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 >

Li Wang^1,*, Xiaoyong Wang²

Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

Haojie Yang, Xiang Wen, Peng Geng^*

Journal on Artificial Intelligence, Vol.6, pp. 283-300, 2024, DOI:10.32604/jai.2024.056303 - 18 October 2024

Abstract To enhance the rationality of the layout of electric vehicle charging stations, meet the actual needs of users, and optimise the service range and coverage efficiency of charging stations, this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms. By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius, the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are… More >

Benxin Li^*, Xuanming Chang

Energy Engineering, Vol.121, No.10, pp. 3001-3018, 2024, DOI:10.32604/ee.2024.051332 - 11 September 2024

Abstract The increasingly large number of electric vehicles (EVs) has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks. To address this issue, an EV charging station load prediction method is proposed in coupled urban transportation and distribution networks. Firstly, a finer dynamic urban transportation network model is formulated considering both nodal and path resistance. Then, a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature. Thirdly, the Monte Carlo method… More > Graphic Abstract

Miao Li, Fanyong Cheng^*, Jiong Yang, Maxwell Mensah Duodu, Hao Tu

Energy Engineering, Vol.121, No.9, pp. 2543-2568, 2024, DOI:10.32604/ee.2024.051231 - 19 August 2024

Abstract Accurate and reliable fault detection is essential for the safe operation of electric vehicles. Support vector data description (SVDD) has been widely used in the field of fault detection. However, constructing the hypersphere boundary only describes the distribution of unlabeled samples, while the distribution of faulty samples cannot be effectively described and easily misses detecting faulty data due to the imbalance of sample distribution. Meanwhile, selecting parameters is critical to the detection performance, and empirical parameterization is generally time-consuming and laborious and may not result in finding the optimal parameters. Therefore, this paper proposes a… More >

Lei Feng¹, Miao Liu¹, Yexun Yuan¹, Chi Zhang², Peng Geng^1,*

Journal on Internet of Things, Vol.6, pp. 1-16, 2024, DOI:10.32604/jiot.2024.053584 - 24 July 2024

Abstract With the rapid development of electric vehicles, the requirements for charging stations are getting higher and higher. In this study, we constructed a charging station topology network in Nanjing through the Space-L method, mapping charging stations as network nodes and constructing edges through road relationships. The experiment introduced five EV charging recommendation strategies (based on distance, number of fast charging piles, user preference, price, and overall rating) used to simulate disordered charging caused by different user preferences, and the impact of the network dynamic robustness in case of node failure is explored by simulating the… More >

Amel Ali Alhussan¹, Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,*, Marwa M. Eid^2,3, Abdelhameed Ibrahim⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1163-1177, 2024, DOI:10.32604/cmc.2024.049001 - 18 July 2024

Abstract To reduce the negative effects that conventional modes of transportation have on the environment, researchers are working to increase the use of electric vehicles. The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge. The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue. Nevertheless, the powering of these terminals presents challenges because of the high energy requirements, which may influence the quality of service. Modelling the maximum hourly capacity of each station based on… More >

Randeep Singh^1,*, Tomoki Oridate², Tien Nguyen²

Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 703-717, 2024, DOI:10.32604/fhmt.2024.050744 - 11 July 2024

Abstract Electrification of vehicles intensifies their cooling demands due to the requirements of maintaining electronics/electrical systems below their maximum temperature threshold. In this paper, passive cooling approaches based on heat pipes have been considered for the thermal management of electric vehicle (EV) traction systems including battery, inverter, and motor. For the battery, a heat pipe base plate is used to provide high heat removal (180 W per module) and better thermal uniformity (<5°C) for the battery modules in a pack while downsizing the liquid cold plate system. In the case of Inverter, two phase cooling system… More > Graphic Abstract

Aneel Kumar¹, Mahesh Kumar¹, Amir Mahmood Soomro¹, Laveet Kumar^2,*

Energy Engineering, Vol.121, No.6, pp. 1439-1457, 2024, DOI:10.32604/ee.2024.049977 - 21 May 2024

Abstract Electric vehicles (EVs) are the most interesting and innovative technology in the 21st century because of their enormous advantages, both technically and economically. Their emissions rate compared to fuel-based vehicles is negligible as they do not consume fuel and hence do not emit any harmful gases. However, their bulk production, adoption and lack of charging stations increase the stress of power stations due to modern-day lifestyles. If Electric vehicles demand increases drastically then conventional power stations will not bear their demand and if they generate electricity by conventional means it will be very costly and… More >