Article
Energy & Fuels
Sinan Cai, Ryuji Matsuhashi
Summary: An optimal dispatching control is proposed for EV aggregators to provide regulation capacity to the power system while ensuring individual EVs have enough SOC before the next trip. The proposed control operates at a faster time-step, allowing more efficient use of EVs and obtaining more market capacity payment for the aggregators. Simulation in Matlab and Simulink validates the performance and effectiveness of the proposed dispatching controller.
Article
Energy & Fuels
Rishabh Ghotge, Koen Philippe Nijssen, Jan Anne Annema, Zofia Lukszo
Summary: This study investigates the acceptance of Vehicle-to-Grid (V2G) charging among electric vehicle (EV) drivers. The findings suggest that clear communication, financial compensation, real-time insight, and user-friendly interface contribute to higher acceptance. However, uncertainty in battery state-of-charge, increased planning requirements, anxiety about reaching destinations, and restrictions on personal vehicle use are major barriers. The study also reveals that actual experience with V2G charging influences perceptions, with concrete factors carrying more weight than abstract concerns.
Article
Engineering, Multidisciplinary
Vaibhav Shah, Saifullah Payami
Summary: This article proposes an integrated converter for electric vehicles (EVs) with various charging capabilities and the use of a switched reluctance motor (SRM). The converter can provide driving power during EV operation and charge the battery when the vehicle is idle. It includes power factor correction (PFC) charging for AC charging, bidirectional switches for both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) charging, and a four-quadrant DC-DC converter (FQDDC) for fast charging via a DC source. The proposed converter eliminates the need for additional non-integrated circuits, reducing the total number of switches and achieving a standstill rotor at appropriate positions.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Energy & Fuels
Bijan Bibak, Lihui Bai
Summary: Recently, the migration from internal combustion cars to electric vehicles (EVs) has gained attention as a viable solution for energy sustainability. However, the short lifespan of EV batteries poses a challenge. This paper proposes an optimal model for a commercial and industrial electric fleet system to reduce total electricity costs by coordinating various energy sources and usage.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Energy & Fuels
Abdullah Dik, Siddig Omer, Rabah Boukhanouf
Summary: Low carbon and renewable energy sources are increasingly being used to meet electricity demands and reduce carbon emissions. The electrification of the transport sector, along with the installation of renewable energy capacity, power storage technology, and energy demand management, presents an opportunity for innovation in the energy market. Electric vehicles have the potential to serve as both means of transportation and dynamic energy vectors interfacing with the grid, buildings, and other systems.
Article
Engineering, Civil
Ifiok Anthony Umoren, Muhammad Zeeshan Shakir, Hina Tabassum
Summary: The paper proposes a method to manage EVs in a V2G communication network using resource efficiency (RE) to balance spectral efficiency and cost efficiency. Through a two-phase algorithm, the downlink of the V2G communication network is considered to maximize RE, improving performance and reducing complexity.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Lou Wei, Chen Yi, Jin Yun
Summary: This paper presents a novel reinforcement learning based approach for energy drive and management in smart grids, utilizing Q-learning technique and dragonfly optimization algorithm for power dispatch optimization and cost minimization.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Francesca Marasciuolo, Maria Dicorato, Gioacchino Tricarico, Pasquale Montegiglio, Giuseppe Forte, Michele Trovato
Summary: This study focuses on analyzing the day-ahead performance of a DC microgrid based on different optimization algorithms, considering technical and economic targets. Statistical analysis of electric vehicle usage based on historical data is conducted to generate scenarios for investigation. The performance is compared using technical and economic indicators.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Anekant Jain, Krishna Kumar Gupta, Sanjay K. Jain, Pallavee Bhatnagar
Summary: A five-level rectifier topology with self-balanced switched capacitors is proposed for EV battery charging applications, showing advantages such as wide output regulation and bidirectional power flow for vehicle-to-grid systems. Experimental results validate the feasibility of the proposed topology and its advantages for electric vehicle battery charging.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Computer Science, Information Systems
Jiangliang Jin, Yunjian Xu
Summary: This study investigates the charging routing problem of a smart electric vehicle, which aims to find an optimal EV charging station based on real-time information from power and intelligent transportation systems. The problem is formulated as a Markov decision process with unknown dynamics and solved using a two-level shortest-path optimization approach and a deep reinforcement learning algorithm. Experimental results demonstrate the superiority of the proposed approach over existing methods.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Yingqi Gu, Mingming Liu
Summary: The implementation of vehicle-to-grid (V2G) functionalities in electric vehicles (EVs) can provide extra freedom for EV owners and reduce operating costs for utility companies. However, devising a fair and optimal EV discharging strategy poses practical challenges.
