Green Energy and Intelligent Transportation最新文献

A survey on hydrogen tanks for sustainable aviation 可持续航空氢气罐的研究进展

IF 16.4

Green Energy and Intelligent Transportation Pub Date : 2025-08-01 DOI: 10.1016/j.geits.2024.100224

Sergio Bagarello , Dario Campagna , Ivano Benedetti

{"title":"A survey on hydrogen tanks for sustainable aviation","authors":"Sergio Bagarello , Dario Campagna , Ivano Benedetti","doi":"10.1016/j.geits.2024.100224","DOIUrl":"10.1016/j.geits.2024.100224","url":null,"abstract":"<div><div>The aviation industry is facing challenges related to its environmental impact and thus the pressing need to develop aircraft technologies aligned with the society climate goals. Hydrogen is emerging as a potential clean fuel for aviation, as it offers several advantages in terms of supply potential and weight specific energy. One of the key factors enabling the use of H<sub>2</sub> in aviation is the development of reliable and safe storage technologies to be integrated into aircraft design. This work provides an overview of the technologies currently being investigated or developed for the storage of hydrogen within the aircraft, which would enable the use of hydrogen as a sustainable fuel for aviation, with emphasis on tanks material and structural aspects. The requirements dictated by the need of integrating the fuel system within existing or ex-novo aircraft architectures are discussed. Both the storage of gaseous and liquid hydrogen are considered and the main challenges related to the presence of either high internal pressures or cryogenic conditions are explored, in the background of recent literature. The materials employed for the manufacturing of hydrogen tanks are overviewed. The need to improve the storage tanks efficiency is emphasized and issues such as thermal insulation and hydrogen embrittlement are covered as well as the reference to the main structural health monitoring strategies. Recent projects dealing with the development of onboard tanks for aviation are eventually listed and briefly reviewed. Finally, considerations on the tank layout deemed more realistic and achievable in the near future are discussed.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 4","pages":"Article 100224"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738862","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

A comprehensive review on deep learning applications in advancing biodiesel feedstock selection and production processes 深度学习在推进生物柴油原料选择和生产过程中的应用综述

Green Energy and Intelligent Transportation Pub Date : 2025-06-01 DOI: 10.1016/j.geits.2025.100260

Olugbenga Akande , Jude A. Okolie , Richard Kimera , Chukwuma C. Ogbaga

{"title":"A comprehensive review on deep learning applications in advancing biodiesel feedstock selection and production processes","authors":"Olugbenga Akande , Jude A. Okolie , Richard Kimera , Chukwuma C. Ogbaga","doi":"10.1016/j.geits.2025.100260","DOIUrl":"10.1016/j.geits.2025.100260","url":null,"abstract":"<div><div>Biodiesel as a renewable alternative to conventional diesel is a growing topic of interest due to its potential environmental benefits. It is typically produced from oilseed crops such as soybean, rapeseed, palm oil, or animal fats. However, its sustainability is debated, primarily because of the reliance on edible oil feedstocks and associated economic and environmental concerns. This study explores alternative, non-edible feedstocks, such as algae and jatropha, that do not compete with food production, offering increased sustainability. Despite their potential, these feedstocks are hindered by high production costs. To address these challenges, innovative approaches in feedstock assessment are imperative for ensuring the long-term viability of biodiesel as an alternative fuel. This review examines explicitly the application of deep learning techniques in selecting and evaluating biodiesel feedstocks. It focuses on their production processes and the chemical and physical properties that impact biodiesel quality. Our comprehensive analysis demonstrates that ANNs provide significant insights into the feedstock assessment process, emerging as a potent tool for identifying new correlations within complex datasets. By leveraging this capability, ANNs can significantly advance biodiesel research, producing more sustainable and efficient feedstock production. The study concludes by highlighting the substantial potential of ANN modeling in contributing to renewable energy strategies and expanding biodiesel research, underscoring its vital role in accelerating the development of biodiesel as a sustainable fuel alternative.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100260"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222124","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

A novel metaheuristic approach for simultaneous loss minimization and torque ripple reduction of DTC- IM driven EV 一种新的元启发式方法，用于同时减小直接转矩控制电机的损耗和转矩脉动

Green Energy and Intelligent Transportation Pub Date : 2025-06-01 DOI: 10.1016/j.geits.2025.100254

