Search Results (212)

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

The market for electric single-person vehicles in Sweden has undergone significant changes, shifting from a rental-dominated model to increasing private ownership. This transformation has resulted in both benefits and challenges, including improved accessibility, evolving consumer behaviour, and increased accident rates, particularly among young users. This study, commissioned by the Swedish government, presents a comprehensive mapping of the availability, usage, and consequences of private electric scooters. Through market surveys, user studies, and accident data analysis, we provide insights into regulatory gaps, consumer awareness, and safety concerns. Our findings highlight the need for clearer communication of existing regulations and improved consumer education to ensure the safe and responsible use of electric single-person vehicles. Full article

Accurately predicting the capacity degradation of lithium-ion batteries is crucial for ensuring the reliability and safety of electric vehicles and energy storage systems. However, existing methods—including those based on physical principles, deep learning, and traditional machine learning—all face challenges in balancing accuracy, computational efficiency, and adaptability to non-linear aging dynamics. This study proposes a new framework that combines multi-scale data preprocessing and a divide-and-conquer strategy to address these limitations. Firstly, a hybrid Wavelet–SG filter is applied to suppress noise, and a set of specialized XGBoost micro models is trained, with each model predicting capacity for a specific cycle, enabling precise trajectory prediction at different aging stages. The evaluation on the Toyota-MIT-Stanford dataset (118 batteries under different operating protocols) shows that this method achieves an average MAPE of 1.16% and a maximum of no more than 2.5% on the unfamiliar protocol test set. In terms of accuracy, it achieves performance comparable to CNN, LSTM, and CNN-LSTM benchmarks. Importantly, its parallel architecture enables fast inference (400 milliseconds on CPU), making it suitable for edge deployment in battery management systems. The model also has interpretability consistent with physical laws and can autonomously capture stage-dependent degradation mechanisms. This work provides a reliable, efficient, and interpretable solution for real-world battery health monitoring. Full article

In battery electric vehicles (BEVs), range-extended electric vehicles (REEVs) are gaining prominence due to range limitations, long charging times, and limited charging infrastructure. Range losses are particularly evident under extreme climate conditions, necessitating the development of efficient range-extender (RE) systems. In this study, a liquefied petroleum gas (LPG)-fueled, recuperator-equipped Micro Gas Turbine (MGT) was modeled as a standalone range-extending power unit using the Simcenter simulation environment, and its thermodynamic performance was examined under extreme climate conditions. While existing MGT studies in the literature generally focus on diesel-fueled systems, this study fills a significant gap in the literature by modeling the effects of using low-carbon, high-energy-density LPG. The performance of the MGT system was analyzed in extreme cold (−10 °C), standard (20 °C), and hot (45 °C) climates; at three different turbine inlet temperatures (1000, 1100, and 1250 K); and at three recuperator effectiveness settings (0.75, 0.85, and 0.95). The developed MGT system achieved a maximum thermal efficiency of 41.1% and a specific fuel consumption (SFC) of 188.67 g/kWh under cold climate conditions of −10 °C (263.15 K), a turbine inlet temperature (TIT) of 1250 K, and a recuperator effectiveness of 0.95. Consequently, specific CO₂ emissions were reduced to 566.01 g/kWh. The study’s most significant contribution to the literature is that the developed system offers high thermal efficiency, low fuel consumption, and low emissions under extremely cold climate conditions (−10 °C), where electric vehicle batteries typically experience performance and range loss. The LPG-fueled micro gas turbine with a recuperator demonstrates the potential to serve as an efficient, low-emission and competitive auxiliary power unit (APU) for range-extender applications, particularly under extreme climatic conditions. Full article

