Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly applying sustainable actions toward emission reduction. However, traditional transportation networks cannot handle right-time updates and act upon the requirements in dynamic conditions. Here, Digital Twin (DT) enables the development of enhanced transportation management via robust modelling and intelligence capabilities. Therefore, we propose a DT-empowered Eco-Regulation (DTER) framework with a novel twinning approach. We define a transport-specific twin sampling rate to catch right-time data in a transportation network. Besides, we perform emission prediction using Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (Bi-LSTM), and BANE embeddings. We perform Laplacian matrix analysis to cluster the risk zones regarding the emissions. Thereafter, we recommend actions by setting the number of vehicle limits of junctions for high-emission areas according to the outputs of Q-learning. In summary, DTER takes control of the emission with its transport-specific twin sampling rate and automated management of transportation actions by considering the emission predictions. We note DTER achieves 19% more successful right-time data capturing, with 30% reduced query time. Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. Lastly, the autonomous recommendations of DTER achieved $sim$ 20% decrease in emissions by presenting an effective carbon tracing framework.
{"title":"Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks","authors":"Kubra Duran;Lal Verda Cakir;Achille Fonzone;Trung Q. Duong;Berk Canberk","doi":"10.1109/OJVT.2024.3484956","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3484956","url":null,"abstract":"Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly applying sustainable actions toward emission reduction. However, traditional transportation networks cannot handle right-time updates and act upon the requirements in dynamic conditions. Here, Digital Twin (DT) enables the development of enhanced transportation management via robust modelling and intelligence capabilities. Therefore, we propose a DT-empowered Eco-Regulation (DTER) framework with a novel twinning approach. We define a transport-specific twin sampling rate to catch right-time data in a transportation network. Besides, we perform emission prediction using Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (Bi-LSTM), and BANE embeddings. We perform Laplacian matrix analysis to cluster the risk zones regarding the emissions. Thereafter, we recommend actions by setting the number of vehicle limits of junctions for high-emission areas according to the outputs of Q-learning. In summary, DTER takes control of the emission with its transport-specific twin sampling rate and automated management of transportation actions by considering the emission predictions. We note DTER achieves 19% more successful right-time data capturing, with 30% reduced query time. Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. Lastly, the autonomous recommendations of DTER achieved \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u0000 20% decrease in emissions by presenting an effective carbon tracing framework.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1109/OJVT.2024.3477268
Nishant Gupta;Satyam Agarwal;Aymen Fakhreddine
Integrating unmanned aerial vehicles (UAVs) with terrestrial networks can enable high-speed communication in various applications. UAVs can serve as aerial base stations (ABSs), offering several benefits to the existing terrestrial networks, such as enhanced coverage, increased capacity, rapid deployment, and mobile communication support. However, this integration presents various technical challenges, including coordination, interference management, and dynamic allocation of resources. To address these key challenges, in this paper, we maximize the network utility by jointly optimizing the scheduling and cell association, transmit power of all base stations, and ABS deployment locations in the presence of co-channel interference. A two-stage approach is proposed to obtain a solution. In the first stage, we propose a heuristic solution by using the clustering algorithm to determine the initial ABS locations and user scheduling while ignoring the co-channel interference. In the second stage, we utilize the solution obtained in the first part and develop an interference-aware iterative scheme to jointly optimize user scheduling, resource allocation, and ABS placement. Given the non-convex nature of this problem, we employ the successive convex approximation technique to approximate the non-convex objectives and constraints. Numerical results show the proposed approach's insights and effectiveness over other schemes. Specifically, our proposed approach provides an average of 25% improvement over the benchmark schemes.
