Search Results (577)

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article

We consider a network of a residential heating system (RHS) composed of two types of agents: a prosumer and a consumer. Both are connected to a community heating system (CHS), which supplies non-intermittent thermal energy for space heating and domestic hot water. The prosumer utilizes a combination of solar thermal collectors and CHS heat, whereas the consumer depends entirely on the CHS. Any excess heat generated by the prosumer can either be stored on-site or fed back into the CHS. Weather conditions, modeled as a common noise term, affect both agents simultaneously. The prosumer’s objective is to minimize the expected discounted total cost, taking into account storage charging and discharging losses as well as uncertainties in future heat production and demand. This leads to a stochastic optimal control problem addressed through dynamic programming techniques. Scenario-based analyses are then performed to examine how different parameters influence both the value function and the resulting optimal control strategies. For a common noise coefficient ${\sigma }_0=0.4$, the prosumer incurs an approximate $16.08\%$ increase in the aggregated discounted cost from the case of no common noise. For a discharging efficiency ${\eta }_E={\displaystyle \frac{1}{0.9}}$, the maximum aggregated discounted cost increases by approximately $1.85\%$ as compared to the perfect discharging efficiency. Similarly, for a charging efficiency ${\eta }_E=0.9$, we observe an approximate $1.94\%$ increase in the aggregated discounted cost as compared to a perfect charging efficiency. Furthermore, we derive insights into the maximum expected discounted investment that a consumer would need to make in renewable technologies in order to transition into a prosumer. Full article

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

The electronic band structure of two-dimensional lithium is calculated using the Dirac equation. Lithium is modeled as a two-dimensional square lattice in which the two strongly bound inner electrons and the fixed nucleus are treated as a positively charged ion (+e), while the outer electron is assumed to be uniformly distributed within the cell. The electronic potential is obtained by considering Coulomb-type interactions between the charges inside the unit cell and those in the surrounding cells. A numerical method that divides the unit cell into small pieces is employed to calculate the potential and then the Fourier coefficients are obtained. The Bloch method is used to determine the energy bands, leading to an eigenvalue matrix equation (in momentum space) of infinite dimension, which is truncated and solved using standard matrix diagonalization techniques. Convergence is analyzed with respect to the key parameters influencing the calculation: the lattice period, the dimension of the eigenvalue matrix, the unit-cell partition used to compute the potential’s Fourier coefficients, and the number of neighboring cells that contribute to the electronic interaction. Full article

The widespread occurrence of pharmaceutical residues in aquatic environments necessitates the development of advanced porous materials for efficient remediation. This study investigates the adsorption mechanisms of ibuprofen and atenolol within the high-silica zeolite Y. Batch adsorption experiments demonstrated significant uptake, with loading capacities of 191.6 mg/g for ibuprofen and 273.0 mg/g for atenolol, confirming the material’s effectiveness. Using a combination of neutron and X-ray powder diffraction, complemented by Rietveld refinement and simulated annealing algorithms, we achieved the exact localization of the guest molecules. While the pristine zeolite maintains cubic symmetry $Fd\overline{3}$, the incorporation of pharmaceutical molecules induces significant residual nuclear density and anisotropic lattice distortions. To accurately model these perturbations, a systematic symmetry reduction to the acentric triclinic space group F1 was implemented. This approach enabled an ab initio refinement of the structure, revealing that drug uptake of each guest is governed by distinct chemical drivers. Ibuprofen is stabilized via steric confinement and long-range dispersive interactions. In contrast, atenolol stability is governed by electrostatic charge compensation within the zeolitic voids. Our results suggest that the final adsorption geometry is dictated by the spatial orientation of functional groups and host–guest proximity rather than molecular chirality. These results provide a microscopic model describing the fundamental host–guest interactions in FAU zeolites. This structural understanding is an essential step towards the potential use of zeolitic materials in environmental remediation and complex guest sequestration. Full article

High-voltage equipment imposes increasingly stringent demands on polymeric insulating materials, particularly in terms of dielectric strength, space charge suppression, thermo-electrical stability, and interfacial reliability. Conventional polymers are prone to critical failure modes under high electric fields, including electrical treeing, partial discharge, interfacial degradation, and thermo-oxidative aging. This review systematically summarizes recent advances in polymer modification strategies specifically designed for high-voltage applications, covering nanofiller reinforcement, plasma surface engineering, and the development of self-healing insulating polymers. Multi-scale structural control and interface engineering, aligned with the specific requirements of high-voltage environments, have emerged as pivotal approaches to enhance insulation performance. Moreover, the integration of artificial intelligence-driven materials design, digital characterization, and application-oriented modeling holds significant promise for accelerating the development of next-generation high-voltage polymeric systems, thereby offering robust materials solutions for the reliable long-term operation of high-voltage equipment. Full article

