Search Results (14,308)

In the current era of energy conservation and emission reduction, the development of electric and other new energy vehicles is booming. With their various attributes, lithium batteries have become the ideal power source for new energy vehicles. However, lithium-ion batteries are highly sensitive to temperature changes. Excessive temperatures, either high or low, can lead to abnormal operation of the batteries, posing a threat to the safety of the entire vehicle. Therefore, developing a reliable and efficient Battery Thermal Management System (BTMS) that can monitor battery status and prevent thermal runaway is becoming increasingly important. In recent years, deep learning has gradually become widely applied in various fields as an efficient method, and it has also been applied to some extent in the development of BTMS. In this work, we discuss the basic principles of deep learning and related optimization principles and elaborate on the algorithmic principles, frameworks, and applications of various advanced deep learning methods in BTMS. We also discuss several emerging deep learning algorithms proposed in recent years, their principles, and their feasibility in BTMS applications. Finally, we discuss the obstacles faced by various deep learning algorithms in the development of BTMS and potential directions for development, proposing some ideas for progress. This paper aims to analyze the advanced deep learning technologies commonly used in BTMS and some emerging deep learning technologies and provide new insights into the current combination of deep learning technology in new energy trams to assist the development of BTMS. Full article

The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods. Full article

Obesity is a global epidemic which induces a multitude of metabolic disorders. Browning of white adipose tissue (WAT) has emerged as a promising therapeutic strategy for promoting weight loss and improving associated metabolic syndromes in people with obesity. However, current methods of inducing white adipose tissue browning have limited applicability. We developed a nanocomplex pTSL@(P+I), which is a temperature-sensitive liposome (TSL) surface-conjugated with an adipocyte-targeting peptide (p) and loaded with both browning-promoting agents (P) and photosensitizing agents (I). This nanocomplex exhibits adipocyte targeting, as well as synergistic pharmacological and photothermal properties to promote browning. pTSL@(P+I) effectively upregulates UCP1 and COX5B expression by activating the transcription axis of PPARγ/PGC1α and HSF1/PGC1α, thereby promoting white adipose tissue browning and reducing obesity. This novel nanocomplex exhibited a uniform spherical shape, with an average diameter of approximately 200 nm. Additionally, the nanocomplexes exhibited remarkable photothermal properties and biocompatibility. Further, when adipocytes were treated with pTSL@(P+I), their triglyceride content decreased remarkably and intracellular mitochondrial activity increased significantly. When applied to diet-induced obesity (DIO) mice, the nanocomplex exhibited significant efficacy, demonstrating a notable 14.4% reduction in body weight from the initial measurement, a decreased fat/lean mass ratio of 20.8%, and no statistically significant disparities (p > 0.05) in associated side effects when compared to the control group. In summary, implementation of the targeted nanocomplex pTSL@(P+I) to enhance energy expenditure by stimulating white adipose tissue browning offers a promising therapeutic approach for the treatment of obesity and related metabolic syndromes. Full article

Due to increasing emissions and global warming, in parallel with the increasing world population and energy needs, IMO has introduced severe rules for ships. Energy efficiency on ships can be achieved using the organic and inorganic Rankine cycle (RC) driven by exhaust heat from marine diesel engines. In this study, toluene, R600, isopentane, and n-hexane as dry fluids; R717 and R718 as wet fluids; and R123, R142b, R600a, R245fa, and R141b as isentropic fluids are selected as the working fluid because they are commonly used refrigerants, with favorable thermal properties, zero ODP, low GWP and are good contenders for this application. The cycle and exergy efficiencies, net power, and irreversibility of marine diesel engine exhaust-driven simple RC and RC with a recuperator are calculated. For dry fluids, the most efficient fluid at low turbine inlet temperatures is n-hexane at 39.75%, while at high turbine inlet temperatures, it is toluene at 41.20%. For isentropic fluids, the most efficient fluid at low turbine inlet temperatures is R123 with 23%, while at high turbine inlet temperatures it is R141b with 23%. As an inorganic fluid, R718 is one of the most suitable working fluids at high turbine inlet temperatures of 300 °C onboard ships with a safety group classification of A1, ODP of 0, and GWP100 of 0, with a cycle efficiency of 33%. This study contributes to significant improvements in fuel efficiency and reductions in greenhouse gas emissions, leading to more sustainable and cost-effective maritime operations. Full article

