Search Results (6,564)

Electrochemical energy storage serves as a foundational technology in contemporary electrical energy storage systems, with its operational safety and stability being crucial to socio-economic development. The estimation of the state of health (SOH) of energy storage batteries is an essential component for ensuring system safety warnings and lifecycle management. To address the challenges of redundant health feature dimensions, insufficient correlation of influencing factors, and limited prediction accuracy in existing SOH estimation methods, in this paper, a novel state of health estimation framework is introduced, leveraging an Improved Gray Wolf Optimization (IGWO) algorithm to optimize the parameters of a Support Vector Machine (SVM). This model achieves precise prediction of battery health states by extracting multidimensional health features, including the differential temperature, incremental capacity, time interval of equal charge voltage difference (DT-IC-TIECVD) and implementing the improved gray wolf optimization algorithm with support vector machine algorithm (IGWO-SVM). Validated on the Oxford battery aging dataset, the proposed model achieves mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) values of 0.43%, 0.55%, and 0.99, respectively. These results confirm the high accuracy and feasibility of the proposed method, while also providing a novel technical pathway for the health management of energy storage batteries. Full article

Conventional black-box models for electric vehicle (EV) battery State-of-Health (SOH) prediction achieve high accuracy but lack interpretability, limiting their practical deployment in Battery Management Systems (BMSs). To circumvent these limitations, this study proposes a novel Grey-Box Residual-Driven Framework (GBRDF) that synergizes Deep Symbolic Regression (DSR) with a residual-learning BiLSTM network with two contributions: (1) the DSR component derives explicit, interpretable mathematical expressions governing global degradation trajectories based on electrochemical features, and (2) the BiLSTM network models the residual errors to capture high-frequency nonlinearities and complex sequential dependencies not addressed by the symbolic baseline. By fusing the physics-informed transparency of DSR with the data-driven refinement of BiLSTM, the GBRDF significantly enhances forecasting precision. Experimental validation across four independent EV datasets shows that the GBRDF achieves the highest coefficient of determination (R²) of 0.982, and the lowest mean absolute error (MAE) of 0.1398 and root mean square error (RMSE) of 0.3176, significantly outperforming existing methods. Furthermore, the DSR-derived SOH equation shows that battery degradation is primarily driven by high voltage exposure and charging time, with mathematical transformations reflecting how degradation accelerates initially then slows, matching real-world aging patterns where voltage stress dominates over temperature and usage variations. Full article

Multi-reference and density functional theory calculations are performed for the diatomic and ligated NpO³⁺ species. The main goal of this study is to provide insights into the stability of the experimentally synthesized N(CH₂CH₂NR)₃NpO (R = SiⁱPr₃) coordination complex and probe its use as a catalyst for the oxidation of methane. The constructed potential energy curves for NpO³⁺ showed the presence of three different types of minima (Np³⁺O, Np⁴⁺O⁻, Np⁵⁺O²⁻) depending on the neptunium–oxygen distance. All these minima are higher in energy than the Np²⁺ + O⁺ fragments, and the more stable Np⁵⁺O²⁻ form is stabilized only due to the presence of the negatively charged -CH₂NR⁻ moiety of the ligand. The C–H bond activation of methane was found to be possible only for the first quintet state of the complex which lies about 30 kcal/mol higher than the ground triplet state. Full article

A three-body classical trajectory Monte Carlo method is used to investigate state-specific electron capture from H₂O by highly charged ions. The radial and momentum distributions of the target electron are modeled using a one-center molecular orbital wave function. Total single-electron capture cross sections, as well as cross sections for capture into specific $nl$-states, are calculated for the highly charged ion projectiles, C⁶⁺, N⁷⁺, Ne¹⁰⁺, and Ar¹⁸⁺, at relative collision energies ranging from 0.01 keV/amu to 50 keV/amu. Comparisons of relative $n$-state capture populations and total single-electron capture cross sections are made with experimental results. The results show a marked improvement in the prediction of relative $n$-states populated, with the overall single-electron single capture cross sections being slightly low compared with experimental values. Overall, this method of calculating $nl$-states of the captured electron appears to be a promising approach for those wishing to model X-ray and Extreme Ultraviolet (EUV) emissions from comets bombarded by solar wind ions, and fusion researchers trying to determine the effects of impurities in Tokomak reactors. Full article

