Green Energy and Intelligent Transportation最新文献

{"title":"Economic dispatch in microgrid with battery storage system using wild geese algorithm","authors":"Vimal Tiwari ,&nbsp;Hari Mohan Dubey ,&nbsp;Manjaree Pandit ,&nbsp;Surender Reddy Salkuti","doi":"10.1016/j.geits.2025.100263","DOIUrl":"10.1016/j.geits.2025.100263","url":null,"abstract":"<div><div>The development of microgrid systems forces to integration of various distributed generators (DG) and battery energy storage (BES) systems. The integration of a BES system in MG provides several benefits such as fast response, short-term power supply, improved power quality, ancillary service, and arbitrage. The system constraints as power balance and the assets constraints as power limit of different DGs, energy, and charge/discharge power limit of BES increase the complexity of the original problem. Therefore, to tackle such a problem an efficient, robust, and strong optimization algorithm is required. In this paper, a recently developed optimization method known as the wild geese algorithm (WGA) has been applied to solve the problem. The WGA is a population-based metaheuristic approach inspired by the different aspects of the living behavior of wild geese. This algorithm has developed with the inspiration of different phases of wild geese's lives, such as their evolution, well-organized and coordinated long-distance group migration, and fatality. The WGA has tested on the MG problem and the obtained simulation results are validated by comparison of results obtained from the other methods. The result shows the WGA is efficiently able to handle the MG operational problem with numerous constraints and shows the potential to produce a high-quality solution in terms of cost reduction. The incorporation of BES reduces operating costs for MG's off-grid and on-grid operational modes by 5.91% and 8.62%, respectively. Further, the analysis for off-grid mode under different seasonality, reduction in the operational cost by 4.47%, 9.28%, 6.37%, and 7.22% was measured in the summer, autumn, winter, and spring seasons, respectively, with the integration of BES. Additionally, the integration of BES in on-grid mode results in a decrease in operating costs by 7.15%, 12.54%, 7.56%, and 11.07% in the summer, autumn, winter, and spring, respectively.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100263"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913804","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":"Utilizing extended theory of planned behavior to evaluate consumers’ adoption intention of electric vehicles","authors":"Apurva Pamidimukkala ,&nbsp;Sharareh Kermanshachi ,&nbsp;Jay Michael Rosenberger ,&nbsp;Greg Hladik","doi":"10.1016/j.geits.2025.100258","DOIUrl":"10.1016/j.geits.2025.100258","url":null,"abstract":"<div><div>The growing apprehension regarding environmental issues is driving global economies to adopt alternative fuel technology to mitigate the release of greenhouse gases from vehicles. Electric vehicles (EVs) provide a practical and eco-friendly solution that can help transition to a sustainable transportation system with minimal emissions, therefore conserving the environment. This study employed the theory of planned behavior (TPB) and incorporated additional factors such as price value, moral norms, and policy incentives (monetary and non-monetary) to examine consumers' intention to adopt EVs. A survey was administered to prospective consumers in March 2023 in Texas and a total of 743 responses were collected. The analysis results revealed that attitudes, perceived behavior control, subjective norms, moral norms, price value, and monetary incentives positively and significantly influenced consumers' intentions to adopt EVs; however, it was also revealed that non-monetary incentives do not have a significant effect on consumers’ propensity to adopt EVs. Furthermore, the research findings from the moderation analysis indicate noteworthy differences in demographic factors along the consumer adoption intention.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100258"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913803","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":"Unveiling the explosion potential of lithium-ion batteries: A quantitative approach to safety assessment","authors":"Zhenpo Wang ,&nbsp;Tongxin Shan ,&nbsp;Shanyu Zhao ,&nbsp;Wim J. Malfait","doi":"10.1016/j.geits.2025.100289","DOIUrl":"10.1016/j.geits.2025.100289","url":null,"abstract":"<div><div>The safety of lithium-ion batteries is a critical and challenging focus of current research. This perspective article systematically summarized and compared evaluation methods for the trinitrotoluene-equivalent (TNT-equivalent) of lithium-ion batteries (LIBs) based on various characteristic parameters and proposed a mechanism-driven calculation approach. Using experimental data for validation, the study input TNT-equivalent values derived from different methods into an explosion dynamics model to predict explosion pressures, identifying the optimal calculation method through comparison with measured data. Results showed that the mechanism-driven approach accurately predicts explosion characteristics at different SOCs, with errors below 3%. This method eliminates the need for complex dynamic testing while providing precise predictions by linking the explosion equivalent to intrinsic thermal runaway (TR) mechanisms. The findings contribute to building safety analysis databases, establishing testing standards, and supporting the safety design of battery systems.