Search Results (133)

The increasing levels of ozone pollution have become a significant environmental issue in urban areas worldwide. Previous studies have confirmed that the urban ozone pollution in China is mainly controlled by volatile organic compounds (VOCs) rather than nitrogen oxides. Therefore, a study on the emission characteristics and source analysis of VOCs is important for controlling urban ozone pollution. In this study, hourly concentrations of 57 VOC species in four groups were obtained in April 2022, a period of high ozone pollution in Kunming, China. The ozone formation potential analysis showed that the accumulated reactive VOCs significantly contributed to the subsequent ozone formation, particularly aromatics (44.16%) and alkanes (32.46%). In addition, the ozone production rate in Kunming is mainly controlled by VOCs based on the results of the empirical kinetic modeling approach (K_NOx/K_VOCs = 0.25). The hybrid single-particle Lagrangian integrated trajectory model and polar coordinate diagram showed high VOC and ozone concentrations from the southwest outside the province (50.28%) and the south in local areas (12.78%). Six factors were obtained from the positive matrix factorization model: vehicle exhaust (31.80%), liquefied petroleum gas usage (24.16%), the petrochemical industry (17.81%), fuel evaporation (11.79%), coal burning (7.47%), and solvent usage (6.97%). These findings underscore that reducing anthropogenic VOC emissions and strengthening controls on the related sources could provide a scientifically robust strategy for mitigating ozone pollution in Kunming. Full article

To address the technical problems of broad droplet size spectrum, insufficient atomization uniformity, and spray drift in plant protection unmanned aerial vehicle (UAV) applications, this study developed a novel two-stage aerial electrostatic spraying device based on the coupled mechanisms of hydraulic atomization and electrostatic induction, and, through the integration of three-dimensional numerical simulation and additive manufacturing technology, a new two-stage inductive charging device was designed on the basis of the traditional hydrodynamic nozzle structure, and a synergistic optimization study of the charging effect and atomization characteristics was carried out systematically. With the help of a charge ratio detection system and Malvern laser particle sizer, spray pressure (0.25–0.35 MPa), charging voltage (0–16 kV), and spray height (100–1000 mm) were selected as the key parameters, and the interaction mechanism of each parameter on the droplet charge ratio (C/m) and the particle size distribution (Dv50) was analyzed through the Box–Behnken response surface experimental design. The experimental data showed that when the charge voltage was increased to 12 kV, the droplet charge-to-mass ratio reached a peak value of 1.62 mC/kg (p < 0.01), which was 83.6% higher than that of the base condition; the concentration of the particle size distribution of the charged droplets was significantly improved; charged droplets exhibited a 23.6% reduction in Dv50 (p < 0.05) within the 0–200 mm core atomization zone below the nozzle, with the coefficient of variation of volume median diameter decreasing from 28.4% to 16.7%. This study confirms that the two-stage induction structure can effectively break through the charge saturation threshold of traditional electrostatic spraying, which provides a theoretical basis and technical support for the optimal design of electrostatic spraying systems for plant protection UAVs. This technology holds broad application prospects in agricultural settings such as orchards and farmlands. It can significantly enhance the targeted deposition efficiency of pesticides, reducing drift losses and chemical usage, thereby enabling agricultural enterprises to achieve practical economic benefits, including reduced operational costs, improved pest control efficacy, and minimized environmental pollution, while generating environmental benefits. Full article

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios. Full article

Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts. Full article

The Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method is employed in this study to investigate the combined effects of region area, the number of COVID-19 infections, and the number of family cars on NO₂ concentration at key highway toll stations in Zhejiang Province, China. By selecting and comparing typical cases, the analysis reveals differentiated characteristics in how various factor combinations influence NO₂ concentration. The main findings are as follows: Under COVID-19 lockdown measures, prolonged vehicle waiting times and a shift towards family car usage among the public have led to a significant increase in NO₂ concentration at highway toll stations. Promoting the Electronic Toll Collection (ETC) system and encouraging public transportation are of great importance. The synergistic effects of COVID-19 lockdown policies and urban land area, resulting in the reduction in the number of family cars and the excellent air circulation conditions in large cities, have contributed to the decrease in NO₂ concentration at highway toll stations. Increasing urban green spaces and promoting clean energy vehicles are crucial for advancing urban sustainable development. The combined analysis of the region area and the number of family cars indicates that a higher proportion of large vehicles contributes to improving transportation efficiency, but also results in elevated NO₂ concentration at highway toll stations due to diesel emissions. Optimizing the transportation structure and reducing reliance on large vehicles are of significant importance. Full article