The transition toward sustainable aviation fuels for unmanned aerial vehicle propulsion requires alternative fuel blends that reduce emissions while maintaining stable power generation. This study investigates the combustion performance, electrical output, emission behavior, and near-field pollutant dispersion of butanol–kerosene blends in a hybrid micro-turboprop propulsion platform representative of UAV applications. Conventional kerosene and three butanol–kerosene blends, containing 10%, 20%, and 30% butanol by volume, were tested under four operating regimes ranging from idle to approximately 2.5 kW electrical load. Exhaust gas temperature, CO, NO, NOx, SO₂, electrical power output, throttle response, and pollutant dispersion behavior were evaluated experimentally, while polynomial regression was applied to quantify throttle–power relationships. The results show that the 20% butanol blend provided the most favorable overall performance. Relative to conventional kerosene, B20 achieved approximately 4.8% higher electrical power output at equivalent throttle settings, reduced fuel demand by nearly 3.9%, and decreased the throttle requirement for 2 kW electrical output by almost 5%. In terms of emissions, B20 reduced CO formation across low and intermediate operating regimes while maintaining moderate NOx levels and stable exhaust gas temperature behavior. Increasing butanol content also improved plume homogenization: the anisotropy index decreased from 2.41 for B10 to 1.96 for B20 and 1.58 for B30, while high-concentration plume regions were reduced by up to 31%. However, B30 introduced stronger evaporative cooling, ignition delay effects, and reduced mid-load responsiveness. Overall, moderate butanol blending, particularly B20, represents the most balanced solution for reducing the environmental footprint of hybrid UAV micro-turboprop propulsion without significant performance penalties. Full article

Given the complex operating conditions and latent faults exhibited by electric vehicle charging infrastructure amid massive order volumes, traditional monitoring methods based on thresholds or single statistical metrics struggle to detect dynamic, time-varying anomalies. This paper proposes a method for identifying and tracing the operational status of charging facilities based on the k-shape time-series clustering algorithm. This method directly uses charging current time series as the research object, eliminating the cumbersome manual feature extraction process. By utilizing a shape-based distance (SBD) metric strategy, it overcomes common time-series data issues such as phase shifts and amplitude scaling while preserving the integrity of the time dimension. Through iterative calculation of cluster centroids, the algorithm successfully and adaptively classifies massive amounts of data into typical clusters such as “standard charging,” “deep oscillation,” and “power-limited.” Based on the clustering results, this paper further constructs a “shape-operating condition” mapping mechanism. Combined with a Bayesian posterior probability model, this enables the localization of high-risk “vehicle-charger” combinations statistically associated with abnormal waveforms. Empirical studies demonstrate that this method can effectively identify equipment performance degradation at the micro-level of waveforms and provide prioritized inspection clues for the intelligent operation and maintenance of charging networks. Full article

With the high penetration of distributed photovoltaic and storage systems, active distribution grids are prone to experiencing “active power surplus and reactive power shortage” during the evening peak, leading to voltage sags at the network end. Although electric vehicle (EV) grid-connected inverters possess four-quadrant reactive power regulation capabilities without causing the additional chemical cyclic aging of the battery cells, existing dispatch systems often treat them as unconditional response resources, overlooking users’ actual willingness to cede control and the associated strategic interactions. To address this, this paper proposes a “grid-load” coordinated reactive power optimization strategy that accounts for EV users’ willingness to respond: a Logit model incorporating price incentives, initial energy consumption, and parking duration is constructed based on discrete choice theory. By combining a truncated normal distribution with the Monte Carlo method to eliminate micro-sampling errors, a model of the expected reactive power capacity of charging stations under dynamic incentives is established; considering the physical constraints of SVCs and EVs, a scalarized single-objective optimization model is constructed with grid loss-equivalent costs, ancillary service costs, and voltage deviation as objectives, and solved using an improved particle swarm optimization algorithm with linearly decreasing weights. Simulations on a modified 33-node IEEE system incorporating storage indicate that this strategy can assign optimal compensation prices to each node based on the spatial value of reactive power. Compared to traditional single-voltage regulation and fixed subsidies, it not only stabilizes the grid-wide voltage within a safe range but also avoids overcompensation, achieving global optimization of both power quality and economic efficiency. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

