Green Energy and Intelligent Transportation最新文献

{"title":"Intelligent prediction of electrode characteristics based on neural networks in the lithium-ion battery production chain","authors":"Tianxin Chen ,&nbsp;Xin Lai ,&nbsp;Fei Chen ,&nbsp;Zhouyang Xu ,&nbsp;Xuebing Han ,&nbsp;Languang Lu ,&nbsp;Yuejiu Zheng ,&nbsp;Minggao Ouyang","doi":"10.1016/j.geits.2025.100294","DOIUrl":"10.1016/j.geits.2025.100294","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) are widespread with the fast development of new energy vehicles. The characteristics of LIB electrodes, including mass load, thickness, and porosity, are critical for battery performance such as energy density and lifespan. These characteristics are greatly influenced by the manufacturing methods and should be carefully considered during the production process development. However, the manufacturing process of electrodes is highly complex, involving a multitude of parameters. The traditional trial-and-error method has proven to be ineffective in improving manufacturing efficiency. In this study, we propose an artificial intelligence-based prediction method for estimating the key characteristics of electrodes. Specifically, it utilizes active material mass content, viscosity, solid-to-liquid ratio, and comma gap as input parameters. Compared to the traditional multiple linear regression method, the proposed method exhibits a significant improvement in accuracy. In certain cases, the root-mean-square error is reduced by an average of 35.5%, highlighting the superior prediction accuracy achieved by our method. Furthermore, we conduct a comparative analysis of different deep neural networks in predicting electrode characteristics. Finally, the importance of input features using the permutation feature importance analysis method is analyzed. By harnessing the powerful generalization ability of artificial intelligence, our method can be effectively applied to the manufacturing process of LIBs, resulting in a significant enhancement of battery production efficiency.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100294"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584458","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":"Lane changing enabled eco-driving control for plug-in hybrid electric vehicle under consecutive signalized intersection conditions","authors":"Yuan Jia ,&nbsp;Yonggang Liu ,&nbsp;Yuanjian Zhang ,&nbsp;Zheng Chen ,&nbsp;Zhenzhen Lei ,&nbsp;Yi Zhang","doi":"10.1016/j.geits.2025.100311","DOIUrl":"10.1016/j.geits.2025.100311","url":null,"abstract":"<div><div>Developing a qualified eco-driving strategy for plug-in hybrid electric vehicles (PHEVs) remains challenging in urban traffic scenarios, due to the comprehensive influence of random traffic flow and signal lights. To solve it, this study develops a hierarchical eco-driving framework integrating lane-changing decisions through a virtual force-based trigger mechanism and an adaptive energy management strategy that dynamically adjusts the equivalence factor. Firstly, considering the interference of neighbor vehicles, the velocity planning layer generates the economic velocity trajectories using dynamic programming in short discrete intervals, enabling the ego-vehicle to navigate through signal intersections smoothly. In addition, the lane changing is properly conducted according to the state of the ego-vehicle, traffic flow, and signal light. In the energy management layer, an adaptive equivalent fuel consumption minimization strategy accounting for trip distance, initial state of charge, and remaining electric mileage is developed to ensure a reasonable power split based on the reference velocity. Simulation and hardware-in-the-loop experimental results indicate that the developed strategy improves the traffic efficiency by 1.68%, while reducing energy consumption by 9.81% and 31.71%, compared with Pontryagin's minimum principle and nonlinear model predictive control based methods.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100311"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618584","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":"Can electric trucks be a viable green logistics and transportation solution? Modeling a joint logistics-and-charging-infrastructure network design problem","authors":"Hao Yu ,&nbsp;Amit Adhikari ,&nbsp;Xu Sun ,&nbsp;Wei Deng Solvang ,&nbsp;Mi Gan ,&nbsp;Nezir Aydin","doi":"10.1016/j.geits.2025.100295","DOIUrl":"10.1016/j.geits.2025.100295","url":null,"abstract":"<div><div>As one of the most substantial contributors to the rapidly increasing carbon emissions, the greening and decarbonization of the transportation and logistics sectors are essential. In this context, electric trucks (E-trucks) have garnered global interest due to their potential to significantly reduce tailpipe emissions in the freight transport sector. However, inadequate