Search Results (1,984)

This paper presents the development of an advanced battery management system (BMS) for electric vehicles (EVs), designed to enhance battery performance, safety, and longevity. Central to the BMS is its precise monitoring of critical parameters, including voltage, current, and temperature, enabled by dedicated sensors. These sensors facilitate accurate calculations of the state of charge (SOC) and state of health (SOH), with real-time data displayed through an IoT cloud interface. The proposed BMS employs data-driven approaches, like advanced Kalman filters (KF), for battery state estimation, allowing continuous updates to the battery state with improved accuracy and adaptability during each charging cycle. Simulation tests conducted in MATLAB’s Simulink across multiple charging and discharging cycles demonstrate the superior accuracy of the advanced Kalman filter (KF), in handling non-linear battery behaviours. Results indicate that the proposed BMS achieves a significantly lower error margin in SOC tracking, ranging from 0.32% to 1%, compared to traditional methods with error margins up to 5%. These findings underscore the importance of integrating robust sensor systems in BMSs to optimise EV battery management, reduce maintenance costs, and improve battery sustainability. Full article

Objectives: Occupational respiratory diseases represent a major public health concern worldwide. This study analyses the hospitalization costs and characteristics of four major occupational respiratory diseases: malignant mesothelioma (MM), sinonasal cancer (SNC), pneumoconiosis (PN), and hypersensitivity pneumonitis (HP). The findings are situated within the context of Italy’s population trends and healthcare system, offering insights into the economic and clinical burden of these diseases. Study Design: This retrospective, population-based study examines Italian hospitalizations for MM, SNC, PN, and HP during the period 2010–2021. The primary outcomes were the number of hospitalizations, length of stay, and associated cost. Costs were derived from charges linked to diagnosis-related groups (version 24) and major diagnostic category coding systems. Results: Though the Italian population is rapidly aging, the annual number and rate of hospitalizations declined by 35% over the study period. SNC hospitalizations aligned with the overall trend, PN and MM experienced faster declines, whereas HP admissions remained steady. MM emerged as the most resource-intensive (EUR 25 million yearly, with 86% attributable to occupation), followed by PN (EUR 10 million, entirely occupational), SNC (EUR 5 million, with EUR 650,000 occupational), and HP (EUR 2 million, with EUR 370,000 occupational). All studied diseases had an average length of stay exceeding the national one. The SNC admissions were the shortest (6.5 days) and least expensive (EUR 3647). In contrast, MM, PN, and HP had a mean length of stay exceeding 10 days, with admission costs averaging EUR 4700 for MM and EUR 4000 for PN and HP. The median age was the highest for PN (78 years) and MM (71 years), while SNC and HP patients had a median age of approximately 65 years. Conclusions: Consistent with their anticipated benefits, Italian workplace health regulations over the last three decades, including the 1992 asbestos ban and D.lgs. 81/2008, are associated with significant reductions in the hospitalization burden and an increased median age at discharge for MM and PN. In contrast, fewer conclusions can be drawn for SNC and HP due to their lower occupational fractions (10–20%). This finding suggests adding an occupational exposure flag in hospital records for acknowledged occupational diseases to enhance surveillance. Finally, this study provides the first estimate of the occupational fraction of hospitalization costs for the studied diseases in Italy. Full article

Accurate estimation of the state of charge (SOC) of lithium batteries is critical for the safe and optimal operation of battery management systems (BMSs). Traditional SOC estimation methods are often limited by model inaccuracy and noise interference. In this study, a novel type-2 fuzzy cerebellar model neural network (Type-2 FCMNN) is proposed for accurately estimating the state of charge of lithium batteries. Based on the traditional fuzzy cerebellar model neural network (FCMNN), type-2 fuzzy rules are innovatively introduced to enhance the robustness of the model against uncertainties and noise disturbances. This enables the model to better cope with nonlinear complexity and external disturbances when dealing with SOC estimation of lithium batteries and significantly improves the accuracy of prediction. On this basis, by analyzing the working principle of lithium batteries, a battery equivalent circuit model is successfully established and simulated and tested by Simulink R2022b, which provides a theoretical basis for the selection of the size of the input parameters of the subsequent neural network. Then, this paper designs and implements the SOC estimation model based on Type-2 FCMNN and tests it in Matlab. Finally, this paper carries out simulation comparison experiments between Type-2 FCMNN and various classical neural network algorithms in Matlab R2022b including traditional FCMNN, a backpropagation neural network, radial basis function neural network, and Kalman filtering algorithm. The simulation results show that Type-2 FCMNN exhibits a significant advantage in SOC estimation accuracy, with mean absolute error and root mean square error values of only 43.1% and 36.0% of FCMNN’s, respectively, while FCMNN achieves the best results among the compared methods. Full article

