage. This paper firstly establishes a whole life cycle model applicable to the distribution grid energy storage system, which fully considers the various types of costs of VSC converter stations. Secondly, for comprehensive reactive power optimization, a sequential quadratic programming algorithm is used, and inequality constraints are added to the objective function in the form of penalty functions to optimize the state of on-load transformers and capacitors. Finally, the effectiveness of its distribution grid energy storage configuration optimization is verified by applying the distribution grid energy storage optimization based on the quadratic programming algorithm to electric vehicle charging piles. The results show that after two stages of optimization, the configuration capacity of distribution grid energy storage is increased by 53.14Kw/h compared with that before optimization, which is a substantial increase. The average annual cost of battery 0’ access decreases by 0.275, and battery 2’ access decreases by 0.581. This shows that the energy storage capacity optimization strategy based on the sequential quadratic programming algorithm proposed in this paper can improve the efficiency of energy storage utilization. It provides the basis for the user to select the energy storage capacity.

FedEVCP: Federated Learning-Based Anomalies Detection for Electric Vehicle Charging Pile

Abstract Vehicle-to-Grid (V2G) is a technology that enables electric vehicles to use smart charging methods to harness low-cost and renewable energy when it is available, and obtain income by feeding energy back into the grid. With the rise of V2G technology, the use of electric vehicles has begun to increase dramatically, which relies on the reliable Electric Vehicle Charging Pile (EVCP). However, most EVCPs are online and networked, introducing many potential network threats, such as Electricity Theft, Identity Theft and False Data Injection etc. Prior work has mostly focused on machine learning, which is not able to effectively capture the relationships and structures in network traffic, making it difficult to deal with the propagation and infection of the novel network attacks. Moreover, most neural network models collect and transfer data from EVCPs to the central server for training, which makes the central server attractive to attackers. It poses a serious threat to user privacy. To address these issues, propose an anomaly detection model that incorporates Federated Learning and Deep Autoencoder, which can increase the amount and diversity of data used to train deep learning models without compromising privacy. The proposed model forms a layer-by-layer unsupervised representation learning algorithm by autoencoder stacking, while batch normalization of hidden layers accelerates the convergence of the model to avoid overfitting and local optima, and introduces an attention mechanism to enhance key features of sequences composed of data vectors to improve the accuracy rate. To prevent the risk of user privacy leakage on the central server, EVCP is allowed to retain local data for model training and send model parameters to the central server for constructing new global models. Experimental results show that the proposed scheme achieves improved detection accuracy with superior performance than other similar models.

Harmonic Resonance Suppression Strategy of the Front-End Vienna Rectifier in EV Charging Piles

Owing to the distributed installation position, the electric vehicle (EV) charging pile is usually connected to the grid via transmission cables of different lengths. The resonance of parasitic inductance and parasitic capacitance on the transmission cables might amplify the background harmonics, which result in a serious distortion of the grid-connected current of the front-end Vienna rectifier of EV charging piles. To overcome the resonance issue, this article proposes a harmonic resonance suppression strategy of the Vienna rectifier, which reshapes the closed-loop input harmonic impedance of the Vienna rectifier to be close to the characteristic impedance of the transmission cable by optimizing the traditional dual closed-loop control strategy of the Vienna rectifier based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> coordinate frame. The proposed suppression strategy can not only ensure the low ripple output voltage of the Vienna rectifier, but also suppress the background harmonic resonance amplification; thus, the power quality of the Vienna rectifier grid-connected current and the voltage at the point of common coupling will be improved effectively. Both the simulation and experimental results verify the effectiveness and correctness of the proposed strategy.

Research on Optimal Control of Dual Active Full-Bridge DCDC Converters

The dual active full-bridge (DAB) DC-DC converter is an important module for realizing bidirectional energy transmission. At present, it has been widely used in solid-state transformers, DC microgrids, electric vehicle charging piles and other fields. It can carry out high-capacity transmission, and can electrically insulate the primary and secondary sides, and has a wide range of uses.