Electrochemical chlorine production is of considerable industrial importance in areas such as water treatment, chemical manufacturing, and disinfection. However, conventional precious metal-based dimensionally stable anodes (DSAs), such as RuO₂- and IrO₂-based systems, are limited by high cost and resource constraints, motivating the development of low-cost alternative catalysts. In this study, Mn₃O₄ electrodes with controllable defect characteristics were fabricated by electrochemical deposition under various processing conditions. The effects of defect modulation and surface modification on the structural, electronic, and electrochemical properties of the electrodes were systematically evaluated. X-ray diffraction analysis confirmed that all deposited films retained a stable tetragonal Mn₃O₄ crystal structure, indicating that the deposition parameters primarily influenced defect states rather than the bulk phase. Mott–Schottky measurements revealed that the Mn₃O₄ electrodes exhibited p-type semiconducting behavior, with charge carrier densities on the order of 10¹⁴ cm⁻³, suggesting that oxygen vacancy-related defect states may contribute to the observed electronic properties of the electrodes. To further enhance anodic performance, Pt was introduced onto the Mn₃O₄ surface via sputtering, resulting in significantly improved charge transfer characteristics. Electrochemical measurements demonstrated that the best performing Pt/Mn₃O₄ electrodes delivered a current density exceeding 100 mA cm⁻² at an applied potential of 1.5 V versus Ag/AgCl. More importantly, defect-enriched Pt/Mn₃O₄ electrodes exhibited markedly enhanced chlorine evolution activity, with the chlorine production rate increasing from approximately 14 µmol cm⁻² to 29 µmol cm⁻², corresponding to an enhancement of about 2.07-fold. Faradaic efficiency analysis further showed that sample (g) and sample (n) achieved chlorine evolution efficiencies of 59.2% and 74.6%, respectively, indicating a higher tendency toward chlorine evolution for the Pt-modified electrodes under the tested conditions. These findings suggest that the synergistic combination of defect engineering and surface modification effectively modulates the electronic structure of Mn₃O₄, providing a viable strategy for improving chlorine evolution performance. Full article

The detection of sulfur hexafluoride (SF₆) decomposition products is critical for diagnosing insulation faults in gas-insulated switchgear (GIS). In this study, a vanadium-doping strategy was incorporated into the graphene/MoS₂ (GM) heterojunction to design a vanadium-doped graphene/MoS₂ (GMV) heterojunction material. Leveraging first-principles density functional theory (DFT), the adsorption behaviors of five characteristic SF₆ and its decomposition gases (H₂S, SO₂, SOF₂, SO₂F₂) on intrinsic GM and GMV were systematically investigated to evaluate their potential for gas sensing applications. Computational results reveal that intrinsic GM exhibits only weak physical adsorption toward all target molecules, with low adsorption energies and negligible charge transfer, which fails to meet practical application requirements. In contrast, GMV demonstrates significantly enhanced adsorption energies for H₂S, SO₂, and SOF₂ at vanadium sites (with a maximum value of −0.388 eV for SO₂) and shorter adsorption distances, while SO₂F₂ and SF₆ preferentially adsorb near electron-deficient carbon regions. Intrinsic GMV displays semimetallic properties, with a Fermi level at 0.126 eV and a band gap of 0.0017 eV. Upon adsorption of H₂S, SOF₂, SO₂F₂, or SF₆, the Fermi level undergoes a moderate shift (ranging from −1.083 eV to +0.349 eV), with minimal changes in the band gap. Conversely, SO₂ adsorption induces a substantial downward shift of the Fermi level to −1.732 eV, accompanied by the emergence of a sharp partial density of states (PDOS) peak near the Fermi level (0–1.5 eV), indicating strong orbital coupling and significant charge transfer. Furthermore, recovery times calculated using classical formulas show that at room temperature and a frequency of 1 × 10⁶ Hz, the recovery time of GMV for SO₂ is 2.43 s, outperforming the other four gases and satisfying practical gas sensing requirements. Through comprehensive analysis of adsorption distances, electronic structure changes, and recovery times, GMV exhibits higher selectivity toward SO₂. Thus, GMV can serve as a sensing material for detecting GIS insulation faults associated with elevated SO₂ concentrations, offering a viable strategy for advancing online monitoring technologies in power systems. Full article

