Search Results (5,356)

Road transport significantly contributes to greenhouse gas emissions in all places where it is used and therefore also in Europe, prompting the EU to set ambitious objectives for CO₂ reduction. In order to reach these objectives, the automotive industry is transitioning to electric vehicles, utilizing electric powertrains powered by battery packs. However, the longevity and reliability of these batteries are critical concerns. This review paper focuses on the advanced diagnostic techniques for effective battery State of Charge (SoC) and State of Health (SoH) monitoring. Accurate SoC/SoH estimation is crucial for optimizing battery performance, avoiding premature degradation, and ensuring driver safety. By investigating these areas, this paper aims to contribute to the development of more sustainable and durable electric vehicles, supporting the transition to cleaner transportation systems. Full article

Retired batteries are approaching the recycling peak, and their secondary utilization can prevent resource waste and environmental pollution from battery retirement. Evaluating the state of health (SOH) of retired batteries is crucial for secondary use. However, estimating the SOH of retired batteries is time-consuming and energy-intensive. To address the problem, this paper proposes a rapid estimation strategy based on resting voltage curves. After discharging retired batteries to the same voltage, variations in the remaining state of charge (SOC) exist among batteries with different SOHs. These SOC differences lead to distinct trends in the resting voltage curves for varying SOH batteries. Our approach analyzes health features from these resting voltage discrepancies, ultimately achieving a fast estimation of retired batteries’ SOH. Additionally, during the data collection of datasets, some batteries may form outliers due to measurement errors. This paper analyzes the impact of outlier quantity on the accuracy of regression models for SOH estimation and proposes using the DBSCAN clustering algorithm to identify and mitigate the influence of outliers, eventually enhancing the precision of SOH estimation. Full article

We explore the equivalence between the low-energy dynamics of strained graphene and a quantum mechanical framework for the 2D Dirac equation in flat space with a deformed momentum operator. By considering some common forms of the anisotropic Fermi velocity tensor emerging from the elasticity theory, we associate such tensor forms with a deformation of the momentum operator. We first explore the bound states of charge carriers in a background uniform magnetic field in this framework and quantify the impact of strain in the energy spectrum. Then, we use a quadrature algebra formula as a mathematical tool to analyze the impact of the deformation attached to the momentum operator and identify physical consequences of such deformation in terms of energy modifications due to the applied strain. Full article

Superjunction (SJ) technology offers a promising solution to the challenges faced by silicon carbide (SiC) Metal Oxide Semiconductor Field-Effect Transistors (MOSFETs) operating at high voltages (>3 kV). However, the fabrication of SJ devices presents significant challenges due to fabrication complexity. This paper presents a comprehensive analysis of a feasible and easy-to-fabricate semi-superjunction (SSJ) design for 3.3 kV SiC MOSFETs. The proposed approach utilizes trench etching and sidewall implantation, with a tilted trench to facilitate the implantation process. Through Technology Computer-Aided Design (TCAD) simulations, we investigate the effects of the p-type sidewall on the charge balance and how it affects key performance characteristics, such as breakdown voltage (BV) and on-state resistance (R_DS-ON). In particular, both planar gate (PSSJ) and trench gate (TSSJ) designs are simulated to evaluate their performance improvements over conventional planar MOSFETs. The PSSJ design achieves a 2.5% increase in BV and a 48.7% reduction in R_DS-ON, while the TSSJ design further optimizes these trade-offs, with a 3.1% improvement in BV and a significant 64.8% reduction in R_DS-ON compared to the benchmark. These results underscore the potential of tilted trench SSJ designs to significantly enhance the performance of SiC SSJ MOSFETs for high-voltage power electronics while simplifying fabrication and lowering costs. Full article

