Green Energy and Intelligent Transportation最新文献_第7页

A strong robust state-of-charge estimation method based on the gas-liquid dynamics model 基于气液动力学模型的强稳健充电状态估算方法

Green Energy and Intelligent Transportation Pub Date : 2024-03-07 DOI: 10.1016/j.geits.2024.100193

Biao Chen , Liang Song , Haobin Jiang , Zhiguo Zhao , Jun Zhu , Keqiang Xu

{"title":"A strong robust state-of-charge estimation method based on the gas-liquid dynamics model","authors":"Biao Chen , Liang Song , Haobin Jiang , Zhiguo Zhao , Jun Zhu , Keqiang Xu","doi":"10.1016/j.geits.2024.100193","DOIUrl":"10.1016/j.geits.2024.100193","url":null,"abstract":"<div><div>Model-based strategies for estimating the state-of-charge (SOC) of Li-ion batteries are essential in real-time applications, such as electric vehicles and large-scale energy storage. However, based on existing models, developing estimation methods with strong robustness to initial and cumulative errors, high SOC estimation accuracy, and adaptability to sparse data remains challenging. Herein, the modeling principles of the gas-liquid dynamics model are systematically clarified, and a SOC estimation method based on this model and a dual extended Kalman filter with a watchdog function is proposed. The proposed method is comprehensively compared with general extended Kalman filter and dual extended Kalman filter methods under five working conditions. The results indicate that all three methods based on the gas-liquid dynamics model have good estimation accuracy, with a maximum SOC error of 0.016 under correct initial conditions. But the proposed method has significant advantages in robustness to large initial errors, cumulative errors, and sparse data. This study provides new insights into efficient online SOC estimation.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140278044","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

Dual-level design for cost-effective sizing and power management of hybrid energy storage in photovoltaic systems 光伏系统中混合储能的成本效益大小和功率管理的双级设计

Green Energy and Intelligent Transportation Pub Date : 2024-03-07 DOI: 10.1016/j.geits.2024.100194

Xiangqiang Wu, Zhongting Tang, Daniel-Ioan Stroe, Tamas Kerekes

{"title":"Dual-level design for cost-effective sizing and power management of hybrid energy storage in photovoltaic systems","authors":"Xiangqiang Wu, Zhongting Tang, Daniel-Ioan Stroe, Tamas Kerekes","doi":"10.1016/j.geits.2024.100194","DOIUrl":"10.1016/j.geits.2024.100194","url":null,"abstract":"<div><div>Integration of hybrid energy storage systems (HESS) into photovoltaic (PV) applications has been a hot topic due to their versatility. However, the proper allocation and power management schemes of HESS are challenges under diverse mission profiles. In this paper, a cost-effectiveness-oriented two-level scheme is proposed as a guideline for the PV-HESS system (i.e., PV, Li-ion battery and supercapacitor), to size the system configuration and extend battery lifespan while considering the power ramp-rate constraint. On the first level, a sizing methodology is proposed to balance the self-sufficiency and the energy throughput between the PV system and the grid to achieve the most cost-effectiveness. On the second level, an improved adaptive ramp-rate control strategy is implemented that dynamically distributes the power between the battery and supercapacitor to reduce the battery cycles. The case study presents the whole two-level design process in detail, and verifies the effectiveness of the proposed strategy, where the results show that the battery cycles are reduced by up to 13% over one year without affecting the self-sufficiency of the PV system.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140273440","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}

引用次数: 0

Optimization of circular coils with ferrite boxes for enhanced efficiency in wireless power transfer for electric vehicles 优化带铁氧体盒的圆形线圈，提高电动汽车的无线电力传输效率

Green Energy and Intelligent Transportation Pub Date : 2024-03-07 DOI: 10.1016/j.geits.2024.100195

Soukaina Jaafari , Hamza El Hafdaoui , Khadija Ajabboune , Ahmed Khallaayoun , Esmail Ahouzi

{"title":"Optimization of circular coils with ferrite boxes for enhanced efficiency in wireless power transfer for electric vehicles","authors":"Soukaina Jaafari , Hamza El Hafdaoui , Khadija Ajabboune , Ahmed Khallaayoun , Esmail Ahouzi","doi":"10.1016/j.geits.2024.100195","DOIUrl":"10.1016/j.geits.2024.100195","url":null,"abstract":"<div><div>This study responds to global climate concerns by addressing the shift towards sustainable transportation, particularly electric vehicles. Focusing on wireless power transfer to overcome charging infrastructure challenges, the research optimizes circular coils for inductive power transfer in electric cars. Utilizing ferrite cores to enhance performance, the study employs ANSYS Electronics Suite R2-202 and the finite element method to analyze circular coils, exploring variations in turns, inner radius, air gap, and misalignment's impact on the coupling coefficient. Introducing ferrite plan cores and boxes, the research finds that ferrite boxes improve coupling efficiency by 50% and electromagnetic field strength by 300%, concentrating the field toward the center. An inequivalent design, enlarging the primary coil, demonstrates significant enhancements, achieving a coupling coefficient increase of 0.183,447 and an electromagnetic field rise of 0.000,40 T. Equivalent coils with ferrite boxes meet a 95% efficiency goal with a strong, narrowed field at a lower cost, while inequivalent coils excel in strengthening and centralizing the field, enhancing misalignment tolerance in distinctive ways.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 2","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140274948","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

