Green Energy and Intelligent Transportation最新文献

{"title":"Autonomous-rail rapid transit tram: System architecture, design and applications","authors":"Jianghua Feng ,&nbsp;Yunqing Hu ,&nbsp;Xiwen Yuan ,&nbsp;Ruipeng Huang ,&nbsp;Lei Xiao ,&nbsp;Chenlin Zhang","doi":"10.1016/j.geits.2024.100161","DOIUrl":"10.1016/j.geits.2024.100161","url":null,"abstract":"<div><div>Autonomous-rail Rapid Transit (ART) tram is a new type of multiple-articulated rubber-tire transit that utilizes intelligent perception, path tracking, and trajectory following control technologies to eliminate reliance on physical railway tracks. The adoption of power batteries, hydrogen energy, wheel-edge motor drive, and other technologies has comprehensively realized the dual advantages of large-capacity rail transportation, which is punctual, high volume, energy-saving, and environmentally friendly, as well as the flexibility and low comprehensive cost of traditional bus operations. This has created a brand-new urban rail transit model. This article first introduces the ART tram systems architecture, operating principles, applicable scenarios. Secondly, it introduces the core subsystems of ART tram vehicle structure, electrical system, and energy storage system. Thirdly, it focuses on analyzing the structure composition and control principles of the Automatic All-Wheel Steering System, which includes two key core subsystems: path tracking control subsystems and trajectory following control subsystems. Then, a horizontal comparison is made between the performance advantages and disadvantages of ART and other transportation systems, and the application status of ART tram is summarized. Finally, some common issues related to the development of ART tram are discussed, and a development plan for future ART systems is proposed to better integrate ART tram into urban transportation and meet people's demands for intelligent, comfortable, fast, and environmentally friendly urban public transportation.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 6","pages":"Article 100161"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139637819","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":"Remote condition monitoring of rail tracks using distributed acoustic sensing (DAS): A deep CNN-LSTM-SW based model","authors":"","doi":"10.1016/j.geits.2024.100178","DOIUrl":"10.1016/j.geits.2024.100178","url":null,"abstract":"<div><p>Railroad condition monitoring is paramount due to frequent passage through densely populated regions. This significance arises from the potential consequences of accidents such as train derailments, hazardous materials leaks, or collisions which may have far-reaching impacts on communities and the surrounding areas. As a solution to this issue, the use of distributed acoustic sensing (DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures. Nevertheless, analyzing DAS data to assess railroad health or detect potential damage is a challenging task. Due to the large amount of data generated by DAS, as well as the unstructured patterns and substantial noise present, traditional analysis methods are ineffective in interpreting this data. This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs, augmented by sliding window techniques (CNN-LSTM-SW), to advance the state-of-the-art in the railroad condition monitoring system. As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks. Extracting insights from the data of High tonnage load (HTL)- a 4.16 ​km fiber optic and DAS setup, we were able to distinguish train position, normal condition, and abnormal conditions along the railroad. Notably, our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup. Moreover, in terms of pinpointing the train's position, the CNN-LSTM architecture showcased an impressive 97% detection rate. Applying a sliding window, the CNN-LSTM labeled data, the remaining 3% of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition. Altogether, these proposed models exhibit promising potential for accurately identifying various railroad conditions, including anomalies and discrepancies that warrant thorough exploration.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100178"},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000306/pdfft?md5=a79391c83814fdcc707b2d1095025afd&pid=1-s2.0-S2773153724000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139636740","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":"Co-optimization of energy management and eco-driving considering fuel cell degradation via improved hierarchical model predictive control","authors":"Caixia Liu ,&nbsp;Yong Chen ,&nbsp;Renzong Xu ,&nbsp;Haijun Ruan ,&nbsp;Cong Wang ,&nbsp;Xiaoyu Li","doi":"10.1016/j.geits.2024.100176","DOIUrl":"10.1016/j.geits.2024.100176","url":null,"abstract":"<div><div>An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel consumption. However, most research on eco-driving focuses on the stability and safety for vehicle operating while disregarding its comfort and economy. To meet the requirements for safety and comfort, at the same time, enhance the economic performance of the vehicles, an improved hierarchical model predictive control cooperative optimization strategy is proposed for fuel cell hybrid electric vehicle with car-following scenario. Specifically, the upper-level model predictive controller controls the velocity, inter-vehicle distance and acceleration to guarantee safety and comfort for driving. According to the velocity information obtained from the upper model predictive controller, the lower-level improved model predictive controller considers the impact of disturbance changes on vehicle economy and aims to minimize the vehicle operating cost considering fuel cell degradation, so as to allocate energy rationally. Finally, the enhancement of economic performance of proposed strategy is verified with the results of comparative study that 3.09 ​% economic improvement on the premise of assuring safety and comfort of driving.