Search Results (88)

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

In this article, the application of virtual reality technology for the realistic and immersive visualization of various tasks and scenarios in fields such as power engineering and fire safety has been examined in order to help prepare students and professional electrical engineers with electrical safety, the operation of electrical substations, potential emergencies, injury prevention, fire safety, and others. Additionally, the use of machine learning algorithms to guide evacuations from hazardous environments, fault prevention, fire prediction, and discovery of conductive materials has been examined. The most frequently used algorithms in these areas have also been described and summarized, and conclusions have been made about the combined advantages of using VR and ML algorithms. Finally, the needs, contributions, and challenges of using machine learning in virtual reality projects have been examined. Full article

Electric bicycles in elevators pose serious safety hazards. Fires in the confined space make escape difficult, and recent accidents involving e-bike fires have caused casualties and property damage. To prevent e-bikes from entering elevators and improve public safety, this design employs the Nezha development board as the upper computer for visual detection. It uses deep learning algorithms to recognize hazards like e-bikes. The lower computer orchestrates elevator controls, including voice alarms, door locking, and emergency halt. The system comprises two parts: the upper computer uses the YOLOv11 model for target detection, trained on a custom e-bike image dataset. The lower computer features an elevator control circuit for coordination. The workflow covers target detection algorithm application, dataset creation, and system validation. The experiments show that the YOLOv11 demonstrates superior e-bike detection performance, achieving 96.0% detection accuracy and 92.61% mAP@0.5, outperforming YOLOv3 by 6.77% and YOLOv8 by 15.91% in mAP, significantly outperforming YOLOv3 and YOLOv8. The system accurately identifies e-bikes and triggers safety measures with good practical effectiveness, substantially enhancing elevator safety. Full article

The layout of an urban waste-fired zero-emission power plant is described in this paper. The principle layout, which is based on similar coal-fired plants retrieved from the literature, integrates gasification with a power-generation section and implements two parallel conversion processes: one relies on the heat developed in the gasifier and consists of a thermoacoustic-magnetohydrodynamic (TA-MHD) generator; the other involves treating syngas to obtain almost pure hydrogen, which is then fed to fuel cells. The CO₂ derived from the oxidation of Carbon is stocked in liquid form. The novelty of the proposed layout lies in the fact that the entire conversion is performed using static equipment. The resulting plant prevents the release of any type of emissions in the atmosphere and increases mechanical efficiency, compared to traditional plants—thanks to the absence of moving parts—resolving, nonetheless, the ever-increasing waste-related pollution issue. A case study of a Union of Municipalities in Southern Lebanon is considered. The ideal cycle handles 65 tons/day of urban waste and is capable of generating 7.71 MW of electric power, with a global efficiency of 52.39%. Full article

Lithium-ion batteries (LIBs) are widely used as energy storage units in electric vehicles, mobile phones, and other electric devices due to their high voltage, large capacity, and long cycle life. Lithium-ion batteries are prone to thermal runway (TR), resulting in fires and explosions, which can seriously hinder the commercial development of LIBs. A series of safety methods has been studied to prevent TR of LIBs. The safety methods for suppressing TR in LIBs were reviewed, including safety equipment method, material modification method, thermal management method, and cooling method. The mechanism, advantages and disadvantages, and future applications of the TR suppression method are discussed. The effectiveness of the proposed safety method was evaluated through technical analysis and experimental testing, and the inhibitory effects of different safety methods on battery TR were summarized. The future trend of suppressing TR is discussed by summarizing and generalizing existing technologies for suppressing thermal runaway. This study provides a reference for exploring more effective methods to mitigate TR in the future. Full article

The study of incipient faults due to insulation defects in cables is crucial for preventing electrical fires and ensuring personal safety. However, research on incipient faults in low-voltage cables remains relatively underexplored compared to that on medium-voltage cables. This paper focuses on low-voltage cross-linked polyethylene (XLPE) cables and investigates the changes in voltage and current caused by insulation defects in different wet conditions. The main findings are that the voltage applied to the cable with defective insulation shows sub-cycle disturbances that become more frequent. The current in the cable conductor shows a pulsed shape, coincident with the voltage disturbances. Over time, the sub-cycle disturbances gradually disappear, instead, the steady-state leakage current emerges. The wet conditions affect waveforms of the voltage/current disturbance and the frequency of occurrence. The findings provide detailed and unique characteristics of the voltage and current during the cable incipient fault, which are different from those of the incipient fault in the medium-voltage cables. The simulation and analysis support the experimental results. Based on the experimental results, a model is developed for further research on LV-cable incipient fault detection and protection. Full article

