Search Results (1,414)

Smart and sustainable mobility increasingly relies on distributed sensing, low-power communication technologies, and cloud-based ICT platforms. This article presents a comprehensive scientific analysis of the technological foundations of sensitized mobility, reviewing the state of the art in embedded sensing, distributed systems, and communication paradigms for future mobility challenges. The research project “MOST” and in particular its subgroup “Spoke 5” falls within this framework of sustainable and sensorized mobility, with numerous activities in data collection, analysis, and field experimentation. In order to allow data collection, retention and analysis, one of the challenges that we must address is the definition of an adequate ICT architecture. The core contribution of this work is the presentation of the MOST ICT architecture, designed as a containerized, scalable, and resilient infrastructure capable of integrating heterogeneous data coming from field-deployed systems. In addition, it discusses the primary research challenges encountered in the definition and development of the presented architecture by examining two representative case studies within the MOST-Spoke 5 research project: renewable energy charging stations for light electric vehicles and cyclists monitoring systems. Full article

Electric vehicles (EVs) are increasingly regarded as a key component of low-carbon mobility and the sustainable energy transition. However, their large-scale deployment raises challenges that extend beyond vehicle technologies and require a system-level understanding of interactions with power networks, energy resources and users. This paper presents a critical review of the literature published since 2012, examining EV development from an integrated energy perspective that includes vehicle technologies, charging infrastructure, power electronics, grid integration, renewable energy coupling and environmental implications. A structured methodology is used to identify and analyze peer-reviewed studies, with particular emphasis on high-impact review articles that consolidate knowledge across disciplines. The analysis shows that, despite significant technological progress, large-scale EV deployment remains constrained by infrastructure limitations, distribution grid readiness, charging coordination strategies, material availability and socio-technical factors. Simulation-based studies play a central role in anticipating these impacts and informing deployment strategies before real-world implementation. Rather than addressing individual components in isolation, this review highlights interdependencies between technologies, control approaches and energy systems. Based on this synthesis, key research priorities and high-level challenges are identified, providing guidance for future research and policy aimed at enabling EVs to effectively support sustainable ambient energy and mobility systems worldwide deployment. Full article

Global growth in electric mobility, portable electronics, and renewable energy storage has increased concerns about the environmental and economic impacts of managing end-of-life lithium-ion and lead-acid batteries. Although these batteries support the transition to renewable energy, their disposal presents significant challenges. Recycling has emerged as a key strategy to reduce resource depletion, limit pollution, and recover valuable materials. This study systematically reviewed and quantitatively synthesised the literature published between 2000 and 2025, assessing the environmental impacts of battery recycling programs. The review followed PRISMA guidelines to ensure a transparent and rigorous study selection process. Data from peer-reviewed articles, industry reports, and policy documents were analysed, focusing on indicators such as greenhouse gas emissions, energy use, material recovery efficiency, and economic returns. Statistical methods, including Hedges’ g, heterogeneity testing, and sensitivity analysis within a random-effects model, were applied to account for variability across technologies and battery types. The results show that recycling generally lowers emissions and improves resource recovery compared to virgin material extraction, though performance varies. Lead-acid recycling demonstrates stronger environmental benefits due to mature technologies and established systems, while lithium-ion recycling shows positive but lower gains, limited by higher energy demands and less-developed processes. Overall, recycling is essential for reducing environmental impacts and supporting a circular economy, though lithium-ion systems require further technological and policy advancements. These findings can be used by governments to strengthen regulatory frameworks to support recycling industries and invest in advanced lithium-ion recycling technologies to improve efficiency. Despite the existing limitations, the benefits of recycling outweigh the drawbacks, making it a necessary strategy for sustainable battery waste management. Full article