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 6","pages":"Article 100176"},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139537044","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":"State of charge estimation for electric vehicles using random forest","authors":"","doi":"10.1016/j.geits.2024.100177","DOIUrl":"10.1016/j.geits.2024.100177","url":null,"abstract":"<div><p>This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precise SOC estimation methods to improve range prediction accuracy and battery management. This study applies a Random Forest (RF) machine learning algorithm to improve SOC estimation. Traditionally, SOC estimation has posed a formidable challenge, particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions. Previous methods, including the Extreme Learning Machine (ELM), have exhibited limitations in providing the accuracy and robustness required for practical EV applications. In contrast, this research introduces the RF model, for SOC estimation approach that excels in real-world scenarios. By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions. Comprehensive comparative analyses showcase the superiority of the RF over ELM. The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability, addressing the pressing needs of the EV industry. The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3. This integration holds the key to more efficient and dependable electric vehicle operations, marking a significant milestone in the ongoing evolution of EV technology. Importantly, the RF model demonstrates a lower Root Mean Squared Error (RMSE) of 5.902,8% compared to 6.312,7% for ELM, and a lower Mean Absolute Error (MAE) of 4.432,1% versus 5.111,2% for ELM across rigorous k-fold cross-validation testing, reaffirming its superiority in quantitative SOC estimation.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277315372400029X/pdfft?md5=14bd47fe21759638310a9a74eebfe9f8&pid=1-s2.0-S277315372400029X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139633379","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":"Potential and challenges of capacitive power transfer systems for wireless EV charging: A review of key technologies","authors":"Wei Zhou ,&nbsp;Mengmeng Li ,&nbsp;Qiang Zhang ,&nbsp;Zhiqiang Li ,&nbsp;Shiyun Xie ,&nbsp;Yuanshuang Fan","doi":"10.1016/j.geits.2024.100174","DOIUrl":"10.1016/j.geits.2024.100174","url":null,"abstract":"","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 6","pages":"Article 100174"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139632768","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":"Critical evaluation of transit policies in Lima, Peru; resilience of rail rapid transit (Metro) in a developing country","authors":"","doi":"10.1016/j.geits.2024.100172","DOIUrl":"10.1016/j.geits.2024.100172","url":null,"abstract":"<div><p>This paper evaluates rail transit within the context of the transit policies implemented in Lima, Peru. First it reviews the implementation of rapid transit, and bus reform. Secondly, it evaluates the outcomes of such policies by using Total Factor Productivity for policy effectiveness, Data Envelopment Analysis for rapid transit performance, and Generalized Cost of Travel for improvements. This paper finds that implementation failed in enforcing key requirements for rail transit regarding penetration of CBD and short transfers to bus transit; and that the basic assumptions of bus reform did not hold regarding bus oversupply, bus congestion or bus pollution. This paper also finds that outcomes of policies failed dramatically in achieving the planning goals; however, rail transit (Metro) shows high level of resilience in serving large ridership at high speed. On the other hand, bus reform was associated with a disproportionate increase of motorization, well over the effect of income growth or car attractiveness, and more related to the excessive reduction of bus transit capacity ill-advised from unproved bus reform assumptions. This paper recommends expanding rail rapid transit due to its intensive use of green renewable energy and its potential of demand growth if combined with modern Intelligent Transportation services, but this opportunity can be wasted without the proposed policy constraint to achieve lower Generalized Cost of Travel at any governmental intervention for bus reform, instead of just reducing bus transit capacity as implemented. Finally, this paper recommends government to government contracts to build rail transit and to enforce proper planning.