IEEE SYSTEMS JOURNAL
(2021)
Article
Green & Sustainable Science & Technology
Hang Yu, Songyan Niu, Yitong Shang, Ziyun Shao, Youwei Jia, Linni Jian
Summary: This paper discusses the challenges and solutions for integrating electric vehicles with distribution grids and V2G operation, evaluates the performance of different architectures in terms of charging demand compatibility, power quality, V2G availability, etc., and provides recommendations for optimal architecture selection.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Engineering, Chemical
Youssra Doudou, Abdeljabbar Cherkaoui, Mostafa Ezziyyani
Summary: Vehicle-to-grid technology (V2G) enables electric vehicles (EVs) to discharge their stored energy to the public grid during periods of high demand, improving energy consumption efficiency. This study explores an optimal energy management strategy for charging and discharging EV batteries in a microgrid, and analyzes the contribution of solar power generation. Using two different driving profiles as case studies, the performance of the proposed strategy and the dependence of V2G technology on EV user driving patterns are demonstrated, along with the contribution of a 6 kW photovoltaic generator in Tangier.
Article
Computer Science, Information Systems
Joseph Antoun, Mohammad Ekramul Kabir, Ribal Atallah, Bassam Moussa, Mohsen Ghafouri, Chadi Assi
Summary: The article discusses the impact of increased electric vehicle charging demand on the distribution grid. It proposes a hybrid method of using V2G and network reconfiguration to handle the new peak load, effectively reducing network losses.
IEEE SYSTEMS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Jens Engel, Thomas Schmitt, Tobias Rodemann, Juergen Adamy
Summary: In this paper, a hierarchical economic model predictive control scheme is proposed for EV charge management in a commercial building energy management system. The scheme considers symmetrical charging objectives and introduces a scalable and adaptable scenario generation approach.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Shao-Di Wang, Hui-Ming Wang, Zhetao Li, Victor C. M. Leung
Summary: For mission-critical massive machine-type communications, a novel UAJ detection method based on JSTS is proposed to address the serious threat of active jamming attack in uplink access. The proposed method detects UAJ in a sequential manner by processing received signals one by one, and does not rely on prior knowledge of the attackers. Numerical results validate the effectiveness of the method.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Jaewook Lee, Haneul Ko, Sangwon Seo, Sangheon Pack
Summary: Federated learning (FL) is an alternative to traditional cloud-centric machine learning (ML) that has gained attention. The performance of FL is affected by the selection of clients with non-independent and identically distributed (non-IID) data. To minimize convergence time and improve learning accuracy, an optimization problem is formulated and a data distribution-aware online client selection (DOCS) algorithm is proposed.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Shao-Di Wang, Hui-Ming Wang, Chen Feng, Victor C. M. Leung
Summary: In this paper, we propose to detect DMRS spoofing in 5G NR by exploiting the spatial sparsity structure of the channel. We first extract the spatial sparsity structure of the channel using a sparse feature retrieval method, and then propose a sequential sparsity structure anomaly detection method to detect DMRS spoofing. Simulation results show that our method outperforms other existing methods.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Wenyu Zhang, Haijun Zhang, Hui Ma, Hua Shao, Ning Wang, Victor C. M. Leung
Summary: This paper proposes a predictive and adaptive deep coding (PADC) framework that achieves flexible code rate optimization with a given target transmission quality requirement. By using a variable code length enabled DeepJSCC model, an Oracle Network model, and a CR optimizer, PADC can minimize bandwidth consumption while guaranteeing the PSNR constraint for each image data in wireless image transmission tasks.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Haneul Ko, Jaewook Lee, Sangwon Seo, Sangheon Pack, Victor C. M. Leung
Summary: In federated learning, low computing power, poor wireless channel conditions, and insufficient data can result in a long convergence time. To address this, a constrained Markov decision process (CMDP) problem is formulated to minimize the average round time while maintaining minimum numbers of trained data and trained data classes. The CMDP problem is converted into a linear programming (LP) to obtain the optimal scheduling policy. Additionally, a joint client selection and bandwidth allocation algorithm (JCSBA) is developed to reduce the curse of dimensionality in CMDP and effectively reduce the convergence time by up to 49%.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Haoyuan Pan, Tse-Tin Chan, Victor C. M. Leung, Jianqiang Li
Summary: This paper investigates the information freshness in two-way relay networks (TWRNs) using physical-layer network coding (PNC). PNC reduces communication latency by converting electromagnetic waves into network-coded messages. The study focuses on the average age of information (AoI) in TWRNs with and without automatic repeat request (ARQ). The proposed uplink-lost-then-drop (ULTD) protocol combines packet drop and ARQ to improve average AoI in TWRNs.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Jingwei Liu, Chuntian Peng, Rong Sun, Lei Liu, Ning Zhang, Schahram Dustdar, Victor C. M. Leung