Public transport corporations in Tamil Nadu face increasing operational and financial pressure due to rising diesel fuel prices, maintenance costs, and operational inefficiencies associated with the conventional bus systems. In many districts, diesel-powered public transport services also suffer from irregular vehicle dispatch and poor timetable adherence, leading to unreliable passenger service. Meanwhile, the rapid penetration of electric two-wheelers and four-wheelers indicates a broader transition towards electrified mobility. Extending electrification to public transport requires prudently designed operational planning, as electric buses operate under battery capacity constraints and charging coordination constraints. In such systems, strict adherence to the scheduling of trips and efficient energy management becomes critical for maintaining service reliability. To address these challenges, this study proposes a Tri-Level Hybrid Electric Bus Scheduling (TLH-EBS) framework integrating Particle Swarm Optimization for global search, Rule-Based Scoring Large Neighborhood Search for adaptive schedule improvement, and Mixed Integer Linear Programming for exact repair optimization. The framework simultaneously optimizes fleet size, depot charging infrastructure allocation, and daily bus assignment under timetable constraints. The proposed model has been applied in three interconnected corridors in Madurai District, which are Thirumangalam, Arapalayam, and Mattuthavani, covering 810 scheduled daily timetabled trips between 05:00 AM and 12:30 AM. Computational results show that the hybrid framework has achieved a 2.8% reduction in annual scheduling cost compared to the best conventional optimization method. Furthermore, compared to equivalent diesel-based operations, the optimized electric system has demonstrated approximately 32.3% annual cost savings, confirming the economic viability of integrated fleet–charger scheduling for district-level electric bus deployment. Full article

The growing deployment of electric vehicles requires battery management systems that maintain cell uniformity while reducing hardware complexity and improving energy efficiency. Many cell-balancing methods rely on converter-based architectures and remain validated only through simulation. This study experimentally validates a previously published adaptive recombination strategy using a prototype with second-life Panasonic NCR18650PF lithium-ion cells. The system employs dynamic series-parallel reconfiguration, relay-based switching, isolated voltage monitoring, and adaptive control to redistribute energy without dedicated balancing converters. Six test cases were evaluated under resting, charging, and discharging conditions using simultaneous and sequential schemes. Complete balancing was achieved in all test cases within the measurement resolution of the prototype. The experiments reproduced the main balancing mechanisms predicted by simulation, particularly under resting and discharging conditions, while also revealing practical deviations under charging operation. These deviations indicate that real current-sharing behavior, cell aging, contact resistance, wiring losses, and measurement constraints can influence recombination performance in ways not fully captured by ideal simulation models. The study therefore provides first-stage hardware evidence for the feasibility of adaptive recombination balancing and identifies key implementation requirements for future real-time, safety-rated, and scalable BMS development. This research contributes to SDG 7 by supporting improved lithium-ion battery utilization and energy efficiency for sustainable electric mobility. Full article

Background/Objectives: Spinal cord injury (SCI) causes heterogeneous motor, sensory, autonomic, and participation limitations; recovery priorities vary by injury level, completeness, time since injury and residual function. Robotic rehabilitation has expanded from assistive technology to restorative, compensatory and health-promoting interventions, but patient-tailored prescription frameworks remain underdeveloped. Methods: PubMed/MEDLINE was searched from database inception to May 2026 using predefined domain-specific strategies, and findings were synthesized narratively to integrate mechanistic, clinical, safety and implementation evidence. Results: Robotic systems can increase task-specific repetition, sensorimotor feedback, active engagement and quantitative monitoring. Upper-limb robotics are feasible in cervical SCI and may support reach, grasp and activities of daily living, although SCI-specific controlled evidence remains limited. Lower-limb exoskeletons and locomotor robots can support gait practice, upright mobility, exercise exposure and selected secondary health outcomes, but walking speed, energy expenditure, cost, supervision needs and community translation remain important barriers. Sensory and non-motor effects, including proprioceptive input, spasticity, pain, bowel routine, cardiometabolic conditioning, participation and psychological well-being, are clinically relevant but should be interpreted according to evidence strength. Robotics combined with functional electrical stimulation, virtual reality, brain–computer interfaces, non-invasive brain stimulation and artificial intelligence-driven adaptation is promising but not yet routine. Conclusions: Robotic rehabilitation in SCI should be prescribed through a severity-based process that considers lesion level, American Spinal Injury Association Impairment Scale grade, residual voluntary and sensory function, safety, patient priorities and measurable goals. The proposed framework supports transparent selection and prospective validation of individualized robotic rehabilitation and shifts decisions beyond device availability toward clinically meaningful and equitable implementation. Full article