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100289"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917574","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":"Understanding spatial–temporal attributes influencing electric vehicle's charging stations utilization: A multi-city study","authors":"Boniphace Kutela ,&nbsp;Abdallah Kinero ,&nbsp;Hellen Shita ,&nbsp;Subasish Das ,&nbsp;Cuthbert Ruseruka ,&nbsp;Tumlumbe Juliana Chengula ,&nbsp;Norris Novat","doi":"10.1016/j.geits.2025.100255","DOIUrl":"10.1016/j.geits.2025.100255","url":null,"abstract":"<div><div>Electric vehicles (EVs) are gaining popularity across the globe. Various initiatives are being implemented to ensure that most of the operating vehicles on public roadways are EVs by 2050. Such initiatives include the construction of charging stations to improve EV charging accessibility. The utilization of the charging stations has not been explored to a great extent, despite its importance in future installations in various cities. This study evaluated the EV station utilization across eleven cities in three countries: the United States, Canada, and Scotland. The Negative Binomial (NB) regression model was applied to understand the influence of the spatial–temporal factors on the daily utilization of EV charging stations. In addition to the overall analysis, country-specific analyses were also performed. It was revealed that there is a great variation in daily EV utilization across the cities in different countries and within the country. In fact, only stations in Crieff, Scotland, showed lower predicted daily utilization, while cities in the United States had over two times predicted daily utilization compared to stations in Aberfeldy, Scotland. Furthermore, the longer the station has been in service, the higher the daily utilization, although there was significant variation across cities. Further, the day of the week and months of the year depicted consistent utilization patterns for Scotland and the United States but showed mixed findings for Canada. The study findings can help planners and policymakers improve the allocation of EV charging stations.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100255"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902445","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":"Power supply for the projectile-borne electromechanical system: A review","authors":"Da Yu ,&nbsp;Xiangyu Han ,&nbsp;Wenrui Wang ,&nbsp;He Zhang ,&nbsp;Peixun Xiong ,&nbsp;Keren Dai","doi":"10.1016/j.geits.2025.100256","DOIUrl":"10.1016/j.geits.2025.100256","url":null,"abstract":"<div><div>Wide-area battlefields, smart ammunition, and precision damage are the new directions of modern warfare, while munition-borne electric systems serve as “decision-makers” for smart ammunition. As the primary energy supplier for the entire system, munition-borne power sources hold a veto power position. The complexity of the application environment for munition-borne power sources involves enduring high overloads, high centrifugal forces, ballistic aerothermal effects, variations in ballistic airflow fields, central blast impacts, complex disturbances in indefinite postures, and even the influence of complex ionized media. These factors represent weak links in research on the entire munition-borne electric system. Therefore, nations around the world attach great importance to developing munition-borne power sources and conducting research on various related aspects, such as technological innovation, digital simulation, and testing techniques. This paper elaborates on the existing technologies and scientific issues facing munition-borne power sources, comparing and analyzing the advantages and disadvantages of liquid reserve batteries, solid-state thermoelectric batteries, and supercapacitors as energy sources for modern warfare systems. It also discusses current technological developments and future challenges. To address the insufficient environmental and spatial adaptability of munition-borne power sources, this paper proposes a design approach that couples excitation with integrated packaging. Specifically, although the diversity of ammunition platforms leads to differences in power source requirements, common problems faced by munition-borne electric systems in modern battlefield environments include extreme impact mechanics, low-temperature rapid activation requirements, and structural size limitations. This paper comprehensively discusses the extreme mechanical environments of ammunition platforms, failure mechanisms