Summary: As a representative application scenario of the Internet of Things (IoT), Internet of Vehicles (IoV) plays an important role in intelligent transportation. However, the data traffic exchanged in IoV is vulnerable to privacy leakage. To address this issue, a conditional privacy-preserving authentication scheme with hierarchical pseudonyms (CPAHP), based on elliptic curve Diffie-Hellman (ECDH) problem, is proposed for 5G-enabled IoV. The scheme ensures protection of real identities and movement tracks of vehicles while allowing recovery of malicious vehicles' identities through corresponding pseudonyms. Furthermore, blockchain technology is introduced to facilitate smooth sharing of traffic information among vehicles. The scheme not only meets security requirements but also provides higher computational efficiency.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Yueqiang Xu, Heli Zhang, Xi Li, F. Richard Yu, Victor C. M. Leung, Hong Ji
Summary: This paper presents a trusted collaboration framework for managing video buffering in VR devices in a distributed environment. By collaboratively processing rendering tasks and using a trust evaluation method for collaborator selection, an optimization problem is solved through joint optimization. Simulation results show that this approach achieves good performance.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Sangwon Seo, Jaewook Lee, Haneul Ko, Sangheon Pack
Summary: The selection of clients and quantization levels in federated learning has a significant impact on learning time and accuracy. Considering factors such as computational power, communication capacity, and data distribution, we propose a joint optimization problem for clustering and selecting clusters with quantization levels. To address the complexity of the problem, we introduce the situation-aware cluster and quantization level selection (SITUA-CQ) algorithm. Simulation results demonstrate that SITUA-CQ reduces round time by up to 80.3% compared to conventional algorithms.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Xu Han, Daxin Tian, Jianshan Zhou, Xuting Duan, Zhengguo Sheng, Victor C. M. Leung
Summary: This paper proposes an aggregated security solution for MEC applications in VANETs, which can protect the privacy of vehicle identities while enabling confidential, efficient, and trustworthy data sharing.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Ping Lang, Daxin Tian, Xuting Duan, Jianshan Zhou, Zhengguo Sheng, Victor C. M. Leung
Summary: In response to the data processing needs of intelligent vehicles, vehicular edge computing (VEC) uses roadside computing resources to provide proximity computing services for vehicles, forming a new computing paradigm. This article proposes a cooperative computation offloading (CO) and secure handover framework utilizing blockchain technology to ensure efficient and secure CO. The framework includes models for vehicle mobility and CO handover, along with a consensus mechanism for data synchronization and immutability. A cooperative CO decision optimization is formulated and solved using multiagent deep reinforcement learning. Extensive simulations validate the performance and effectiveness of the proposed method.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Lijia Ma, Zengyang Shao, Xiaocong Li, Qiuzhen Lin, Jianqiang Li, Victor C. M. Leung, Asoke K. Nandi
Summary: This article proposes an evolutionary deep reinforcement learning algorithm called EDRL-IM for influence maximization in complex networks. By combining evolutionary algorithm and deep reinforcement learning algorithm, EDRL-IM outperforms state-of-the-art methods in finding seed nodes.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Shihao Shen, Yiwen Han, Xiaofei Wang, Shiqiang Wang, Victor C. M. Leung
Summary: This paper introduces KaiS, a learning-based scheduling framework, to improve the long-term throughput rate of edge-cloud networks. KaiS utilizes a coordinated multi-agent actor-critic algorithm for decentralized request dispatch and dynamic dispatch spaces within the edge cluster. It also employs graph neural networks to embed system state information and reduce orchestration dimensionality through stepwise scheduling.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Information Systems
Ling Fan, Xuxun Liu, Huan Zhou, Victor C. M. Leung, Jian Su, Alex X. Liu
Summary: In this paper, an exchange-free resource scheduling scheme is proposed to address the interference problem in dynamic coexisting WBANs. Transmission channel/slot allocation scheme based on a Latin square and retransmission time-slot selection scheme based on a hash function are designed for data transmission and retransmission respectively. Compared with existing solutions, this work enables independent resource allocation and coordination, ensuring adaptability to fast changes in WBANs, and guarantees contention-free resource allocation for both transmission and retransmission, effectively addressing the interference problem.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Cybernetics
Ye Wang, Yingmin Zhou, Mengzhu Wang, Zhenghan Chen, Zhiping Cai, Junyang Chen, Victor C. M. Leung
Summary: Multidocument aspect-based summarization aims to generate focused summaries based on target aspects from relevant documents. A two-stage framework is proposed that first discovers the latent relationship among aspects and then uses relevant sentences to generate abstractive summaries. The model utilizes tag mask training strategy to exploit latent dependencies among aspects and improve the interpretability of the model. Experimental results show accurate aspect discovery and improvements in summarization compared to strong baselines.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