{"title":"Fairness-Aware Utility Maximization for Multi-UAV-Aided Terrestrial Networks","authors":"Nishant Gupta;Satyam Agarwal;Aymen Fakhreddine","doi":"10.1109/OJVT.2024.3477268","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3477268","url":null,"abstract":"Integrating unmanned aerial vehicles (UAVs) with terrestrial networks can enable high-speed communication in various applications. UAVs can serve as aerial base stations (ABSs), offering several benefits to the existing terrestrial networks, such as enhanced coverage, increased capacity, rapid deployment, and mobile communication support. However, this integration presents various technical challenges, including coordination, interference management, and dynamic allocation of resources. To address these key challenges, in this paper, we maximize the network utility by jointly optimizing the scheduling and cell association, transmit power of all base stations, and ABS deployment locations in the presence of co-channel interference. A two-stage approach is proposed to obtain a solution. In the first stage, we propose a heuristic solution by using the clustering algorithm to determine the initial ABS locations and user scheduling while ignoring the co-channel interference. In the second stage, we utilize the solution obtained in the first part and develop an interference-aware iterative scheme to jointly optimize user scheduling, resource allocation, and ABS placement. Given the non-convex nature of this problem, we employ the successive convex approximation technique to approximate the non-convex objectives and constraints. Numerical results show the proposed approach's insights and effectiveness over other schemes. Specifically, our proposed approach provides an average of 25% improvement over the benchmark schemes.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the major challenges in multiple input multiple output (MIMO) system design is the salient trade-off between performance and computational complexity. For instance, the maximum likelihood (Max-L) detection is capable of achieving optimal performance based on exhaustive search, but its exponential computational complexity renders it impractical. By contrast, zero-forcing detection has low computational complexity, while having significantly worse performance compared to that of the Max-L. The recent developments in deep learning (DL) based detection techniques relying on back propagation neural networks (BPNN) constitute promising candidates for the open challenge of the MIMO detection performance versus complexity trade-off. Against this background, in this paper, we propose a novel partial learning (PL) model for MIMO detection with soft-bit decisions that can be incorporated into channel-coded communication systems. More explicitly, the proposed PL model consists of two parts: first, a subset of the transmitted MIMO symbols is detected by the data-driven DL technique and then the detected symbols are removed from the received MIMO signals for the sake of interference cancellation. Afterwards, the classic model-based zero-forcing detector is invoked to detect the remaining symbols at a linear complexity. As a result, near-optimal MIMO performance can be achieved with substantially reduced computational complexity compared to Max-L and BPNN. The proposed solution is adapted to both accept and produce soft information, so that iterative detection can be performed, where the iteration gain is analyzed by extrinsic information transfer (EXIT) charts. Our simulation results demonstrate that the proposed partial learning-based iterative detection is capable of attaining near-Max-L performance while attaining a flexible performance versus complexity trade-off.
多输入多输出（MIMO）系统设计的主要挑战之一是性能与计算复杂度之间的突出权衡。例如，最大似然（Max-L）检测能够在穷举搜索的基础上实现最佳性能，但其指数级的计算复杂度使其不切实际。相比之下，零强迫检测的计算复杂度较低，但性能却比 Max-L 差很多。基于反向传播神经网络（BPNN）的深度学习（DL）检测技术的最新发展，为解决 MIMO 检测性能与复杂性权衡这一难题带来了希望。在此背景下，我们在本文中提出了一种新颖的部分学习（PL）模型，用于可纳入信道编码通信系统的具有软位决策的 MIMO 检测。更明确地说，所提出的 PL 模型由两部分组成：首先，通过数据驱动的 DL 技术检测传输的 MIMO 符号子集，然后为了消除干扰，从接收的 MIMO 信号中去除检测到的符号。之后，再使用基于模型的经典零干扰检测器，以线性复杂度检测剩余的符号。因此，与 Max-L 和 BPNN 相比，在大幅降低计算复杂度的同时，还能实现近乎最佳的 MIMO 性能。所提出的解决方案既能接受软信息，也能产生软信息，因此可以进行迭代检测，迭代增益通过外在信息传输（EXIT）图进行分析。我们的仿真结果表明，所提出的基于部分学习的迭代检测能够达到接近 Max-L 的性能，同时实现灵活的性能与复杂性权衡。