The expected growth of electric vehicle (EV) usage will not only increase the energy demand but also bring the requirement to provide the necessary electrical infrastructure to handle the load. While charging infrastructure is becoming increasingly present in urban areas, special attention is required for transit traffic, not just for passengers but also for freight transport. Differences in the nature of battery charging compared to that of classical refueling require careful planning in order to provide a resilient electrical infrastructure that will supply enough energy at critical locations during peak hours. This paper presents a hybrid simulation model for analyzing fast-charging demand based on traffic flow, projected EV adoption, battery characteristics, and environmental conditions. The model integrates a probabilistic model for evaluating the charging requirements based on traffic flows with a discrete-event simulation (DES) framework to analyze charger utilization, waiting queues, and energy demand. The presented case of traffic flow on Slovenian motorways explored the expected power demands at various seasonal traffic intensities. The findings provide valuable insight for planning the charging infrastructure, the electrical grid, and also the layout by anticipating the number of vehicles seeking charging services. The modular design of the model allowed replacing key parameters with different traffic projections, supporting a robust scenario analysis and adaptive infrastructure planning. Replacing the parameters with real-time data opens the path for integration into a digital twin framework of individual EV charging hubs, providing the basis for development of an EV charging hub network digital twin. Full article

The usage rates of shared autonomous vehicles (SAVs) have become a pressing concern following their increased deployment. While prior research has focused on initial user acceptance, post-adoption behavior remains underexplored. As SAV deployment matures, public concerns have expanded beyond safety to encompass service requirements, challenging the relevance of earlier findings to current commercialization efforts. This study investigates the factors shaping SAV utilization through an empirical study in Norway, where autonomous buses have operated for several years. Through mixed methods, we first analyzed responses from 106 participants to 43 SAV users and 63 witnesses of SAV operations. The results revealed that concerns had shifted from technological anxiety to service-related factors. Through purposive interviews with individuals who showed acceptance of SAVs but did not adopt them as their primary mode of transportation, we explored the gap between high acceptance and low usage. Our findings provide insights into long-term SAV deployment and guidelines for improving usage rates, highlighting the importance of addressing service characteristics such as information transparency, vehicle appearance, speed, and convenience, rather than focusing solely on safety in commercial settings. Full article

This study selected green spaces from three residential areas in Hefei as the research subjects, combining behavioral observation methods and a natural experiment to collect behavioral data from 2010 and 2024. The data were then compared using Poisson regression models. Additionally, home visits were conducted to gather residents’ perceptions of the factors contributing to the decline in vitality. Based on the survey data, multilevel regression analysis was performed to explore the decline in RQGS usage vitality and its influencing factors in the context of rapid urbanization. This study found a significant decline in green space visits, particularly during the afternoon (16:00–18:00) and in areas adjacent to roadways. The main influencing factors include emerging leisure choices (such as taking the subway to large parks or preferring indoor activities) and residents’ satisfaction with RQGS characteristics (such as functional zoning, noise pollution, and neighborhood familiarity). Notably, there was no significant correlation between “disposable leisure time” and visit frequency. These findings suggest that, despite the inherent advantages of proximity, the vitality of RQGS faces increasing challenges due to emerging diverse leisure demands and growing environmental disturbances. In contrast to the traditional emphasis on accessibility, this study recommends that future RQGS planning prioritize functional zoning (e.g., dog-walking areas, sports zones), address the needs of vulnerable groups, and focus on mitigating vehicle noise and air pollution rather than merely expanding parking facilities. Interventions should be scheduled for the afternoon and emphasize strengthening community interaction and cohesion to enhance user experience. This research provides valuable scientific evidence and practical guidance for urban planners and policymakers to optimize residential green spaces in the context of rapid urbanization, offering new perspectives for the empirical evaluation of RQGS upgrades. Full article