This paper addresses the problem of improving the efficiency of the traction electric drive of an agricultural electric vehicle operating under variable load conditions typical of agricultural transportation. The study substantiates the feasibility of employing a low-power (2 kW) induction motor as a cost-effective, technically robust, and reliable solution for mobile power systems. Particular attention is given to the operating characteristics of the traction drive under fluctuating loading conditions, which significantly affect the energy efficiency and overall performance of agricultural electric vehicles. A comprehensive structural and mathematical model of the induction motor was developed based on a proprietary implementation without the use of standard MATLAB R2020b/Simulink library blocks. The model was formulated using the transformation of a three-phase coordinate system into a two-phase stationary α–β reference frame, enabling a more accurate representation of the electromagnetic and electromechanical processes occurring within the machine. The analysis was carried out with consideration of transient processes, dynamic characteristics, and energy performance under realistic conditions regarding the influence of control strategies on the energy consumption of the electric drive system. The results of this can be applied to the design and optimization of electric transportation systems for agricultural applications, as well as to the development of energy-efficient control algorithms for traction electric motors. Full article

Accessibility in bus-based public transport is essential for inclusive and sustainable urban mobility, yet bus stops and stations are often overlooked compared with vehicle modernization and network planning. This study presents a bibliometric and thematic review of research (2020–2026) on accessibility in bus stop and station infrastructure across European cities. Literature indexed in the Web of Science Core Collection was analyzed using thematic synthesis supported by bibliometric network analysis. A total of 685 publications were examined to identify research trends, barriers, regional differences, assessment methods, and planning implications. The findings show that accessibility is a multidimensional concept shaped by physical design, information systems, user experience, first- and last-mile connectivity, and governance capacity. Persistent barriers include poor boarding interfaces, inadequate maintenance, weak wayfinding, safety concerns, and uneven implementation of accessibility standards. The reviewed studies suggest that evidence of stronger integration and higher user satisfaction is more frequently reported in Northern and some Western European contexts, whereas rural and smaller cities often face lower service accessibility. Emerging topics include real-time passenger information, crowding management, electric buses, and smart mobility technologies. The review concludes that bus stops and stations should be treated as strategic mobility assets. Integrated planning, participatory design, and territorially differentiated policy support are essential to advance inclusive bus-based public transport across Europe. Full article

Electric vehicle (EV) usage has increased significantly in the past few years, which may create challenges for distribution system operators due to EV charging needs. In this paper, we propose an approach for planning and operating EV charging stations, considering the challenges that distribution networks may face. A two-step framework is proposed in this paper. First, the optimal size and location of a charging station is determined using a multi-objective optimization problem considering minimizing power losses and voltage drop while maximizing load placements. Then, an optimal scheduling scheme is employed to charge and discharge the vehicles on the selected buses. Simulation studies were conducted using IEEE 33- and 123-bus systems; the results show that the proposed framework significantly enhances the buses’ voltages and line power flows. In order to plan for charging stations, several factors need to be considered, such as optimal size and location, the daily load curve for the given system, the time of use (TOU), and the charging patterns of EV owners. Without careful planning and operation, the system may suffer vulnerability and line overloading, which may lead, eventually, to cascading outages and interruptions. By improving grid utilization, reducing losses, and enabling coordinated EV charging and discharging, the proposed framework supports more sustainable energy use and facilitates the integration of electric mobility into future low-carbon power systems. Full article

The increasing frequency of high-impact, low-probability climate events has highlighted the limitations of conventional reliability criteria, including N-1 planning assumptions, and the need for dynamic resilience architectures in electrical systems. This article analyzes the evolution, trends, and technological challenges associated with microgrid resilience under extreme-weather disruptions. The study adopts a hybrid research design that combines quantitative bibliometric mapping of 283 Scopus-indexed article records for 2016–2025 using CiteSpace version 7 with a structured technical synthesis of the selected literature. The structural analysis identified nine thematic clusters and indicated a transition from service restoration and component-level recovery toward multi-energy energy-management systems, resilient distribution-system planning, and mobile restoration resources. The technical synthesis shows that modern resilience is increasingly associated with hierarchical control architectures: optimization and forecasting methods support tertiary-level energy management, while grid-forming inverters can provide primary-layer voltage references that support islanded operation and black-start sequences under appropriate design, protection, and validation conditions. The article concludes that future research should bridge stochastic planning and real-time physical operation through standardized interoperability frameworks, reproducible dynamic metrics, and experimentally validated control strategies for autonomous critical microgrid operation under extreme-weather conditions. Full article

