Green Energy and Intelligent Transportation最新文献

{"title":"Intelligent traffic signal controller for heterogeneous traffic using reinforcement learning","authors":"Savithramma R M,&nbsp;R. Sumathi","doi":"10.1016/j.geits.2023.100124","DOIUrl":"10.1016/j.geits.2023.100124","url":null,"abstract":"<div><p>A traffic signal controller is an essential part of a signalized intersection to alleviate congestion and pollution by ensuring safety. However, the available research solutions are focused on homogeneous traffic scenarios, whereas heterogeneous traffic is the reality in most countries. Hence, a traffic signal control scheme suitable for heterogeneous traffic conditions is proposed in the current study using Reinforcement Learning. A novel reward function with an objective to reduce the traffic residual is defined and a combination of exploration and exploitation optimal policy is applied which made the system learn quickly. The proposed scheme can choose the appropriate phase sequence with optimal signal lengths based on traffic demand on each approaching road. The simulation results proved that the proposed model is well-suited for heterogeneous traffic conditions and its performance against the actuated traffic signal controller is significant in reducing the green time wastage and mean waiting time at the intersection.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 6","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153723000609/pdfft?md5=9266d8308d9cea5c3a8166cdd672a434&pid=1-s2.0-S2773153723000609-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91154996","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}

{"title":"Autonomous eVTOL: A summary of researches and challenges","authors":"Senwei Xiang ,&nbsp;Anhuan Xie ,&nbsp;Minxiang Ye ,&nbsp;Xufei Yan ,&nbsp;Xiaojia Han ,&nbsp;Hongjiao Niu ,&nbsp;Qiang Li ,&nbsp;Haishan Huang","doi":"10.1016/j.geits.2023.100140","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100140","url":null,"abstract":"<div><p>Due to the rising concept of advanced air mobility (AAM), electric vertical take-off and landing (eVTOL) aircraft has become the hotspot for academic research and commercial application. This paper provides a comprehensive review of latest researches related to autonomous eVTOL. It examines key technologies involved in autonomous eVTOL, including automated flight control, sensing &amp; perception, safety &amp; reliability, and decision making. It also addresses the technical, regulatory, and societal challenges associated with the wholesale adoption of autonomous eVTOL into AAM. The paper concludes with a discussion of future trends and recommendations, including the importance of integration with air traffic management, urban infrastructure and human–machine interaction. It aims to be a useful resource for those involved in the research, policy, and industry of autonomous eVTOL technology.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 1","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153723000762/pdfft?md5=7430a64a385a1170d5f1bba7c6d0835f&pid=1-s2.0-S2773153723000762-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139487118","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}

{"title":"Barriers and drivers for sustainable public transportation in Indian context","authors":"Gopi R,&nbsp;Dev Vrat Pathak,&nbsp;Saurabh Pratap,&nbsp;Lakshay","doi":"10.1016/j.geits.2023.100141","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100141","url":null,"abstract":"<div><p>Rising negative externalities, including greenhouse gas emissions, climate change, and environmental pollution, shows the need for sustainable and environmentally friendly modes of transportation. Adopting zero-emission, environmentally friendly electric buses in public transportation systems can be an effective solution for both developing and developed countries, including India. While the Indian government is making numerous efforts to promote electric buses in commercial and public transportation systems, it faces several formidable obstacles. This research objective is to analyze and evaluate the primary factors influencing the adoption and usage of electric buses in the Indian public transportation system, within the limited available resources. A survey questionnaire is prepared with several perceptual subjects for the key perceived barriers. An empirical analysis using Structural Equation Modeling (SEM) is then performed to identify the critical barriers. The result of this study demonstrates that the infrastructural barriers substantially impact the adoption and utilization of electric buses. Further, the study provides critical insights and managerial implications for decision-makers.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 1","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153723000774/pdfft?md5=45f237525a4eadcd10f8e6a26a0759e1&pid=1-s2.0-S2773153723000774-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139494179","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}