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter’s ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system’s state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems. Full article

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models. Full article

Cisplatin-based platinum compounds are important clinical chemotherapeutic agents that participate in most tumor chemotherapy regimens. Through density-functional theory calculations, the formation and stability of the inorganic oxide carrier, the mechanisms of the hydrolysis reaction of the activated platinum compound, and its binding mechanism with DNA bases can be studied. The higher the oxidation state of Pt (II to IV), the more electrons transfer from the magnesia–gold composite material to the platinum compound. After adsorption on the composite carrier, 5d←2p coordination bonds of Pt-N are strengthened. For flat and oblique adsorption modes of cisplatin, there is no significant difference in the density of states of the gold and magnesium oxide film, indicating the maintenance of the heterojunction structural framework. However, there are significant changes in the electronic states of cisplatin itself with different adsorption configurations. In the flat configuration, the band gap width of cisplatin is larger than that of the oblique configuration. The Cl-Pt bond range in the Pt(III) compound shows a clear charge reduction on the magnesia film, indicating the Cl-Pt bond is an active site with the potential for decomposition and hydrolysis. The substitution of chloride ions by water can lead to hydrolysis products, enhancing the polarization of the composite and showing strong charge separation. The hydrolysis of the free platinum compound is endothermic by 0.309 eV, exceeding the small activation energy barrier of 0.399 eV, indicating that hydrolysis of this platinum compound is easily achievable. ADME (absorption, distribution, metabolism, and excretion) prediction parameters indicate that hydrolysis products have good ESOL (Estimated SOLubility) solubility and high gastrointestinal absorption, consistent with Lipinski’s rule. During the coordination reaction process, there are significant changes in the distribution of frontier molecular orbitals, with the HOMO (highest occupied molecular orbital) of the initial state primarily located on the purine base, providing the possibility for electron transfer to the empty orbitals of the platinum compound in the LUMO (lowest unoccupied molecular orbital). The HOMO and HOMO-1 of the transition state and product are mainly distributed on the platinum compound, indicating clear electron transfer and orbital rearrangement. The activation energy barrier for the purine coordination reaction with the hydrolysis products is reduced to 0.61 eV, and the dipole moment gradually decreases to 6.77 Debye during the reaction, indicating a reduction in the system’s charge separation and polarization. This contribution is anticipated to provide a new theoretical clue for developing inorganic oxide carriers of platinum compounds. Full article

This study focuses on the effect of pre-deformation on hydrogen diffusion and hydrogen embrittlement of the high alloy austenitic TRIP steel X3CrMnNiMo17-8-4. Different cold-rolled steel sheets with thicknesses of ≤400 µm were electrochemically charged on both sides in 0.1 M sodium hydroxide with hydrogen for two weeks. Comparative measurements on uncharged and immersed samples prove that hydrogen causes embrittlement in this steel for all investigated states. The embrittlement increases with increasing pre-deformation and is accompanied by deformation-induced martensite formation. The corresponding fractured surfaces were examined using electron microscopy and compared to modelled hydrogen distributions with previously determined diffusion coefficients. For this purpose, various diffusion coefficients are determined using the Devanathan–Stachurski permeation test and hot extraction in order to describe the diffusion process. The hydrogen concentration profiles and the fractographic analyses show a good agreement, so this study provides a basis for estimating the embrittlement behaviour for later application. Full article

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, have become a core technology driving this transformation. In lithium-ion battery energy storage systems, precise state estimation, such as state of charge, state of health, and state of power, is crucial for ensuring system safety, extending battery lifespan, and improving energy efficiency. Although physics-based state estimation techniques have matured, challenges remain regarding accuracy and robustness in complex environments. With the advancement of hardware computational capabilities, data-driven algorithms are increasingly applied in battery management, and multi-model fusion approaches have emerged as a research hotspot. This paper reviews the fusion application between physics-based and data-driven models in lithium-ion battery management, critically analyzes the advantages, limitations, and applicability of fusion models, and evaluates their effectiveness in improving state estimation accuracy and robustness. Furthermore, the paper discusses future directions for improvement in computational efficiency, model adaptability, and performance under complex operating conditions, aiming to provide theoretical support and practical guidance for developing lithium-ion battery management technologies. Full article