A micro electric vehicle (micro-EV) is a small electric car with one or two seats designed for short-to-medium-distance trips. Micro-EVs produce relatively less pollution during operation and, due to their compact size, offer greater mobility in narrow areas compared to conventional transportation. These advantages have led to a continuous increase in the number of micro-EVs. However, their small battery capacity results in a limited driving range per charge, and restrictions on power and speed lead to lower driving performance. Due to these drawbacks, micro-EVs still hold a small share of the overall vehicle market. Therefore, it is necessary to evaluate the strengths of micro-EVs and analyze how they should be utilized to promote their widespread adoption. Therefore, this study analyzed the strengths of micro-EVs and identified the types of services where they can be effectively utilized to promote the use of micro-EVs as a smart mobility option. This study focused on micro-EVs used as a shared transport service, delivery service, and in public service, as part of an R&D project on micro-EVs conducted by the Ministry of Trade, Industry, and Energy. A total of 106 micro-EVs were deployed for each service type: 57 for shared transport, 13 for delivery, and 36 for public service. Each micro-EV was equipped with a GPS device, and the analysis was conducted using GPS data collected from January 2021 to October 2021. Micro-EVs with missing data due to GPS device malfunctions were excluded from the analysis. As a result, two micro-EVs from the shared transport service and one from the public service were excluded. The study compared the travel characteristics of micro-EVs across the three different service types. Additionally, a comparative analysis of the driving characteristics of micro-EVs and conventional vehicles was conducted to assess the advantages of micro-EVs over traditional vehicles. The results of the analyses showed that micro-EVs were more utilized for the delivery service type than other service types in terms of daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), trip amounts (24.1 trips/day), and number of trips per trip chain (9.4 trips/trip chain). Moreover, micro-EVs have their strengths compared to other modes of transportation when traveling narrow roads. Analysis of the roads around the areas where micro-EVs were located showed that the micro-EVs were exposed to narrow roads with a width of under 5 m (among the total road link extensions, 57% consisted of road links with a width of less than 5 m), especially the micro-EVs used for delivery service. It is expected that the findings of this study will serve as a foundational resource for developing strategies to promote the adoption of micro electric vehicles. Full article

In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV. Full article

Simulation is a cornerstone of planning and facilitating the transition towards electric mobility in sub-Saharan Africa’s informal public transport. The primary objective of this study is to validate and refine the electro-kinetic model used to simulate electric versions of the sector’s minibuses. A systematic simulation methodology is also developed to correct the simulation parameters and improve the high-frequency GPS data used with the model. A retrofitted electric minibus was used to capture high-frequency GPS mobility data and power draw from the battery. The method incorporates key refinements such as corrections for gross vehicle mass, elevation and speed smoothing, radial drag, hill-climb forces, and the calibration of propulsion and regenerative braking parameters. The refined simulation demonstrates improved alignment with measured power draw and trip energy usage, reducing error margins and enhancing model reliability. Factors such as trip characteristics and environmental conditions, including wind resistance, are identified as potential contributors to observed discrepancies. These findings highlight the importance of precise data handling and model calibration for accurate energy simulation and decision making in the transition to electric public transport. This work provides a robust framework for future studies and practical implementations, offering insights into the technical and operational challenges of electrifying informal public transport systems in resource-constrained regions. Full article

Despite the fact that countless IoT applications are arising frequently in various fields, such as green cities, net-zero decarbonization, healthcare systems, and smart vehicles, the smart grid is considered the most critical cyber–physical IoT application. With emerging technologies supporting the much-anticipated smart energy systems, particularly the smart grid, these smart systems will continue to profoundly transform our way of life and the environment. Energy systems have improved over the past ten years in terms of intelligence, efficiency, decentralization, and ICT usage. On the other hand, cyber threats and attacks against these systems have greatly expanded as a result of the enormous spread of sensors and smart IoT devices inside the energy sector as well as traditional power grids. In order to detect and mitigate these vulnerabilities while increasing the security of energy systems and power grids, a thorough investigation and in-depth research are highly required. This study offers a comprehensive overview of state-of-the-art smart grid cybersecurity research. In this work, we primarily concentrate on examining the numerous threats and cyberattacks that have recently invaded the developing smart energy systems in general and smart grids in particular. This study begins by introducing smart grid architecture, it key components, and its security issues. Then, we present the spectrum of cyberattacks against energy systems while highlighting the most significant research studies that have been documented in the literature. The categorization of smart grid cyberattacks, while taking into account key information security characteristics, can help make it possible to provide organized and effective solutions for the present and potential attacks in smart grid applications. This cyberattack classification is covered thoroughly in this paper. This study also discusses the historical incidents against energy systems, which depicts how harsh and disastrous these attacks can go if not detected and mitigated. Finally, we provide a summary of the latest emerging future research trend and open research issues. Full article