Climate change has become a global nightmare, and the awareness of the causes of carbon emissions has resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon-growth plan or emission-reduction strategy. The world is transitioning toward a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. This study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. In order to examine the detrimental effects of carbon emissions on ecosystems and the ways in which economic expansion contributes to carbon footprints, more than three hundred research papers were gathered using several search engines, including Elsevier and Google Scholar. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All the results suggest that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment. Full article

The maritime industry is a major source of greenhouse gas (GHG) emissions, largely due to ships running on fossil fuels. Transitioning to hydrogen-powered marine transportation in the Mediterranean Sea requires the development of a network of hydrogen refueling stations across the region to ensure a steady supply of green hydrogen. This paper explores the technoeconomic viability of harnessing wave energy from the Mediterranean Sea to produce green hydrogen for hydrogen-powered ships. Four promising island locations—near Sardegna, Galite, Western Crete, and Eastern Crete—were selected based on their favorable wave potential for green hydrogen production. A thorough analysis of the costs associated with wave power facilities and hydrogen production was conducted to accurately model economic viability. The techno-economic results suggest that, with anticipated cost reductions in wave energy converters, the levelized cost of hydrogen could decrease to as low as 3.6 €/kg, 4.3 €/kg, 5.5 €/kg, and 3.9 €/kg for Sardegna, Galite, Western Crete, and Eastern Crete, respectively. Furthermore, the study estimates that, in order for the hydrogen-fueled ships to compete effectively with their oil-fueled counterparts, the levelized cost of hydrogen must drop below 3.5 €/kg. Thus, despite the competitive costs, further measures are necessary to make hydrogen-fueled ships a viable alternative to conventional diesel-fueled ships. Full article

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. Full article

The need to limit anthropogenic climate change to 1.5–2 °C, as agreed in the Paris Agreement, requires a significant reduction of CO₂ emissions resulting from the use of fossil carbon. However, based on current knowledge, carbon is expected to remain crucial in certain industrial sectors, e.g., the chemical industry. Consequently, it is essential to identify and utilize sustainable carbon sources in the future. In this context, various carbon sources were examined and classified in terms of their disruption of the Earth’s (fast) carbon cycle. Furthermore, the examined carbon sources were qualitatively analyzed with regard to their technical readiness level, their energy expenditure, and their current and future availability, as well as legal regulation within the European Union. As a result, only biogenic and mixed carbon from the ambient air can be considered genuinely sustainable within the Earth’s (fast) carbon cycle. Mixed carbon streams, e.g., from waste recycling, fall into a gray area. The same applies to certain process-related emissions that originally descend from fossil fuel energy. In terms of energy considerations, technical maturity, and exploitable potentials, prioritizing the utilization of biogenic carbon sources is advisable for the time being, especially for CO₂ produced as a by-product originating from biogenic carbon carriers. Full article

This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to improve the solar radiation prediction made by the CMA-WSP v2.0. It is found that the performance of the solar radiation forecasting from the CMA-WSP v2.0 is closely related to the weather conditions, with notable diurnal fluctuations. The mean absolute percentage error (MAPE) produced by the CMA-WSP v2.0 is approximately 74% between 11:00 and 13:00. However, the MAPE ranges from 193% to 242% at 07:00–08:00 and 17:00–18:00, which is greater than that observed at other daytime periods. The MAPE is relatively low (high) for both sunny and cloudy (overcast and rainy) conditions, with a high probability of an absolute percentage error below 25% (above 100%). The forecasts tend to underestimate (overestimate) the observed solar radiation in sunny and cloudy (overcast and rainy) conditions. By applying machine learning models (such as linear regression, decision trees, K-nearest neighbors, random forests regression, adaptive boosting, and gradient boosting regression) to revise the solar radiation forecasts, the MAPE produced by the CMA-WSP v2.0 is significantly reduced. The reduction in the MAPE is closely connected to the weather conditions. The models of K-nearest neighbors, random forests regression, and decision trees can reduce the MAPE in all weather conditions. The K-nearest neighbor model exhibits the most optimal performance among these models, particularly in rainy conditions. The random forest regression model demonstrates the second-best performance compared to that of the K-nearest neighbor model. The gradient boosting regression model has been observed to reduce the MAPE of the CMA-WSP v2.0 in all weather conditions except rainy. In contrast, the adaptive boosting (linear regression) model exhibited a diminished capacity to improve the CMA-WSP v2.0 solar radiation prediction, with a slight reduction in MAPE observed only in sunny (sunny and cloudy) conditions. In addition, the input feature selection has a considerable influence on the performance of the machine learning model. The incorporation of the time series data associated with the diurnal variation of solar radiation as an input feature can further improve the model’s performance. Full article