Near-infrared light-triggered photocatalytic water treatment has attracted significant attention in recent years. In this novel research, rational sonochemical fabrication of Ag₂S/g-C₃N₄ nanocomposites with various compositions of Ag₂S (0–25) wt% was carried out to eliminate hazardous rhodamine B dye in a cationic organic pollutant model. g-C₃N₄ sheets were synthesized via controlled thermal annealing of microcrystalline urea. However, black Ag₂S nanoparticles were synthesized through a precipitation-assisted sonochemical route. The chemical interactions between various compositions of Ag₂S and g-C₃N₄ were carried out in an ultrasonic bath with a power of 300 W. XRD, PL, DRS, SEM, HRTEM, mapping, BET, and SAED analysis were used to estimate the crystalline, optical, nanostructure, and textural properties of the solid specimens. The coexistence of the diffraction peaks of g-C₃N₄ and Ag₂S implied the successful production of Ag₂S/g-C₃N₄ heterojunctions. The band gap energy of g-C₃N₄ was exceptionally reduced from 2.81 to 1.5 eV with the introduction of 25 wt% of Ag₂S nanoparticles, implying the strong absorbability of the nanocomposites to natural solar radiation. The PL signal intensity of Ag₂S/g-C₃N₄ was reduced by 40% compared with pristine g-C₃N₄, implying that Ag₂S enhanced the electron–hole transportation and separation. The rate of the photocatalytic degradation of rhodamine B molecules was gradually increased with the introduction of Ag₂S on the g-C₃N₄ surface and reached a maximum for nanocomposites containing 25 wt% Ag₂S. The radical trapping experiments demonstrated the principal importance of reactive oxygen species and hot holes in destroying rhodamine B under natural solar radiation. The charge transportation between Ag₂S and g-C₃N₄ semiconductors proceeded through the type I straddling scheme. The enriched photocatalytic activity of Ag₂S/g-C₃N₄ nanocomposites resulted from an exceptional reduction in band gap energy and controlling the electron–hole separation rate with the introduction of Ag₂S as an efficient photothermal photocatalyst. The novel as-synthesized nanocomposites are considered a promising photocatalyst for destroying various types of organic pollutants under low-cost sunlight radiation. Full article

Due to the increasing share of battery electric buses (BEBs) in cities, depots need to be adapted to the increasing load demand. The integration of renewable energy sources (RESs) into a depot can increase the self-consumption, but optimal sizing is required for a cost-efficient and reliable operation. Accordingly, this paper introduces a co-design optimization framework for a depot microgrid, equipped with photovoltaics (PVs) and an energy storage system (ESS). Three European cities are considered to evaluate the effect of different environmental conditions and electricity prices on the optimal microgrid design. Accurate models of the different subsystems are created to estimate the load demand and the power generation. Different energy management strategies (EMSs), developed to properly control the power flow within the microgrid, are compared in terms of operational costs reduction, one of which was also experimentally validated using a hardware-in-the-loop (HiL) test setup. In addition, the total cost of ownership (TCO) of the depot microgrid is analyzed, showing that an optimally designed depot microgrid can reduce the charging-related expenses for the public transport operator (PTO) by 30% compared to a scenario in which only the distribution grid supplies the BEB depot. Full article

Electric vehicles (EVs) are rising in the automotive industry, replacing combustion engines and increasing their global market presence. These vehicles offer zero emissions during operation and more straightforward maintenance. However, for such systems that rely heavily on battery capacity, precisely determining the battery’s state of charge (SOC) presents a significant challenge due to its essential role in lithium-ion batteries. This research introduces a dual-phase testing approach, considering factors like HVAC use and road topography, and evaluating machine learning models such as linear regression, support vector regression, random forest regression, and neural networks using datasets from real-world driving conditions in European (Germany) and African (Morocco) contexts. The results validate that the proposed neural networks model does not overfit when evaluated using the dual-phase test method compared to previous studies. The neural networks consistently show high predictive precision across different scenarios within the datasets, outperforming other models by achieving the lowest mean squared error (MSE) and the highest R² values. Full article

