Green Energy and Intelligent Transportation最新文献

{"title":"Economical vehicle-side strategies for an electric bus charging station in vehicle-to-grid services based on reinforcement learning","authors":"Taotao Li ,&nbsp;Xiaoqi Zeng ,&nbsp;Xiao Qi ,&nbsp;Tianyang Zhao ,&nbsp;Zhengmao Li ,&nbsp;You Lv ,&nbsp;Yajun Qiao ,&nbsp;Zijian Tan ,&nbsp;Jizhen Liu ,&nbsp;Jinghan He ,&nbsp;Weixiong Wu","doi":"10.1016/j.geits.2025.100387","DOIUrl":"10.1016/j.geits.2025.100387","url":null,"abstract":"<div><div>The power batteries of idle electric vehicles can be utilized as distributed energy storage units to deliver Vehicle-to-Grid (V2G) services, enabling bidirectional energy flow between the grid and EV batteries. Existing studies on the economic feasibility of V2G focus on conventional passenger vehicles, which have highly random idle times and lack scalability. The distinctiveness of this study is selecting a specific electric bus charging station in the Pearl River Delta region as the research object. This station provides comprehensive data on basic station information, bus batteries, charging, driving, bus departure times, and electricity prices. Compared to existing V2G economic strategy research, it demonstrates operational regularity and scalability, offering strong practical application value. First, this study collected data from bus charging stations covering 25 routes and 377 electric buses. A power battery electrical-health-economic model was developed to simulate electrical behavior, health degradation, and economic performance under various charging and discharging conditions. Then, the constraints of charging stations and electric buses are set according to operational scenarios. The charging station uses reinforcement learning to implement health-aware V2G (V2G-H). Furthermore, strategies including no V2G, uncoordinated V2G, deep-discharge V2G, and V2G-H are compared and analyzed in terms of economic performance. The results show that the reinforcement learning-based V2G-H strategy saves $1,539 in total cost per bus over the entire lifecycle and extends battery life by over 21 months compared to the traditional approach. Finally, this study analyzes the effects of electricity price fluctuations, departure frequency, and battery cost on the V2G strategy, providing a feasible solution for implementing V2G services at charging stations.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 2","pages":"Article 100387"},"PeriodicalIF":16.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078640","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":"A Fourier graph neural network for SOH estimation of lithium-ion batteries simultaneously considering spatio-temporal features","authors":"Wanglin Liu ,&nbsp;Jindong Tian ,&nbsp;Xiaoyu Li ,&nbsp;Yong Tian ,&nbsp;Guang Li","doi":"10.1016/j.geits.2025.100301","DOIUrl":"10.1016/j.geits.2025.100301","url":null,"abstract":"<div><div>Lithium-ion batteries have been commonly applied in electric vehicles and renewable energy systems. To ensure the operating reliability and efficiency of lithium-ion batteries, it is imperative to estimate their state of health (SOH) online. Among various SOH estimation methods, data-driven methods have been given considerable attention. However, they often rely on time series neural networks, failing to capture inter-series (spatial) dynamics and intra-series (temporal) dependencies within health feature sequences. In this study, a Fourier graph neural network (FourierGNN) is proposed for SOH estimation of lithium-ion batteries. A hypervariable graph is constructed to represent the spatial and temporal correlations of multivariate features related to capacity degradation of lithium-ion batteries. The node dependencies in the hypervariable graph are refined into fully connected node-to-node dependencies, thus addressing the uncertainty and compatibility issues in spatiotemporal modeling, and establishing adaptive spatiotemporal dependencies. Experimental results show that the FourierGNN model performs well on multiple datasets. Compared with three existing neural networks, FourierGNN model averagely achieves about 35% and 52% reduction in MAE and RMSE errors on NASA B5 battery, respectively. In addition, when the models trained by NASA B5 battery dataset are directly applied to other batteries, FourierGNN can reduce the average MAE and RMSE errors about 50% and 46%, respectively.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 2","pages":"Article 100301"},"PeriodicalIF":16.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886233","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 status of bidirectional wireless power transfer technology","authors":"Yang Wu ,&nbsp;Udaya K. Madawala ,&nbsp;Lei Zhao ,&nbsp;Xin Dai","doi":"10.1016/j.geits.2025.100266","DOIUrl":"10.1016/j.geits.2025.100266","url":null,"abstract":"<div><div>With the expansion of the application field of wireless power transmission (WPT) technology, there is an urgent need for bidirectional wireless power transmission technology (BWPT) to realize the energy interaction between wireless charging devices. This paper first analyses the development status of bidirectional capacitive power transfer (BCPT) system and the key issues that need to be addressed, briefly describes the basic operating principle of BWPT system, specifically discuss the main research achievements in terms of typical bidirectional conversion topologies, resonant networks, power control strategies and application scenarios of BWPT system. Finally, prospects for research directions worthy of attention in the future are presented.