{"title":"Partial Learning-Based Iterative Detection of MIMO Systems","authors":"Abdulaziz Babulghum;Chao Xu;Soon Xin Ng;Mohammed El-Hajjar","doi":"10.1109/OJVT.2024.3482008","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3482008","url":null,"abstract":"One of the major challenges in multiple input multiple output (MIMO) system design is the salient trade-off between performance and computational complexity. For instance, the maximum likelihood (Max-L) detection is capable of achieving optimal performance based on exhaustive search, but its exponential computational complexity renders it impractical. By contrast, zero-forcing detection has low computational complexity, while having significantly worse performance compared to that of the Max-L. The recent developments in deep learning (DL) based detection techniques relying on back propagation neural networks (BPNN) constitute promising candidates for the open challenge of the MIMO detection performance versus complexity trade-off. Against this background, in this paper, we propose a novel partial learning (PL) model for MIMO detection with soft-bit decisions that can be incorporated into channel-coded communication systems. More explicitly, the proposed PL model consists of two parts: first, a subset of the transmitted MIMO symbols is detected by the data-driven DL technique and then the detected symbols are removed from the received MIMO signals for the sake of interference cancellation. Afterwards, the classic model-based zero-forcing detector is invoked to detect the remaining symbols at a linear complexity. As a result, near-optimal MIMO performance can be achieved with substantially reduced computational complexity compared to Max-L and BPNN. The proposed solution is adapted to both accept and produce soft information, so that iterative detection can be performed, where the iteration gain is analyzed by extrinsic information transfer (EXIT) charts. Our simulation results demonstrate that the proposed partial learning-based iterative detection is capable of attaining near-Max-L performance while attaining a flexible performance versus complexity trade-off.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/OJVT.2024.3481884
Kai Lennert Bober;Anselm Ebmeyer;Falko Dressler;Ronald Freund;Volker Jungnickel
As industrial communication continues to evolve to increase flexibility through wireless communication, networked optical wireless communication (OWC), also known as LiFi, has emerged as a promising candidate technology due to its unlicensed spectrum and relatively deterministic propagation. The inherent containment of light improves security, enables dense cellular networks with spatial reuse, and results in reduced sporadic interference while providing high-capacity short range communication links to mobile end devices. This paper outlines the features of the new IEEE Std 802.15.13-2013, suitable for industrial OWC, and presents details of our prototype implementation along with initial experiments. The standard specifies deterministic medium access control (MAC), based on dynamic time division multiple access (TDMA), as well as two physical layers (PHYs) for extended range and robustness, and for spectral efficiency, respectively. Our prototype includes a central coordinator, implemented entirely in software, running on commodity server hardware. It connects to distributed ceiling-mounted optical wireless frontends via a packet-switched network (Ethernet) and is capable of forming them into adaptive virtual cells on a per-user basis. This approach enhances reliability through multiple-input multiple-output (MIMO) transmission and allows for smooth mobility. We implemented the Pulsed Modulation PHY (PM-PHY) on a commercially available field programmable gate array (FPGA) evaluation board. Initial test results indicate that the PM-PHY supports typical distances of up to 6 m between the ceiling and the mobile device. The MAC achieves deterministic latency values below 4 ms.