and protection methods under high-impact conditions for munition-borne power sources, low-temperature rapid activation, and miniaturization design and proposes protective design concepts such as elastic skeleton structures and high-pressure sealed secondary packaging. Additionally, these findings suggest the use of capillary microarray structures with electrode membranes to increase infiltration rates and further improve the activation rate of munition-borne power sources. Lastly, this paper outlines future directions for the development of munition-borne electrical system power sources, primarily from the perspectives of non-reserve primary batteries, non-bottle-breaking reserve batteries, new system batteries, and the advantages of battery-supercapacitor composite energy, providing a reference for the design of munition-borne electrical system power sources used in diversified weapon system platforms.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100256"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010154","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":"Enhancing vehicle Re-identification by pair-flexible pose guided vehicle image synthesis","authors":"Baolu Li ,&nbsp;Ping Liu ,&nbsp;Lan Fu ,&nbsp;Jinlong Li ,&nbsp;Jianwu Fang ,&nbsp;Zhigang Xu ,&nbsp;Hongkai Yu","doi":"10.1016/j.geits.2025.100269","DOIUrl":"10.1016/j.geits.2025.100269","url":null,"abstract":"<div><div>Vehicle Re-identification (Re-ID) has drawn extensive exploration recently; nevertheless, the issue of accurately distinguishing features in latent space across varying vehicle poses, remains a challenging hurdle for real-world application of Vehicle Re-ID. To address this challenge, we supply a novel idea which projects the various-pose vehicle images into a unified target pose so as to promote the discriminative capability of vehicle Re-ID model. Acknowledging the labor and cost of paired data for the same vehicle images across different traffic surveillance cameras in practical scenarios, we propose the pioneering Pair-flexible Pose Guided Image Synthesis for vehicle Re-ID, denominated as VehicleGAN. Our method is adept at both supervised (paired images of same vehicle) and unsupervised (unpaired images of any vehicle) settings, and bypasses the need of geometric 3D model information. Furthermore, we propose a novel Joint Metric Learning (JML) method to facilitate the effective fusion of both real and synthetic data. Comprehensive experimental analyses conducted on the public VeRi-776 and VehicleID datasets substantiate the precision and efficacy of our proposed VehicleGAN and JML.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100269"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010151","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":"Cycle-efficient modeling for degradation staging and early life prediction of lithium batteries","authors":"Can Wang ,&nbsp;Renjie Wang ,&nbsp;Jianming Li ,&nbsp;Zhuangzhuang Li ,&nbsp;Quanqing Yu","doi":"10.1016/j.geits.2025.100338","DOIUrl":"10.1016/j.geits.2025.100338","url":null,"abstract":"<div><div>An effective and time-saving early life prediction model is crucial for rapid battery assessment. However, existing models face a dilemma: they either rely heavily on extensive historical data or provide limited predictive insights into battery degradation. To address this, this study proposes a cycle-efficient battery life assessment framework integrating data-driven and empirical models. The framework consists of two components: degradation stage detection relying solely on data from one cycle and early life prediction using five-cycle data. The early life prediction model is capable of achieving joint prediction of the battery's remaining useful life and the cycle to knee point. Experimental results demonstrate that the degradation staging model achieves an accuracy of 0.977,6 for lithium iron phosphate batteries. Meanwhile, the early life prediction model yields mean absolute percentage errors of 10.5% for remaining useful life and 12.8% for the cycle to knee predictions. The model's accuracy and generalizability have been validated across diverse battery types, health states, and operating conditions. This proposed framework exhibits excellent generalizability capability under all evaluated scenarios, establishing a robust foundation for rapid battery design assessment and retirement decisions.