charging infrastructure is a substantial barrier to the widespread adoption of E-trucks. This paper investigates a charging infrastructure development model led by an industry cluster in order to better meet the emission target. To this end, a new optimization model is formulated for a joint logistics-and-charging-infrastructure network design problem. The model aims to minimize the total cost of operating the logistics system and charging infrastructure network while simultaneously ensuring accessibility to charging stations. Numerical experiments based on a case study in Nepal were conducted to validate the proposed optimization model. The results demonstrate potential reductions of up to 33.3% in total logistics costs and 55.9% in emissions related to transportation through the transition to electric power. This analysis highlights the economic viability and environmental benefits of adopting E-trucks in green logistics and transportation, supported by an industry-spearheaded business model for developing charging infrastructure.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100295"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685435","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":"Smart parking systems: A comprehensive review of digitalization of parking services","authors":"Sai Sneha Channamallu ,&nbsp;Sharareh Kermanshachi ,&nbsp;Jay Michael Rosenberger ,&nbsp;Apurva Pamidimukkala","doi":"10.1016/j.geits.2025.100293","DOIUrl":"10.1016/j.geits.2025.100293","url":null,"abstract":"<div><div>Smart parking systems (SPS) address issues that plague traditional parking methods by offering data on real-time parking availability, optimizing the use of space, and facilitating convenient payment solutions. Despite the timeliness and importance of the systems, however, the literature fails to adequately identify areas within SPS that can be vastly improved by innovation. This study addresses the research gap by identifying the key limitations of 124 comprehensively reviewed academic papers and offering innovative solutions. For sensor technology, the challenge of environmental effects and camera line-of-sight issues is tackled with a proposed integrated sensor framework, combining radar precision with camera coverage, all enhanced by AI for greater detection accuracy. Communication networks, currently hindered by scalability and node failure, could be improved with a mesh network architecture for better reliability. To address data management concerns, specifically data integrity and security, the integration of blockchain technology is suggested to protect against data breaches and boost user confidence. Lastly, to simplify complex SPS user interfaces, AI-driven adaptive interfaces are recommended to personalize the user experience and improve system engagement. The findings of this study will be instrumental for city planners, SPS developers, and parking authorities who are tasked with implementing efficient and reliable smart parking solutions.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100293"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685436","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 static current error elimination algorithm for predictive current control in PMSM for electric vehicles","authors":"Lin Shen ,&nbsp;Jian Zhang ,&nbsp;Kai Chen ,&nbsp;Xuhui Wen","doi":"10.1016/j.geits.2025.100306","DOIUrl":"10.1016/j.geits.2025.100306","url":null,"abstract":"<div><div>Permanent magnet synchronous motors (PMSM), especially interior PMSM, offer several benefits, including a compact design, light weight, simple construction, high efficiency, and versatile control capabilities, which make them ideal for use in electric vehicle drive systems. A robust predictive current control approach for PMSM drives is proposed. The finite control set-model predictive current control (FS-MPC) system's sensitivity to the PMSM model's parameters was initially examined, and the current response characteristics were assessed. On this basis, a <strong>static error elimination algorithm</strong> based on PI controller and particle swarm optimization (PSO) is proposed to compensate the parameter mismatches between actual parameters and model parameters adaptively. By combining the proposed algorithm to the FS-MPC, error elimination FS-MPC (FS-EEMPC) can significantly reduce the current static error. Simulation and experimental comparisons between the FS-MPC and FS-EEMPC approaches were conducted to confirm the parameter robustness of the proposed method. The results demonstrate that FS-EEMPC improves steady-state performance while maintaining the good dynamic performance of FS-MPC, with minimal additional computational load.