Anjan Kumar Sahoo

{"title":"A novel metaheuristic approach for simultaneous loss minimization and torque ripple reduction of DTC- IM driven EV","authors":"Anjan Kumar Sahoo","doi":"10.1016/j.geits.2025.100254","DOIUrl":"10.1016/j.geits.2025.100254","url":null,"abstract":"<div><div>The efficiency and torque ripple of an electric vehicle (EV) determine its performance and driving range. An optimum reference flux increases efficiency and decreases torque ripple and harmonics. This strategy used in the current literature is based on either a lookup table or a search control approach. However, these methods have convergence issues at optimal values, require large memory spaces, have higher computational complexity, and are difficult to implement. In the recent literature, efforts have been made to improve either the efficiency or the ripple, whereas in this paper, a multi-objective dynamic reference flux selection algorithm based on teamwork optimization is used to improve the efficiency and ripples simultaneously for a wide range of operating scenarios. The proposed dynamic reference flux selection algorithm is evaluated numerically and compared using standard drive cycles, and the amount of energy a vehicle uses during different drive cycles is compared. The results obtained justify the effectiveness and feasibility of the proposed algorithm over a wide range of driving conditions.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511036","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

The investigation of reinforcement learning-based end-to-end decision-making algorithms for autonomous driving on the road with consecutive sharp turns 基于强化学习的连续急转弯道路自动驾驶端到端决策算法研究

Green Energy and Intelligent Transportation Pub Date : 2025-06-01 DOI: 10.1016/j.geits.2025.100288

Tongyang Li, Jiageng Ruan, Kaixuan Zhang

{"title":"The investigation of reinforcement learning-based end-to-end decision-making algorithms for autonomous driving on the road with consecutive sharp turns","authors":"Tongyang Li, Jiageng Ruan, Kaixuan Zhang","doi":"10.1016/j.geits.2025.100288","DOIUrl":"10.1016/j.geits.2025.100288","url":null,"abstract":"<div><div>Learning-based algorithm attracts great attention in the autonomous driving control field, especially for decision-making, to meet the challenge in long-tail extreme scenarios, where traditional methods demonstrate poor adaptability even with a significant effort. To improve the autonomous driving performance in extreme scenarios, specifically consecutive sharp turns, three deep reinforcement learning algorithms, i.e. Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic policy gradient (TD3), and Soft Actor-Critic (SAC), based decision-making policies are proposed in this study. The role of the observation variable in agent training is discussed by comparing the driving stability, average speed, and consumed computational effort of the proposed algorithms in curves with various curvatures. In addition, a novel reward-setting method that combines the states of the environment and the vehicle is proposed to solve the sparse reward problem in the reward-guided algorithm. Simulation results from the road with consecutive sharp turns show that the DDPG, SAC, and TD3 algorithms-based vehicles take 367.2, 359.6, and 302.1 s to finish the task, respectively, which match the training results, and verifies the observation variable role in agent quality improvement.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241793","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

A comparative review of user acceptance factors for drones and sidewalk robots in autonomous last mile delivery 无人机和人行道机器人在自主最后一英里配送中的用户接受度比较分析

Green Energy and Intelligent Transportation Pub Date : 2025-04-12 DOI: 10.1016/j.geits.2025.100310

Didem Cicek , Burak Kantarci , Sandra Schillo

引用次数: 0

Optical communication based V2V for vehicle platooning 基于光通信的V2V车辆队列

Green Energy and Intelligent Transportation Pub Date : 2025-04-10 DOI: 10.1016/j.geits.2025.100278

Jiajun Zhang , Ran Zhan , Yuhao Wang , Xiaobo Qu

{"title":"Optical communication based V2V for vehicle platooning","authors":"Jiajun Zhang , Ran Zhan , Yuhao Wang , Xiaobo Qu","doi":"10.1016/j.geits.2025.100278","DOIUrl":"10.1016/j.geits.2025.100278","url":null,"abstract":"<div><div>Vehicle platooning offers significant advantages, including improved fuel economy, reduced congestion and collisions, and decreased air resistance, owing to synchronized acceleration and braking within the platoon. In a vehicle platoon, vehicle-to-vehicle (V2V) communication plays a pivotal role in facilitating information transmission between leading vehicle (L+V) and following vehicle (FV). However, existing V2V communication solutions, such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C–V2X), and Visible Light Communication (VLC), face limitations, including high costs, infrastructural and network demands, and privacy concerns. To overcome these challenges, our research introduces a novel vision-based, network-independent information transmission approach. This method can be used as a complement to traditional V2V methods, especially under poor network conditions or potential attacks from adversaries. Simulations and experiments reveal that our approach can facilitate vehicle-to-vehicle information transmission even when network conditions are completely absent, thereby enhancing driving safety. This is achieved through the use of an LED matrix embedded in the leading vehicle′s taillight for communication. This innovative approach holds promise as a solution to the challenges associated with conventional V2V communication methods.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 4","pages":"Article 100278"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472397","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