The fracture mechanism and macro-properties of SVSAC were studied using a novel test system combined with numerical simulations, which included three-point bending beam tests, the digital image correlation (DIC) technique, scanning electron microscopy (SEM), and ABAQUS analyses. In total, 9 groups and 36 specimens were fabricated by considering two critical parameters: initial notch-to-depth ratios (a₀/h) and concrete mix components (seawater and volcanic scoria coarse aggregate (VSCA)). Changes in fracture parameters, such as the load-crack mouth opening displacement curve (P-CMOD), load-crack tip opening displacement curve (P-CTOD), and fracture energy (G_f), were obtained. The typical double-K fracture parameters (i.e., initial fracture toughness ($K_{\mathrm{IC}}^{\mathrm{ini}}$) and unstable fracture toughness ($K_{\mathrm{IC}}^{\mathrm{un}}$)) and tension-softening (σ-CTOD) curve were analyzed. The test results showed that the initial cracking load (P_ini), G_f, and characteristic length (L_ch) of the SVSAC increased with decreasing a₀/h. Compared with the ordinary concrete (OC) specimen, the P-CMOD and P-CTOD curves of the specimen changed after using seawater and VSCA. The evolution of the crack propagation length was obtained through the DIC technique, indicating cracks appeared earlier and the fracture properties of specimen decreased after using VSCA. Generally, the $K_{\mathrm{IC}}^{\mathrm{un}}$ and $K_{\mathrm{IC}}^{\mathrm{ini}}$ of SVSAC were 36.17% and 8.55% lower than those of the OC specimen, respectively, whereas the effects of a₀/h were negligible. The reductions in P_ini, G_f, and L_ch of the specimen using VSCA were 10.94%, 32.66%, and 60.39%, respectively; however, seawater efficiently decreased the negative effect of VSCA on the fracture before the cracking width approached 0.1 mm. Furthermore, the effects of specimen characteristics on the fracture mechanism were also studied through numerical simulations, indicating the size of the beam changed the fracture toughness. Finally, theoretical models of the double-K fracture toughness and the σ-CTOD relations were proposed, which could prompt their application in marine structures. Full article

The expansion of the digital economy is crucial for halting climate change, as carbon emissions from urban energy use contribute significantly to global warming. This study uses the Difference-in-Differences Model and the Spatial Durbin Model determine whether the digital economy may support the development of reducing carbon emissions and its geographic spillover effects in Chinese cities on the east coast. In addition, it looks more closely at the effects of lowering carbon emissions in space by separating them into direct, indirect, and spatial impact parts. The findings show that (1) from 2012 to 2021, the digital economy favored carbon emission reductions in China’s eastern coastline cities, as supported by the robustness test. (2) The link between digital economy growth and carbon emissions is highly variable, with smart city development and urban agglomeration expansion both cutting city carbon emissions considerably. Successful digital economy strategies can lower CO₂ emissions from nearby cities. (3) Eastern coastal cities have a considerable spatial spillover impact, and the digital economy mitigates local energy consumption and carbon emissions while simultaneously enhancing environmental quality in nearby urban areas. This analysis proposes that the peak carbon and carbon neutrality targets can be met by increasing the digital economy and enhancing regional environmental governance cooperation. Full article