{"title":"LiFi for Industry 4.0: Main Features, Implementation and Initial Testing of IEEE Std 802.15.13","authors":"Kai Lennert Bober;Anselm Ebmeyer;Falko Dressler;Ronald Freund;Volker Jungnickel","doi":"10.1109/OJVT.2024.3481884","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3481884","url":null,"abstract":"As industrial communication continues to evolve to increase flexibility through wireless communication, networked optical wireless communication (OWC), also known as LiFi, has emerged as a promising candidate technology due to its unlicensed spectrum and relatively deterministic propagation. The inherent containment of light improves security, enables dense cellular networks with spatial reuse, and results in reduced sporadic interference while providing high-capacity short range communication links to mobile end devices. This paper outlines the features of the new IEEE Std 802.15.13-2013, suitable for industrial OWC, and presents details of our prototype implementation along with initial experiments. The standard specifies deterministic medium access control (MAC), based on dynamic time division multiple access (TDMA), as well as two physical layers (PHYs) for extended range and robustness, and for spectral efficiency, respectively. Our prototype includes a central coordinator, implemented entirely in software, running on commodity server hardware. It connects to distributed ceiling-mounted optical wireless frontends via a packet-switched network (Ethernet) and is capable of forming them into adaptive virtual cells on a per-user basis. This approach enhances reliability through multiple-input multiple-output (MIMO) transmission and allows for smooth mobility. We implemented the Pulsed Modulation PHY (PM-PHY) on a commercially available field programmable gate array (FPGA) evaluation board. Initial test results indicate that the PM-PHY supports typical distances of up to 6 m between the ceiling and the mobile device. The MAC achieves deterministic latency values below 4 ms.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/OJVT.2024.3479927
Yakov Miron;Dan Navon;Yuval Goldfracht;Dotan Di Castro;Itzik Klein
Multi-agent collaboration involves multiple participants working together in a shared environment to achieve a common goal. These agents share information, divide tasks, and synchronize their actions. Key aspects of multi-agent collaboration include coordination, communication, task allocation, cooperation, adaptation, and decentralization. On construction sites, surface grading is the process of leveling sand piles to increase a specific area's height. There, a bulldozer grades while a dumper allocates sand piles. Our work aims to utilize a multi-agent approach to enable these vehicles to collaborate effectively. To this end, we propose a decentralized and asymmetric multi-agent learning approach for construction sites (DAMALCS). We formulate DAMALCS to reduce expected collisions for operating vehicles. Therefore, we develop two heuristic experts capable of achieving their joint goal optimally, by applying an innovative prioritization method. In this approach, the bulldozer's movements take precedence over the dumper's operations. This enables the dozer to clear the path for the dumper and ensure continuous operation of both vehicles. As heuristics alone are insufficient in real-world scenarios, we utilize them to train AI agents, which proves to be highly effective. We simultaneously train dozer and dumper agents to operate within the same environment, aiming to avoid collisions and optimizing performance in terms of time efficiency and sand volume handling. Our trained agents and heuristics are evaluated in both simulation and real-world lab experiments, testing them under various conditions such as visual noise and localization errors. The results demonstrate that our approach significantly reduces collision rates for these vehicles.
{"title":"Decentralized and Asymmetric Multi-Agent Learning in Construction Sites","authors":"Yakov Miron;Dan Navon;Yuval Goldfracht;Dotan Di Castro;Itzik Klein","doi":"10.1109/OJVT.2024.3479927","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3479927","url":null,"abstract":"Multi-agent collaboration involves multiple participants working together in a shared environment to achieve a common goal. These agents share information, divide tasks, and synchronize their actions. Key aspects of multi-agent collaboration include coordination, communication, task allocation, cooperation, adaptation, and decentralization. On construction sites, surface grading is the process of leveling sand piles to increase a specific area's height. There, a bulldozer grades while a dumper allocates sand piles. Our work aims to utilize a multi-agent approach to enable these vehicles to collaborate effectively. To this end, we propose a decentralized and asymmetric multi-agent learning approach for construction sites (DAMALCS). We formulate DAMALCS to reduce expected collisions for operating vehicles. Therefore, we develop two heuristic experts capable of achieving their joint goal optimally, by applying an innovative