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100172"},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000240/pdfft?md5=03b70c9cd281200c3af5c8159ae47506&pid=1-s2.0-S2773153724000240-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634265","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":"Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving","authors":"","doi":"10.1016/j.geits.2024.100162","DOIUrl":"10.1016/j.geits.2024.100162","url":null,"abstract":"<div><p>Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000148/pdfft?md5=2ffa7b0f6b9565e20dbc0b08fa505cc3&pid=1-s2.0-S2773153724000148-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139539646","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":"A demand forecasting model for urban air mobility in Chengdu, China","authors":"Wenqiu Qu ,&nbsp;Jie Huang ,&nbsp;Chenglong Li ,&nbsp;Xiaohan Liao","doi":"10.1016/j.geits.2024.100173","DOIUrl":"10.1016/j.geits.2024.100173","url":null,"abstract":"<div><p>The successful application of new technologies such as remotely piloted aircraft systems, distributed electric propulsion systems, and automatic control systems on electric vertical take-off and landing(eVTOL) aircraft has prompted Urban Air Mobility (UAM) to be mentioned frequently. UAM is a newly raised transport mode of using eVTOL aircraft to transport people and cargo in urban areas, which is thought to share some of the traffic on the ground. One of the prerequisites for <span>UAM</span> to operate on a regular basis is that its demand can support the operating costs, so forecasting <span>UAM</span> demand is necessary. We conduct UAM demand forecasting based on the four-step method, focusing on improving the third-step modal split, and propose a demand forecasting model based on the logit model. The model combines a nested logit (NL) model with a multinomial logit (MNL) model to solve the problem of non-existent UAM sharing rates. We use Chengdu, China as an example, and focus on forecasting the UAM traffic demand in 2030 with the help of the four-step method. The results show that UAM is suitable for shared operation during the early stages. With a fully shared operation, the UAM share rate increases by 0.73% for every kilometer increase in distance. Moreover, UAM is more competitive than other modes for delivery distances exceeding 15 ​km. Finally, using the distributions of the share rate and traffic flow pattern from the simulation, we propose the routes that can be prioritized for UAM operations in Chengdu.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 3","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000252/pdfft?md5=2fbc57c5b174ea771587b6af6e9bd4a6&pid=1-s2.0-S2773153724000252-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139639075","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":"A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve","authors":"","doi":"10.1016/j.geits.2024.100169","DOIUrl":"10.1016/j.geits.2024.100169","url":null,"abstract":"<div><p>The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 5","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000215/pdfft?md5=340544f53cc025b057c2325e7c2cc83e&pid=1-s2.0-S2773153724000215-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139538131","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":"Preparation of N-B doped composite electrode for iron-chromium redox flow battery","authors":"Yingchun Niu ,&nbsp;Senwei Zeng ,&nbsp;Guangfu Wu,&nbsp;Qingtan Gao,&nbsp;Ruichen Zhou,&nbsp;Chuanyuan Li,&nbsp;Yang Zhou ,&nbsp;Quan Xu","doi":"10.1016/j.geits.2024.100158","DOIUrl":"10.1016/j.geits.2024.100158","url":null,"abstract":"<div><p>Iron-chromium redox flow battery (ICRFB) is an electrochemical energy storage technology that plays a vital role in dealing with the problems of discontinuity and instability of massive new energy generation and improving the acceptance capacity of the power grid. Carbon cloth electrode (CC) is the main site where the electrochemical reaction occurs, which always suffers from the disadvantages of poor electrochemical reactivity. A new N-B co-doped co-regulation Ti composite CC electrode (T-B-CC) is firstly generated and applied to ICRFB, where the REDOX reaction can be promoted significantly owing to the plentiful active sites generated on the modified electrode. As contrasted with ICRFB with normal CC electrode, after 50 battery charge/discharge cycles, the discharge capacity (1,990.3 ​mAh vs 1,155.8 ​mAh) and electrolyte utilization (61.88% vs 35.94%) of ICRFB with CC electrode (T-B-CC) are significantly improved. Furthermore, the energy efficiency (EE) is maintained at about 82.7% under 50 cycles, which is 9.3% higher than that of the pristine electrically assembled cells. The co-modulation of heteroatom doping and the introduction of Ti catalysts is a simple and easy method to improve the dynamics of the Cr³⁺/Cr²⁺ and Fe³⁺/Fe²⁺ reactions, enhancing the performance of ICRFBs.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 3","pages":"Article 100158"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000100/pdfft?md5=ad5860747cfdfa29c2e887edd1ce970e&pid=1-s2.0-S2773153724000100-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516420","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}