Method of Location and Capacity Determination of Intelligent Charging Pile Based on Recurrent Neural Network

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 short supply, unreasonable distribution areas, and insufficient power supply. In response to these problems, this research proposes a recurrent neural network algorithm with an integrated firefly algorithm. Based on these two algorithms, a charging pile location and capacity model was established, and users’ travel habits were analyzed according to the model. In the simulation experiment, the PR curve analysis of the algorithm was carried out first. The analysis results showed that the AP value of the recurrent neural network algorithm combined with the firefly algorithm was increased from 0.9324 to 0.9972. In addition, it had higher accuracy and stability than before, which also verified the feasibility of the algorithm. Finally, through the model, the user’s travel habits were analyzed in detail. From the perspective of total demand, the charging demand of commercial centers was the highest, with a peak of about 537 kw, followed by 501 kw in office areas and then about 379 kw in parks. The kw charging demand in other areas was below 200 kw. The above results show that the recursive neural network can effectively determine the location and capacity of the charging pile, which is of great value to the development of transportation and new energy.

Application of Blockchain Technology in Electric Vehicle Charging Piles Based on Electricity Internet of Things

With the continuous development of urban intelligence, as traffic, power grids, and electric vehicles are new ideas to solve energy shortages and air control problems, they have received widespread attention from the society and strong support from the government. The charging pile is a key hub for data exchange and has typical characteristics of IoT terminals. However, the guidance of the grid connection of electric vehicles is not standardized, and the security and stability of the power grid will inevitably be affected. The blockchain has the characteristics that data is difficult to tamper with and decentralized. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable. This paper is aimed at realizing the global unified identification and management of large terminal equipment based on ubiquitous power Internet of Things equipment with the help of blockchain technology and at providing safe, efficient, and stable online management services for a large number of devices in the ubiquitous power Internet of Things. In this paper, we analyze the application possibility of blockchain technology in the current electricity market and apply blockchain technology to the electricity market to solve the drawbacks of the electricity market, as well as adding blockchain analytics to the renewable energy electricity market. The experimental data analysis shows that with the increase of the length of the blockchain, the blocking time of the new area increases correspondingly, but the overall performance declines slightly, so this scheme can meet the practical needs of a large number of concurrent access terminals in the ubiquitous power Internet of Things. It can be seen that the successful application of blockchain technology based on the power Internet of Things in electric vehicle charging piles has greatly improved work efficiency.

Day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles

Abstract The day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles is studied to effectively solve the day-ahead and real-time congestion scheduling problem of distribution network. The charge adjustment strategy of charge and discharge service fee is established to realize the double response regulation between the distribution system’s scheduling organization and the charging pile operator; considering the adjustment strategy of charging service fee, a day-ahead congestion scheduling model is established with the goal of minimizing the charging cost of electric vehicles; based on the day-ahead congestion scheduling, a real-time congestion scheduling model is established to minimize the regional power fluctuation; the day-ahead and real-time congestion scheduling model is solved by differential evolution algorithm, and the optimal scheduling scheme is obtained. Experiments show that this method can reduce the line load rate of distribution network, avoid re-congestion, reduce the congestion scheduling cost and improve the security and economy of power grid operation.

Location of Electric Vehicle Charging Piles Based on Set Coverage Model

Electric vehicles are rapidly popping up in the market as a new alternative to fossil fuels, in order to reduce carbon emissions in urban areas. However, the improper placement of charging piles has impeded the development of electric vehicles. In this paper, 12 indicators from 4 categories, namely economy, environment, cost, and service quality are selected to form an index system for evaluating the location of electric vehicle charging piles. The entropy weight-TOPSIS method is also applied for the same purpose. On the basis of the evaluation, this paper proposes a set coverage model and adopts a greedy heuristic algorithm to find out the optimal location of charging piles. Finally, the paper verifies the reasonability and feasibility of this model by studying the existing location of electric vehicle charging piles in northeast China. The evaluation is based on the Liaoning Province Electric Vehicle Big Data Supervision Platform, which has data that are official and scientifically based. The set coverage model proposed, based on the evaluation, is a new solution to finding out the optimal location of electric vehicle charging piles across China. This study aims to provide a theoretical basis for the development of this new energy industry.

