Search Results (1,439)

The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ (x = 0.03–0.15) ceramics were fabricated via the traditional solid reaction method. Characterization results revealed that each component exhibited a pure perovskite structure, and the average grain size significantly diminishes with increasing x. The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramics exhibited prominent relaxor ferroelectric behavior, whose characteristic narrow hysteresis loops effectively enhanced the energy storage performance of the material. Most importantly, the composition with x = 0.10 demonstrated exceptional energy storage properties at 150 kV/cm, achieving a high recoverable energy storage density (W_rec = 1.91 J/cm³) and excellent energy efficiency (η = 90.87%). Under the equivalent electric field, this composition also displayed a superior pulsed discharge performance, including a high current density (871 A/cm²), a high power density (67.3 MW/cm³), an ultrafast discharge time (t_0.9 = 109 ns), and a discharged energy density of 1.47 J/cm³. These results demonstrate that the (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramic system establishes a promising design paradigm for the creation and refinement of next-generation dielectrics for pulse power applications. Full article

In the context of urban decarbonization, multi-energy microgrids (MEMGs) are gaining increasing relevance due to their ability to enhance synergies across multiple energy vectors. This study presents a block-based MILP framework developed to optimize the operations of a real MEMG, with a particular focus on accurately modeling the structure of electricity and natural gas bills. The objective is to assess the added economic value of integrating a battery energy storage system (BESS) under the assumption it is employed to provide implicit flexibility—namely, bill management, energy arbitrage, and peak shaving. Results show that under assumed market conditions, tariff schemes, and BESS costs, none of the analyzed BESS configurations achieve a positive net present value. However, a 2 MW/4 MWh BESS yields a 3.8% reduction in annual operating costs compared to the base case without storage, driven by increased self-consumption (+2.8%), reduced thermal energy waste (–6.4%), and a substantial decrease in power-based electricity charges (–77.9%). The performed sensitivity analyses indicate that even with a significantly higher day-ahead market price spread, the BESS is not sufficiently incentivized to perform pure energy arbitrage and that the effectiveness of a time-of-use power-based tariff depends not only on the level of price differentiation but also on the BESS size. Overall, this study provides insights into the role of BESS in MEMGs and highlights the need for electricity bill designs that better reward the provision of implicit flexibility by storage systems. Full article

Electrohydrodynamic atomization (EHDA) has significant advantages for microencapsulating compounds in various structures using biopolymers, where more research using pure starch is required. Concerning this, jicama starch and its hydrolysates have not yet been tested, despite their unique characteristics, which come from an alternative low-value-added crop source. Rapid acid hydrolysis of jicama starch with H₂SO₄ resulted in dextrins with a degree of hydrolysis (DE) from 0.4 to 19% within 1–12 h, and syrup solids at 24 h (DE = 42%). This process modifies the water retention capacity of jicama starch, gel viscosity, surface tension, and electrical conductivity. Hydrolyzed starch particles obtained by electrospraying (10 kV, L = 10 cm, Q = 2 mL/h) showed Feret diameters and roundness significantly influenced (p ≤ 0.05) by the degree of hydrolysis rather than the concentration of solids. It was found that hydrolyzed jicama starch with a DE < 6.3% can be used as the sole wall material to form particles by electrospraying, as they facilitate the formation of stable and rounded like-microspheres particles; this was not feasible above this threshold. The results suggest that the jicama starch’s ability to be used as a wall material in the electrospray synthesis of particles or microspheres appears to be determined by the degree of hydrolysis. Full article

Edible mushrooms are well-known for their culinary and nutritional values. Additionally, they serve as a natural source of polyphenols, a group of bioactive compounds that significantly treat diseases associated with oxidative stress. The polyphenolic profile of mushrooms mainly consists of phenolic acids and flavonoids, whose chemical properties have attracted the attention of both the food and pharmaceutical industries. Consequently, methods for extracting polyphenols from mushrooms encompass conventional techniques (maceration and Soxhlet extraction) as well as innovative or green methods (ultrasound-assisted extraction, microwave-assisted extraction, pressurized liquid extraction, supercritical fluid extraction, enzyme-assisted extraction, and pulsed electric field extraction). Nonetheless, extraction with pressurized liquids and supercritical fluids is considered the most suitable method, as they function in a gentle and selective manner, preserving the integrity of the phenolic compounds. The use of mushroom-derived phenolic compounds in food and pharmaceutical formulations continues to face challenges concerning the safety of these extracts, as they might contain unwanted substances. Future applications should incorporate purification systems to yield highly pure extracts, thereby creating safe polyphenol carriers (for food and pharmaceutical products) for consumers. Full article