prioritization method. In this approach, the bulldozer's movements take precedence over the dumper's operations. This enables the dozer to clear the path for the dumper and ensure continuous operation of both vehicles. As heuristics alone are insufficient in real-world scenarios, we utilize them to train AI agents, which proves to be highly effective. We simultaneously train dozer and dumper agents to operate within the same environment, aiming to avoid collisions and optimizing performance in terms of time efficiency and sand volume handling. Our trained agents and heuristics are evaluated in both simulation and real-world lab experiments, testing them under various conditions such as visual noise and localization errors. The results demonstrate that our approach significantly reduces collision rates for these vehicles.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10715664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1109/OJVT.2024.3477937
Emmanouel T. Michailidis;Konstantinos Maliatsos;Demosthenes Vouyioukas
During the last few years, Unmanned Aerial Vehicles (UAVs) have increasingly become primary components of various critical civilian and military applications. As technology rapidly evolves, particularly in the realm of Software-Defined Radio (SDR) and Field-Programmable Gate Arrays (FPGAs), advanced communication protocols and signal processing methods are expected to emerge within UAV-based systems. Crucially, UAVs are expected to capitalize on SDR to enhance communication, sensing, data processing, and defense mechanisms. With this perspective in mind, this paper provides a comprehensive up-to-date review of the integration of SDR technology in UAV-based systems, encompassing the latest techniques, methodologies, and challenges. Specifically, this paper examines case studies and real-world implementations of SDR-assisted UAV-based systems across various domains, including communication, security, detection, classification, and localization, elucidating their efficacy, constraints, and areas for potential improvement. Through this review, valuable insights are offered to researchers, engineers, and practitioners interested in harnessing the synergies between SDR and UAV technologies to address the evolving requirements of contemporary applications and pave the path for future innovations in the field.
{"title":"Software-Defined Radio Deployments in UAV-Driven Applications: A Comprehensive Review","authors":"Emmanouel T. Michailidis;Konstantinos Maliatsos;Demosthenes Vouyioukas","doi":"10.1109/OJVT.2024.3477937","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3477937","url":null,"abstract":"During the last few years, Unmanned Aerial Vehicles (UAVs) have increasingly become primary components of various critical civilian and military applications. As technology rapidly evolves, particularly in the realm of Software-Defined Radio (SDR) and Field-Programmable Gate Arrays (FPGAs), advanced communication protocols and signal processing methods are expected to emerge within UAV-based systems. Crucially, UAVs are expected to capitalize on SDR to enhance communication, sensing, data processing, and defense mechanisms. With this perspective in mind, this paper provides a comprehensive up-to-date review of the integration of SDR technology in UAV-based systems, encompassing the latest techniques, methodologies, and challenges. Specifically, this paper examines case studies and real-world implementations of SDR-assisted UAV-based systems across various domains, including communication, security, detection, classification, and localization, elucidating their efficacy, constraints, and areas for potential improvement. Through this review, valuable insights are offered to researchers, engineers, and practitioners interested in harnessing the synergies between SDR and UAV technologies to address the evolving requirements of contemporary applications and pave the path for future innovations in the field.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/OJVT.2024.3474426
I. Safak Bayram;Xiang Shi
In light of governmental policies phasing out petrol/diesel car sales, the vehicle retail sector is transforming to focus solely on electric vehicles (EVs). Given their available physical space and access to a high volume of EVs, future vehicle retailers are ideally positioned to operate as bidirectional charging hubs. This paper explores the challenges and opportunities this presents for EV retailers. Current EV battery technology is examined, including degradation mechanisms associated with grid-to-vehicle and vehicle-to-everything applications. Next, bidirectional chargers and relevant industry protocols are analyzed in detail. The U.K. energy market's ancillary services are also investigated, with a focus on the specific performance requirements of different market types. Leveraging publicly available datasets from six mainstream EV models, the suitability of various EV fleets for each market is assessed. Finally, recent V2G projects are analyzed, and the broader societal implications of bidirectional charging hubs are discussed.