Deadbeat predictive power control for Vienna rectifier under unbalanced power grid condition

Due to the low output distortion and switch voltage stress, Vienna rectifiers have been widely utilized in high-power applications such as communication power supplies, wind power systems and electric vehicle charging piles. However, Vienna rectifiers still have to face tough challenges under unbalanced grid considering the inherit operating range limitation caused by its structure. Consequently, a deadbeat predictive power control (DBP-PC) for the Vienna rectifier under unbalanced grid is proposed. Using the positive and negative sequence double dq decoupling current control strategy, the operating range of the Vienna rectifier under unbalanced grid is analyzed. Furthermore, an improved DBP-PC for Vienna rectifiers under unbalanced grid is proposed. By the improved DBP-PC, the stable operating range can be effectively extended. Finally, the corresponding simulations and experiments are executed to demonstrate the performance of the proposed control strategy.

Comprehensive Analyses of the Spatio-Temporal Variation of New-Energy Vehicle Charging Piles in China: A Complex Network Approach

This study collects data on electric vehicle (EV) charging piles for various provinces in China and analyzes the development of the network of EV chargers from the perspective of a complex network. Features of the distribution of EV charging piles for the period from May 2016 to April 2019 and the spatio-temporal variations across provinces are thus analyzed. The study then transforms time-series data of the EV charging piles into a complex network by applying a visibility graph, uses several clustering methods to categorize different provinces, and predicts the future development of the network of EV charging piles in China. Additionally, the distribution of EV charging piles across time is analyzed for a combination of national policies and new-energy vehicles. The results of the study will guide provincial governments in creating policies that develop relevant industries progressively and promote the sustainable development of EVs and green-energy industry.

Research on the Design and Execution of Distributed Electric Vehicle Charging Pile Operation Management System Based on WebGIS

The integration of communication tech, data acquisition tech, Web and GIS tech is helpful to improve the automatic operation and management of electric vehicle charging pile, so it has momentous research value. Based on this, this paper first analyses the concept and connotation of WebGIS, then studies the architecture of distributed Electric Vehicle (EV) charging pile operation management system based on WebGIS, and finally gives the function design of distributed EV charging pile operation management system based on WebGIS.

Analysis and Design of EIT-Like Magnetic Coupling Wireless Power Transfer Systems

In order to improve the effective transfer range and then enhance the interoperability of magnetic coupling wireless power transfer (MC-WPT) systems, an electromagnetically induced transparency (EIT)-like MC-WPT system is proposed in this paper, which can maintain a reasonable level of power and efficiency over a wide range of transfer distance. Firstly, the circuit model of the tri-coil MC-WPT system is presented along with several key concepts, including EIT mechanism and eigen parameters. Then the energy efficiency characteristic of the system in eigen state is investigated, based on which the energy efficiency improvement principle and corresponding parameter design condition of the EIT-like mechanism are derived. Finally, practical systems are designed, simulated and measured to show the accuracy and effectiveness of the proposed method. The simulation and experimental results show that, with the proposed method, the effective transfer range that takes into account both output power and transfer efficiency is greatly improved. The method is particularly suitable for the design of electric vehicle charging piles, based on which the effective charging of vehicles with different chassis heights on a charging pile can be achieved.

Optimal Configuration Method for EV Charging Station in Distribution Network Considering User Adjustment under V2G mode

The existing charging piles planning method of distribution network does not consider the influence of user participation in regulation. Hence, an optimal planning method of electric vehicle charging piles in V2G mode is proposed, considering users’ influence of regulation. An EV charging pile planning model is established to minimize the investment and operation cost of distribution network and charging station, and the genetic algorithm with elitist retention strategy is used to solve the model. The examples show the effectiveness of the proposed method.