Cybersecurity in Smart Railways: Exploring risks, vulnerabilities and mitigation in the data communication services 智能铁路中的网络安全：探索数据通信服务中的风险、漏洞和缓解措施

Green Energy and Intelligent Transportation Pub Date : 2025-04-02 DOI: 10.1016/j.geits.2025.100305

Tiago Fernandes , João Paulo Magalhães , Wellington Alves

{"title":"Cybersecurity in Smart Railways: Exploring risks, vulnerabilities and mitigation in the data communication services","authors":"Tiago Fernandes , João Paulo Magalhães , Wellington Alves","doi":"10.1016/j.geits.2025.100305","DOIUrl":"10.1016/j.geits.2025.100305","url":null,"abstract":"<div><div>Smart trains and railways are gaining increasing significance in major global cities as they offer solutions to issues like traffic congestion and environmental pollution. Technological advancements have facilitated the transition from conventional systems to more advanced, highly efficient, and personalized railway systems. However, the complexity of these systems presents challenges, especially in terms of reliability, interoperability security, and privacy. With the potential vulnerability of railway systems to cyberattacks, it becomes crucial for these emerging smart systems to establish stringent privacy and security requirements. Cybersecurity is a key requirement to enable railways to deploy and take advantage of the full extent of a connected, digital environment. This research explores the cybersecurity landscape within Smart Railways aiming to identify potential threats and associated risks on these systems, focusing on analyzing the current literature related to Smart Railways and cybersecurity aspects, then listing key technologies used by smart systems, and finally proposing an illustration of use cases application to call attention to the impact of attacks, providing then as a set of good practices that must be followed to reduce risks and to the safeguard the operability for Rail Transportation. The research findings suggest that over the last few years, there has been a significant increase in research activity in this area, indicating a growing recognition of the importance of cybersecurity in the railway industry. The results also pointed out several gaps related to this topic, namely the lack of standardization in cybersecurity practices and limited consideration of human factors that can impact cybersecurity.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 4","pages":"Article 100305"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948849","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

A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors 一种基于物理信息神经网络的超级电容器退化轨迹和剩余使用寿命预测方法

Green Energy and Intelligent Transportation Pub Date : 2025-03-26 DOI: 10.1016/j.geits.2025.100291

Lixin E , Jun Wang , Ruixin Yang , Chenxu Wang , Hailong Li , Rui Xiong

{"title":"A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors","authors":"Lixin E , Jun Wang , Ruixin Yang , Chenxu Wang , Hailong Li , Rui Xiong","doi":"10.1016/j.geits.2025.100291","DOIUrl":"10.1016/j.geits.2025.100291","url":null,"abstract":"<div><div>Supercapacitors are widely used in transportation and renewable energy fields due to their high power density, stable cycling performance, and rapid charge–discharge capabilities. To ensure efficient applications of supercapacitors, accurately predicting their degradation trajectories and remaining useful life (RUL) is crucial. For this purpose, a physics-informed neural network (PINN) model is developed using Long Short-Term Memory (LSTM) as the base architecture. Physical equations are embedded into the loss function to ensure consistency with domain knowledge, allowing the loss function to incorporate both physical and data-driven components. The balance between these two loss components is dynamically determined through Bayesian optimization, to enhance the model's accuracy further. Validation results show a root mean square error (RMSE) of 3 mF (the rated capacity is 1 F) in the degradation trajectory prediction and a RMSE of 269 cycles (the average cycle life is 5180 cycles) for the RUL. Ablation experiments were conducted to validate the effectiveness of integrating physical information into the LSTM framework. Results demonstrate that the proposed model outperforms both the data-driven LSTM method and the empirical equation-based method that the PINN model can reduce the RMSE by 85% and 87.5% for degradation trajectory prediction, and 86.5% and 94.6% for RUL prediction, respectively. In addition, a comparison with advanced models demonstrates that our model reduces the requirement significantly on training data while maintaining comparable prediction accuracy, which favors scenarios where data is scarce.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100291"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852097","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