{"title":"Bidirectional Charging Hubs in the Electric Vehicle Retail Landscape: Opportunities and Challenges for the U.K. Case","authors":"I. Safak Bayram;Xiang Shi","doi":"10.1109/OJVT.2024.3474426","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3474426","url":null,"abstract":"In light of governmental policies phasing out petrol/diesel car sales, the vehicle retail sector is transforming to focus solely on electric vehicles (EVs). Given their available physical space and access to a high volume of EVs, future vehicle retailers are ideally positioned to operate as bidirectional charging hubs. This paper explores the challenges and opportunities this presents for EV retailers. Current EV battery technology is examined, including degradation mechanisms associated with grid-to-vehicle and vehicle-to-everything applications. Next, bidirectional chargers and relevant industry protocols are analyzed in detail. The U.K. energy market's ancillary services are also investigated, with a focus on the specific performance requirements of different market types. Leveraging publicly available datasets from six mainstream EV models, the suitability of various EV fleets for each market is assessed. Finally, recent V2G projects are analyzed, and the broader societal implications of bidirectional charging hubs are discussed.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a preamble arbitration rule and interference suppression (PARIS) method for ultra-wideband (UWB) in-vehicle networks. Advancements in the automotive technology have led to increased reliance on wire harnesses, resulting in higher costs, electronic integration challenges, and adverse environmental effects. To address these problems, we explored the use of UWB wireless networks, which are characterized by low transmission power and superior signal penetration capabilities. A significant challenge associated with implementing UWB in automotive environments is the increased frame error rate (FER) caused by UWB interference. Our experiments indicate that vehicles equipped with identical UWB networks exhibit an FER of approximately 6% when positioned closely. This level of FER is problematic for automotive applications, where reliable communication is paramount. To mitigate this problem, we developed an PARIS communication algorithm that is robust against interference. As identified in this study, PARIS leverages two key characteristics of UWB. First, it prioritizes the timing of signal reception over radio signal power, enhancing interference suppression by activating the receiver at the optimal moment before the desired frame arrives, thereby minimizing data loss. Second, the algorithm exploits the hierarchical nature of preamble codes in simultaneously received frames, reducing data loss rate to the order of $10^{-5}$ by prioritizing frames from critical communication devices based on the preamble code hierarchy. Implementing the UWB-based PARIS method in wireless vehicle networks can reduce the weight of the wire harnesses by approximately 20%, offering a promising solution to the challenges posed by traditional wiring systems.
本文介绍了一种用于超宽带（UWB）车载网络的前导码仲裁规则和干扰抑制（PARIS）方法。汽车技术的进步使人们越来越依赖线束，导致成本上升、电子集成难题和不利的环境影响。为了解决这些问题，我们探索使用 UWB 无线网络，其特点是传输功率低、信号穿透能力强。在汽车环境中实施 UWB 的一个重大挑战是 UWB 干扰导致的帧误码率（FER）增加。我们的实验表明，配备相同 UWB 网络的车辆在紧密定位时，FER 约为 6%。对于通信可靠性至关重要的汽车应用来说，这种水平的 FER 是个问题。为了缓解这一问题，我们开发了一种具有抗干扰能力的 PARIS 通信算法。正如本研究中所确定的，PARIS 利用了 UWB 的两个关键特性。首先，它优先考虑信号接收时间而不是无线电信号功率，通过在所需帧到达前的最佳时机启动接收器来增强干扰抑制能力，从而最大限度地减少数据丢失。其次，该算法利用了同时接收的帧中前导码的层次性，通过根据前导码层次对来自关键通信设备的帧进行优先级排序，将数据丢失率降低到 10^{-5}$ 的数量级。在无线车载网络中实施基于 UWB 的 PARIS 方法，可将线束重量减轻约 20%，为应对传统布线系统带来的挑战提供了一个前景广阔的解决方案。
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Pub Date : 2024-09-30DOI: 10.1109/OJVT.2024.3471252
Xianhao Lu;Longjun Wang
With the increasing integration of renewable energy into power grids, ensuring the stability and reliability of power grids has become crucial. The intermittency of renewable energy poses a challenge for the frequency and voltage control of power grids. As an adjustable flexible load, electric vehicles (EVs) have emerged as an important solution for grid frequency and voltage control. A joint control and optimization strategy for electric vehicle aggregators (EVAs) to participate in grid frequency and voltage regulation based on a cloud-edge collaborative hierarchical scheduling architecture is proposed, and a multi-timescale EV charging pile cluster (EVC) scheduling model is established with the goal of maximizing the EVA profit. The strategy and model are grounded in the ancillary service market process. The EVA forecasts and optimizes to declare the active and reactive power capacities of the EVC to the market before the day and hour and controls the EVC to respond quickly and accurately to the frequency and voltage regulation instructions in the real-time stage. The methods of rolling optimization, model predictive control, evaluation of the feasible energy region and real-time capacity correction are adopted to coordinate the active and reactive power of EVC. The feasibility and effectiveness of the strategy are verified by an example, which provides an important reference for EVAs participating in power grid interactions.