The demand for lower-impact materials in mobility has increased interest in the lightweight composite structures for electric vehicles (EVs). This study presents an extended and revised dataset for biaxial non-crimp fabric (NCF) composite laminates intended for two-wheeled EV chassis applications, building on earlier published results by repeating all mechanical tests and recalculations and by adding a full stress–strain analysis, a repeatability assessment across multiple specimens, and a digital image correlation (DIC)-based strain evaluation. Three material families, represented by four laminate conditions, were investigated: carbon/epoxy composites post-cured for 4 h and 10 h, glass-fiber composites, and linen (flax) composites. The tensile and flexural behaviors were characterized according to ISO 527-4 and ISO 14125, respectively, while a GOM ARAMIS optical system was used to obtain the axial strain, transverse strain, and Poisson’s ratio. Carbon laminates showed the highest performance, with the 10 h post-cure condition reaching 1126 MPa tensile strength, up to 60 GPa Young’s modulus, 696 MPa flexural strength, and 43 GPa flexural modulus. Glass laminates provided intermediate properties, whereas flax laminates showed lower strength but higher compliance and deformation capacity. The obtained results show that the biaxial NCF composites studied in this work offer weight-saving potential for micro-mobility chassis and provide a standard-based benchmark for future durability and life-cycle studies. Full article

The Multi-Destination Vehicle Routing Problem (MD-VRP) with a heterogeneous electric fleet is a critical challenge in optimizing commuter services for large-scale institutions and logistics operations. To address the complexities of electric fleet composition uncertainty and multi-center routing in “micro-city” campus environments, this paper establishes a robust multi-objective programming model. The model aims to simultaneously minimize three conflicting objectives, the total number of vehicles, total driving distance, and total electric energy consumption (kWh), under constraints of flow conservation and vehicle availability. Considering the nondeterministic polynomial-time hard (NP-hard) nature of the problem, a novel two-stage hybrid heuristic algorithm is proposed. In the first stage, a Modified Kruskal’s algorithm is employed to aggregate scattered stops into optimized clusters to reduce dimensionality. In the second stage, a State-Compressed Dynamic Programming (SC-DP) algorithm is applied to determine the optimal routing and electric vehicle type selection for each cluster. The methodology is validated using a case study of a large-scale campus network with 100 nodes. The optimization results identify an optimal fleet configuration of 41 campus electric commuter vehicles across three specific types (capacities of 45, 55, and 60), resulting in an annual total energy consumption of 5893.98 kWh. Compared with a global Ant Colony Optimization (ACO) baseline in this case study, the proposed framework reduces the required fleet size by 22.6% and annual energy consumption by 9.2%; however, this comparison should be interpreted as a preliminary case-study benchmark because the proposed method adopts a decomposition-based “Cluster-First, Route-Second” strategy. The results indicate that the approach achieves higher solution efficiency, offering an economically and environmentally friendly scheme for electric vehicle fleet operations. Full article

The large-scale integration of renewable energy and electric vehicles introduces grid-connected power fluctuations in microgrids. To address this, this paper proposes a novel two-stage optimization scheduling strategy that balances economic efficiency and grid compatibility. In the first stage, a multi-objective optimization model is formulated to minimize both operating costs and power fluctuations, and the Improved Multi-Objective Grey Wolf Optimization algorithm—incorporating the Bernoulli chaotic map—is employed to solve it efficiently. In the intra-day phase, a rolling tracking strategy based on model predictive control is proposed to address ultra-short-term forecasting errors, and a multi-unit hierarchical error compensation mechanism is designed. This mechanism prioritizes the use of supercapacitors to absorb high-frequency fluctuations, followed by the coordinated use of batteries, electric vehicle clusters, and micro gas turbines to mitigate residual deviations, thereby effectively reducing the operational burden on individual energy storage devices. Finally, a comparative analysis of six simulation cases was conducted using a weighted evaluation metric that integrates average power deviation values and interconnection line power fluctuations. The results confirm that this strategy not only significantly smooths grid-connected power fluctuations but also demonstrates exceptional robustness and adaptability under extreme forecast error scenarios. Full article