An HVDC-GIL system with a conical spacer in a radioactive environment is studied in this work using simulated data on COMSOL^® Multiphysics. Electromagnetic simulations on a 2D model were performed with varying ion-pair generation rates and potential applied to the system. This article explores machine learning methods to derive time to steady state, dark current, gas conductivity, and surface charge density expressions. The focus was on constructing symbolic representations, which could be interpretable and less prone to overfitting, using the symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy) algorithms. The study successfully derived the intended expressions, demonstrating the power of symbolic regression. Predictions of dark currents in the gas–ground electrode interface reported an absolute error and mean absolute percentage error (MAPE) of 1.04 × ${10}^{-4}$ pA and 0.01%, respectively. The solid–ground electrode interface reported an error of 8.99 × ${10}^{-5}$ pA and MAPE of 0.04%, showing strong agreement with simulation data. Expressions for time to steady state had a test error of approximately 110 h with MAPE of around 3%. Steady-state gas conductivity expression achieved an absolute error of 0.55 log(S/m) and MAPE of 1%. An interpretable equation was created with SINDy to model the time evolution of surface charge density, achieving a root mean squared error of 1.12 nC/m²/s across time-series data. These results demonstrate the capability of SR and SINDy to provide interpretable and computationally efficient alternatives to time-consuming numerical simulations of HVDC systems under radiation conditions. While the model provides useful insights, performance and practical applications of the expressions can improve with more diverse datasets, which might include experimental data in the future. Full article

The AZnOS:Bi³⁺ (A = Ba, Ca) phosphors exhibit mono- and dual-emission phenomena based on the different choices of cation, making them an ideal prototype for dual-emission mechanism studies of Bi³⁺ ions. Here, first-principles calculations were performed to investigate the site occupancy, defect levels, and luminescence properties of the AZnOS:Bi³⁺ systems. The formation energy calculations show that the bismuth dopants are mainly in the trivalent charge state, with the Bi³⁺ ions preferring the Ca sites in CaZnOS but the Zn sites in BaZnOS. Such cation-selective occupancy mainly results in the difference between the mono- and dual-emission phenomena in the two hosts. The excitation and emission energies predicted by calculations are in good agreement with the measurements. Our calculations show that the lowest excited state ³P_0,1 provides the dominant emission in both CaZnOS:Bi³⁺ and BaZnOS:Bi³⁺ phosphors. In light of the experimental and theoretical results, the metastable excited state of Bi²⁺ + h_VBM (hole at the valence band maximum) is the origin of the additional emission bands in BaZnOS:Bi³⁺. These results provide the basis of emission band tuning and novel material design for Bi³⁺-doped phosphors. Full article

Kalman filter (KF) is an effective way to estimate the state-of-charge (SOC), but its performance is heavily dependent on the state-space model parameters. One of the factors that causes the model parameters to change is battery aging, which is individually and non-uniformly experienced by the cells inside the battery pack. To mitigate this issue, this paper proposes an online calibration method considering the impact of cell aging and cell inconsistency. In this method, the state-of-health (SOH) levels of the individual cells are estimated using the deep learning method, and the historical parameter loop-up table is constructed to update the state-space model. The proposed calibration framework provides enhanced accuracy for cell-by-cell SOC estimation by lightweight computing devices. The SOC estimation errors of the calibrated EKF reduce to 1.81% compared to 12.1% of the uncalibrated algorithms. Full article

Quantum batteries are quantum systems designed to store energy and release it on demand. The optimization of their performance is an intensively studied topic within the realm of quantum technologies. Such optimization forces the question: how do quantum many-body systems work as quantum batteries? To address this issue, we rely on symmetry and symmetry breaking via quantum phase transitions. Specifically, we analyze a dimerized quantum XY chain in a transverse field as a prototype of an energy storage device. This model, which is characterized by ground states with different symmetries depending on the Hamiltonian parameters, can be mapped onto a spinless fermionic chain with superconducting correlations, displaying a rich quantum phase diagram. We show that the stored energy strongly depends on the quantum phase diagram of the model when large charging times are considered. Full article

When a liquid film is thinning, the charge and the potential of its surfaces change simultaneously due to the interaction between the two surfaces. This phenomenon is an example for charge regulation and has been known for half a century for systems featuring aqueous solutions in contact with metals, salts, biological surfaces covered by protolytes, etc. Few studies, however, investigated regulation in foam and emulsion films, where the charge is carried by soluble ionic surfactants. This work presents an analysis of the phenomenon for surfactants that follow the classical Davies adsorption isotherm. The electrostatic disjoining pressure Π_el was analyzed, and the Davies isotherm was shown to lead to Π_el ∝ h^−1/2 behavior at a small film thickness h. As usual, the charge regulation regime (constant chemical potential of the surfactant) corresponded to a dependence of Π_el on h between those for constant charge and constant electric potential regimes. The role of the background electrolyte was also studied. At the water–air interface, many ionic surfactants exhibit a surface phase transition. We show that the interaction between the two surfaces of a foam film can trigger the phase transition (i.e., the film changes its charge abruptly), and two films of different h values can coexist in equilibrium with each other—one covered by surfactant in the 2D gaseous state and another in the 2D liquid state. Full article

