Jaeseung Lee¹, Jehyeok Rew^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077691 - 09 April 2026

Abstract The accelerating transition toward electrified mobility has positioned electric vehicles (EVs) as a primary technology in modern transportation systems. In this context, ensuring the reliability of EV drive motors (EVDMs) becomes increasingly critical, given their central role in propulsion performance and operational safety. Accurate and interpretable fault diagnosis of EVDMs is therefore essential for enabling effective maintenance and supporting the broader sustainability and resilience of EVs. This study presents a novel framework that combines decision tree-based fault classification with a multi-agent large language model (LLM) interpretation architecture to deliver transparent and human-readable diagnostic explanations. The… More >

He Tian^1,2, Ziliang Zhu^1,2, Jiangping Li^1,2, Ziyun Li^1,2, Baofeng Tang^1,2, Pengfei Ju^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075309 - 09 April 2026

Abstract As the number of electric vehicles continues to rise, pressure on charging infrastructure grows increasingly intense. Mobile charging technology, with its flexibility and deployability, has emerged as an effective solution. Within this technology, charging robots or vehicles must autonomously locate and dock with charging ports. Consequently, precise and stable charging port recognition constitutes both a prerequisite and the core bottleneck for achieving automated operations in mobile charging systems. However, in practical scenarios, charging ports often prove difficult to detect reliably due to factors such as physical obstructions, variations in lighting, and long shooting distances. To… More >

Chin-Wen Liao^1,2,3, En-Shiuh Lin¹, Wei-Lun Huang^4,5,6, I-Chi Wang⁷, Bo-Siang Chen^8,*, Wei-Sho Ho^1,2,9,*

Journal of Polymer Materials, Vol.43, No.1, 2026, DOI:10.32604/jpm.2026.076807 - 03 April 2026

Abstract The demand for extended electric vehicle (EV) range necessitates advanced lightweighting strategies. This study introduces a materials genome approach, augmented by machine learning (ML), for optimizing lightweight composite designs for EVs. A comprehensive materials genome database was developed, encompassing composites based on carbon, glass, and natural fibers. This database systematically records critical parameters such as mechanical properties, density, cost, and environmental impact. Machine learning models, including Random Forest, Support Vector Machines, and Artificial Neural Networks, were employed to construct a predictive system for material performance. Subsequent material composition optimization was performed using a multi-objective genetic More >

Naveen Guguloth¹, Bishwajit Dey², Fausto Pedro García Márquez^3,*, Prasenjit Dey¹, Isaac Segovia Ramírez⁴

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.073971 - 27 February 2026

Abstract The increasing integration of electric vehicle (EV) loads into power systems necessitates understanding their impact on stability. Small-magnitude perturbations, if persistent, can cause low-frequency oscillations, leading to synchronism loss and mechanical stress. This work analyzes the effect of voltage-dependent EV loads on this small-signal stability. The study models an EV load within a Single-Machine Infinite Bus (SMIB) system. It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller (UPFC), a key device for damping oscillations. The system’s performance is compared to a modified version equipped with More >

Lei Su^1,2,3, Wanli Feng^1,2,3, Cao Kan^1,2,3, Mingjiang Wei^1,2,3, Jihai Wang⁴, Pan Yu⁴, Lingxiao Yang^5,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071006 - 27 February 2026

Abstract To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources (DERs) (such as photovoltaic (PV) systems, wind turbines (WT), and energy storage (ES) devices), and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles (EVs) charging, this paper proposes a novel dual-scale hierarchical collaborative optimization strategy. This strategy decouples system-level economic dispatch from distributed EV agent control, effectively solving the resource coordination conflicts arising from the high computational complexity, poor scalability of existing centralized optimization, or the reliance on local information… More >