{"title":"Cooperative THz communication for UAVs in 6G and beyond","authors":"Nazifa Mustari ,&nbsp;Muhammet Ali Karabulut ,&nbsp;A.F.M. Shahen Shah ,&nbsp;Ufuk Tureli","doi":"10.1016/j.geits.2023.100131","DOIUrl":"10.1016/j.geits.2023.100131","url":null,"abstract":"<div><p>One of the key components of any smart city is considered to be its intelligent transportation systems (ITSs). Unmanned aerial vehicles (UAVs) are envisioned in several ITS application fields because of their autonomous operation, mobility, communication/processing capabilities, and other factors. In this paper, cooperative terahertz (THz) communication is proposed for flying ad hoc networks (FANETs), which is a particular kind of network made up of a collection of small UAVs linked in an ad hoc fashion and working together to accomplish high-level objectives. The frequency spectrum for wireless communication has been expanding continuously in order to meet the demand for bandwidth. For the forthcoming 6G and beyond, communications in the THz range will be vital, similar to how mmWave-band communications are currently influencing the 5G of wireless mobile communications. The finite state machine (FSM) of the proposed cooperative communication system for THz band is presented. A Markov chain model-based analytical study is carried out, which derives relationships among parameters. Furthermore, numerical results are provided to support the analytical study.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 1","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153723000671/pdfft?md5=03f9814b98508e50c5c56b775dcc2ed9&pid=1-s2.0-S2773153723000671-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136009408","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}

{"title":"GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks","authors":"Pratik Thantharate,&nbsp;Anurag Thantharate,&nbsp;Atul Kulkarni","doi":"10.1016/j.geits.2023.100130","DOIUrl":"10.1016/j.geits.2023.100130","url":null,"abstract":"<div><p>Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking traditional infrastructure. However, UAVs’ limited onboard energy storage necessitates optimized, energy-efficient communication strategies and intelligent energy expenditure to maximize productivity. This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations. The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure. By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints, the mixed integer linear programming approach reduces energy usage by 9.1 ​% versus conventional greedy heuristics. The key results provide insights into separating charging strategies based on UAV mobility patterns, fully utilizing all available infrastructure through balanced distribution, and strategically leveraging existing base stations before deploying dedicated charging assets. Compared to myopic localized decisions, the globally optimized solution extends battery life and enhances productivity. Overall, this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets. The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 1","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277315372300066X/pdfft?md5=056ba1e19a7d0f6f30c190c9be6fe427&pid=1-s2.0-S277315372300066X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135762521","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}

{"title":"Extracting multi-objective multigraph features for the shortest path cost prediction: Statistics-based or learning-based?","authors":"Songwei Liu,&nbsp;Xinwei Wang,&nbsp;Michal Weiszer,&nbsp;Jun Chen","doi":"10.1016/j.geits.2023.100129","DOIUrl":"10.1016/j.geits.2023.100129","url":null,"abstract":"<div><p>Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation; (ii) A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns; and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 1","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153723000658/pdfft?md5=95a680849ef5e061e0984408fd673bbb&pid=1-s2.0-S2773153723000658-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134977812","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}