Autism spectrum disorder (ASD) is an early-onset neurodevelopmental condition that now impacts 1 in 36 children in the United States and is characterized by deficits in social communication, repetitive behaviors, and restricted interests. Children with ASD also frequently experience co-morbidities including anxiety and ADHD, and up to 80% experience gastrointestinal (GI) symptoms such as constipation, diarrhea, and/or abdominal pain. Systemic immune activation and dysregulation, including increased pro-inflammatory cytokines, are frequently observed in ASD. Evidence has shown that the innate immune system may be impacted in ASD, as altered monocyte gene expression profiles and cytokine responses to pattern recognition ligands have been observed compared to typically developing (TD) children. In humans, circulating monocytes are often categorized into three subpopulations—classical, transitional (or “intermediate”), and nonclassical monocytes, which can vary in functions, including archetypal inflammatory and/or reparative functions, as well as their effector locations. The potential for monocytes to contribute to immune dysregulation in ASD and its comorbidities has so far not been extensively studied. This study aims to determine whether these monocyte subsets differ in frequency in children with ASD and if the presence of GI symptoms alters subset distribution, as has been seen for T cell subsets. Whole blood from ASD children with (ASD⁺GI⁺) and without gastrointestinal symptoms (ASD⁺GI⁻) and their TD counterparts was collected from children enrolled in the Childhood Autism Risk from Genetics and Environment (CHARGE) study. Peripheral blood mononuclear cells were isolated and stained for commonly used subset identifiers CD14 and CD16 as well as activation state markers CCR2, HLA-DR, PD-1, and PD-L1 for flow cytometry analysis. We identified changes in monocyte subpopulations and their expression of surface markers in children with ASD compared to TD children. These differences in ASD appear to be dependent on the presence or absence of GI symptoms. We found that the ASD⁺GI⁺ group have a different monocyte composition, evident in their classical, transitional, and nonclassical populations, compared to the ASD⁺GI⁻ and TD groups. Both the ASD⁺GI⁺ and ASD⁺GI⁻ groups exhibited greater frequencies of classical monocytes compared to the TD group. However, the ASD⁺GI⁺ group demonstrated lower frequencies of transitional and nonclassical monocytes than their ASD⁺GI⁻ and TD counterparts. CCR2⁺ classical monocyte frequencies were highest in the ASD⁺GI⁻ group. HLA-DR⁺ classical, transitional, and nonclassical monocytes were statistically comparable between groups, however, HLA-DR⁻ nonclassical monocyte frequencies were lower in both ASD groups compared to TD. The frequency of classical monocytes displaying exhaustion markers PD-1 and PD-L1 were increased in the ASD⁺GI⁺ group compared to ASD⁺GI⁻ and TD, suggesting potentially impaired ability for clearance of foreign pathogens or debris, typically associated with worsened inflammation. Taken together, the findings of differential proportions of the monocyte subpopulations and altered surface markers may explain some of the characteristics of immune dysregulation, such as in the gastrointestinal tract, observed in ASD. Full article

To handle and manage battery degradation in electric vehicles (EVs), various capacity estimation methods have been proposed and can mainly be divided into traditional modeling methods and data-driven methods. For realistic conditions, data-driven methods take the advantage of simple application. However, state-of-the-art machine learning (ML) algorithms are still kinds of black-box models; thus, the algorithms do not have a strong ability to describe the inner reactions or degradation information of batteries. Due to a lack of interpretability, machine learning may not learn the degradation principle correctly and may need to depend on big data quality. In this paper, we propose a physics-informed recurrent neural network (PIRNN) with a fractional-order gradient for fast battery degradation estimation in running EVs to provide a physics-informed neural network that can make algorithms learn battery degradation mechanisms. Incremental capacity analysis (ICA) was conducted to extract aging characteristics, which could be selected as the inputs of the algorithm. The fractional-order gradient descent (FOGD) method was also applied to improve the training convergence and embedding of battery information during backpropagation; then, the recurrent neural network was selected as the main body of the algorithm. A battery dataset with fast degradation from ten EVs with a total of 5697 charging snippets were constructed to validate the performance of the proposed algorithm. Experimental results show that the proposed PIRNN with ICA and the FOGD method could control the relative error within $5\%$ for most snippets of the ten EVs. The algorithm could even achieve a stable estimation accuracy (relative error < 3%) during three-quarters of a battery’s lifetime, while for a battery with dramatic degradation, it was difficult to maintain such high accuracy during the whole battery lifetime. Full article