During the last few years, the requirements for highly efficient, sustainable, and versatile materials in modern biomedicine, aircraft and aerospace industries, automotive production, and electronic and electrical engineering applications have increased. This has led to the development of new and innovative methods for material modification and optimization. This can be achieved in many different ways, but one such approach is the application of surface thin films. They can be conductive (metallic), semi-conductive (metal-ceramic), or isolating (polymeric). Special emphasis is placed on applying semi-conductive thin films due to their unique properties, be it electrical, chemical, mechanical, or other. The particular thin films of interest are composite ones of the type of transition metal oxide (TMO) and transition metal nitride (TMN), due to their widespread configurations and applications. Regardless of the countless number of studies regarding the application of such films in the aforementioned industrial fields, some further possible investigations are necessary to find optimal solutions for modern problems in this topic. One such problem is the possibility of characterization of the applied thin films, not via textbook approaches, but through a simple, modern solution using their electrical properties. This can be achieved on the basis of measuring the films’ electrical impedance, since all different semi-conductive materials have different impedance values. However, this is a huge practical work that necessitates the collection of a large pool of data and needs to be based on well-established methods for both characterization and formation of the films. A thorough review on the topic of applying thin films using physical vapor deposition techniques (PVD) in the field of different modern applications, and the current results of such investigations are presented. Furthermore, current research regarding the possible methods for applying such films, and the specifics behind them, need to be summarized. Due to this, in the present work, the specifics of applying thin films using PVD methods and their expected structure and properties were evaluated. Special emphasis was paid to the electrical impedance spectroscopy (EIS) method, which is typically used for the investigation and characterization of electrical systems. This method has increased in popularity over the last few years, and its applicability in the characterization of electrical systems that include thin films formed using PVD methods was proven many times over. However, a still lingering question is the applicability of this method for backwards engineering of thin films. Currently, the EIS method is used in combination with traditional techniques such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDX), and others. There is, however, a potential to predict the structure and properties of thin films using purely a combination of EIS measurements and complex theoretical models. The current progress in the development of the EIS measurement method was described in the present work, and the trend is such that new theoretical models and new practical testing knowledge was obtained that help implement the method in the field of thin films characterization. Regardless of this progress, much more future work was found to be necessary, in particular, practical measurements (real data) of a large variety of films, in order to build the composition–structure–properties relationship. Full article

Nickel is used in aerospace, military, energy, and chemical sectors. Commercially pure (CP) Ni, and its alloys, including solid-solution strengthened (SSS), precipitation strengthened (PS), and specialty alloys (SA), are widely utilized, typically at elevated temperatures, in corrosive settings and in cryogenic milieu. Ni or Ni-based alloys frequently require welding realized, inter alia, via methods using electric arc and beam power. Tungsten inert gas (TIG) and Electron-beam welding (EBW) have been utilized most often. Friction stir welding (FSW) is the most promising solid-state welding technique for connecting Ni and its alloys. The primary weldability issues related to Ni and its alloys are porosity, as well as hot and warm cracking. CP Ni exhibits superior weldability. It is vulnerable to porosity and cracking during the solidification of the weld metal. Typically, SSS alloys demonstrate superior weldability when compared to PS Ni alloys; however, both types may experience weld metal solidification cracking, liquation cracking in the partially melted and heat-affected zones, as well as ductility-dip cracking (DDC). Furthermore, PS alloys are prone to strain-age cracking (SAC). The weldability of specialty Ni alloys is limited, and brazing might provide a solution. Employing appropriate filler metal, welding settings, and minimal restraint can reduce or avert cracking. Full article

With the increasing environmental pollution and resource consumption caused by automobiles, a lightweight design of automobiles is the best solution at present. In this paper, the load-bearing frame of pure electric SUVs is taken as the research object. The finite element analysis method is used to analyze the strength, stiffness and modal performance of the load-bearing frame, and the material selection of the frame is optimized according to the analysis results to achieve a lightweight design. First, a three-dimensional model of the pure electric SUV frame is established using SolidWorks software 2019 and then imported into ANSYS 2024 R1 Workbench for meshing and material property definition. Then, through finite element static analysis, the various force conditions of the frame under three typical working conditions of full-load bending, full-load braking and full-load turning are simulated; the stress distribution and deformation of the frame under different working conditions are confirmed; and the strength and stiffness performance of the frame are evaluated. After the above analysis, a modal analysis of the frame is carried out, and the natural frequency and vibration mode of the frame are finally obtained. According to the analysis results, the material replacement method is selected to optimize the lightweight design of the frame. The results show that the weight of the frame is significantly reduced after material optimization, while still meeting the strength, stiffness and modal performance requirements. This article provides a certain reference value for the lightweight design of pure electric SUV frames in the future. Full article