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 2","pages":"Article 100266"},"PeriodicalIF":16.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886231","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":"Energy-saving control of intelligent connected plug-in hybrid electric vehicle via fusing driving intention of front vehicle","authors":"Guanying Liu ,&nbsp;Shiquan Shen ,&nbsp;Yonggang Liu ,&nbsp;Yuanjian Zhang ,&nbsp;Yu Liu ,&nbsp;Zheng Chen ,&nbsp;Fengxiang Guo","doi":"10.1016/j.geits.2025.100330","DOIUrl":"10.1016/j.geits.2025.100330","url":null,"abstract":"<div><div>Velocity variation of front vehicle substantially influences the driving performance and energy consumption of the following vehicle, particularly for hybrid electric vehicles (HEVs). Fusing the intention identification of front vehicle into the control of following HEV can facilitate speed optimization, energy saving and operation efficiency promotion. Inspired by this, an effective speed optimization and energy management approach is designed for the following plug-in HEV (PHEV) with the support of front vehicle's driving style identification. To this end, an improved K-means clustering approach and the support vector machine algorithm are respectively employed to cluster and distinguish the driving intention. Next, the next step velocity of front vehicle is predicted by Elman neural network, based on the current velocity and the identified driving intention. Subsequently, a multi-objective speed optimization problem is formulated with the consideration of powertrain efficiency, vehicle comfort, tracking capability and vehicle safety. Then, a power distribution strategy is designed based on model predictive control with a clipped double Q-learning algorithm to allocate the energy flow in different energy sources. The simulation findings demonstrate that the proposed strategy not only achieves preferable following effect and comfort, but also leads to 97.01% energy economy optimality supplied by dynamic programming.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 2","pages":"Article 100330"},"PeriodicalIF":16.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886230","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":"Eco-driving framework for hybrid electric vehicles in multi-lane scenarios by using deep reinforcement learning methods","authors":"Weiqi Chen ,&nbsp;Jiankun Peng ,&nbsp;Yuhan Ma ,&nbsp;Hongwen He ,&nbsp;Tinghui Ren ,&nbsp;Chunhai Wang","doi":"10.1016/j.geits.2025.100309","DOIUrl":"10.1016/j.geits.2025.100309","url":null,"abstract":"<div><div>The eco-driving strategy is crucial for hybrid electric vehicles to save energy and reduce emissions. Most studies focused on longitudinal car-following or lane-changing maneuvers, lacking the consideration of continuous lateral dynamics, leading to insufficient optimization of energy-saving. This paper proposes an integrated eco-driving framework for fuel cell hybrid electric vehicles in multi-lane highway scenarios, in which trajectory planning and energy management are synchronously optimized by unified continuous control variables: acceleration, steering angle, and engine power, so as to maximize vehicle energy economy in real traffic environments. The key features of spatial traffic information and vehicular power conditions are extracted and formulated as the decision-making input. Then, the Soft Actor-Critic algorithm is utilized to optimize the eco-driving framework due to its good ability to explore complex strategy spaces for multi-objective optimization tasks. Analyses of the co-optimization process for motion trajectory planning and energy management show that, the proposed eco-driving strategy achieves better transverse-longitudinal comfort and energy economy by sacrificing 14.07% of the average speed, which results in an 87.65% improvement in the State-of-Health performance of the power system, and a reduction in the hydrogen consumption and the driving cost by 86.17% and 89.58%, respectively. This project is available at <span><span>https://github.com/sicilyala/EcoAD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 2","pages":"Article 100309"},"PeriodicalIF":16.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886286","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":"Design and implementation of interoperable high-efficiency bidirectional wireless power transfer systems for multiple vehicles","authors":"Baokun Zhang ,&nbsp;Junjun Deng ,&nbsp;Mengchen Duan ,&nbsp;Chang Li ,&nbsp;Yi Zheng ,&nbsp;Shuo Wang ,&nbsp;David Dorrell","doi":"10.1016/j.geits.2025.100307","DOIUrl":"10.1016/j.geits.2025.100307","url":null,"abstract":"<div><div>The rapid growth of electric vehicle ownership and advancements in vehicle-to-grid (V2G) technologies have created an urgent demand for bidirectional charging–discharging interfaces. Wireless power transfer (WPT) technology, known for its convenience, safety, and flexibility, is a promising solution for energy transfer between vehicles and