Green Energy and Intelligent Transportation最新文献

{"title":"A comprehensive review on deep learning applications in advancing biodiesel feedstock selection and production processes","authors":"Olugbenga Akande ,&nbsp;Jude A. Okolie ,&nbsp;Richard Kimera ,&nbsp;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}

{"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,&nbsp;Jiageng Ruan,&nbsp;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}

{"title":"A comparative review of user acceptance factors for drones and sidewalk robots in autonomous last mile delivery","authors":"Didem Cicek ,&nbsp;Burak Kantarci ,&nbsp;Sandra Schillo","doi":"10.1016/j.geits.2025.100310","DOIUrl":"10.1016/j.geits.2025.100310","url":null,"abstract":"<div><div>Autonomous delivery technologies play a pivotal role in meeting the high expectations of customers while addressing the sustainability challenges posed by last-mile delivery traffic, particularly in urban areas. Over the past five years, research on user acceptance of these groundbreaking technologies has surged. This paper represents the first comprehensive review that consolidates and compares user acceptance factors related to deliveries by drones and sidewalk robots, drawing from global questionnaire-based studies. Our research reveals some common factors that consistently influence user acceptance for both drone and sidewalk robot deliveries and also sheds light on technology-specific acceptance factors. However, it's important to recognize that some of these factors may vary depending on the demographics and location of the studies conducted. Our findings intend to provide managerial insights to technology and policy makers, enabling strategic planning for the adoption of these innovative technologies.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 4","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948850","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":"Cybersecurity in Smart Railways: Exploring risks, vulnerabilities and mitigation in the data communication services","authors":"Tiago Fernandes ,&nbsp;João Paulo Magalhães ,&nbsp;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}

{"title":"A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors","authors":"Lixin E ,&nbsp;Jun Wang ,&nbsp;Ruixin Yang ,&nbsp;Chenxu Wang ,&nbsp;Hailong Li ,&nbsp;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}

{"title":"Multi-objective electric vehicle charge scheduling for photovoltaic and battery energy storage based electric vehicle charging stations in distribution network","authors":"Sigma Ray ,&nbsp;Kumari Kasturi ,&nbsp;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 &amp; 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 &amp; 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}

{"title":"Big data generation platform for battery faults under real-world variances","authors":"Daniel Luder ,&nbsp;Praise Thomas John ,&nbsp;Paul Busch ,&nbsp;Martin Börner ,&nbsp;Wenjiong Cao ,&nbsp;Philipp Dechent ,&nbsp;Elias Barbers ,&nbsp;Stephan Bihn ,&nbsp;Lishuo Liu ,&nbsp;Xuning Feng ,&nbsp;Dirk Uwe Sauer ,&nbsp;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}

