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 >

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 >

Yi-Feng Luo^1,*, 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 >

Rajanand Patnaik Narasipuram^1,*, 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 >

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 >

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 >

Shiqian Wang¹, Bo Liu¹, Yuanpeng Hua¹, Qiuyan Li¹, Binhua Tang^2,*, 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 >

Annan Yang¹, Jiawei Zhang^2,*, Haojie Yang^3,*, 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 >