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  • Open Access

    ARTICLE

    Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

    Li Wang1,*, 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 >

  • Open Access

    ARTICLE

    An Optimisation Strategy for Electric Vehicle Charging Station Layout Incorporating Mini Batch K-Means and Simulated Annealing Algorithms

    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 >

  • Open Access

    ARTICLE

    EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

    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

    EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

  • Open Access

    ARTICLE

    A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

    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 >

  • Open Access

    ARTICLE

    Research on Robustness of Charging Station Networks under Multiple Recommended Charging Methods for Electric Vehicles

    Lei Feng1, Miao Liu1, Yexun Yuan1, Chi Zhang2, Peng Geng1,*

    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 >

  • Open Access

    ARTICLE

    Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

    Amel Ali Alhussan1, Doaa Sami Khafaga1, El-Sayed M. El-kenawy2,*, Marwa M. Eid2,3, 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 >

  • Open Access

    ARTICLE

    Drive Train Cooling Options for Electric Vehicles

    Randeep Singh1,*, 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

    Drive Train Cooling Options for Electric Vehicles

  • Open Access

    ARTICLE

    Techno-Economic Optimization of Novel Stand-Alone Renewable Based Electric Vehicle Charging Station near Bahria Town Karachi, Sindh Pakistan

    Aneel Kumar1, Mahesh Kumar1, Amir Mahmood Soomro1, Laveet Kumar2,*

    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 >

  • Open Access

    ARTICLE

    Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

    Shuwei Zhong, Yanbo Che*, Shangyuan Zhang

    Energy Engineering, Vol.121, No.3, pp. 603-618, 2024, DOI:10.32604/ee.2023.044667 - 27 February 2024

    Abstract Electric vehicle (EV) is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future. However, a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff. Therefore, this paper proposes a dynamic time-of-use tariff mechanism, which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean (FCM) clustering algorithm, and then dynamically adjusts the peak and valley tariffs according to the actual load of each time… More >

  • Open Access

    ARTICLE

    A Predictive Energy Management Strategies for Mining Dump Trucks

    Yixuan Yu, Yulin Wang*, Qingcheng Li, Bowen Jiao

    Energy Engineering, Vol.121, No.3, pp. 769-788, 2024, DOI:10.32604/ee.2023.044042 - 27 February 2024

    Abstract The plug-in hybrid vehicles (PHEV) technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks. Meanwhile, plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies (EMS). Therefore, a series hybrid system is constructed based on a 100-ton mining dump truck in this paper. And inspired by the dynamic programming (DP) algorithm, a predictive equivalent consumption minimization strategy (P-ECMS) based on the DP optimization result is proposed. Based on the optimal control manifold and the SOC reference trajectory obtained by the More >

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