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO₂/NO_x will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results. Full article

Due to the absence of engine noise in new energy vehicles, road noise and wind noise become particularly noticeable. Therefore, studying the noise transmission through car doors is essential to effectively reduce the impact of these noises on the passenger compartment. To address the optimization of the sound absorption performance of single-layer porous plates combined with lightweight glass wool used in the doors of electric vehicles, this study established a microscopic acoustic performance analysis model based on the transfer matrix method and sound transmission loss theory. The effects of medium type, perforation rate, perforation radius, material thickness, and porosity on the sound absorption coefficient, impedance characteristics, and reflection coefficient were systematically investigated. Results indicate that in the high-frequency range (above 1200 Hz), the sound absorption coefficients of both rigid and flexible media can reach up to 0.9. When the perforation rate increases from 0.01 to 0.2, the peak sound absorption coefficient in the high-frequency band (1400–2000 Hz) rises from 0.45 to 0.85. Increasing the perforation radius to 0.03 m improves acoustic impedance matching. This research provides theoretical support and a parameter optimization basis for the design of acoustic packaging materials for electric vehicles, contributing significantly to enhancing the interior acoustic environment. Full article

Water, acetone, and TiO₂/nano-silver water (NSW) nanofluids were investigated as working fluids in loop thermosyphon battery thermal management systems (LTBMS) under simulated electric vehicle (EV) conditions to evaluate scalability and robustness across inclinations (0° to 60°) and ambient temperatures (−10 °C to 20 °C). Experimental conditions were established with 60 °C as the reference temperature, corresponding to the onset of battery thermal runaway, to ensure relevance to critical thermal management scenarios. Results indicate that LTBMS A maintained battery cell temperatures at 50.4 °C with water and 31.6 °C with acetone under a 50 W heat load. In contrast, LTBMS B achieved cell temperatures of 41.8 °C with water and 42.8 °C with 0.01 vol% TiO₂ nanofluid, however, performance deteriorated at higher nanofluid concentrations due to increased viscosity and related thermophysical constraints. In heating mode, LTBMS A elevated cell temperatures by 16 °C at an ambient temperature of −10 °C using acetone, while LTBMS B attained 52–55 °C at a 100 W heat load with nanofluids. The lightweight LTBMS design demonstrated superior thermal performance compared to conventional air-cooling systems and performance comparable to liquid-cooling systems. Pure water proved to be the most effective working fluid, while nanofluids require further optimization to enhance their practical applicability in EV thermal management. Full article

Polystyrene (PS), a thermoplastic polymer, is used in many applications due to its mechanical performance, good chemical inertness, and excellent processability. However, it is doped with different nanomaterials for reasons such as improving its electrical conductivity and mechanical properties. In this study, carbon nanotube (CNT)-added PS composites were produced with the aim of combining the properties of CNTs, such as their low weight and high tensile strength and Young’s modulus, with the versatility, processability, and mechanical properties of PS. In this study, multi-walled carbon nanotube (MWCNT)-reinforced polystyrene (PS) composites with different percentage ratios (0.1, 0.2, and 0.3 wt%) were prepared by a plastic injection molding method. The mechanical, microstructural, and thermal properties of the fabricated PS/MWCNT composites were characterized by Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) Spectroscopy, Atomic Force Microscopy (AFM) and Thermogravimetric Analysis (TGA) techniques. AFM analyses were carried out to investigate the surface properties of MWCNT-reinforced composite materials by evaluating the root mean square (RMS) values. These analyses show that the RMS value for MWCNT-reinforced composite materials decreases as the weight percentage of MWCNTs increases. The TGA results show that there is no change in the degradation temperature of the 0.1%- and 0.2%-doped MWCNT composites compared to pure polystyrene, but the degradation of the 0.3%-doped MWCNT composite is almost complete at a temperature of 539 °C. Among the PS/MWCNT composites, the 0.3%-doped MWCNT composite exhibits more thermal stability than pure PS and other composites. Similarly, the values of the percentage elongation and tensile strength of 0.3% MWCNT-doped composites was obtained as 1.91% and 12.174% mm², respectively. These values are higher than the values of 0.1% and 0.2% MWCNT-doped composite materials. In conclusion, the mechanical and thermal properties of MWCNT-reinforced PS polymers provide promising results for researchers working in this field. Full article

