Green Energy and Intelligent Transportation最新文献_第2页

Annotated survey and perspectives on rail transport energy system RAMS evaluation technology 铁路运输能源系统 RAMS 评估技术的附加说明的调查和展望

Green Energy and Intelligent Transportation Pub Date : 2024-06-01 DOI: 10.1016/j.geits.2024.100164

Ruifeng Shi , Lingzhi Zhang , Feng Lin , Jin Ning , Limin Jia , Kwang Y. Lee

{"title":"Annotated survey and perspectives on rail transport energy system RAMS evaluation technology","authors":"Ruifeng Shi , Lingzhi Zhang , Feng Lin , Jin Ning , Limin Jia , Kwang Y. Lee","doi":"10.1016/j.geits.2024.100164","DOIUrl":"10.1016/j.geits.2024.100164","url":null,"abstract":"<div><p>The rail transit system plays a crucial role in modern transportation. With the increasing demand for clean and green energy in the transport sector, its energy system is expected to achieve low-carbon and highly efficient energy utilization in rail transit. However, the gradual development of the rail transport energy system has led to an increase in its complexity, and the rising difficulty of system assessment has faced the limitations of traditional assessment methods. Hence, it is essential to develop effective assessment methods. This paper begins by providing a systematic review of the development status of Reliability, Availability, Maintainability and Safety (RAMS) assessment and analyzing the shortcomings of traditional RAMS assessment technology in the context of rail transit energy systems. Subsequently, based on the four fundamental properties of RAMS, it summarizes the current state of key assessment technologies in the field of rail transit. Moreover, the paper delves into the challenges and potential solutions concerning the implementation of RAMS assessment technology for rail transit energy systems. Finally, the paper offers an outlook on the future development of RAMS assessment for rail transport energy systems. By comprehensively analyzing these aspects, the paper aims to contribute valuable insights into optimizing the rail transit energy system, promoting its sustainable and efficient operation in the context of clean and green energy utilization.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000161/pdfft?md5=ff189dbab44ff188118f4e3b0aae26db&pid=1-s2.0-S2773153724000161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139454852","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}

引用次数: 0

Fault detection of new and aged lithium-ion battery cells in electric vehicles 电动汽车中新老锂离子电池芯的故障检测

Green Energy and Intelligent Transportation Pub Date : 2024-06-01 DOI: 10.1016/j.geits.2024.100165

Sara Sepasiahooyi , Farzaneh Abdollahi

{"title":"Fault detection of new and aged lithium-ion battery cells in electric vehicles","authors":"Sara Sepasiahooyi , Farzaneh Abdollahi","doi":"10.1016/j.geits.2024.100165","DOIUrl":"10.1016/j.geits.2024.100165","url":null,"abstract":"<div><p>In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging. Battery aging primarily affects capacity and resistance, becoming more pronounced in the later stages of a battery lifespan. By incorporating aging effects into our fault diagnosis scheme, our proposed approach prevents false or missed alarms for the aged battery cells. The aging effect of battery, capacity fading and resistance growth, are considered unknown parameters. An adaptive observer is employed to design a fault detector, considering unknown parameters in the battery model. The adaptive observers are designed for two different scenarios: In the first scenario, it is presumed that aging effects remain constant over time due to their slow rate of change. Then, it is assumed that aging effects are time-varying. Therefore, the fault detection scheme can detect faults of new battery cells as well as aged cells. Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack, to demonstrate the performance of the proposed approach in more real-world scenarios. The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000173/pdfft?md5=2457a5d15917c4653e8c47b7a2af194b&pid=1-s2.0-S2773153724000173-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139539634","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}

引用次数: 0

Receding Horizon Based Collision Avoidance for UAM Aircraft at Intersections 基于后退地平线的 UAM 飞机交叉路口防撞系统

Green Energy and Intelligent Transportation Pub Date : 2024-05-01 DOI: 10.1016/j.geits.2024.100205