Gold is a significant revenue source for custom copper smelters facing profitability challenges due to low treatment and refining charges, stricter regulations, and rising costs. Gold is also often blended with copper concentrates, but precise recovery rates from smelting processes are poorly documented despite gold critical economic importance. This paper aims to provide the first comprehensive estimates of gold first-pass recovery across various operational units within the copper sulfide concentrate processing flowsheet. It evaluates the effectiveness of different copper smelting and converting technologies in recovering gold. Optimizing gold first-pass recovery is especially important to enhance immediate financial returns and responsiveness to market dynamics, allowing companies to capitalize on favorable gold prices without delays. Given the absence of direct measurements for gold recovery rates, this research develops an estimation method based on understanding gold loss mechanisms during smelting. This study identifies and analyzes key input and output parameters by examining data from various copper producers. By correlating these parameters with gold loss, the research estimates gold first-pass recovery rates within the copper smelting process. Among integrated smelting-converting routes, the flash smelting to Peirce–Smith converting route achieves the highest gold first-pass recovery (98.8–99.5%), followed by the Mitsubishi continuous smelting and converting process (94.3–99.8%), bottom-blowing smelting to bottom-blowing converting (95.8%), flash smelting to flash converting (95.5%), Teniente smelting to Peirce–Smith converting (95.2%), and the Noranda continuous smelting and converting process (94.8%). The final recovery rates are expected to be higher considering the by-products’ internal recirculation and post-processing within the copper flow sheet. Additionally, superior gold recoveries are attributed to advanced metallurgical practices and control systems, which vary even among companies with similar technologies. This research demonstrates that copper smelting can effectively recover over 99% of gold from sulfide concentrates. Gold accumulates up to 1000 times its original concentration in anode slime during electrolytic refining, generating 5–10 kg of slime per ton of copper, which is further processed to recover gold and other by-products. Major smelters operate precious metal plants where recovering gold from highly concentrated anode slime is both cost-effective and efficient. Full article

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination ($R^2$) exceeding 97%, significantly improving prediction accuracy and efficiency. Full article

We present first-principles results on the electronic and magnetic properties of the cubic bulk $\beta$-phase of ${\mathrm{Fe}}_2{\mathrm{O}}_3$. Given that all Fe–Fe magnetic couplings are expected to be antiferromagnetic within this high-symmetry crystal structure, the system may exhibit some signature of magnetic frustration, making it challenging to identify its magnetic ground state. We have analyzed the possible magnetic phases of the $\beta$-phase, among which there are ferrimagnets, altermagnets, and Kramers antiferromagnets. While the $\alpha$-phase is an altermagnet and the $\gamma$-phase is a ferrimagnet, we conclude that the magnetic ground state for the bulk $\beta$-phase of ${\mathrm{Fe}}_2{\mathrm{O}}_3$ is a Kramers antiferromagnet. Moreover, we find that close in energy, there is a bulk d-wave altermagnetic phase. We report the density of states and the evolution band gap as a function of the electronic correlations. For suitable values of the Coulomb repulsion, the system is a charge-transfer insulator with an indirect band gap of 1.5 eV. More in detail, the unit cell of the $\beta$-phase is composed of $8{\mathrm{Fe}}_a$ atoms and $24{\mathrm{Fe}}_b$ atoms. The $8{\mathrm{Fe}}_a$ atoms lie on the corner of a cube, and their magnetic ground state is a G-type. This structural phase is composed of zig-zag chains ${\mathrm{Fe}}_a‐{\mathrm{Fe}}_b‐{\mathrm{Fe}}_a‐{\mathrm{Fe}}_b$ with spin configuration ↑-↑-↓-↓ along the 3 directions such that for every ${\mathrm{Fe}}_a$ atoms there are $3{\mathrm{Fe}}_b$ atoms. As the opposite to the $\gamma$-phase, the magnetic configuration between the first neighbor of the same kind is always antiferromagnetic while the magnetic configuration between ${\mathrm{Fe}}_a$ and ${\mathrm{Fe}}_b$ is ferro or antiferro. In this magnetic arrangement, first-neighbor interactions cancel out in the mean-field estimation of the Néel temperature, leaving second-neighbor magnetic exchanges as the primary contributors, resulting in a Néel temperature lower than that of other phases. Our work paves the way toward the ab initio study of nanoparticles and alloys for the $\beta$-phase of ${\mathrm{Fe}}_2{\mathrm{O}}_3$. Full article

This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from the offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to metaheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that incorporates both charging time and charging temperature was constructed to facilitate the use of DO in obtaining the OCP. To verify the performance of the proposed method, various algorithms, including the constant current-constant voltage (CC-CV) technique, formula method (FM), particle swarm optimization (PSO), war strategy optimization (WSO), jellyfish search algorithm (JSA), grey wolf optimization (GWO), beluga whale optimization (BWO), levy flight distribution algorithm (LFDA), and African gorilla troops optimizer (AGTO), were introduced. Based on the OCP extracted from the simulations using these MOAs for the specified ECM model, a charging experiment was conducted on the Panasonic NCR18650PF LiB to evaluate the charging performance in terms of charging time, temperature, and efficiency. The results demonstrate that the proposed DO technique offers superior charging performance compared to other charging methods. Full article