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids. Full article

This paper presents the design, development, and evaluation of a Reinforcement Learning (RL)–based torque-split controller for the regenerative braking system (RBS) in battery electric vehicles (BEVs). The controller employs a Deep Deterministic Policy Gradient (DDPG) agent to distribute the braking demand between regenerative and frictional braking systems with the aim of maximizing energy recovery while adhering to the physical and operational constraints. To capture the charging limitation of the battery, a State-of-Power (SoP) calculation mechanism is incorporated, providing a time-varying bound on the regenerative charge power. The agent is trained in a MATLAB/Simulink environment representing the digital twin of a BEV drivetrain, and considers a mix of different braking scenarios, i.e., light braking, medium braking, hard braking, and emergency braking. The RL’s reward shaping promotes efficient utilization of the SoP-limited regenerative capability while discouraging constraint violations and aggressive control behavior. Across a range of State-of-Charge (SoC) conditions and driving cycles, including the Worldwide Harmonized Light–Vehicle Test Procedure (WLTP) and synthetic random-rich driving cycle, the RL controller consistently delivers promising performance, yielding energy recovery of up to ~98% of the total braking energy available on WLTP type 3 driving cycle while being able to operate closely to the battery SoP limit. The results demonstrate the proposed controller’s capability for adaptive, constraint-aware energy management in BEVs and underline its potential for future intelligent braking strategies. Full article

The adsorption of rare-earth Y-based inclusions (Y, Y₂O₃, Y₂O₂S) on Al₂O₃, YAlO₃, and Y₂O₃ refractory surfaces is a primary cause of nozzle clogging during the continuous casting of rare-earth steels. Conventional anti-clogging strategies, being passive and offline, lack real-time adjustability. This study aims to elucidate the mechanism by which external positive charge modulates interfacial adsorption. Using first-principles calculations combined with partial density of states, charge density difference, and thermodynamic analyses, we investigated the adsorption behavior of Y, Y₂O₃, and Y₂O₂S on Al₂O₃ (001), YAlO₃ (001), and Y₂O₃ (001) surfaces under neutral and positively charged states (+2, +4). A triple inhibition mechanism is revealed: electronically, external charge disrupts O-p and Y-d orbital hybridization, attenuating interfacial covalent bonding; electrostatically, the net positive charge shifts the interfacial interaction from attraction to repulsion, creating a physical barrier; and thermodynamically, the Gibbs free energy change ΔG increases under charged conditions, indicating a quantifiable reduction in adsorption spontaneity. These findings provide a theoretical basis for the development of active anti-clogging strategies in rare-earth steel production. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

The energy-dependent population of fine quantum states in single electron capture (SEC) reflects the intrinsic collision dynamics. Here we report experimental studies of Ar⁷⁺ ions colliding with He in the energy range of 1.05–17.5 keV/u. Owing to the high resolution of a recoil-ion momentum spectrometer, the n-, l-, and spin-state electron capture populations are well resolved, and a strong energy dependence of the SEC cross sections is observed. Most importantly, a clear inversion of the cross-section ratio between the spin-resolved triplet and singlet 3s3d configurations is found, demonstrating a breakdown of spin statistics. Together with recent spin-resolved studies of C³⁺-He collisions (PRL 133, 173002 (2024)), these results suggest that the breakdown of spin statistics is likely a general feature of charge exchange in open-shell highly charged ion systems. Full article

It is critical to accurately estimate the state of charge (SOC) and state of health (SOH) of lithium-ion batteries to ensure the safety and reliability of marine power systems, where the inherent symmetry of lithium-ion battery charge–discharge dynamics is often disrupted. However, the accuracy of conventional methods significantly deteriorates under dynamic current rates induced by fluctuating electrical loads, leading to unreliable SOC and SOH estimates. This article proposes a novel SOC and SOH joint estimation method based on a long short-term memory network with a rate awareness attention mechanism (RAAM-LSTM) and support vector regression optimized by greylag goose algorithm (GGO-SVR). RAAM-LSTM improves SOC estimation accuracy by adaptively weighting enhanced rate-related features. For SOH estimation, the GGO-SVR model incorporates the SOC as a coupling feature and applies physical constraints to ensure consistency with irreversible battery degradation. The comparative experimental results show that the error of the SOC is less than 1.6%, and that of the SOH is less than 0.5%, which are much smaller compared with those of conventional methods. Full article