the grid. This paper presents the design and demonstration of a highly interoperable and high-efficiency bidirectional WPT system, addressing key challenges such as wide voltage output adaptation, multi-power level compatibility, and efficient operation over a broad power range. The front-end converter uses a power module combining a three-phase fully controlled rectifier and a cascaded buck converter to provide a wide DC voltage range. Modular activation technology ensures the grid interface operates efficiently under varying power demands. For the bidirectional inductive power transfer (BIPT) link, an integrated scheme for the resonant networks in the ground assembly (GA) with cross-frequency compatibility is proposed, and its performance is validated through calculations and simulations. A bidirectional power flow control strategy is implemented, with voltage regulation and operation mode switching as the main method. Experimental results demonstrate interoperability between the same grid-side equipment and different vehicle-side equipment rated at 6, 11, and 30 ​kW. Under specified operating conditions at the aligned position, the system achieves a grid-to-battery efficiency from 91.7% to 94.3%, and a battery-to-grid efficiency ranging from 89.5% to 93.5%.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 2","pages":"Article 100307"},"PeriodicalIF":16.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886235","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":"An analytical-iterative method for accurate parameter estimation of the single-diode model in photovoltaic modules: Application to monocrystalline and polycrystalline modules under various environmental conditions","authors":"Imade Choulli,&nbsp;Mustapha Elyaqouti,&nbsp;El Hanafi Arjdal,&nbsp;Driss Saadaoui,&nbsp;Dris Ben hmamou,&nbsp;Souad Lidaighbi,&nbsp;Abdelfattah Elhammoudy,&nbsp;Ismail Abazine,&nbsp;Brahim Ydir","doi":"10.1016/j.geits.2025.100285","DOIUrl":"10.1016/j.geits.2025.100285","url":null,"abstract":"<div><div>This article presents a methodology, both analytical and iterative, aimed at estimating the five parameters of the single-diode model for photovoltaic modules. This approach, devoid of preliminary external measurements and based on minimal available information, enables the complete determination of parameters without resorting to prior estimations. Its principle lies in the analytical estimation of the photocurrent (<span><math><msub><mi>I</mi><mtext>phn</mtext></msub></math></span>), reverse saturation current of the diode (<span><math><msub><mi>I</mi><mtext>on</mtext></msub></math></span>), and shunt resistance (<span><math><msub><mi>R</mi><mtext>shn</mtext></msub></math></span>) under standard test conditions. Simultaneously, the ideality factor of the diode (<span><math><msub><mi>a</mi><mi>n</mi></msub></math></span>) and series resistance (<span><math><msub><mi>R</mi><mtext>sn</mtext></msub></math></span>) are obtained through a two-dimensional iterative process. A reformulation of parameters under normal conditions accounts for the impact of metrological conditions. Applied to SP140 monocrystalline and S75 polycrystalline modules, this method demonstrates notable precision, assessed using criteria such as root mean square error and relative error, compared to other approaches. These results underscore the reliability of this methodology for precise modeling of photovoltaic behavior, highlighting its potential for design, optimization, and prediction of photovoltaic system performance.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100285"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584457","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":"Clustering methodologies for flexibility characterization of electric vehicles supply equipment","authors":"Marcelo Forte ,&nbsp;Cindy P. Guzman ,&nbsp;Alexios Lekidis ,&nbsp;Hugo Morais","doi":"10.1016/j.geits.2025.100304","DOIUrl":"10.1016/j.geits.2025.100304","url":null,"abstract":"<div><div>The continuous growth of electric vehicles (EVs) poses new challenges to power systems planning and operation due to the need to meet society's decarbonization goals. In this context, clustering has emerged as a powerful tool to help understand and categorize the uncertain behavior of EV users and the electric vehicle supply equipment (EVSE) needs. However, previous studies lack empirical European EV charging data and relevance for practical applications.Therefore, to address such issues, this study evaluates different clustering techniques to identify typical EV charging profiles and, mainly, usage flexibility. The defined methodology comprises three major stages: data preprocessing, clustering application, and validation of results. We conduct benchmarking based on EV energy consumption, arrival, and sojourn times, using K-means, Gaussian mixture model, and Hierarchical clustering. This method allows greater applicability to various datasets from different regions, producing more comprehensive profiles that can provide empirical flexibility data in a visual, intuitive, and relevant approach.A use case considering EV charging data from Caltech University and Greece is utilized to test the proposed methods, demonstrating the versatility of our methodology.Specifically, Caltech features highly flexible prolonged charging sessions, while Greece exhibits quick-stay sessions with less flexibility potential. Both contexts offer opportunities to use the available flexibility for coordination with renewable energy sources and help balance the grid. This information unlocks the potential for future studies, enabling distribution system operators and charge point operators to intelligently and successfully integrate EVs into the energy system.