Impact and integration of electric vehicles on renewable energy based microgrid: Frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach 电动汽车对基于可再生能源的微电网的影响和整合：用优化的 FO-Fuzzy PSS 方法改善频率曲线

Green Energy and Intelligent Transportation Pub Date : 2024-03-03 DOI: 10.1016/j.geits.2024.100191

Prakash Chandra Sahu

{"title":"Impact and integration of electric vehicles on renewable energy based microgrid: Frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach","authors":"Prakash Chandra Sahu","doi":"10.1016/j.geits.2024.100191","DOIUrl":"10.1016/j.geits.2024.100191","url":null,"abstract":"<div><div>The modelling of an electric vehicle along with its integration and impact over a renewable energy based microgrid topology is well addressed in this manuscript. The frequent charging and discharging of the electric vehicle makes an oscillation over grid frequency. The performance especially frequency of an islanded AC microgrid is also affected seriously under the actions of different uncertainties like load dynamics, wind fluctuation in wind plant, solar intensity variation of PV plant etc. In order to maintain standard frequency, this research work aims to regulate the net power generation of the system in response to total demand. To monitor net generation, this work has intended a Fractional order fuzzy power system stabilizer (FO-Fuzzy PSS) control scheme in several dynamic situations. The proposed FO-Fuzzy PSS control scheme acts as most potential candidate to pertain stability in system frequency in above discussed disturbances. The controller gains are tuned optimally with suggesting an advanced-Sine Cosine Algorithm (a-SCA) under different conditions. The performance of the optimal FO-Fuzzy PSS controller is compared over standard fuzzy controller and PID controller in regard to frequency regulation of microgrid system. It is observed that proposed FO-Fuzzy PSS control scheme has the credential to reduce settling time of Δ<em>F</em><sub>1</sub> (area1 microgrid frequency) by 98.60% and 250.82% over fuzzy controller & PID controller correspondingly. Further, the dynamic optimal performance of the proposed a-SCA is compared over original SCA and PSO techniques to justify its superiority.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 2","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090752","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

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 : 2024-02-12 DOI: 10.1016/j.geits.2024.100182

Jianfang Jiao , Hongwei Wang , Feng Gao , Serdar Coskun , Guang Wang , Jiale Xie , Fei Feng

{"title":"Layered energy equalization structure for series battery pack based on multiple optimal matching","authors":"Jianfang Jiao , Hongwei Wang , Feng Gao , Serdar Coskun , Guang Wang , Jiale Xie , Fei Feng","doi":"10.1016/j.geits.2024.100182","DOIUrl":"10.1016/j.geits.2024.100182","url":null,"abstract":"<div><div>The equalization management system is an essential guarantee for the safe, stable, and efficient operation of the power battery pack, mainly composed of the topology of the equalization circuit and the corresponding control strategy. This article proposes a novel active balancing control strategy to address the issue of individual cell energy imbalance in battery packs. Firstly, to achieve energy equalization under complex conditions, a two-layer equalization circuit topology is designed, and the efficiency and loss of energy transfer in the equalization process are studied. Furthermore, a directed graph-based approach was proposed to represent the circuit topology equivalently as a multi-weighted network. Combined with a multi-weighted optimal matching algorithm, aims to determine the optimal energy transfer path and reduce equalization losses. In addition, a fuzzy controller that can dynamically adjust the equalization current with the state parameter of the cell as the input condition is designed to optimize the equalization efficiency. Matlab/Simulink software is used to build and simulate the model. The experimental results indicate that, under the same static state, the newly proposed control strategy improves efficiency by 6.08% and enhances equalization speed by 42.03% compared to the maximum value equalization method. The method also effectively improves energy utilization under the same charging and discharging states.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 2","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139827655","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

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 : 2024-02-10 DOI: 10.1016/j.geits.2024.100181

Mince Li, Yujie Wang, Pei Peng, Zonghai Chen

{"title":"Toward efficient smart management: A review of modeling and optimization approaches in electric vehicle-transportation network-grid integration","authors":"Mince Li, Yujie Wang, Pei Peng, Zonghai Chen","doi":"10.1016/j.geits.2024.100181","DOIUrl":"10.1016/j.geits.2024.100181","url":null,"abstract":"<div><div>The increasing scale of electric vehicles (EVs) and their stochastic charging behavior have resulted in a growing coupling between the transportation network and the grid. Consequently, effective smart management in the EV-transportation network-grid integration system has become paramount. This paper presents a comprehensive review of the current state of the art in system modeling and optimization approaches for the smart management of this coupled system. We begin by introducing the types of EVs that impact the transportation and grid systems through their charging behavior, along with an exploration of charging levels. Subsequently, we delve into a detailed discussion of the system model, encompassing EV charging load forecasting models and transportation-grid coupling models. Furthermore, optimization technologies are analyzed from the perspectives of system planning and EV charging scheduling. By thoroughly reviewing these key scientific issues, the latest theoretical techniques and application results are presented. Additionally, we address the challenges and provide future outlooks for research in modeling and optimization, aiming to offer insights and inspiration for the development and design of the EV-transportation network-grid integration system.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"3 6","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139881002","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 : 2024-02-09 DOI: 10.1016/j.geits.2024.100180

Junren Ye , Zhitao Liu , Shan Lu , Hongye Su

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":"3 1","pages":"Article 100151"},"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