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  • Open Access

    ARTICLE

    Comprehensive Black-Box Fuzzing of Electric Vehicle Charging Firmware via a Vehicle to Grid Network Protocol Based on State Machine Path

    Yu-Bin Kim, Dong-Hyuk Shin, Ieck-Chae Euom*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2217-2243, 2025, DOI:10.32604/cmc.2025.063289 - 03 July 2025

    Abstract The global surge in electric vehicle (EV) adoption is proportionally expanding the EV charging station (EVCS) infrastructure, thereby increasing the attack surface and potential impact of security breaches within this critical ecosystem. While ISO 15118 standardizes EV-EVCS communication, its underspecified security guidelines and the variability in manufacturers’ implementations frequently result in vulnerabilities that can disrupt charging services, compromise user data, or affect power grid stability. This research introduces a systematic black-box fuzzing methodology, accompanied by an open-source tool, to proactively identify and mitigate such security flaws in EVCS firmware operating under ISO 15118. The proposed… More >

  • Open Access

    ARTICLE

    Data-Driven Digital Evidence Analysis for the Forensic Investigation of the Electric Vehicle Charging Infrastructure

    Dong-Hyuk Shin1, Jae-Jun Ha1, Ieck-Chae Euom2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3795-3838, 2025, DOI:10.32604/cmes.2025.066727 - 30 June 2025

    Abstract The accelerated global adoption of electric vehicles (EVs) is driving significant expansion and increasing complexity within the EV charging infrastructure, consequently presenting novel and pressing cybersecurity challenges. While considerable effort has focused on preventative cybersecurity measures, a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents, a gap exacerbated by system heterogeneity, distributed digital evidence, and inconsistent logging practices which hinder effective incident reconstruction and attribution. This paper addresses this critical need by proposing a novel, data-driven forensic framework tailored to the EV charging infrastructure, focusing on the systematic identification, classification,… More >

  • Open Access

    ARTICLE

    Machine Learning-Optimized Energy Management for Resilient Residential Microgrids with Dynamic Electric Vehicle Integration

    Mohammed Moawad Alenazi*

    Journal on Artificial Intelligence, Vol.7, pp. 143-176, 2025, DOI:10.32604/jai.2025.066067 - 27 June 2025

    Abstract This paper presents a novel machine learning (ML) enhanced energy management framework for residential microgrids. It dynamically integrates solar photovoltaics (PV), wind turbines, lithium-ion battery energy storage systems (BESS), and bidirectional electric vehicle (EV) charging. The proposed architecture addresses the limitations of traditional rule-based controls by incorporating ConvLSTM for real-time forecasting, a Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning agent for optimal BESS scheduling, and federated learning for EV charging prediction—ensuring both privacy and efficiency. Simulated in a high-fidelity MATLAB/Simulink environment, the system achieves 98.7% solar/wind forecast accuracy and 98.2% Maximum Power Point… More >

  • Open Access

    ARTICLE

    Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks

    Sahbi Boubaker1,*, Adel Mellit2,3,*, Nejib Ghazouani4, Walid Meskine5, Mohamed Benghanem6, Habib Kraiem7,8

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2237-2259, 2025, DOI:10.32604/cmes.2025.064530 - 30 May 2025

    Abstract Electric vehicles (EVs) are gradually being deployed in the transportation sector. Although they have a high impact on reducing greenhouse gas emissions, their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging. To cope with these problems, this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting. The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’ charging scheduling task. By using predictive algorithms for solar generation and load demand… More >

  • Open Access

    EDITORIAL

    AI-Driven Interaction and Collaborative Optimization of Vehicle, Charging Station and Grid: Challenges and Prospects

    Bo Yang1,*, Zhe Jiang1, Jianfeng Wen2, Ning Yang3, Kaiping Qu4, Shuai Zhou5

    Energy Engineering, Vol.122, No.6, pp. 2187-2195, 2025, DOI:10.32604/ee.2025.065489 - 29 May 2025

    Abstract This article has no abstract. More >

  • 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

    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 >

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