This comprehensive systematic review explores the multifaceted impacts of electric vehicle (EV) adoption across technological, environmental, organizational, and policy dimensions. Drawing from 88 peer-reviewed articles, the study addresses a critical gap in the existing literature, which often isolates the impact of EV adoption without considering holistic effects. Technological advancements include innovations in the battery technology and energy storage systems, enhancing EV performance and mitigating range anxiety. The environmental analysis reveals substantial reductions in greenhouse gas emissions, with lifecycle assessments showing significant reductions for EVs compared to internal combustion engine vehicles, particularly when charged with renewable energy sources. Key comparisons include lifecycle emissions between mid-size battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), and global average lifecycle emissions by powertrain under various policy scenarios. The organizational implications are evident, as businesses adopt new models for fleet management and logistics, leveraging EVs for operational efficiency and sustainability. Policy analysis underscores the crucial role of government incentives, regulatory measures, and infrastructure investments in accelerating EV adoption. The review identifies future research areas such as efficient battery recycling methods, the potential impact of EVs on grid stability, and long-term economic implications. This study offers insights for stakeholders aiming to foster sustainable transportation and achieve global climate goals. Full article

This study introduces an innovative energy harvesting system designed for industrial applications such as fluid pipelines, air conditioning ducts, sewer systems, and subsea oil pipelines. The system integrates magneto-electric flow coupling and utilizes a dynamic vibration absorber (DVA) to mitigate the vibrations induced by fluid flow while simultaneously harvesting energy through magnetic dipole–dipole interactions in a vibration energy harvester (VEH). The theoretical models, based on Hamilton’s Principle and the Biot–Savart Law, were validated through comprehensive experiments. The results indicate the superior performance of the small-magnet system over the large-magnet system in both damping and power generation. The study analyzed the frequency response and energy conversion efficiency across different parameters, including the DVA mass, spring constant, and placement location. The experimental findings demonstrated significant vibration reduction and increased voltage output, validating the theoretical model. This research offers new avenues for energy harvesting systems in pipeline infrastructures, potentially enhancing energy efficiency and structural integrity. Full article

Ice accumulation poses considerable challenges in transportation, notably in the domain of general aviation. The present study combines the strengths and limitations of conventional aircraft deicing techniques with the emerging trend toward all-electric aircraft. This study aims to utilize laser-induced graphene (LIG) technology to create a multifunctional surface, seamlessly integrating hydrophobic properties with efficient electrical heating to mitigate surface icing effectively. We investigated the utilization of a 10.6 μm CO₂ laser for direct writing on polyimide (PI), a widely used insulating encapsulation material. From the thermomechanical perspective, our initial analysis using COMSOL Multiphysics software (V5.6) revealed that when the laser power P exceeds 5 W, the PI substrate experiences ablative damage. The experimental results show that when P ≤ 5 W, an increase in power has a positive impact on the quality, surface porosity, roughness reduction, line-spacing reduction, and water contact-angle enhancement of the graphene. Conversely, when P > 5 W, higher power negatively affects both the substrate and the graphene structure by inducing excessive ablation. However, it influences the graphene line height positively and is consistent with overall experimental–simulation congruence. Furthermore, the incorporation of high-quality graphene resulted in a surface that exhibited higher contact angles (CA > 120°), lower energy consumption, and higher heating efficiency compared to the use of traditional electrically heated materials for anti-icing applications. The potential applications of this one-step fabrication method extend across various industries, particularly aviation, marine engineering, and other ice-prone domains. Moreover, the method has extensive prospects for addressing pivotal challenges associated with ice formation and serves as an innovative and efficient anti-icing technology. Full article

Bio-energy systems with carbon capture and storage (BECCS) will be essential if countries are to meet the gas emission reduction targets established in the 2015 Paris Agreement. This study seeks to carry out a thermodynamic optimization and analysis of a BECCS technology for a typical Brazilian cogeneration plant. To maximize generated net electrical energy (MWe) and carbon dioxide CO₂ capture (Mt/year), this study evaluated six cogeneration systems integrated with a chemical absorption process using MEA. A key performance indicator (gCO₂/kWh) was also evaluated. The set of optimal solutions shows that the single regenerator configuration (REG1) resulted in more CO₂ capture (51.9% of all CO₂ emissions generated by the plant), penalized by 14.9% in the electrical plant’s efficiency. On the other hand, the reheated configuration with three regenerators (Reheat3) was less power-penalized (7.41%) but had a lower CO₂ capture rate (36.3%). Results showed that if the CO₂ capture rates would be higher than 51.9%, the cogeneration system would reach a higher specific emission (gCO₂/kWh) than the cogeneration base plant without a carbon capture system, which implies that low capture rates (<51%) in the CCS system guarantee an overall net reduction in greenhouse gas emissions in sugarcane plants for power and ethanol production. Full article