Dual-motor electric vehicles enhance power performance and overall output capabilities by enabling the real-time control of the torque distribution between the front and rear wheels, thereby improving handling, stability, and safety. In addition to increased energy efficiency, a dual-motor system provides redundancy: if one motor fails, the other can still supply partial power, further enhancing driving safety. This study aimed to optimize the energy management strategies of the front- and rear-axis motors, examining the application effects of rule-based control (RBC), global grid search (GGS), and the whale optimization algorithm (WOA). A simulation platform based on MATLAB/Simulink^® (R2021b, MATLAB, Natick, MA, USA) was constructed and validated through hardware-in-the-loop (HIL) testing to ensure the authenticity and reliability of the simulation results. Detailed tests and analyses of the dual-motor system were conducted under FTP-75 driving cycles. Compared to the RBC strategy, GGS and WOA achieved energy efficiency improvements of 9.1% and 8.9%, respectively, in the pure simulation, and 4.2% and 3.8%, respectively, in the HIL simulation. Compared to the pure RBC strategy, the RBC and GGS strategies incorporating regenerative braking achieved energy efficiency improvements of 26.1% and 29.4%, respectively, in the HIL simulation. Overall, GGS and WOA each present distinct advantages, with WOA emerging as a highly promising alternative energy management strategy. Future research should further explore WOA applications to enhance energy savings in real-world vehicle operations. Full article

This work reports the fabrication and optimization of nylon/multi-walled carbon nanotube (MWCNT) nanocomposite-based triboelectric nanogenerators (TENGs) using a spin coating method. By carefully tuning the MWCNT concentration, the device achieved a substantial enhancement in electrical output, with open-circuit voltage and short-circuit current peaking at 29.7 V and 3.0 μA, respectively, at 0.05 wt% MWCNT loading on the surface of nylon. The corresponding power density reached approximately 13.9 mW/m², representing a significant improvement over pure nylon-based TENGs. The enhanced performance is attributed to improved charge trapping and dielectric properties due to well-dispersed MWCNTs on the surface of nylon, while excessive loading caused agglomeration, reducing efficiency. This lightweight, flexible nanocomposite TENG offers a promising solution for efficient, sustainable energy harvesting in wearable electronics and self-powered sensor systems, highlighting its potential for practical energy applications. Full article

To enhance the accuracy of SOC prediction in EVs, which often suffers from significant discrepancies between displayed and actual driving ranges, this study proposes a data-driven model guided by an energy consumption framework. The approach addresses the problem of inaccurate remaining range prediction, improving drivers’ travel planning and vehicle efficiency. A PCA-GA-K-Means-based driving cycle clustering method is introduced, followed by driving style feature extraction using a GMM to capture behavioral differences. A coupled library of twelve typical driving cycle style combinations is constructed to handle complex correlations among driving style, operating conditions, and range. To mitigate multicollinearity and nonlinear feature redundancies, a Pearson-DII-based feature extraction method is proposed. A stacking ensemble model, integrating Random Forest, CatBoost, XGBoost, and SVR as base models with ElasticNet as the meta model, is developed for robust prediction. Validated with real-world vehicle data across −21 °C to 39 °C and four driving cycles, the model significantly improves SOC prediction accuracy, offering a reliable solution for EV range estimation and enhancing user trust in EV technology. Full article

For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, this study proposes a joint estimation framework that integrates data-driven and modified recursive subspace identification algorithms. Firstly, based on the electromechanical coupling mechanism, an electric drive wheel dynamics model (EDWM) is constructed, and multidimensional driving data is collected through a chassis dynamometer experimental platform. Secondly, an improved proportional integral observer (PIO) is designed to decouple the longitudinal force from the system input into a state variable, and a subspace identification recursive algorithm based on correction term with forgetting factor (CFF-SIR) is introduced to suppress the residual influence of historical data and enhance the ability to track time-varying parameters. The simulation and experimental results show that under complex working conditions without noise and interference, with noise influence (5% white noise), and with interference (5% irregular signal), the mean and mean square error of longitudinal force estimation under the CFF-SIR algorithm are significantly reduced compared to the correction-based subspace identification recursive (C-SIR) algorithm, and the comprehensive estimation accuracy is improved by 8.37%. It can provide a high-precision and highly adaptive longitudinal force estimation solution for vehicle dynamics control and intelligent driving systems. Full article