Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (56)
  • Open Access

    REVIEW

    The Electric Vehicle Surge: Effective Solutions for Charging Challenges with Advanced Converter Technologies

    Rajanand Patnaik Narasipuram1,*, Md M. Pasha2, Saleha Tabassum3, Amit Singh Tandon4

    Energy Engineering, Vol.122, No.2, pp. 431-469, 2025, DOI:10.32604/ee.2025.055134 - 31 January 2025

    Abstract The global adoption of Electric Vehicles (EVs) is on the rise due to their advanced features, with projections indicating they will soon dominate the private vehicle market. However, improper management of EV charging can lead to significant issues. This paper reviews the development of high-power, reliable charging solutions by examining the converter topologies used in rectifiers and converters that transfer electricity from the grid to EV batteries. It covers technical details, ongoing developments, and challenges related to these topologies and control strategies. The integration of rapid charging stations has introduced various Power Quality (PQ) issues,… More >

  • Open Access

    ARTICLE

    Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

    Jing Guo*, Ziying Wang, Yajuan Guo, Haitao Jiang

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 427-442, 2025, DOI:10.32604/cmc.2024.057045 - 03 January 2025

    Abstract The rapid proliferation of electric vehicle (EV) charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system. This study presents an innovative anomaly detection framework for EV charging stations, addressing the unique challenges posed by third-party aggregation platforms. Our approach integrates node equations-based on the parameter identification with a novel deep learning model, xDeepCIN, to detect abnormal data reporting indicative of aggregation attacks. We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation. The xDeepCIN model, incorporating a Compressed Interaction Network, has the ability… More >

  • Open Access

    ARTICLE

    Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles

    Shukang Lyu*, Fei Zeng, Huachun Han, Huiyu Miao, Yi Pan, Xiaodong Yuan

    Energy Engineering, Vol.122, No.1, pp. 221-242, 2025, DOI:10.32604/ee.2024.056380 - 27 December 2024

    Abstract The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle (EV) charging resources further aggravate the voltage fluctuation of the distribution network, and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics, and failed to realize the multi-timescale synergistic control with other regulating means, For this reason, this paper proposes a multilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network. Firstly, a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic (PV) active-reactive power regulation model are… More >

  • Open Access

    ARTICLE

    Dispatchable Capability of Aggregated Electric Vehicle Charging in Distribution Systems

    Shiqian Wang1, Bo Liu1, Yuanpeng Hua1, Qiuyan Li1, Binhua Tang2,*, Jianshu Zhou2, Yue Xiang2

    Energy Engineering, Vol.122, No.1, pp. 129-152, 2025, DOI:10.32604/ee.2024.054867 - 27 December 2024

    Abstract This paper introduces a method for modeling the entire aggregated electric vehicle (EV) charging process and analyzing its dispatchable capabilities. The methodology involves developing a model for aggregated EV charging at the charging station level, estimating its physical dispatchable capability, determining its economic dispatchable capability under economic incentives, modeling its participation in the grid, and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable capability. The results indicate that using economic dispatchable capability reduces charging prices by 9.7% compared to physical dispatchable capability and 9.3% compared to disorderly More >

  • Open Access

    ARTICLE

    Optimization of Electric Vehicle Charging Station Layout Based on Point of Interest Data and Location Entropy Evaluation

    Annan Yang1, Jiawei Zhang2,*, Haojie Yang3,*, Keyi Tao1, Mengna Xu1, Yuyu Zhao1

    Journal on Big Data, Vol.6, pp. 21-41, 2024, DOI:10.32604/jbd.2024.057612 - 31 December 2024

    Abstract This study introduces an electric vehicle charging station layout optimization method utilizing Point of Interest (POI) data, addressing traditional design limitations. It details the acquisition and visualization of POI data for Yancheng’s key locations and charging stations. Employing a hybrid K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, the study determines areas requiring optimization through location entropy and overlap analysis. The research shows that the integrated clustering approach can efficiently guide the fair distribution of charging stations, enhancing service quality and supporting the sustainable growth of the electric vehicle sector. More >

  • Open Access

    ARTICLE

    Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

    Li Wang1,*, Xiaoyong Wang2

    Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

    Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

  • Open Access

    ARTICLE

    An Optimisation Strategy for Electric Vehicle Charging Station Layout Incorporating Mini Batch K-Means and Simulated Annealing Algorithms

    Haojie Yang, Xiang Wen, Peng Geng*

    Journal on Artificial Intelligence, Vol.6, pp. 283-300, 2024, DOI:10.32604/jai.2024.056303 - 18 October 2024

    Abstract To enhance the rationality of the layout of electric vehicle charging stations, meet the actual needs of users, and optimise the service range and coverage efficiency of charging stations, this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms. By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius, the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are… More >

  • Open Access

    ARTICLE

    EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

    Benxin Li*, Xuanming Chang

    Energy Engineering, Vol.121, No.10, pp. 3001-3018, 2024, DOI:10.32604/ee.2024.051332 - 11 September 2024

    Abstract The increasingly large number of electric vehicles (EVs) has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks. To address this issue, an EV charging station load prediction method is proposed in coupled urban transportation and distribution networks. Firstly, a finer dynamic urban transportation network model is formulated considering both nodal and path resistance. Then, a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature. Thirdly, the Monte Carlo method… More > Graphic Abstract

    EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

  • Open Access

    ARTICLE

    A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

    Miao Li, Fanyong Cheng*, Jiong Yang, Maxwell Mensah Duodu, Hao Tu

    Energy Engineering, Vol.121, No.9, pp. 2543-2568, 2024, DOI:10.32604/ee.2024.051231 - 19 August 2024

    Abstract Accurate and reliable fault detection is essential for the safe operation of electric vehicles. Support vector data description (SVDD) has been widely used in the field of fault detection. However, constructing the hypersphere boundary only describes the distribution of unlabeled samples, while the distribution of faulty samples cannot be effectively described and easily misses detecting faulty data due to the imbalance of sample distribution. Meanwhile, selecting parameters is critical to the detection performance, and empirical parameterization is generally time-consuming and laborious and may not result in finding the optimal parameters. Therefore, this paper proposes a… More >

  • Open Access

    ARTICLE

    Research on Robustness of Charging Station Networks under Multiple Recommended Charging Methods for Electric Vehicles

    Lei Feng1, Miao Liu1, Yexun Yuan1, Chi Zhang2, Peng Geng1,*

    Journal on Internet of Things, Vol.6, pp. 1-16, 2024, DOI:10.32604/jiot.2024.053584 - 24 July 2024

    Abstract With the rapid development of electric vehicles, the requirements for charging stations are getting higher and higher. In this study, we constructed a charging station topology network in Nanjing through the Space-L method, mapping charging stations as network nodes and constructing edges through road relationships. The experiment introduced five EV charging recommendation strategies (based on distance, number of fast charging piles, user preference, price, and overall rating) used to simulate disordered charging caused by different user preferences, and the impact of the network dynamic robustness in case of node failure is explored by simulating the… More >

Displaying 1-10 on page 1 of 56. Per Page