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100306"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618585","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":"Modeling and simulation of sodium-ion batteries based on the combination of electrochemical mechanism and machine learning","authors":"Meiwen Liu ,&nbsp;Junfu Li ,&nbsp;Yaxuan Wang ,&nbsp;Shilong Guo ,&nbsp;Lei Zhao ,&nbsp;Zhenbo Wang","doi":"10.1016/j.geits.2025.100299","DOIUrl":"10.1016/j.geits.2025.100299","url":null,"abstract":"<div><div>Sodium-ion batteries have gained increasing attention due to their advantages, such as abundant raw material reserves and low costs. As a new battery system, the electrochemical and thermal properties of its electrodes and the entire cell, as well as their variations over both short and long periods, still contain many unknowns. Similar to lithium-ion batteries, sodium-ion batteries also experience performance degradation over time. To ensure the long-term, safe, and stable operation of batteries in service, health and safety management are necessary. Modeling and simulation can accurately predict the multi-scale behavior of battery characteristics, and thus, serve as an important theoretical foundation for battery management. Therefore, modeling and simulation of sodium-ion batteries are crucial.</div><div>This paper first considers the temperature changes during battery operation and, based on the fundamental working principles of the battery, develops an electrochemical-thermal coupling model by retaining the main physical processes while ignoring secondary processes. Then, to identify and optimize the highly sensitive model parameters, a weighted particle swarm optimization algorithm is used, ensuring that the parameters are valid and reasonable. Finally, to address the differences among individual cells and the uncertainties in the measured data, machine learning algorithms are introduced into battery mechanism modeling. Specifically, a dynamic residual forest model (DRF) for sodium-ion batteries is constructed using random forest and incremental learning algorithms, which iteratively learns from errors to reduce simulation errors in voltage and temperature.</div><div>In the DRF model, the random forest algorithm initially performs a preliminary prediction, followed by the use of incremental learning algorithms to correct prediction errors, thereby continuously optimizing the prediction accuracy of battery terminal voltage and temperature. The key feature of this model is its ability to handle real-time data streams, adapt to dynamic changes in data distribution, and reduce the need for retraining on new data, all while maintaining high prediction accuracy. This allows the model to simulate the complex operating conditions during the actual use of the battery. By using the DRF model to correct the outputs of the electrochemical-thermal coupling model, the final predictions of terminal voltage and temperature are obtained. Validation results show that the hybrid model provides better predictions of terminal voltage and temperature for different individual cells with higher accuracy.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100299"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618583","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":"Conceptualizing UAM: Technologies and methods for safe and efficient urban air transportation","authors":"Lidia Zewde,&nbsp;Ioannis A. Raptis","doi":"10.1016/j.geits.2025.100265","DOIUrl":"10.1016/j.geits.2025.100265","url":null,"abstract":"<div><div>The effective integration of Urban Air Mobility (UAM) hinges upon the adoption of a comprehensive approach that harmoniously melds various network components, placing paramount importance on the pillars of safety, sustainability, and efficiency. Existing technologies are currently undergoing a transformative evolution to cater to the distinctive requirements of UAM, with an unwavering commitment to enhancing safety, sustainability, and efficiency. This paper meticulously elucidates the extant technologies and methodologies that pertain to the safe and efficient realm of air transportation while delving into the perspective of key UAM network components: (1) Aircraft classification, range, and operational technology; (2) Airspace typology and structural intricacies; and (3) Air Traffic Management (ATM) services. In conclusion, this paper culminates by offering insights into prospective research directions in this burgeoning field.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100265"},"PeriodicalIF":16.