Negasa Yahi, Jose Matute, Ali Karimoddini

引用次数: 0

Overall eVTOL aircraft design for urban air mobility 用于先进空中机动性的 eVTOL 飞机总体设计

Green Energy and Intelligent Transportation Pub Date : 2024-04-01 DOI: 10.1016/j.geits.2024.100150

Jiechao Zhang, Yaolong Liu, Yao Zheng

{"title":"Overall eVTOL aircraft design for urban air mobility","authors":"Jiechao Zhang, Yaolong Liu, Yao Zheng","doi":"10.1016/j.geits.2024.100150","DOIUrl":"10.1016/j.geits.2024.100150","url":null,"abstract":"<div><p>Electric vertical takeoff and landing (eVTOL) aircraft have emerged as a potential alternative to the existing transportation system, offering a transition from two-dimensional commuting and logistics to three-dimensional mobility. As a groundbreaking innovation in both the automotive and aviation sectors, eVTOL holds significant promise but also presents notable challenges. This paper aims to address the overall aircraft design (OAD) approach specifically tailored for eVTOL in the context of Urban Air Mobility (UAM). In contrast to traditional OAD methods, this study introduces and integrates disciplinary methodologies specifically catered to eVTOL aircraft design. A case study is conducted on a tilt-duct eVTOL aircraft with a typical UAM mission, and the disciplinary performance, including initial sizing, aerodynamics, electric propulsion systems, stability and control, weight, mission analysis and noise, is examined using the OAD methodologies. The findings demonstrate that the current approach effectively evaluates the fundamental aircraft-level performance of eVTOL, albeit further high-fidelity disciplinary analysis and optimization methods are required for future MDO-based eVTOL overall aircraft design.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000021/pdfft?md5=34eda0b9ee4b3c073328ee184fd1864e&pid=1-s2.0-S2773153724000021-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455849","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}

引用次数: 0

Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows 时空受限的 A* 算法作为混合流交叉口无车道交通的支持算法

Green Energy and Intelligent Transportation Pub Date : 2024-04-01 DOI: 10.1016/j.geits.2024.100159

Haifei Chi , Pinlong Cai , Daocheng Fu , Junda Zhai , Yadan Zeng , Botian Shi

{"title":"Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows","authors":"Haifei Chi , Pinlong Cai , Daocheng Fu , Junda Zhai , Yadan Zeng , Botian Shi","doi":"10.1016/j.geits.2024.100159","DOIUrl":"10.1016/j.geits.2024.100159","url":null,"abstract":"<div><p>Improving the capacity of intersections is the key to enhancing road traffic systems. Benefiting from the application of Connected Automated Vehicles (CAVs) in the foreseeing future, it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs. Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries. However, it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles (HVs). To address the research gap, this paper proposes a spatiotemporal-restricted A∗ algorithm to obtain efficient and flexible lane-free trajectories for CAVs. First, we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors. Second, we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm. Third, we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs. The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A∗ algorithm, while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency. The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000112/pdfft?md5=2c8f0d5905097a25e192b5b1df224d57&pid=1-s2.0-S2773153724000112-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456100","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}

引用次数: 0

A reinforcement learning approach to vehicle coordination for structured advanced air mobility 结构化先进空中机动的车辆协调强化学习方法

Green Energy and Intelligent Transportation Pub Date : 2024-04-01 DOI: 10.1016/j.geits.2024.100157