Our Sun is the closest X-ray astrophysical source to Earth. As such, it makes for a strong case study to better understand astrophysical processes. Solar flares are particularly interesting as they are linked to coronal mass ejections as well as magnetic field reconnection sites in the solar atmosphere. Flares can therefore provide insightful information on the physical processes at play on their production sites but also on the emission and acceleration of energetic charged particles towards our planet, making it an excellent forecasting tool for space weather. While solar flares are critical to understanding magnetic reconnection and particle acceleration, their hard X-ray polarization—key to distinguishing between competing theoretical models—remains poorly constrained by existing observations. To address this, we present the CUbesat Solar Polarimeter (CUSP), a mission under development to perform solar flare polarimetry in the 25–100 keV energy range. CUSP consists of a 6U-XL platform hosting a dual-phase Compton polarimeter. The polarimeter is made of a central assembly of four 4 × 4 arrays of plastic scintillators, each coupled to multi-anode photomultiplier tubes, surrounded by four strips of eight elongated GAGG scintillator bars coupled to avalanche photodiodes. Both types of sensors from Hamamatsu are, respectively, read out by the MAROC-3A and SKIROC-2A ASICs from Weeroc. In this manuscript, we present the preliminary spectral performances of single plastic and GAGG channels measured in a laboratory using development boards of the ASICs foreseen for the flight model. Full article

In the pursuit of cleaner and more efficient internal combustion engines, dual-fuel strategies combining diesel and hydrogen are gaining increasing attention. This study employs detailed computational fluid dynamics (CFD) simulations to investigate the behaviour of pilot diesel injections in dual-fuel diesel–hydrogen engines. The study aims to characterize spray formation, ignition delay and early combustion phenomena under various energy input levels. Two combustion models were evaluated to determine their performance under these specific conditions: Tabulated Well Mixed (TWM) and Representative Interactive Flamelet (RIF). After an initial numerical validation using dual-fuel constant-volume vessel experiments, the models are further validated using in-cylinder pressure measurements and high-speed natural combustion luminosity imaging acquired from a large-bore optical engine. Particular attention was given to ignition location due to its influence on subsequent hydrogen ignition. Results show that both combustion models reproduce the experimental behavior reasonably well at high energy input levels (EILs). At low EILs, the RIF model better captures the ignition delay; however, due to its single-flamelet formulation, it predicts an abrupt ignition of all available premixed charge in the computational domain once ignition conditions are reached in the mixture fraction space. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

In this study, a three-dimensional nonlinear finite element (FE) model was developed using Abaqus/Explicit to simulate the effects of internal blasts. The numerical model was validated against two previously published numerical and experimental works, demonstrating strong agreement in deformation results. A parametric study was carried out to evaluate the influence of several key factors on the deformation of the receiver tunnel subjected to an explosion in the adjacent donor tunnel. The investigation considered critical variables such as lining material, tunnel inner diameter, cross-sectional shape, spacing between tunnels, and TNT charge weight. The results clearly indicate that expanded polystyrene (EPS) foam, across various densities, demonstrates superior capacity for absorbing blast waves compared to polyurethane and aluminum foams. Furthermore, it was found that lower-density EPS foam provides enhanced mitigation of deformation in tunnel linings. The findings also revealed that damage to the tunnel walls is more strongly correlated with the tunnel shape where the circular tunnel exhibited the best performance. It showed the lowest deformation and delayed peak response. In addition, tunnel deformation increases markedly with higher TNT charge weights. A blast of 1814 kg produced approximately five times the deformation compared to a 454 kg charge. Moreover, it is seen that increasing the spacing between donor and receiver tunnels from 1.5 D to 2.5 D led to a 38.7% reduction in maximum deformation. Full article

Residential space heating in Northern Europe requires long-duration thermal storage to align summer solar gains with winter heating demand. This study investigates a compact sand-based seasonal thermal energy storage integrated with flat-plate solar collectors for an A+ class single-family house in Kaunas, Lithuania. An iterative co-design couples collector sizing with the seasonal charging target and a 3D COMSOL Multiphysics model of a 300 m³ sand-filled, phenolic foam-insulated system, with a 1D conjugate model of a copper pipe heat-exchanger network. The system was charged from March to September and discharged from October to February under measured-weather boundary conditions across three consecutive annual cycles. During the first year, the storage supplied the entire winter heating demand, though 35.2% of the input energy was lost through conduction, resulting in an end-of-cycle average sand temperature slightly below the initial state. In subsequent years, both the peak sand temperature and the residual end-of-cycle temperature increased by 3.7 °C and 3.2 °C, respectively, by the third year, indicating cumulative thermal recovery and improved retention. Meanwhile, the peak conductive losses rate decreased by 98 W, and cumulative annual losses decreased by 781.4 kWh in the third year, with an average annual reduction of 4.15%. These results highlight the progressive self-conditioning of the surrounding soil and demonstrate that a low-cost, sand-based storage system can sustain a complete seasonal heating supply with declining losses, offering a robust and scalable approach for residential building heating applications. Full article