Supriya Wadekar^1,*, Shailendra Mittal¹, Ganesh Wakte², Rajshree Shinde²

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.071761 - 27 December 2025

Abstract Rapid evolutions of the Internet of Electric Vehicles (IoEVs) are reshaping and modernizing transport systems, yet challenges remain in energy efficiency, better battery aging, and grid stability. Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand, thus increasing energy costs and battery aging. This study proposes a smart charging station with an AI-powered Battery Management System (BMS), developed and simulated in MATLAB/Simulink, to increase optimality in energy flow, battery health, and impractical scheduling within the IoEV environment. The system operates through… More >

Hongyu Wang¹, Wenwu Cui¹, Kai Cui¹, Zixuan Meng^2,*, Bin Li², Wei Zhang¹, Wenwen Li¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069576 - 27 December 2025

Abstract To achieve low-carbon regulation of electric vehicle (EV) charging loads under the “dual carbon” goals, this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multi-objective optimization. First, a dual-convolution enhanced improved Crossformer prediction model is constructed, which employs parallel 1 × 1 global and 3 × 3 local convolution modules (Integrated Convolution Block, ICB) for multi-scale feature extraction, combined with an Adaptive Spectral Block (ASB) to enhance time-series fluctuation modeling. Based on high-precision predictions, a carbon-electricity cost joint optimization model is further designed to balance economic, environmental, and grid-friendly objectives.… More > Graphic Abstract

Federico Sacchelli¹, Luca Cattani^1,2, Matteo Malavasi¹, Fabio Bozzoli^1,2,*, Corrado Sciancalepore¹

Frontiers in Heat and Mass Transfer, Vol.23, No.5, pp. 1351-1364, 2025, DOI:10.32604/fhmt.2025.064154 - 31 October 2025

Abstract This study investigates the feasibility of a novel dual two-phase cooling system for thermal management in lithium-ion batteries used in electric vehicles (EVs). The proposed system aims to combine low-boiling dielectric fluid immersion cooling and pulsating heat pipes (PHPs), in order to leverage the advantages of both technologies for efficient heat dissipation in a completely passive configuration. Experimental evaluations conducted under different discharge conditions demonstrate that the system effectively maintains battery temperatures within the optimal range of 20–40°C, with enhanced temperature uniformity and stability. While the PHP exhibited minimal impact at low power, its role More >

Jack Kiing Teck Wei¹, Mohanad Taher Mohamed Sayed Roshdy¹, Bryan Ho Liang Hui¹, Jalal Tavalaei¹, Hadi Nabipour Afrouzi^2,*

Energy Engineering, Vol.122, No.11, pp. 4755-4775, 2025, DOI:10.32604/ee.2025.069980 - 27 October 2025

Abstract In this article, a hybrid energy storage system powered by renewable energy sources is suggested, which is connected to a grid-tied electric vehicle charging bay (EVCB) in Sarawak and is examined for its techno-economic effects. With a focus on three renewable energy sources, namely hydrokinetic power, solar power, and hydrogen fuel cells, the study seeks to minimize reliance on the electrical grid while meeting the growing demand from the growing electric vehicle (EV) infrastructure. A hybrid renewable energy storage system that combines solar power, hydrogen fuel cells, hydrokinetic power, and the grid was simulated and… More >

J. S. V. Siva Kumar¹, Mahmad Mustafa², Sk. M. Unnisha Begum³, Badugu Suresh⁴, Rajanand Patnaik Narasipuram^5,*

Energy Engineering, Vol.122, No.10, pp. 3891-3904, 2025, DOI:10.32604/ee.2025.070052 - 30 September 2025

Abstract Electric vehicle (EV) monitoring systems commonly depend on IoT-based sensor measurements to track key performance parameters such as vehicle speed, state of charge (SoC), battery temperature, power consumption, motor RPM, and regenerative braking. While these systems enable real-time data acquisition, they are often hindered by sensor noise, communication delays, and measurement uncertainties, which compromise their reliability for critical decision-making. To overcome these limitations, this study introduces a comparative framework that integrates reference signals, a digital twin model emulating ideal system behavior, and real-time IoT measurements. The digital twin provides a predictive and noise-resilient representation of More >