{"title":"Battery SOH estimation method based on gradual decreasing current, double correlation analysis and GRU","authors":"Chaolong Zhang ,&nbsp;Laijin Luo ,&nbsp;Zhong Yang ,&nbsp;Shaishai Zhao ,&nbsp;Yigang He ,&nbsp;Xiao Wang ,&nbsp;Hongxia Wang","doi":"10.1016/j.geits.2023.100108","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100108","url":null,"abstract":"<div><p>In intelligent lithium-ion battery management, the state of health (SOH) of battery is essential for the batteries’ running in electric vehicles. Popularly, the battery SOH is estimated by using suitable features and data-driven methods. However, it is difficult to extract appropriate features characterizing battery SOH from the charging and discharging data of batteries owing to various state of charges (SOCs) and working conditions of batteries. In order to effectively estimate the battery SOH, an estimation method based on gradual decreasing current, double correlation analysis and gated recurrent unit (GRU) is proposed in this paper. Firstly, gradual decreasing current in the constant voltage charging phase is measured as the raw data. Then, the double correlation analysis method is proposed to select combined features characterizing the battery SOH from different categories of features. Meanwhile, the number of input features is also ensured by the method. Finally, the GRU algorithm is employed to set up a SOH estimation model whose learning rate is improved by using a sparrow search algorithm (SSA) for the purpose of capturing the hidden relationship between features and SOH. The adaptability of the proposed method is validated by SOH estimation experiments of a single battery and a battery pack. Additionally, contrast experiments are performed to show the advanced estimation performance of the proposed method.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 5","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49716924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and quantitative diagnosis of micro-short-circuit faults in lithium-ion battery packs considering cell inconsistency","authors":"Dongxu Shen ,&nbsp;Dazhi Yang ,&nbsp;Chao Lyu ,&nbsp;Gareth Hinds ,&nbsp;Lixin Wang ,&nbsp;Miao Bai","doi":"10.1016/j.geits.2023.100109","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100109","url":null,"abstract":"<div><p>Micro short circuit (MSC) fault diagnosis is thought functional in preventing thermal runaway of lithium-ion battery packs. Inconsistencies in the initial state-of-charge and aging state inevitably exist among cells of a battery pack. The existing method for MSC diagnosis disregards the symptoms originating from cell-to-cell inconsistency, which may lead to misdiagnosing inconsistent cells as MSC cells and vice versa. This work presents a method for detecting and quantitatively diagnosing MSC faults in lithium-ion battery packs, while taking cell inconsistency into consideration. Initially, the median incremental capacity (IC), derived based on ranking the terminal voltages of cells, is used as a benchmark representing the state of normal cells. Subsequently, the correlation coefficients between the ICs of individual cells and their median IC are calculated in both the time and frequency domains, as to distinguish the normal, inconsistent, and MSC cells. After detecting the MSC cell, an algorithm, which is based on a recursive least squares algorithm with forgetting factor and an adaptive H_<em>∞</em> Kalman filtering, is designed to calculate the short-circuit resistance online. The experimental results demonstrate that the short-circuit resistance estimated by the proposed algorithm exhibits rapid convergence to the actual values, thereby confirming the utility of the proposed algorithm in real-life contexts.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 5","pages":"Article 100109"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49716927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances of ionic liquids in zinc ion batteries: A bibliometric analysis","authors":"Chang Su ,&nbsp;Xuan Gao ,&nbsp;Kejiang Liu ,&nbsp;Alexender He ,&nbsp;Hongzhen He ,&nbsp;Jiayan Zhu ,&nbsp;Yiyang Liu ,&nbsp;Zhiyuan Chen ,&nbsp;Yifan Zhao ,&nbsp;Wei Zong ,&nbsp;Yuhang Dai ,&nbsp;Jie Lin ,&nbsp;Haobo Dong","doi":"10.1016/j.geits.2023.100126","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100126","url":null,"abstract":"<div><p>Due to their potential for high energy density, low cost, and environmental sustainability, zinc-ion batteries (ZIBs) have emerged as a promising energy storage technology. The performance, safety, and overall efficiency of ZIBs are significantly impacted by the properties of the electrolyte, such as ionic conductivity, electrochemical stability window, viscosity, and compatibility with other battery components. The use of ionic liquids (ILs) in ZIBs has gained extensive attention in recent years due to their desirable properties, such as high thermal stability, low volatility, wide electrochemical window, and tunable physicochemical properties. Therefore, this paper provides a bibliometric analysis of recent advances in the use of ILs as electrolytes in ZIBs. Current research trends, authorship patterns, and publications of ILs in ZIBs are analyzed. Our review reveals a growing interest in the use of ILs as electrolytes in ZIBs, and the development of novel ILs with tailored properties to meet the specific requirements of ZIBs is of a specific focus. This paper provides insights into the recent advancements and future research directions in the field of ILs as electrolytes for ZIBs.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 5","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49735179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep transfer learning for intelligent vehicle perception: A survey","authors":"Xinyu Liu ,&nbsp;Jinlong Li ,&nbsp;Jin Ma ,&nbsp;Huiming Sun ,&nbsp;Zhigang Xu ,&nbsp;Tianyun Zhang ,&nbsp;Hongkai Yu","doi":"10.1016/j.geits.2023.100125","DOIUrl":"https://doi.org/10.1016/j.geits.2023.100125","url":null,"abstract":"<div><p>Deep learning-based intelligent vehicle perception has been developing prominently in recent years to provide a reliable source for motion planning and decision making in autonomous driving. A large number of powerful deep learning-based methods can achieve excellent performance in solving various perception problems of autonomous driving. However, these deep learning methods still have several limitations, for example, the assumption that lab-training (source domain) and real-testing (target domain) data follow the same feature distribution may not be practical in the real world. There is often a dramatic domain gap between them in many real-world cases. As a solution to this challenge, deep transfer learning can handle situations excellently by transferring the knowledge from one domain to another. Deep transfer learning aims to improve task performance in a new domain by leveraging the knowledge of similar tasks learned in another domain before. Nevertheless, there are currently no survey papers on the topic of deep transfer learning for intelligent vehicle perception. To the best of our knowledge, this paper represents the first comprehensive survey on the topic of the deep transfer learning for intelligent vehicle perception. This paper discusses the domain gaps related to the differences of sensor, data, and model for the intelligent vehicle perception. The recent applications, challenges, future researches in intelligent vehicle perception are also explored.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"2 5","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49717166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}