The article addresses the usage patterns of plug-in hybrid vehicles (PHEVs) under Polish conditions. The conventional approach to operating such vehicles assumes that they are used with a fully charged battery at the start. However, the economic circumstances of Polish users often do not allow for daily charging of vehicles from the domestic power grid. As a result, these vehicles are used not only in a mode powered solely by the internal combustion engine but also in a mode where the internal combustion engine is primarily utilized to charge the battery. An analysis was conducted on various ways of operating plug-in vehicles, evaluating not only harmful emissions but also fuel consumption (for battery states of charge: SOC = 100%, SOC = 50%, SOC = 0%, and SOC = 0 → 100%—forced charging mode). The study focused on the most characteristic vehicle segment in Poland, SUVs, and employed a methodology for determining exhaust emissions under real-world driving conditions. Results indicate that forced charging of such a vehicle’s battery leads to over a 25-fold increase in carbon dioxide emissions (fuel consumption) in urban areas compared to operating the vehicle with a fully charged battery (CO—25× increase, NOx—12× increase, PN—11× increase). Operating a plug-in SUV without charging it from the power grid results in a 13-fold increase in fuel consumption compared to using the vehicle with a fully charged battery (CO—10× increase, NOx—6× increase, PN—4× increase). The emission results were used to evaluate Poland’s charging infrastructure in the context of PHEV usage. The current state of the infrastructure and its development plans for 2030 and 2040 were analyzed. It was found that significant reductions in fuel consumption (by approximately 30%) and CO₂ emissions are achievable by 2040. Emissions of CO, NOx, and PN are expected to decrease by about 10%, primarily due to the internal combustion engine operating at high load conditions in non-urban or highway scenarios. Full article

Beyond portable mobile devices, lithium-ion batteries play a crucial role in electric vehicle operations and stationary grid power generation. However, the aging of lithium-ion batteries, often accelerated by extreme temperatures and load current influences, requires thorough examination and solution. The high load current, cycling, temperature differential, and operational conditions are factors contributing to the reduction in capacity and shortened lifespan of lithium-ion batteries. In this study, a lithium-ion (LiNi_xMn_yCo_zO₂) battery was modeled by using the MATLAB/Simulink model technique. In order to investigate the effect of resistance build-up in the batteries, the capacity of the batteries (old and new batteries) was analyzed over different usage periods: 360 cycles, 1000 cycles, and 2000 cycles. A cooling system was introduced to explicitly carry out an inductive analysis of the effect of temperature on the performances of the batteries. The effect of load current on the capacity of the battery was examined between 30 A and 100 A. The results showed that the available capacity of a battery is proportional to its usage rate. Generally, when the load current on the batteries (old and new batteries) was 30 A, the battery was ideally in good health even after 1000 cycles for a 2 h discharge time. In addition, the old battery, however, showed a capacity decrease to about 74.15% and 74.94% for scenarios 1 and 2 after 1000 cycles for a 2 h discharge time when the batteries were subjected to a 100 A discharge current. Amongst other factors, scenarios 1 and 2 can be differentiated by whether the battery pack discharges uniformly or non-uniformly, whether the individual cells operate under the same or different discharge cycles, and whether the batteries are with cooling or without a cooling system. The voltage and temperature differences between the old and new batteries, after 2000 cycles for the 100 A load current, are 4.0 V and 5.3 °C (scenario 2), respectively. Moreover, after 360 cycles at a 100 A discharge current, the temperature difference between the old and new batteries was 4.5 °C in scenario 1 and 2.3 °C in scenario 2. Based on the results obtained in this study, useful equations for proper calibration, voltage, and cooling switching time characteristics were proposed. Additionally, the study results indicated that at higher load currents, battery degradation became less affected by temperature differentials. The results of this study will aid in the adequate load optimization and thermal management of lithium-ion batteries for electric vehicle applications. Full article

This study examines the key factors influencing e-bike adoption and explores how advancing e-bike usage in Henan Province, China, can foster sustainable urban transportation and contribute to urban environmental preservation. Utilizing data from an online survey, binary logistic regression analyzes the impact of socio-demographic characteristics, perceived advantages, neighborhood environmental attributes, and vehicle ownership on e-bike usage. The findings indicate that socio-demographic factors, such as family size and occupation, significantly influence adoption, with workmen more likely than office workers to choose e-bikes. Cost savings emerged as the primary motivator for e-bike use, overshadowing environmental concerns, which unexpectedly negatively affected usage patterns. However, the presence of supportive infrastructure—particularly charging stations and dedicated lanes—proves crucial for promoting e-bike usage, highlighting the importance of accessible, environmentally supportive urban design. Vehicle ownership characteristics further illuminate how access to e-bikes correlates with regular usage. These findings suggest that, beyond cost efficiency, targeted awareness campaigns and strategic infrastructure improvements are essential for embedding e-bikes into sustainable urban transport systems. By fostering adoption and supporting e-bike infrastructure, cities can significantly reduce urban pollution, improve air quality, and advance toward sustainable mobility goals in Henan Province and beyond. Full article