随着可再生能源越来越多地并入电网，确保电网的稳定性和可靠性变得至关重要。可再生能源的间歇性给电网的频率和电压控制带来了挑战。作为一种可调节的灵活负载，电动汽车（EV）已成为电网频率和电压控制的重要解决方案。本文提出了一种基于云边协作分层调度架构的电动汽车聚合器（EVA）参与电网频率和电压调节的联合控制和优化策略，并以电动汽车聚合器利润最大化为目标，建立了一个多时间尺度的电动汽车充电桩集群（EVC）调度模型。该策略和模型以辅助服务市场流程为基础。EVA 通过预测和优化，在日、小时前向市场申报 EVC 的有功和无功功率容量，并在实时阶段控制 EVC 快速、准确地响应频率和电压调节指令。采用滚动优化、模型预测控制、可行能量区域评估和实时容量修正等方法来协调 EVC 的有功和无功功率。通过实例验证了该策略的可行性和有效性，为参与电网互动的 EVA 提供了重要参考。
{"title":"Cloud-Edge Collaboration Control Strategy for Electric Vehicle Aggregators Participating in Frequency and Voltage Regulation","authors":"Xianhao Lu;Longjun Wang","doi":"10.1109/OJVT.2024.3471252","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3471252","url":null,"abstract":"With the increasing integration of renewable energy into power grids, ensuring the stability and reliability of power grids has become crucial. The intermittency of renewable energy poses a challenge for the frequency and voltage control of power grids. As an adjustable flexible load, electric vehicles (EVs) have emerged as an important solution for grid frequency and voltage control. A joint control and optimization strategy for electric vehicle aggregators (EVAs) to participate in grid frequency and voltage regulation based on a cloud-edge collaborative hierarchical scheduling architecture is proposed, and a multi-timescale EV charging pile cluster (EVC) scheduling model is established with the goal of maximizing the EVA profit. The strategy and model are grounded in the ancillary service market process. The EVA forecasts and optimizes to declare the active and reactive power capacities of the EVC to the market before the day and hour and controls the EVC to respond quickly and accurately to the frequency and voltage regulation instructions in the real-time stage. The methods of rolling optimization, model predictive control, evaluation of the feasible energy region and real-time capacity correction are adopted to coordinate the active and reactive power of EVC. The feasibility and effectiveness of the strategy are verified by an example, which provides an important reference for EVAs participating in power grid interactions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.
{"title":"A Physics-Informed Cold-Start Capability for xEV Charging Recommender System","authors":"Raik Orbay;Aditya Pratap Singh;Johannes Emilsson;Michele Becciani;Evelina Wikner;Victor Gustafson;Torbjörn Thiringer","doi":"10.1109/OJVT.2024.3469577","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3469577","url":null,"abstract":"An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10697286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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