Sabrullah Deniz, Yufei Wu, Yang Shi, Zhenbo Wang

{"title":"A reinforcement learning approach to vehicle coordination for structured advanced air mobility","authors":"Sabrullah Deniz, Yufei Wu, Yang Shi, Zhenbo Wang","doi":"10.1016/j.geits.2024.100157","DOIUrl":"10.1016/j.geits.2024.100157","url":null,"abstract":"<div><p>Advanced Air Mobility (AAM) has emerged as a pioneering concept designed to optimize the efficacy and ecological sustainability of air transportation. Its core objective is to provide highly automated air transportation services for passengers or cargo, operating at low altitudes within urban, suburban, and rural regions. AAM seeks to enhance the efficiency and environmental viability of the aviation sector by revolutionizing the way air travel is conducted. In a complex aviation environment, traffic management and control are essential technologies for safe and effective AAM operations. One of the most difficult obstacles in the envisioned AAM systems is vehicle coordination at merging points and intersections. The escalating demand for air mobility services, particularly within urban areas, poses significant complexities to the execution of such missions. In this study, we propose a novel multi-agent reinforcement learning (MARL) approach to efficiently manage high-density AAM operations in structured airspace. Our approach provides effective guidance to AAM vehicles, ensuring conflict avoidance, mitigating traffic congestion, reducing travel time, and maintaining safe separation. Specifically, intelligent learning-based algorithms are developed to provide speed guidance for each AAM vehicle, ensuring secure merging into air corridors and safe passage through intersections. To validate the effectiveness of our proposed model, we conduct training and evaluation using BlueSky, an open-source air traffic control simulation environment. Through the simulation of thousands of aircraft and the integration of real-world data, our study demonstrates the promising potential of MARL in enabling safe and efficient AAM operations. The simulation results validate the efficacy of our approach and its ability to achieve the desired outcomes.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000094/pdfft?md5=ca43f6257641dbaba04d1fd52c0b85ec&pid=1-s2.0-S2773153724000094-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634766","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}

引用次数: 0

State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine 基于粒子群优化算法和极端学习机的锂离子电池健康状况评估

Green Energy and Intelligent Transportation Pub Date : 2024-02-01 DOI: 10.1016/j.geits.2024.100151

Kui Chen , Jiali Li , Kai Liu , Changshan Bai , Jiamin Zhu , Guoqiang Gao , Guangning Wu , Salah Laghrouche

{"title":"State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine","authors":"Kui Chen , Jiali Li , Kai Liu , Changshan Bai , Jiamin Zhu , Guoqiang Gao , Guangning Wu , Salah Laghrouche","doi":"10.1016/j.geits.2024.100151","DOIUrl":"10.1016/j.geits.2024.100151","url":null,"abstract":"<div><p>Lithium-ion battery State of Health (SOH) estimation is an essential issue in battery management systems. In order to better estimate battery SOH, Extreme Learning Machine (ELM) is used to establish a model to estimate lithium-ion battery SOH. The Swarm Optimization algorithm (PSO) is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy. Firstly, collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve. Use Grey Relation Analysis (GRA) method to analyze the correlation between battery capacity and five characteristic quantities. Then, an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics, and a PSO is introduced to optimize the parameters of the capacity estimation model. The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions. The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation, and the average absolute percentage error is less than 1%.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000033/pdfft?md5=ad2fa31d5c48320930ba2e666cec2038&pid=1-s2.0-S2773153724000033-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455840","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}

引用次数: 0

Metal object detection with high sensitivity and blind-zone free for DD coil-based wireless electric vehicle chargers 为基于 DD 线圈的无线电动汽车充电器提供高灵敏度和无盲区的金属物体检测功能

Green Energy and Intelligent Transportation Pub Date : 2024-02-01 DOI: 10.1016/j.geits.2024.100180

Junren Ye, Zhitao Liu, Shan Lu, Hongye Su

引用次数: 0

Toward Efficient Smart Management: A Review of Modeling and Optimization Approaches in Electric Vehicle-Transportation Network-Grid Integration 实现高效智能管理：电动汽车-交通网络-电网集成中的建模和优化方法综述

Green Energy and Intelligent Transportation Pub Date : 2024-02-01 DOI: 10.1016/j.geits.2024.100181

Mince Li, Yujie Wang, Pei Peng, Zonghai Chen

引用次数: 0

Layered energy equalization structure for series battery pack based on multiple optimal matching 基于多重优化匹配的串联电池组分层能量均衡结构

Green Energy and Intelligent Transportation Pub Date : 2024-02-01 DOI: 10.1016/j.geits.2024.100182

J. Jiao, Hongwei Wang, Feng Gao, S. Coskun, Guang Wang, Jiale Xie, Fei Feng

引用次数: 0