With the development of society and the advancement of technology, the application of electricity in modern life has become increasingly widespread. However, the risk of electrical fires has also significantly increased. This paper thoroughly investigates the causes, classifications, and challenges of electrical fires in special environments, and summarizes advanced detection and extinguishing technologies. The study reveals that the causes of electrical fires are complex and diverse, including equipment aging, improper installation, short circuits, and overloading. In special environments such as submarines, surface vessels, and aircraft, the risk of electrical fires is higher due to limited space, dense equipment, and difficult rescue operations. This paper also provides a detailed analysis of various types of electrical fires, including cable fires, electrical cabinet fires, transformer fires, battery fires, data center fires, and residential fires, and discusses their characteristics and prevention and control technologies. In terms of detection technology, this paper summarizes the progress of technologies such as arc detection, video detection, and infrared thermography, and emphasizes the importance of selecting appropriate technologies based on specific environments. Regarding extinguishing technologies, this paper discusses various means of extinguishing, such as foam extinguishing agents, dry powder extinguishing agents, and fine water mist technology, and highlights their advantages, disadvantages, and applicable scenarios. Finally, this paper identifies the limitations in the current field of electrical fire prevention and control, emphasizes the importance of interdisciplinary research and the development of advanced risk assessment models, and outlines future research directions. Full article

Multi-threshold segmentation of color images is a critical component of modern image processing. However, as the number of thresholds increases, traditional multi-threshold image segmentation methods face challenges such as low accuracy and slow convergence speed. To optimize threshold selection in color image segmentation, this paper proposes a multi-strategy improved Electric Eel Foraging Optimization (MIEEFO). The proposed algorithm integrates Differential Evolution and Quasi-Opposition-Based Learning strategies into the Electric Eel Foraging Optimization, enhancing its search capability, accelerating convergence, and preventing the population from falling into local optima. To further boost the algorithm’s search performance, a Cauchy mutation strategy is applied to mutate the best individual, improving convergence speed. To evaluate the segmentation performance of the proposed MIEEFO, 15 benchmark functions are used, and comparisons are made with seven other algorithms. Experimental results show that the MIEEFO algorithm outperforms other algorithms in at least 75% of cases and exhibits similar performance in up to 25% of cases. To further explore its application potential, a multi-level Kapur entropy-based MIEEFO threshold segmentation method is proposed and applied to different types of benchmark images and forest fire images. Experimental results indicate that the improved MIEEFO achieves higher segmentation quality and more accurate thresholds, providing a more effective method for color image segmentation. Full article

Grassland fires represent a significant natural disaster affecting global grassland ecosystems, posing serious threats to ecological safety, livestock production, and the lives and property of herders. This study focuses on Qinghai Province, utilizing historical grassland fire data to pre-screen the factors influencing both natural and anthropogenic grassland fires. By applying the maximum entropy model and analyzing data from 150 fire incidents, this research predicts the spatial distribution of grassland fire susceptibility within the study area. The outcomes were as follows. (1) A maximum entropy–Kvamme gain statistical model was developed and validated for reliability. (2) The natural grassland fire-prone areas are predominantly located in southern Qinghai, covering a vast portion of the Guoluo Tibetan Autonomous Prefecture, as well as Zeku County, Henan Mongolian Autonomous County, Yushu City, and Mado County in the Yushu Tibetan Autonomous Prefecture. These regions are distinguished by their proximity to rivers and the presence of abundant vegetation. In contrast, grassland areas prone to anthropogenic fires are mainly concentrated in eastern Qinghai Province, where human activities are more intensive and population centers are located nearby. (3) The prediction results identify dominant grassland fire factors and their thresholds. (4) Natural grassland fires in Qinghai Province primarily result from spontaneous combustion, while anthropogenic grassland fires are primarily caused by electrical faults from high-voltage power lines, heating activities related to grazing, and the use of firecrackers. This study presents a disaster prediction model to support grassland management and fire prevention in Qinghai Province, providing a scientific basis for effective fire control strategies. Full article

Lithium-ion batteries (LIBs), valued for their high energy density, long lifespan, and low environmental impact, are widely used in electric vehicles (EVs) and energy storage. However, increased energy density has exacerbated thermal runaway (TR) issues, hindering large-scale applications. This paper systematically analyzes the mechanisms of TR and strategies for early warning and prevention to enhance battery safety. It begins by detailing TR mechanisms and their triggers, then reviews various TR early warning technologies, fire prevention methods, and the effectiveness and mechanisms of novel extinguishing agents such as hydrogels, perfluorohexanone, liquid nitrogen (LN), dry powder, and aqueous vermiculite dispersion (AVD). The study also explores advancements in new fire-retardant coatings for batteries. Finally, it summarizes current challenges and forecasts future research directions in battery technology. This review offers readers a clear, systematic overview of TR mechanisms, warning systems, and prevention technologies, providing comprehensive insights into TR management. Full article

