Pralay Roy¹, Pabitra Kumar Biswas¹, Chiranjit Sain^2,*, Taha Selim Ustun^3,*

Energy Engineering, Vol.122, No.10, pp. 4035-4060, 2025, DOI:10.32604/ee.2025.068989 - 30 September 2025

Abstract This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form, Quasi Oppositional Sine Cosine Algorithm, to automatic generation control of a multiple-source-based interconnected power system that consists of thermal, gas, and hydro power plants. The Proportional-Integral-Derivative controller, which is utilized for automated generation control in an interconnected hybrid power system with a DC link connecting two regions, has been tuned using the proposed optimization technique. An Electric Vehicle is taken into consideration only as an electrical load. The Quasi Oppositional Sine Cosine method’s performance and efficacy… More >

Salam Al-E’mari¹, Yousef Sanjalawe^2,*, Budoor Allehyani³, Ghader Kurdi⁴, Sharif Makhadmeh², Ameera Jaradat⁵, Duaa Hijazi⁶

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3289-3320, 2025, DOI:10.32604/cmc.2025.067950 - 23 September 2025

Abstract Electric Vehicle Charging Systems (EVCS) are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet of Things (IoT) environments, raising significant security challenges. Most existing research primarily emphasizes network-level anomaly detection, leaving critical vulnerabilities at the host level underexplored. This study introduces a novel forensic analysis framework leveraging host-level data, including system logs, kernel events, and Hardware Performance Counters (HPC), to detect and analyze sophisticated cyberattacks such as cryptojacking, Denial-of-Service (DoS), and reconnaissance activities targeting EVCS. Using comprehensive forensic analysis and machine learning models, the proposed framework significantly outperforms existing More >

Harendra Pratap Singh^1,2, Ishfaq Hussain Rather³, Sushil Kumar¹, Mohammad Aljaidi⁴, Omprakash Kaiwartya^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5577-5595, 2025, DOI:10.32604/cmc.2025.065741 - 30 July 2025

Abstract Electric Vehicles (EVs) have emerged as a cleaner, low-carbon, and environmentally friendly alternative to traditional internal combustion engine (ICE) vehicles. With the increasing adoption of EVs, they are expected to eventually replace ICE vehicles entirely. However, the rapid growth of EVs has significantly increased energy demand, posing challenges for power grids and infrastructure. This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road. To address these challenges, various deep… More >

Yanjie Liu, Ximin Cao^*, Yanchi Zhang

Energy Engineering, Vol.122, No.8, pp. 3377-3398, 2025, DOI:10.32604/ee.2025.066577 - 24 July 2025

Abstract Under the “dual carbon” goals, this paper constructs an optimization model of the comprehensive energy system in the park. A stepwise carbon excess rate mechanism and an electric vehicle coupling strategy are proposed: A carbon quota trading system is established based on the baseline method, and the stepwise function is adopted to quantify the cost of excess carbon emissions; Introduce the price demand response and the two-way interaction mechanism of electric Vehicle vehicle-to-grid (V2G) to enhance the flexible regulation ability. Aiming at the uncertainty of wind and solar output, a typical scene set is generated… More >

Zhenhao Wang¹, Hongwei Li^1,*, Dan Pang², Jinming Ge¹

Energy Engineering, Vol.122, No.8, pp. 3223-3242, 2025, DOI:10.32604/ee.2025.063615 - 24 July 2025

Abstract Aiming at the problem of increasing the peak-to-valley difference of grid load and the rising cost of user charging caused by the disorderly charging of large-scale electric vehicles, this paper proposes a coordinated charging scheduling strategy for multiple types of electric vehicles based on the degree of urgency of vehicle use. First, considering the range loss characteristics, dynamic time-sharing tariff mechanism, and user incentive policy in the low-temperature environment of northern winter, a differentiated charging model is constructed for four types of vehicles: family cars, official cars, buses, and cabs. Then, we innovatively introduce the… More >

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 >

Dong-Hyuk Shin¹, Jae-Jun Ha¹, Ieck-Chae Euom^2,*

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 >

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 >

Sahbi Boubaker^1,*, Adel Mellit^2,3,*, Nejib Ghazouani⁴, Walid Meskine⁵, Mohamed Benghanem⁶, Habib Kraiem^7,8

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 >

Bo Yang^1,*, 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 >