Electric vehicles (EVs) are essential for sustainable urban mobility, coordinating transportation demands with energy distribution networks. However, uncoordinated EV charging neglects trip chain continuity, inducing spatial–temporal congestion and overloading local charging capacities. Thus, effectively guiding EVs is a key problem in mitigating traffic emissions and preventing power grid-side stress. In this paper, a two-stage dynamic routing framework within a traffic–energy coordination architecture is proposed, integrating an AHP–Entropy–TOPSIS model for station selection and an Improved Ant Colony Optimization algorithm for trajectory execution. Using this framework, a series of macro–micro simulations on the Sioux Falls network was conducted alongside a congestion-driven dynamic pricing mechanism. The results indicate that the pricing strategy facilitates spatial load balancing through peak shaving at core nodes. Compared to conventional standard meta-heuristic baselines, this framework reduces average economic costs by 28.9% while ensuring battery safety and limiting indirect carbon emissions. The proposed framework provides a multi-objective navigation solution that prevents cross-layer decision fragmentation, supporting the sustainable development of smart city infrastructure. Full article

Urban roundabouts present significant design challenges for the integration of micro-mobility, yet comparative evidence regarding user behavior remains limited. As cities transition toward sustainable transport networks, understanding the operational needs of different micromobility modes is essential for urban planning. This study investigates the dynamic strategies of micromobility users through a controlled field experiment at a mini-roundabout in Gravina di Catania, Italy. Twenty experienced riders executed crossings using conventional bicycles and electric scooters. Utilizing drone recordings and open-source tracking, the analysis extracted speed, longitudinal acceleration, and path radius across 80 maneuvers. The findings reveal that behavior is highly dependent on vehicle type and geometric deflection. On highly deflected trajectories, e-scooters selected wider radii and achieved up to 15% higher speeds and accelerations than bicycles, whereas on gentler trajectories, they adopted more conservative, tighter lines with intense braking. Bicycles exhibited smaller, less systematic adjustments. These significant kinematic differences indicate that bicycles and e-scooters possess distinct performance envelopes. Treating them as a single legal or design class obscures stability disparities influencing conflict risk. Ultimately, this research provides empirical insights to guide urban planners in redesigning intersections, emphasizing that tailored infrastructure and targeted speed management are critical steps toward safer, truly sustainable urban mobility. Full article

For electric vehicles, accurate real-time estimation of the road friction coefficient is critical for maintaining stability, as the millisecond-level response of electric motors and the integration of regenerative braking demand higher perception fidelity than traditional internal combustion vehicles. This paper proposes a methodological framework for road friction estimation specifically designed for intelligent E-Chassis based on micro-signal features of intelligent tires and deep learning. An intelligent tire system, integrated with tri-axial accelerometers and strain gauges, was installed on the front-left wheel of a test vehicle to capture raw dynamic signals during transitions from cement to snow-covered surfaces across a velocity gradient of 10–50 km/h. The Savitzky–Golay convolutional smoothing algorithm was applied to reconstruct the high-frequency raw signals, enabling the extraction of a five-dimensional feature vector comprising vehicle velocity, peak strain, contact patch width, peak-to-peak acceleration, and signal standard deviation. The study revealed a natural filtering effect originating from the porous elastic properties of snow, resulting in a 60–70% reduction in signal standard deviation compared to cement, accompanied by a cliff-like feature collapse at the moment of snow entry. A BP neural network model with a 5-7-1 architecture achieved an identification accuracy of 96.2% on the test set, facilitating a rapid real-time prediction of the friction coefficient transitioning from 0.75 to 0.23. Unlike traditional methods, the proposed approach does not rely on high slip ratios and can complete identification within the first physical rotation cycle. This provides a robust physical criterion for the torque vectoring and regenerative braking stability of intelligent electric vehicles in extreme environments. Full article

This paper presents an experimental investigation of the level flight speed and endurance characteristics of a micro-class unmanned aerial vehicle as a function of operating weight. Wind tunnel experiments were conducted to determine the aerodynamic performance and power requirements of the UAV over a range of operating weight configurations. The tested vehicle, a fixed-wing micro UAV, was examined under steady, level flight conditions, with particular emphasis on identifying variations in the minimum power required to sustain level flight. Measured aerodynamic forces and moments were used to derive drag polars and the corresponding power curves for each mass configuration. Based on these results, endurance estimates were obtained by coupling the experimentally derived power requirements with the characteristics of the onboard electric propulsion system. The study demonstrates a clear shift in flight speeds with increasing operating weight, as well as a reduction in achievable endurance, highlighting the sensitivity of micro-class UAV performance to mass variations, and therefore energy consumption. Full article