Multi-objective electric vehicle charge scheduling for photovoltaic and battery energy storage based electric vehicle charging stations in distribution network 基于光伏和电池储能的配电网电动汽车充电站多目标充电调度

Green Energy and Intelligent Transportation Pub Date : 2025-03-26 DOI: 10.1016/j.geits.2025.100296

Sigma Ray , Kumari Kasturi , Manas Ranjan Nayak

{"title":"Multi-objective electric vehicle charge scheduling for photovoltaic and battery energy storage based electric vehicle charging stations in distribution network","authors":"Sigma Ray , Kumari Kasturi , Manas Ranjan Nayak","doi":"10.1016/j.geits.2025.100296","DOIUrl":"10.1016/j.geits.2025.100296","url":null,"abstract":"<div><div>Recently, with the increasing demand of the electric vehicle (EV) in transportation, the power grid faces critical challenges in meeting the extra power demand. Companies are focusing on expanding EV charging infrastructure to meet customer requirements. Ensuring power supply security, reliability, and economics for EV charging stations remains a challenge, despite efforts to align photovoltaic (PV) and battery energy storage system (BESS) based designs with distribution system requirements. A criteria weight ranking mechanism has been designed to accept charging requests for EVs depending on the criteria weights specified by the EV owner. This paper uses a multi-objective remora optimization algorithm (MOROA) to determine the optimal location of two electric vehicle charging stations (EVCS) in the distribution system, and capacity of PV & BESS units in two EVCS for optimizing three conflicting objective functions, such as (1) minimizing total power loss; (2) minimizing annual substation power cost, and annual capital, operation & maintenance cost of the PV and BESS, and (3) minimizing emission from upstream grid. Moreover, the EVs are also scheduled optimally at each charging station. The effectiveness of these methodologies has been demonstrated through four case studies using IEEE 33 bus radial distribution system (RDS). Furthermore, the smart EV charge scheduling reduces the overall load burden on the grid network and the benefit of EVCS operators and EV owners.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 4","pages":"Article 100296"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134654","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

Big data generation platform for battery faults under real-world variances 真实方差下电池故障大数据生成平台

Green Energy and Intelligent Transportation Pub Date : 2025-02-21 DOI: 10.1016/j.geits.2025.100282

Daniel Luder , Praise Thomas John , Paul Busch , Martin Börner , Wenjiong Cao , Philipp Dechent , Elias Barbers , Stephan Bihn , Lishuo Liu , Xuning Feng , Dirk Uwe Sauer , Weihan Li

{"title":"Big data generation platform for battery faults under real-world variances","authors":"Daniel Luder , Praise Thomas John , Paul Busch , Martin Börner , Wenjiong Cao , Philipp Dechent , Elias Barbers , Stephan Bihn , Lishuo Liu , Xuning Feng , Dirk Uwe Sauer , Weihan Li","doi":"10.1016/j.geits.2025.100282","DOIUrl":"10.1016/j.geits.2025.100282","url":null,"abstract":"<div><div>There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability. However, such prediction methods require large amounts of data, generally obtained through experiments or during the operation phase, resulting in substantial economic and time efforts. In this context, generating realistic battery pack data that covers all sensor values a battery management system receives, as well as including fault models, is of particular interest and can mitigate the need to perform extensive laboratory testing. This paper focuses on the systematic development of a data generation platform capable of simulating a large scale of battery packs with random battery faults and generating big data for the following battery fault diagnostics. Initially, the electrical, thermal, and aging modeling of a battery pack is performed. After this, four types of faults, namely hard short circuit, soft short circuit, abnormal internal resistance, and abnormal contact resistance, are modeled using equivalent circuit models. To generate realistic data, both cell-to-cell variations and pack-level variations are considered. Variations included are, for example, the manufacturing quality, temperatures, aging processes, road conditions, state of charge, and fault severity. By combining the battery pack models, fault models, and the different variations through Monte Carlo simulations, a large data set representing different packs with varying levels of inconsistencies is generated.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100282"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934930","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