To address the inherent low voltage and poor energy density of LiFePO₄, LiFe_0.6Mn_0.4PO₄ (LFMP) has emerged as a promising cathode for next-generation lithium-ion batteries. However, its practical application is severely hindered by intrinsic limitations such as low electronic conductivity and sluggish Li⁺ diffusion. To address these challenges, this study investigates the effects of silver (Ag) doping on the structural and electrochemical performance of LFMP. Through a facile high-temperature solid-state approach, Ag⁺ ions are successfully incorporated into the LFMP matrix, and the resulting material (LFMP-Ag) is systematically characterized. The results reveal that partial Ag is doped into the LFMP lattice while an Ag-rich secondary phase within LFMP particles is detected, significantly enhancing the charge transfer kinetics. The Ag-doped LFMP cathodes exhibit superior discharge capacity of 142.1 mAh g⁻¹ at 0.1 C, enhanced rate capability, better cyclic stability (92.3% retention after 300 cycles) and enhanced thermal stability, surpassing the undoped LFMP counterparts. These findings demonstrate that Ag doping is an effective strategy for optimizing the electrochemical performance of LFMP cathodes, offering a viable pathway toward advanced battery technologies. Full article

Accurate assessment of the State of Health (SOH) is critical for battery management systems in aviation. As a step towards this goal, this study presents a proof-of-concept for a novel SOH estimation method based on an Improved Particle Swarm Optimization-Extreme Learning Machine (IPSO-ELM) model, validated under controlled laboratory cycling conditions. Although traditional Extreme Learning Machines (ELM) are widely used due to their fast computation and good generalization, their random parameter initialization often leads to unstable convergence and limited accuracy. To address these limitations, this paper proposes a novel SOH estimation method based on an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the key parameters of ELM. Three health indicators (HI)—constant-current charging time, equal-voltage-drop discharge time, and average discharge voltage—were extracted from charge–discharge curves as model inputs. The IPSO algorithm dynamically adjusts the inertia weight, introduces a constriction factor and a termination counter to enhance global search capability and avoid local optima. Experimental results on open-source datasets (B005, B007, B0018) and laboratory datasets (A001, A002) demonstrate that the proposed IPSO-ELM model achieves a Root-Mean-Square Error (RMSE) below 0.7% and a Mean Absolute Percentage Error (MAPE) below 0.5%. Compared with standard ELM and PSO-ELM models, it significantly outperforms them in accuracy (e.g., for B0018, RMSE is reduced to 0.21% and MAPE to 0.14%), convergence speed, and robustness, establishing a foundation for future development of aviation-ready SOH estimators. Full article

The study presents a comprehensive first-principles investigation of the structural, electronic, and magnetic properties of rocksalt scandium nitride (ScN) and its Cr- and Mn-doped derivatives using spin-polarized density-functional theory within the GGA + U (U_Cr = 3.5 eV, U_Mn = 2.7 eV) and HSE06 frameworks. Pristine ScN crystallizes in the cubic Fm$\bar{3}$m structure and exhibits narrow-gap semiconducting behavior, with an indirect band gap of 0.82 eV obtained from hybrid-functional calculations, in excellent agreement with reported theoretical values. Substitutional doping with Cr and Mn introduces localized 3d states near the Fermi level, driving a transition toward spin-polarized metallic or half-metallic behavior accompanied by robust ferromagnetism. Density-of-states and band-structure analyses reveal that magnetism and charge transport in the doped systems are dominated by exchange-split transition-metal 3d states hybridized with N-2p orbitals. Total energy calculations confirm ferromagnetic ground states for both Cr- and Mn-doped ScN, with Mn substitution yielding stronger exchange stabilization and higher magnetic moments. Magnetocrystalline anisotropy energies, evaluated using the force-theorem approach, are found to be negligibly small, indicating weak anisotropy consistent with the moderate spin–orbit coupling strength in ScN-based nitrides. Nevertheless, symmetry breaking around dopant sites gives rise to a finite Dzyaloshinskii–Moriya interaction, leading to weak spin canting and non-collinear magnetic tendencies. The interplay between magnetic exchange coupling, spin–orbit interaction, and local inversion symmetry breaking positions of Cr- and Mn-doped ScN as promising dilute magnetic semiconductors with tunable spin polarization and chiral magnetic interactions, offering a viable platform for nitride-based spintronic and magneto-electronic applications. Full article