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100338"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885772","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":"Lithium-ion battery remaining useful life prediction based on data-driven and particle filter fusion model","authors":"Chunling Wu ,&nbsp;Chenfeng Xu ,&nbsp;Liding Wang ,&nbsp;Juncheng Fu ,&nbsp;Jinhao Meng","doi":"10.1016/j.geits.2025.100267","DOIUrl":"10.1016/j.geits.2025.100267","url":null,"abstract":"<div><div>To improve the accuracy and stability of battery remaining useful life (RUL) prediction for lithium-ion batteries, this paper proposes a new convolutional neural network-gated recurrent unit-particle filter (CNN-GRU-PF) fusion prediction model. First, the battery capacity series is decomposed and reconstructed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and Pearson correlation coefficient method, which reduces the influence of noise on RUL prediction. Then, the capacity is predicted by CNN-GRU, and the CNN-GRU prediction value is used as the observation value of PF, and the prediction error of CNN-GRU is corrected by the state prediction ability of PF. A moving window is used to iteratively update the training set, and the PF optimization value is added to the CNN-GRU training set, forming an iterative training and dynamic updating between them, which improves the long-term prediction performance of CNN-GRU. To verify the effectiveness of proposed method, CNN-GRU-PF model is applied to predict the battery's RUL. The experiments show that CNN-GRU-PF improves the prediction accuracy of battery B5 by 87.27%, 82.88%, and 55.43% respectively compared with GRU, PF and GRU-PF, and also achieves significant improvement for other batteries. The new model is an effective RUL prediction method with good accuracy and robustness.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100267"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010152","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":"Research on vertical strategy for left turn at signal-free T-shaped intersections based on multi-layer reinforcement learning methods","authors":"Xuemei Chen,&nbsp;Jia Wu,&nbsp;Jiachen Hao,&nbsp;Yixuan Yang","doi":"10.1016/j.geits.2025.100261","DOIUrl":"10.1016/j.geits.2025.100261","url":null,"abstract":"<div><div>The task of executing left turns at signal-free T-shaped intersections without protective signals poses a critical challenge in the realm of autonomous driving. Conventional rule-based approaches tend to be excessively cautious, rendering them inadequate for effectively managing driving tasks within unpredictable T-shaped intersection environments. In the case of complex traffic scenarios, a single model is less effective in convergence and has a lower pass rate and poorer safety. Thus, this study introduces a multi-layer reinforcement learning model, employing D3QN (Dueling Double DQN) and TD3 (Twin Delayed Deep Deterministic policy gradient algorithm) for advanced behavioral decision-making and vertical acceleration planning, respectively. In our experimental investigation, we designed four simulation scenarios based on the driving behavior of the Carla simulator to replicate real-world driving conditions. Verification and test simulation outcomes substantiate that, in comparison to other single-trained reinforcement learning models, the multi-layer reinforcement learning model proposed in this study attains the highest success rate. Specifically, the pass rate in the verification scenario, consistent with the training conditions, achieves an impressive 99.5%. Furthermore, the pass rate in the comprehensive test scenario reaches 89.6%. These experiments unequivocally demonstrate the considerable enhancement in T-shaped intersections pass rates achieved by the proposed method while ensuring both traffic efficiency and safety.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100261"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913805","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":"Optimal allocation of distributed generation units and fast electric vehicle charging stations for sustainable cities","authors":"Isaac Prempeh ,&nbsp;Albert K. Awopone ,&nbsp;Patrick N. Ayambire ,&nbsp;Ragab A. El-Sehiemy","doi":"10.1016/j.geits.2025.100281","DOIUrl":"10.1016/j.geits.2025.100281","url":null,"abstract":"<div><div>The rise of electric vehicles (EVs) in sustainable cities has fuelled interest in Distributed Generation (DG) units allocation. A well-planned and efficient charging infrastructure is required for effective e-mobility. The paper examined the single-objective frameworks of optimal simultaneous allocation of DG units and fast EV charging stations (EVCS). The applications are employed on the IEEE 69 bus network and a real part of the Ghana network in the Ashanti region. The optimization tasks are carried out by using Particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. The impact of optimal placement on the networks was analysed. The results show that with high penetration levels of DG units (up to 40%) and fast EVCS, PSO, and ABC can achieve a significant power loss reduction that reaches 68%. Furthermore, PSO outperforms ABC in relation to the voltage deviation index on both the test network and the 33 ​kV Ashanti region network, while still satisfying the IEC standards' 5% margins. The results indicate that PSO and ABC are viable swarm algorithms for mitigating active power loss and enhancing the voltage profile of a system through concurrent allocation.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100281"},"PeriodicalIF":16.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925937","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}