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Design of Double Integral Sliding Mode Controller for Energy Storage System of a Novel Multisource Hybrid Electric Vehicle

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Transportation research Procedia 2025年 84卷 551-558页

作者： Saif Ullah Kamran Zeb Muhammad Farooq Siddique Muhammad Khalid Jaeyoung Kim Jong-Myon Kim Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan 44610 Republic of Korea Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals (KFUPM) Dhahran 31261 Saudi Arabia Electrical Engineering Department KFUPM Dhahran 31261 Saudi Arabia Prognosis and Diagnostic Technologies Co. Ulsan 44610 Republic of Korea

The swift depletion of oil, natural gas, and fossil fuels caused by their excessive use serves as a strong motivation to seek potential alternatives. Emissions from traditional vehicles are unhealthy and include greenhouse gases. Therefore, various studies have been carried out on hybrid electric vehicles powered by fuel cells to overcome the problems of depleting natural resources and emissions of harmful gases. In this study, the proposed model consists of four sources: a fuel cell, a solar panel, a battery, and a supercapacitor. The fuel cell is the primary source while the remaining sources are secondary sources. DC converters are used to link the sources to the ssDC bus. A double integral sliding mode non-linear controller is designed and formulated for the hybrid energy storage system. The system’s overall stability has been ensured using Lyapunov criteria. The proposed model simulations are performed in MATLAB/Simulink 2024b to verify the controller’s performance. The results obtained authenticate stability, robustness, and efficient performance under various dynamic conditions while tracking the DC reference bus voltage of 400V.

关键词： Fuel Cell Photovoltaic Battery Supercapacitor Double Integral Sliding Mode Controller Hybrid Electric Vehicle

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A New Look for Outage and Ergodic Improvements in Coordinated IoT Paradigms with Hybrid Multiple Access

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IEEE Transactions on Vehicular Technology 2025年

作者： Vu, Thai-Hoc Le, Anh-Tu Huynh-The, Thien da Costa, Daniel Benevides Kim, Sunghwan Kyonggi University School of Electronic Engineering Korea Republic of VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science 17. Listopadu 2172/15 Ostrava708 00 Czech Republic Ho Chi Minh City University of Technology and Education Department of Computer and Communications Engineering Viet Nam Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia

As spectrum utilization becomes increasingly scarce due to the exponential growth of intelligent connected devices in the Internet of Things (IoT), developing efficient communication protocols with simultaneous improvements in energy and spectrum usage is critical in response to the demands of cost-effective IoT devices. In this work, we propose a hybrid orthogonal and non-orthogonal multiple-access protocol in coordinated direct uplink/downlink and relay transmission for low-cost IoT paradigms, where a source node engages in direct transmission with proximate IoT users and relies on a relay node for distant connections. This protocol offers a successive interference cancellation–free approach and does not require any channel disparity for power allocation. Exact and asymptotic analytical expressions for the outage probability (OP) and ergodic capacity (EC) are derived. Numerical results demonstrate that the proposed protocol significantly improves OP–by 8 folds for the respective proximate users while maintaining the same OP for distant users over classical coordinated non-orthogonal multiple access–and delivers superior EC performance for proximate IoT users at low to moderate signal-to-noise ratios (SNRs) and distant users at most SNR. Moreover, critical tasks for minimizing the maximal OP and maximizing the ergodic sum capacity have been undertaken to achieve outage fairness and enhance the overall system's capacity. These non-convex problems are efficiently addressed by applying the analytical asymptotic-inspired alternative optimization. © 1967-2012 IEEE.

关键词： Signal to noise ratio

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IntSHU: A Security-Enabled Intelligent Soft Handover Approach for UAV-Aided 5G and Beyond

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Karmakar, Raja Kaddoum, Georges Akhrif, Ouassima Heritage Institute of Technology Department of Computer Science and Engineering IoT and Cyber Security including Blockchain Technology Kolkata700107 India University of Quebec Department of Electrical Engineering ETS Montreal Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon

Unmanned aerial vehicles (UAVs) play a significant role in serving as gNodeBs (gNBs), or user devices (UDs). However, considering the high data rates and low latency required by fifth generation (5G) and beyond, it is essential to maintain a reliable connection at UAVs, particularly due to frequent handovers. To minimize handover failures, the hysteresis margin and time-to-trigger (TTT) should be dynamically adjusted. Furthermore, to prevent vulnerabilities to threats, UAV and gNB authentication and secure communication should be addressed during handovers. However, available literature uses a learning-based approach to handover management in UAV-aided 5G networks, thereby neglecting security, or offers a handover authentication mechanism, but lacks a novel handover strategy. To fill this gap in the literature, in this study, we design an Intelligent Soft Handover for UAV-enabled cellular networks (IntSHU), a generative adversarial network (GAN)-based scheme designed for intelligent soft handovers in UAV-enabled 5G and beyond. IntSHU uses adapted hysteresis margins and TTT values, dynamically generated through an -greedy policy. In addition, we implement a lightweight, physically unclonable function (PUF)-based message encryption and authentication scheme via blockchain for streamlined access control during handovers. The results of our simulation indicate that IntSHU significantly enhances network performance by facilitating reliable soft handovers. © 2015 IEEE.

关键词： 5G mobile communication systems

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Enhanced Voltage Stability for DC Microgrids Integrating Hybrid Electric Vehicles with Virtual Inertia and Damping Control

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Transportation research Procedia 2025年 84卷 571-578页

作者： Hossam Ali Mohamed Zaery Syed Muhammad Amrr S.M. Suhail Hussain Vijay Vital Mohammad A. Abido Department of Electrical Engineering Faculty of Energy Engineering Aswan University 81528 Aswan Egypt School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85281 USA K.A.CARE Energy Research & Innovation Center King Fahd University of Petroleum and Minerals (KFUPM) Dhahran 31261 Saudi Arabia Control & Instrumentation Engineering Department KFUPM Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems KFUPM Dhahran 31261 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran 31261 Saudi Arabia

DC microgrids (MGs) have experienced swift growth, driven by the expanding integration of energy storage devices(ESD), renewable energy sources (RESs), and localized loads. However, it suffers from insufficient inertia owing to less rotating mass sources, which can lead to poor voltage stability. This paper introduces a control method that emulates both inertia and damping to mitigate fluctuations in DC voltage, enhance system stability, and address the low inertia concern. The proposed virtual inertia and damping (VID) control system is adopted through using hybrid electric vehicle (HEV). The suggested HEV comprises three power sources: a battery, a fuel cell (FC), and a supercapacitor (SC). In this setup, both the battery and the FC are employed to supply virtual damping owing to their high-energy density, and the SC is utilized to support virtual inertial characteristics due to its high-power density. Through this approach, enhancements in the DC voltage stability of an islanded DC MG can be achieved. Simulations demonstrate that the proposed control system significantly outperforms existing works utilizing virtual inertia (VI) based on an EV’s battery only, as well as over systems lacking VI. The proposed control system effectively reduces voltage fluctuations and improves system stability, specifically in terms of the rate of change of voltage (ROCOV) and accelerated settling time.

关键词： Voltage Stability DC Microgrids Virtual Inertia Damping Hybrid Electric Vehicle Renewable Energy Sources

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Data-Driven Power Flow Estimation for MVDC Distribution systems Based on Physics-embedded FCN

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IEEE Transactions on Smart Grid 2025年

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

Heart Rhythm O2

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Heart Rhythm O2 2025年第5期6卷 678.0-686.0页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

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Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 568-584页

作者： Letafati, Mehdi Amirhossein Ameli Kalkhoran, Seyyed Erdemir, Ecenaz Hossein Khalaj, Babak Behroozi, Hamid Gunduz, Deniz Sharif University of Technology Computer Engineering Department Tehran14588-89694 Iran Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Sharif University of Technology Department of Electrical Engineering Tehran14588-89694 Iran Sharif University of Technology Sharif Center for Information Systems and Data Science Tehran14588-89694 Iran Institute for Research in Fundamental Sciences School of Computer Science Tehran19538-33511 Iran

Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Unlike prior works that assume perfectly known and independent identically distributed (i.i.d.) source and channel statistics, the proposed scheme operates under unknown and non-i.i.d. conditions, making it more applicable to real-world scenarios. The goal is to transmit images with minimum distortion, while simultaneously preventing eavesdroppers from inferring certain private attributes of images. Simultaneously generalizing the ideas of privacy funnel and wiretap coding, a multi-objective optimization framework is expressed that characterizes the trade-off between image reconstruction quality and information leakage to eavesdroppers, taking into account the structural similarity index (SSIM) for improving the perceptual quality of image reconstruction. Extensive experiments on the CIFAR-10 and CelebA, along with ablation studies, demonstrate significant performance improvements in terms of SSIM, adversarial accuracy, and the mutual information leakage compared to benchmarks. Experiments show that the proposed scheme restrains the adversarially-trained eavesdroppers from intercepting privatized data for both cases of eavesdropping a common secret, as well as the case in which eavesdroppers are interested in different secrets. Furthermore, useful insights on the privacy-utility trade-off are also provided. © 2023 CCBY.

关键词： Deep neural networks

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A Data-and-Model-Driven Acceleration Approach for Large-Scale Network-Constrained Unit Commitment Problem With Uncertainty

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IEEE Transactions on Sustainable Energy 2025年

作者： Zhou, Yuzhou Han, Zhihan Zhai, Qiaozhu Wu, Lei Cao, Xiaoyu Guan, Xiaohong Xi'an Jiaotong University Systems Engineering Institute MOEKLINNS Lab Xi'an710049 China Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Xi'an Jiaotong University Systems Engineering Institute Xi'an710049 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken07030 United States Tsinghua University Department of Automation Center for Intelligent and Networked Systems Beijing100084 China

Numerous binary variables and constraints greatly affect the computation of large-scale network-constrained unit commitment (NCUC) problems. With the integration of massive renewable energy and distributed loads, binary variables and network power flows face more frequent changes to cope with uncertainties. How to ensure the calculation efficiency and accuracy of NCUC problems against uncertainties and frequent changes becomes a common challenge for academia and industry. Inspired by data-driven and model-driven technologies, this paper presents a hybrid reduction approach for variables and constraints to accelerate the solving of large-scale NCUC problems, which achieves "one plus one much greater than two" effect. Specifically, an offline database of nodal net load clusters and the corresponding NCUC solutions is established, which also captures the impacts of changes in renewables, loads, topology, etc. on UC solutions. With this database, a data-and-modeldriven reduction method is proposed to fix binary variables and reduce constraints, while considering the changes of prices and net loads in online NCUC instances. Moreover, the reduction method was further developed to accelerate various NCUC problems, including scenario-based methods, robust optimization methods, etc. Thus, the computation burden of online large-scale NCUC problems is greatly mitigated while ensuring good solution quality. Numerical tests implemented on IEEE 24-bus system, 118-bus system, and Polish 2383-bus system illustrate the efficiency of the proposed approach. © 2025 IEEE. All rights reserved.

关键词： Mendelevium

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Asymptotic Leader-Following With Continuous Control Inputs in Networked Euler-Lagrangian systems: The Case of Directed Topologies

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IEEE Transactions on Control of Network systems 2025年

作者： Rezaee, Hamed Parisini, Thomas Polycarpou, Marios M. Imperial College London Department of Mechanical Engineering LondonSW7 2AZ United Kingdom Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy University of Cyprus KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering NicosiaCY-1678 Cyprus

In most practical dynamic leader-following scenarios, the control input or dynamical model of the neighboring agents are not known to the followers. In such scenarios, asymptotic leader-following requires employing discontinuous robust mechanisms to cope with unknown parts of the neighbors trajectories, leading to chattering in the followers inputs. Few studies already addressed asymptotic leader-following with continuous control inputs;however, they require the communication topology to be undirected, which is a restrictive assumption in leader-following. In this paper, asymptotic dynamic leader-following in Euler-Lagrangian multiagent systems is addressed. We assume that the leader control input is determined by the leader locally and is unknown to the followers. The contribution of this paper is that whereas the unknown input and model of the neighboring agents are compensated by a robust mechanism in the followers control strategy, the continuity in the followers control inputs is guaranteed and the interaction in the network is under directed topologies. Simulation results for a network of manipulator robots illustrate the performance of the proposed control strategy. © 2014 IEEE.

关键词： Lagrange multipliers

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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