In this report, a thickness-driven, aggregation–structure–transport optimum in sonicated poly(3-hexylthiophene) (P3HT) FETs was investigated. Mobility peaks at ~10–20 nm, coincident with a minimum in the photoluminescence (PL) vibronic ratio $I_{0-0}/I_{0-1}$ (strong H-aggregate interchain coupling) and X-ray diffraction sharpening of the (100) lamellar peak with slightly reduced d-spacing, indicate tighter π–π stacking and larger crystalline coherence. Absorption analysis (Spano model) is consistent with this enhanced interchain order. The mobility maximum arises from an optimal balance: J-aggregate–like intrachain planarity supports along-chain transport, while H-aggregates provide interchain connectivity for efficient hopping. Below this thickness, insufficient interchain coupling limits transport; above it, over-aggregation and disorder introduce traps and weaken gate control. The sharp rise in threshold voltage beyond the critical thickness indicates more trap states or fixed charges forming within the film bulk. As a result, a larger gate bias is needed to deplete the channel (remove excess holes) and switch the device off. These results show that electrical gating can be tuned via solution processing (sonication) and film thickness—guiding the design of P3HT devices for photovoltaics and sensing. Full article

The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the first stage, a metric learning-based distributed model predictive control (ML-DMPC) strategy is proposed to enable cooperative longitudinal control among heterogeneous vehicles, explicitly incorporating inter-vehicle interactions to improve speed tracking, ride comfort, and platoon-level energy efficiency. In the second stage, a multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is developed for real-time energy management, achieving an optimal power split between the engine and battery while reducing Q-value overestimation and accelerating learning convergence. Simulation results across multiple standard driving cycles demonstrate that the proposed framework outperforms conventional distributed model predictive control (DMPC) and multi-agent deep deterministic policy gradient (MADDPG)-based methods in fuel economy, stability, and convergence speed, while maintaining battery state of charge (SOC) within safe limits. To facilitate future experimental validation, a dSPACE-based hardware-in-the-loop (HIL) architecture is designed to enable real-time deployment and testing of the proposed control framework. Full article

The continued growth of international maritime trade has driven automated container terminals (ACTs) to pursue more efficient operational management strategies. In practice, the horizontal yard layout in ACTs significantly enhances transshipment efficiency. However, the more complex horizontal transporting system calls for an effective approach to enhance automated guided vehicle (AGV) scheduling. Considering AGV charging and path conflicts, this paper proposes a multi-agent reinforcement learning (MARL) approach to address the AGV dispatching and path planning (VD2P) problem under a horizontal layout. The VD2P problem is formulated as a Markov decision process model. To mitigate the challenges of high-dimensional state-action space, a multi-agent framework is developed to control the AGV dispatching and path planning separately. A mixed global–individual reward mechanism is tailored to enhance both exploration and corporation. A proximal policy optimization method is used to train the scheduling policies. Experiments indicate that the proposed MARL approach can provide high-quality solutions for a real-world-sized scenario within tens of seconds. Compared with benchmark methods, the proposed approach achieves an improvement of 8.4% to 53.8%. Moreover, sensitivity analyses are conducted to explore the impact of different AGV configurations and charging strategies on scheduling. Managerial insights are obtained to support more efficient terminal operations. Full article

Ionic propulsion, where charged particles, ions, are produced between electrodes and accelerate towards the negative electrode, has practical applications as a propulsion system in the space industry; however, its adoption to in-atmosphere ionic propulsion is relatively new and faces different challenges. A high potential difference is required to achieve a corona discharge between a positive and negative electrode. In this work, we will explore the feasibility of ionic propulsion using CFD modelling to replicate the effect of the ions, with a future aim of improving efficiency. The ionization region is modelled for a 15 kV potential difference, which is replicated with a velocity inlet, based on experimental data. The output velocity from the numerical simulation shows the same trend as theoretical predictions but significantly underestimates the magnitude of the ionic wind when compared with theoretical estimates. Further modelling is highlighted to improve predictions and assess if the theoretical model overestimates the ionic wind. Full article