{"title":"The road to net zero in a renewable energy-dominated electricity system: Impact of EV charging and social cost of emission on the optimal economic dispatch","authors":"Malolan Sundararaman ,&nbsp;Balasubramanian Sambasivam","doi":"10.1016/j.geits.2025.100280","DOIUrl":"10.1016/j.geits.2025.100280","url":null,"abstract":"<div><div>This study explores the intersection of two pivotal interventions aimed at achieving carbon neutrality: the electric vehicles (EVs) adoption and the renewable energy (RE) electricity generation. Focusing on a Renewable Energy-Dominated (RED) electricity system, the research examines the interdependence between these interventions and their collective impact on economic dispatch. The study's objective is to determine optimal economic dispatch strategies that meet hourly electricity demand, considering two distinct supply scenarios across eight supply options. The first scenario assesses the maximum possible supply, while the second contemplates the minimum possible supply from each option. Additionally, the study delves into the influence of social cost of emissions on these economic dispatches. Employing an experimental design, the study generates representative load curves that incorporate EV charging demands for varied levels of EV penetration, alongside regular electricity demand. Data from Karnataka's RED electricity system provides a basis for the supply-side analysis. The economic dispatch for each supply scenario is formulated as a Mixed Integer Linear Program (MILP), aiming to minimize both costs for generation and social costs of emissions, while adhering to operational constraints of the supply options. Key findings from this approach, highlight several critical insights: the significant role of incorporating social costs in economic dispatch decisions, the tangible impact of EV demand on supply shortages, and the importance of maintaining supply capacity to minimize these shortages.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100280"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924604","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":"Evaluation of eco-driving performance of electric vehicles using driving behavior-enabled graph spectrums: A naturalistic driving study in China","authors":"Hui Zhang ,&nbsp;Yiyue Luo ,&nbsp;Naikan Ding ,&nbsp;Toshiyuki Yamamoto ,&nbsp;Chenming Fan ,&nbsp;Chunhui Yang ,&nbsp;Wei Xu ,&nbsp;Chaozhong Wu","doi":"10.1016/j.geits.2024.100246","DOIUrl":"10.1016/j.geits.2024.100246","url":null,"abstract":"<div><div>Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision, especially in developing countries. Specifically, it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions. However, current researches on ecological driving behavior mostly use structured data to reflect the characteristics of ecological driving behavior, and it is difficult to accurately reveal the recessive relationship between driving behavior and energy consumption. One promising and prevalent method for comprehensively and in-depth characterizing driving behaviors is “graph spectrums”, which allows for an effective and illustrative representation of complex driving behavior characteristics. This study presented an assessment method of ecological driving for electric vehicles based on the graph. Firstly, a multi-source refined data set was constructed through naturalistic driving experiments (NDE). Four typical traffic state (CCCF: congested close car-following; CSSF: constrained slow free-flow; CSCF: constrained slow car-following; UFFF: unconstrained fast free-flow) were classified through longitudinal acceleration data, and driving behavior graph was constructed to realize the visual representation of driving behavior. Then, the energy consumption graph was constructed using the energy loss of 100 ​km (EL) index. After the six drivers with the highest and lowest ecological assessment of driving behavior using the behavior graph and energy consumption graph, proposing the quantitative analysis of fifteen drivers' ecology driving behavior. The results show that: 1) The graphical method can describe the individual features of a driver’s ecological driving behavior; 2) Rapid acceleration of driving behavior leads to high energy consumption; 3) In the comparison among the six eco-drivers and energy-intensive drivers, founding that the energy-intensive drivers accelerate and decelerate significantly more in CCCF traffic state; 4) The driving behavior was more complex and unecological in CCCF traffic state; 5) Fifteen drivers had lower ecological scores in start-up driving. This study proposes a method for visualizing ecology driving behavior that not only help understand the individual characteristics of ecological driving behaviors, but also offers substantial application value for the subsequent construction of Ecological driving behavior regulation models.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling mechanisms of electrolyte wetting process in three-dimensional electrode structures: Insights from realistic architectures","authors":"Fei Chen ,&nbsp;Tianxin Chen ,&nbsp;Zhenxuan Wu ,&nbsp;Zihan Zhou ,&nbsp;Kunjie Lu ,&nbsp;Jinyao Su ,&nbsp;Yihua Wang ,&nbsp;Jianfeng Hua ,&nbsp;Xin Lai ,&nbsp;Xuebin Han ,&nbsp;Minggao Ouyang ,&nbsp;Yuejiu Zheng","doi":"10.1016/j.geits.2024.100248","DOIUrl":"10.1016/j.geits.2024.100248","url":null,"abstract":"<div><div>The advancement of lithium-ion batteries (LIBs) towards larger structures is considered the most efficient approach to enhance energy density in clean energy storage systems. However, this advancement poses significant challenges in terms of the filling and wetting processes of battery electrolytes. The intricate interplay between electrode microstructure and electrolyte wetting process still requires further investigation. This study aims to systematically investigate the primary mechanisms influencing electrolyte wetting on porous electrode structures produced through different manufacturing processes. Using advanced X-ray computed tomography, three-dimensional electrode structures are reconstructed, and permeability and capillary action are evaluated as key parameters. It is observed that increasing calendering pressure and active material content reduces electrode porosity, thereby decreasing permeability and penetration rate; however, it simultaneously enhances capillary action. The interplay between these indicators contributes to the complexity of wetting behavior. Incomplete wetting of electrolytes arises from two primary factors elucidated by further simulations: partial closure of pores induced by the calendering process impedes complete wetting, while non-wetting phase gases become trapped within the electrolyte during the wetting process hindering their release and inhibiting full penetration of the electrolyte. These findings have significant implications for designing and optimizing LIBs while offering profound insights for future advancements in battery technology.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}