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  • Open Access

    ARTICLE

    Reactive Power Optimization Model of Active Distribution Network with New Energy and Electric Vehicles

    Chenxu Wang*, Jing Bian, Rui Yuan

    Energy Engineering, Vol.122, No.3, pp. 985-1003, 2025, DOI:10.32604/ee.2025.059559 - 07 March 2025

    Abstract Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load, a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed. Firstly, the k-medoids clustering algorithm is used to divide the reduced power scene into periods. Then, the discrete variables and continuous variables are optimized in the same period of time. Finally, the number of input groups of parallel capacitor banks (CB) in multiple periods is fixed, and then the secondary static reactive power optimization correction is carried out by… More >

  • Open Access

    ARTICLE

    Enhancing Safety in Electric Vehicles: Multi-Tiered Fault Detection for Micro Short Circuits and Aging in Battery Modules

    Yi-Feng Luo1,*, Jyuan-Fong Yen2, Wen-Cheng Su3

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3069-3087, 2025, DOI:10.32604/cmes.2025.061180 - 03 March 2025

    Abstract This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article More >

  • Open Access

    ARTICLE

    A Novel Proactive AI-Based Agents Framework for an IoE-Based Smart Things Monitoring System with Applications for Smart Vehicles

    Meng-Hua Yen1,*, Nilamadhab Mishra2,*, Win-Jet Luo3, Chu-En Lin1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1839-1855, 2025, DOI:10.32604/cmc.2025.060903 - 17 February 2025

    Abstract The Internet of Everything (IoE) coupled with Proactive Artificial Intelligence (AI)-Based Learning Agents (PLAs) through a cloud processing system is an idea that connects all computing resources to the Internet, making it possible for these devices to communicate with one another. Technologies featured in the IoE include embedding, networking, and sensing devices. To achieve the intended results of the IoE and ease life for everyone involved, sensing devices and monitoring systems are linked together. The IoE is used in several contexts, including intelligent cars’ protection, navigation, security, and fuel efficiency. The Smart Things Monitoring System… More >

  • Open Access

    ARTICLE

    A Perspective-Aware Cyclist Image Generation Method for Perception Development of Autonomous Vehicles

    Beike Yu1, Dafang Wang1,*, Xing Cui2, Bowen Yang1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2687-2702, 2025, DOI:10.32604/cmc.2024.059594 - 17 February 2025

    Abstract Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective… More >

  • Open Access

    ARTICLE

    MG-SLAM: RGB-D SLAM Based on Semantic Segmentation for Dynamic Environment in the Internet of Vehicles

    Fengju Zhang1, Kai Zhu2,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2353-2372, 2025, DOI:10.32604/cmc.2024.058944 - 17 February 2025

    Abstract The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2,… More >

  • 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

    A Support Vector Machine (SVM) Model for Privacy Recommending Data Processing Model (PRDPM) in Internet of Vehicles

    Ali Alqarni*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 389-406, 2025, DOI:10.32604/cmc.2024.059238 - 03 January 2025

    Abstract Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacy-preserving measures. The model analyzes data based on user demands and interactions with service More >

  • Open Access

    ARTICLE

    Optimization of an Artificial Intelligence Database and Camera Installation for Recognition of Risky Passenger Behavior in Railway Vehicles

    Min-kyeong Kim1, Yeong Geol Lee2, Won-Hee Park2,*, Su-hwan Yun2, Tae-Soon Kwon2, Duckhee Lee2

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1277-1293, 2025, DOI:10.32604/cmc.2024.058386 - 03 January 2025

    Abstract Urban railways are vital means of public transportation in Korea. More than 30% of metropolitan residents use the railways, and this proportion is expected to increase. To enhance safety, the government has mandated the installation of closed-circuit televisions in all carriages by 2024. However, cameras still monitored humans. To address this limitation, we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof. We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway 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 >

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