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100304"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584455","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":"Data-driven machine learning techniques for fuel economy prediction in sustainable transportation systems","authors":"Muhammad Sohaib Zahid ,&nbsp;Umar Jamil","doi":"10.1016/j.geits.2025.100303","DOIUrl":"10.1016/j.geits.2025.100303","url":null,"abstract":"<div><div>Sustainable transportation aims to reduce greenhouse gas emissions and improve air quality. While developed countries focus on transitioning to electric vehicles, undeveloped and some developing countries face challenges due to energy crises and high costs, making immediate adoption difficult. In response to the growing demand for vehicles, automotive industries are encountering diverse challenges, including high initial costs due to the integration of intelligent technologies for enhanced vehicle performance. Concurrently, consumers prioritize vehicles with improved fuel economy, aiming to minimize fuel expenses and mitigate environmental impacts like air pollution. The fuel economy of vehicles depends on different features such as their vehicle class, engine size, cylinders, fuel type and city fuel consumption. In this research work, various Machine Learning (ML) techniques such as Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR) are used to predict the fuel economy of vehicles based on the factors mentioned above. After a comparative study, the RFR demonstrated superior performance compared to other machine learning models, such as LR and SVR, using most of the input features. Specifically, with city fuel consumption (L/(100 ​km)) as the input, RFR achieved a Mean Squared Error (MSE) of 0.839,4, a Mean Absolute Error (MAE) of 0.66, and an R-Squared (<em>R</em>²) score of 0.984,3 on the 2000–2022 dataset. In comparison, LR resulted in an MSE of 7.375,4, an MAE of 1.754,9, and an <em>R</em>² score of 0.856,7, while SVR yielded an MSE of 0.976,1, an MAE of 0.69, and an <em>R</em>² score of 0.981,9. On the validated 2023–2024 dataset, RFR maintained superior performance with an MSE of 0.848,6, an MAE of 0.66, and an <em>R</em>² score of 0.984,9. In contrast, LR achieved an MSE of 10.504,5, an MAE of 1.950,7, and an <em>R</em>² score of 0.827,3, whereas SVR obtained an MSE of 1.104,7, an MAE of 0.75, and an <em>R</em>² score of 0.980,9.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100303"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584456","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":"State of charge estimation for lithium-ion batteries using an adaptive cubature Kalman filter based on improved generalized minimum error entropy criterion","authors":"Chen Chen ,&nbsp;Qiang Zhang ,&nbsp;Wei Liao ,&nbsp;Feng Zhu ,&nbsp;Menghan Li ,&nbsp;Hanming Wu","doi":"10.1016/j.geits.2025.100292","DOIUrl":"10.1016/j.geits.2025.100292","url":null,"abstract":"<div><div>Accurate and robust estimation of the State of Charge (SOC) in complex environments is vital to achieving high battery performance, extended lifespan, enhanced safety, and improved user experience. Conventional estimation methods often neglect the impact of temperature on battery during the modeling process. To address this, this paper presents a battery modeling method applicable across a comprehensive temperature range that encompasses all potential operating temperatures. A second-order equivalent circuit model (ECM) is developed for the battery, with parameters defined within a specified temperature range. Furthermore, an adaptive cubature Kalman filter based on an improved minimum error entropy criterion (IMEF-ACKF) is introduced to address the significant accuracy degradation of traditional methods under non-Gaussian noise and substantial outlier interference. The generalized minimum error entropy criterion is combined with the generalized maximum correntropy criterion to replace the traditional minimum mean-square error (MMSE) criterion, addressing the influence of non-Gaussian noise. Then, exponential transformation of system residual is used to mitigate the impact of large outliers, and adaptive filter is incorporated to improve the stability of the calculation process. Predictions at various cycle tests and temperatures show that RMSE values below 0.3% and MAX values below 0.6% could be achieved by the proposed method in environments without additional noises. Even under non-Gaussian and impulsive noise conditions, the RMSE value of the optimized method remains below 0.9%. The results indicate that this method consistently maintains excellent estimation accuracy and robustness across all application scenarios.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100292"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685437","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}