Electrical vehicle (EV) batteries, particularly lithium-ion batteries, pose significant environmental challenges due to their hazardous components, the effects of initial building-material fabrication, and the difficulties of recycling and disposal. Policies and legislative strategies adopted by different governments to solve these issues are investigated in this manuscript, specifically based on circularity and resource use. Important steps are end-of-life management, safe disposal and transportation, avoidance of hazardous gas emissions, circularity, resource use, fire prevention, and expanded producer accountability. As of February 2024, New Jersey is the first and only state in the United States that has adopted a thorough legislative framework for EV battery management, therefore establishing a standard for other states. California passed major laws encouraging Zero-Emission Vehicle (ZEV) battery manufacture and recycling. Other states are likewise trying to show initiative by implementing and changing laws. Globally, the European Union is leading, while Canada, Australia, China, and others have created strong rules of regulation. This paper looks at and contrasts the environmental problems of lithium-ion electric vehicles with the legislative actions made by different nations and states to solve these problems. By means of a thorough examination of these policies, this paper seeks to present a whole picture of the current scene and the best techniques for lifetime management of EV batteries that can be embraced by different governments. In this manuscript, a comparison is made between two leading legislations, specifically that of the state of New Jersey and the European Union. To achieve the most beneficial outcome, it is the responsibility of stakeholders to promote rules; emphasize battery recycling, secure disposal, and extended producer accountability; promote innovation in sustainable battery technology; and try to build a pragmatic approach to battery management to mitigate environmental impacts based on a hybrid version of the legislations from the state of New Jersey and the European Union. Full article

Roll-on/Roll-off passenger vessels transporting electric vehicles (Ro-Ro EVs) face unique fire hazards, challenging traditional fire risk management strategies. This study integrates fault tree analysis (FTA) with Fuzzy Bayesian Network (FBN) to assess the fire risks of Ro-Ro EVs across the entire hazard chain. Given limited historical accident data, five experts familiar with the Shanghai Baoshan–Chongming ferry route refine fault tree models to visualize key fire hazard chain mechanisms and estimate risk probabilities. The FBN incorporates fault tree hierarchical structures, EV and Ro-Ro vessel-related risk factors, and applies a nine-level fuzzy scoring system to assess these risks. The FTA-FBN model offers a comprehensive framework for evaluating emerging fire risks specific to Ro-Ro EVs. Findings indicate that the highest risk occurs during the ignition phase. Primary triggers include external heat sources, improper vehicle securing, and vehicle collisions, leading to thermal runaway in lithium batteries. Failures in extinguishing and detecting lithium battery fires exacerbate fire spread. Effective fire compartmentalization and flammable material management are essential to prevent uncontrolled fires. Recommendations for fire prevention and control include shipboard battery level monitoring, charging restrictions, explosion-proof electrical installations, enhanced ventilation, lithium battery fire suppression systems, and vehicle securing. Full article

In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention. Full article

Domestic electrical fires can occur for a variety of reasons, including faulty wiring and plugs, overloaded circuits, and malfunctioning electrical appliances. In this article, the circumstances of domestic electrical fire injuries between 2011 and 2022 in the county of Merseyside in Northwestern England were examined in order to inform fire prevention activities. Householder carelessness appeared to be less of a factor in electrical fire injury compared to other types of fire injury such as cooking or smoking fire injury. Faulty electricity supplies were the main cause of electrical fire injuries. Male residents were slightly more likely to sustain injury in an electrical fire in comparison to females (1.25 to 1). Those aged 75+ appeared to be more at risk of electrical fire injuries compared to other age groups. Full article

A large amount of energy can accumulate and be stored during underground coal fires. As thermal energy cannot be easily removed using the traditional technologies of fire prevention and extinguishment, there is a potential benefit to collecting and utilizing thermal energy from coal fires and converting it to electrical energy. Thus, this work proposes a thermoelectric generator as a solution to convert thermal energy from coal fires to electrical energy. To improve the thermal energy conversion efficiency, an experimental test system was established using a thermosyphon, an electric heating module, a cooling circulation module, a thermoelectric module, and a data acquisition module. Under the condition of ensuring the same input heat and cooling boundary conditions, the influence of three factors, namely the cooling method, the connection method, and the coverage rate of thermoelectric devices, on the performance of the coal fire waste heat conversion system was studied. The results show that, compared with air cooling, water cooling provides a greater temperature difference for the thermoelectric module, and the maximum temperature difference can reach 65.90 °C. Series connection between thermoelectric devices will generate a higher open-circuit voltage and output voltage. The maximum horizontal open-circuit voltage value can reach 3.34 V, and the maximum output voltage is 2.61 V. Compared with the coverage rates of thermoelectric devices of 15.0% and 30.0%, the output power under the coverage rate of 22.5% is the largest at 0.35 W, and its thermoelectric conversion efficiency is also the largest at 0.35%. The optimal combination of thermoelectric modules obtained from the research results can provide ideas for the application of in situ coal fire prevention and control. Full article