The accelerating adoption of electric vehicles (EVs) presents significant challenges for maintaining grid stability and optimizing charging infrastructure. Accurate short-term forecasting of EV charging demand is therefore critical to support reliable grid operation and effective energy management in urban environments. However, existing forecasting models often fail to capture the intricate interdependencies among mobility patterns, weather variations, and real-world charging behaviors, which constrains their generalizability and robustness. This study develops a multi-model forecasting framework that leverages Transformer-based deep learning architectures to integrate real-world charging data with traffic flow and meteorological variables for predicting short-term EV charging demand across metropolitan areas. To benchmark performance, two additional machine learning models—CatBoost and convolutional neural networks (CNNs)—are systematically evaluated using datasets from urban EV supply equipment (EVSE) and electric bus systems. The results indicate that Transformer-based models deliver superior predictive accuracy, temporal consistency, and adaptability compared with CNNs and CatBoost. Furthermore, sensitivity analysis reveals that traffic dynamics and user charging behavior exert the strongest influence on forecast performance. The proposed framework offers actionable insights for utilities and urban planners, facilitating resilient grid operation, optimized charging infrastructure deployment, and accelerated integration of EVs into the power system. Full article

This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual inspection is time-consuming and subjective. To address these limitations, we systematically evaluate three lightweight convolutional neural network (CNN) architectures MobileNetV2, MobileNetV3, and EfficientNet using transfer learning on a domain-specific image dataset. The models are assessed based on accuracy, loss, precision, recall, and F1-score. Experimental results show that EfficientNet achieves the highest classification accuracy, while MobileNetV3 demonstrates the lowest validation loss and superior generalization stability. Based on a performance–efficiency trade-off analysis, MobileNetV3 is deployed on an Android platform using TensorFlow Lite, enabling real-time, offline, and on-device inference. To the best of our knowledge, this is among the first studies to integrate lightweight CNN-based short-circuit trace classification with real-time mobile deployment for on-site energy system fault analysis. By bridging the gap between deep learning and field deployment, the proposed mobile system ensures low-latency execution and provides a rapid, reliable, and portable solution for improving operational safety in electrical fire investigations. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article

This study examines the development of urban air mobility, which requires the creation of vertiports capable of ensuring the safe operation of electric vertical takeoff and landing (eVTOL) systems. Key operational constraints include unstable power supply, external climatic conditions, and reliance on battery systems. This study aims to develop a risk-based model for vertiport planning those accounts for the stochastic nature of eVTOL operational safety. A continuous-time Markov model incorporating nominal operational characteristics, system constraints, and transitions into emergency and catastrophic flight modes is proposed. State transitions within the model are primarily driven by climatic indicators, power supply reliability, battery parameters, maintenance quality, and diagnostic coverage. To interpret the low probabilities of transitioning to a catastrophic mode, this study introduces a safety index (integrated safety index), which facilitates the comparison of various operational scenarios and regulatory maturity levels. The practical importance of the research lies in applying the proposed model to precisely select vertiport locations; assess energy infrastructure requirements; and organize onboard monitoring, robotic preflight inspection systems, and decision support systems. The results demonstrate that eVTOL operational safety is assessed not only through spatial and infrastructure metrics but also through an integrated indicator encompassing power supply, climate, battery degradation, diagnostics, and hardware–software reliability of the entire vertiport system. Full article

Electric vehicle (EV) charging demand introduces significant variability in power systems, requiring forecasting approaches capable of representing both aggregated consumption trends and stochastic charging behaviors. While machine learning methods often provide strong predictive performance, they generally require large datasets and substantial computational resources. This paper proposes a stochastic framework based on compound Poisson and Cox processes to model EV charging demand using real charging station data collected at one-minute resolution. The proposed methodology jointly models charging-event arrivals, charging duration, and charging power through probabilistic distributions calibrated from historical observations. A compound homogeneous Poisson process (CHPP) and a double stochastic compound Poisson process (Cox process) are investigated and compared for the generation of synthetic EV charging profiles and short-term forecasting applications. The framework is validated using 1863 charging sessions recorded at a workplace charging infrastructure composed of 37 charging terminals. Monte Carlo simulations are performed to generate synthetic daily charging profiles and evaluate the capability of the models to reproduce key operational indicators, including daily energy consumption and peak grid power demand. The CHPP process achieves average forecasting errors up to 0.8% for daily energy and 6.2% for maximum grid power demand. The results show that Poisson-based stochastic models can generate diverse and realistic charging profiles while requiring only limited historical data and having low computational complexity. The proposed approach provides an interpretable and computationally efficient probabilistic framework for EV charging demand forecasting, synthetic profile generation, and power system operational studies. Stochastic compound Poisson processes may therefore constitute a valuable tool to support the ongoing electrification of mobility and the digital transformation of future smart grids and smart cities. Full article