Dehydration is known to affect the rate of electron transfer backreaction from the light-induced charge separation state P⁺Q_A⁻ to the neutral ground state PQ_A in photosynthetic bacterial Reaction Centers. On the other hand, a 20 s continuous illumination period has been demonstrated to induce (at 297 K) formation of one or more light-adapted states at different levels of dehydration; these light-adapted states are believed to be related to peculiar response(s) from the protein. In this work, we applied time-resolved rapid-scan FTIR difference spectroscopy to investigate the protein response under dehydrated conditions (RH = 11%) at 281 K both after a flash and under prolonged continuous illumination. Time-resolved FTIR difference spectra recorded after a laser flash show a protein recovery almost synchronous to the electron transfer backreaction P⁺Q_A⁻ → PQ_A. Time-resolved FTIR difference spectra recorded after 20.5 s of continuous illumination (RH = 11%, T = 281 K) surprisingly show almost the same kinetics of electron transfer back reaction compared to spectra recorded after a laser flash. This means that the mechanism of formation of a light-adapted stabilized state is less effective compared to the same hydration level at 297 K and to the RH = 76% hydration level (both at 281 K and 297 K). Time-resolved FTIR difference spectra after continuous illumination also suggest that the 1666 cm⁻¹ protein backbone band decays faster than marker bands for the electron transfer back reaction P⁺Q_A⁻ → PQ_A. Finally, FTIR double-difference spectra (FTIR difference spectrum recorded after 18.4 s illumination minus flash-induced FTIR difference spectrum) suggest that at RH = 11%, a light-adapted state different from the one observed at RH = 76% is formed. A possible interpretation is that at RH = 11%, the protein response is modified by the fact that only protons can move easily, differently from water molecules, as instead observed for RH = 76%. This probably makes the formation of a real light-adapted P⁺Q_A⁻ stabilized state at RH = 11% unfeasible. Full article

The high penetration and uncertainty of renewable energy sources, such as wind, in modern power systems make traditional automatic generation control (AGC) methods more challenging. In order to improve the frequency stability of the power system under the high proportion of wind power penetration, the inherent fast-response characteristics of energy storage allow bidirectional adjustments with the system. However, storage power becomes insufficient when the state of charge (SOC) approaches its upper or lower limits, at this time, it is difficult to take into account both the state of charge protection of the energy storage and the effect of frequency regulation. Based on the purpose of testing the grid frequency containment reserve (FCR) performance and efficient use of frequency modulation resources, this paper proposes a wind power high penetration system frequency modulation control strategy considering the storage state of charge by constructing a two-zone automatic generation control system model containing wind power, using an improved adaptive ocean predator algorithm to optimise the frequency modulation responsibility allocation method in real time, and formulating a real-time management scheme for storage state of charge. Finally, the different control strategies are compared and analysed by MATLAB 2018b/Simulink under different loads and wind speeds, and their effectiveness is verified by the frequency offset and state of charge offset, so as to optimise the effect of frequency modulation while maintaining the state of charge of energy storage. Full article

The transport sector is responsible for nearly a quarter of global CO₂ emissions annually, underscoring the urgent need for cleaner, more sustainable alternatives such as electric vehicles (EVs). However, the electrification of heavy goods vehicles (HGVs) has been slow due to the substantial power and battery capacity required to match the large payloads and extended operational ranges. This study addresses the research gap in battery pack design for commercial HGVs by investigating the electrical and thermal behaviour of a novel battery pack configuration using an electro-thermal model based on the equivalent circuit model (ECM). Through computationally efficient 1D modelling, this study evaluates critical factors such as cycle ageing, state of charge (SoC), and their impact on the battery’s range, initially estimated at 285 km. The findings of this study suggest that optimal cooling system parameters, including a flow rate of 18 LPM (litres per minute) and actively controlling the inlet temperature within ±7.8 °C, significantly enhance thermal performance and stability. This comprehensive electro-thermal assessment and the advanced cooling strategy set this work apart from previous studies centred on smaller EV applications. The findings provide a foundation for future research into battery thermal management system (BTMS) design and optimised charging strategies, both of which are essential for accelerating the industrial deployment of electrified HGVs. Full article

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features. Full article