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618582","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":"Remaining mileage estimation for electric vehicles based on dual extended Kalman filter and eXtreme gradient boosting","authors":"Zhiqiang Han ,&nbsp;Zeyu Chen ,&nbsp;Zhou Yang ,&nbsp;Zilu Zhang ,&nbsp;Bo Zhang","doi":"10.1016/j.geits.2025.100259","DOIUrl":"10.1016/j.geits.2025.100259","url":null,"abstract":"<div><div>Precisely estimating the remaining mileage of electric vehicles is highly important for vehicle control and battery recharging determinations. Remaining mileage estimation (RME) is a technique difficulty in practice since it is impacted by many factors, including the battery state of charge (SOC), state of health (SOH), ambient temperature, and traffic condition, etc. In this study, an online RME method is proposed based on dual extended Kalman filter (DEKF) and extreme gradient boosting (XGB) algorithms. Firstly, the battery SOC and SOH are co-estimated based on DEKF with considering the impacts of ambient temperature. Secondly, the current traffic condition are analyzed by using a historical data segement, and then the energy consumpation rate is predicted by XGB algorithm. The XGB algorithm's accuracy under the varying length of data segment is analyzed for determining the proper algorithm parameters. The presented method is evaluated by a simulation study. The results under several typical driving cycles indicate that the precise RME can be achieved with the maximum error less than 1.2%. The method is expected to be useful in providing credible mileage estimation in electric vehiecle applications.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 6","pages":"Article 100259"},"PeriodicalIF":16.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519801","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 security in the ISO 15118–20 ​EV charging system","authors":"Ross Porter ,&nbsp;Morteza Biglari-Abhari ,&nbsp;Benjamin Tan ,&nbsp;Duleepa Thrimawithana","doi":"10.1016/j.geits.2025.100262","DOIUrl":"10.1016/j.geits.2025.100262","url":null,"abstract":"<div><div>Electric Vehicle (EV) ‘DC Fast Charging’ systems directly connect an EV's battery to an external charger. A compromised EV charger may damage the EV or be used as part of a demand-side power grid attack. We show that the newest charging standard ISO 15118–20 is not sufficient to prevent charging attacks, as it provides no mechanism to verify charger integrity. We present system and threat models for the attack, before defining an extension to ISO 15118–20 that adds support for firmware integrity verification through remote attestation, while remaining interoperable with non-supporting devices. A proof of concept implementation demonstrates the security improvement by protecting against the specified attack while requiring only 85 bytes of secure storage, 8 ​kB of working memory, and adding less than 0.5 ​s to the length of a charging session. Backwards compatibility with an implementation of the original standard is also demonstrated.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 6","pages":"Article 100262"},"PeriodicalIF":16.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417330","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 joint time-frequency analysis of the mechanical-electrochemical coupling mechanism from particles to electrodes for the Li-ion battery","authors":"Zihan Meng ,&nbsp;Yuxuan Bai ,&nbsp;Fangzhou Zhang ,&nbsp;Jiujun Zhang ,&nbsp;Qiu-An Huang","doi":"10.1016/j.geits.2025.100322","DOIUrl":"10.1016/j.geits.2025.100322","url":null,"abstract":"<div><div>Diffusion-induced stress (DIS) originates from the shrinkage/expand during Li extraction/insertion from/into the active particle for the Li-ion battery (LIB). Till today, the two-way coupled mechanical-electrochemical mechanism is still unclear. The above challenge can be decomposed into 2W ​+ ​lH as follows: (i) Why need to reveal the two-way coupled mechanical-electrochemical mechanism? (ii) What is the two-way coupled mechanical-electrochemical mechanism? (iii) How to reveal the two-way coupled mechanical-electrochemical mechanism. In the process of answering the above 2W ​+ ​lH, the following contributions have been made in this work: (i) An electro-chemo-mechanical (ECM) model is established for the LIB, in which the mechanical-electrochemical coupling is two-way; (ii) The mechanical-electrochemical responses are solved for the ECM model in the time/frequency domain, respectively; (iii) The time-domain analysis shows that DIS enhances Li diffusion at the early and middle stages of discharge, while DIS inhibits Li diffusion at the end of discharge; (iv) The frequency-domain analysis shows that stress mainly affects solid-phase diffusion instead of electrolyte-phase diffusion. In a word, the multi-scale analysis quantitatively analyzes the impact of DIS on Li diffusion on the particle scale and reveals the two-way coupled mechanical-electrochemical mechanism on the electrode scale. The above results provide theoretical support for the battery manufacture and stress monitoring.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 6","pages":"Article 100322"},"PeriodicalIF":16.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222064","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}