Search Results (22,650)

Objectives: Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, and cardiometabolic comorbidity, have been increasingly associated with cognitive impairment and dementia. These associations, however, remain underexplored and underappreciated in middle-aged individuals with AF. This study aimed to explore the associations of early cognitive impairment with the presence of cardiometabolic comorbidities and potential associations with echocardiographic markers in middle-aged patients with and without AF. Methods: Between 2023–2024, fifty-six consecutive outpatients with a diagnosis of AF aged 45–65 years underwent clinical evaluation, transthoracic echocardiography, and comprehensive neuropsychological assessment using the Montreal Cognitive Assessment (MoCA) and the Consortium to Establish a Registry for Alzheimer’s Disease battery (CERAD). A control group of 58 age group-matched individuals without known cardiometabolic disease was included in comparative cognitive analyses. Results: Patients with AF and cardiometabolic comorbidities demonstrated early cognitive deficits, particularly in episodic memory and visuospatial functions, detectable even in individuals with normal MoCA scores, compared with the control group. However, no associations were observed between cognitive performance and conventional echocardiographic parameters in the group with AF. Conclusions: This study corroborated prior evidence of an association between cardiometabolic impairment and subtle cognitive impairment, but did not identify a specific contribution of echocardiography markers. More extensive and sensitive biomarkers of left atrial structure and function may be required to detect harmful associations with subtle cognitive impairment in middle-aged individuals. Further prospective studies, with a more balanced control for comorbidities, are warranted to clarify the clinical relevance of atrial structural remodeling in this context. Full article

The transition from traditional synchronous generators to intermittent renewable sources, combined with increasingly variable and difficult-to-control energy demand, is creating a growing need for large-scale reserves and energy storage. At the same time, reduced system inertia and evolving electricity market regimes are emerging as important challenges that may affect grid stability, reliability, and economic performance. Advanced storage technologies, particularly those with fast ramping and high-response capabilities, offer a potential means of providing near-instantaneous support in response to unexpected system disturbances or market signals, thereby helping to mitigate inertia-related risks. This paper investigates four technologies: pumped hydroelectric storage, battery energy storage systems, synchronous condensers, and special protection schemes, with a focus on their capability to deliver rapid responses to large-scale disturbances. The analysis is conducted using a deliberately simplified power system model to provide qualitative insights into system behavior and control interactions. The results indicate that automation-enabled responses to system imbalances, including support from synchronous condensers and the rapid activation of additional generation, can enhance system performance under disturbance conditions within the considered framework. These findings demonstrate the feasibility and potential value of such approaches; however, further validation using higher-fidelity models and system-specific data is required to quantify their operational and economic impacts. Full article

This paper presents the toolset, methodology and procedure followed to create a dataset from battery electric vehicle trajectories, called DEVRT—Dataset of Electric Vehicle Real Trips. Understanding the behaviour of electric vehicles and their battery consumption under real-life conditions and journeys is required in the shift towards the electrification of transport of people and goods. This paper aims to contribute with the provision of real measurements in different types of routes and environmental contexts at the time of driving to support data analytics and modelling techniques, essential for extracting actionable insights from electric vehicle battery consumption. The preparation, on-route and post-processing steps of the followed methodology are depicted. The outcome dataset consists of probe data collected over 4 days following heterogeneous routes performed by four different drivers using two electric vehicles (one more suitable to city usage and the other one more suitable for longer trips). This probe data is complemented with associated road network characterisation information, traffic flow measurements and weather extracted from auxiliary data sources. The paper presents a comprehensive description of the geographical characteristics of the trajectories, qualitative and quantitative characterisation of planned routes to create these trajectories, and criteria used to select them. Full article

Nanotechnology has emerged as a key driver in radioisotope batteries, which offer unique advantages for long-term, maintenance-free energy supply in deep space exploration, medical implants, and nuclear waste utilization. This review summarizes recent progress in applying nanomaterials and nanostructures to overcome the limitations of nuclear batteries, including low energy conversion efficiency and poor stability. The main content focuses on the three primary conversion mechanisms of thermoelectric, radio-voltaic, and radio-photovoltaic batteries, discussing high-performance thermoelectric nanomaterials such as SiGe alloys, wide-bandgap semiconductors including diamond and SiC for enhanced carrier collection, and nanoscale radionuclide ources to mitigate self-absorption losses. This review further elaborates on how nanostructure regulation and interface engineering have significantly improved carrier collection efficiency and device stability. These advances have enabled notable civilian applications, such as the BV100 and “Zhulong No.1” nuclear batteries. Despite this progress, challenges remain in ensuring long-term material stability under extreme environments, maintaining performance consistency during macroscopic device integration, and addressing the high fabrication costs. The review concludes by outlining future research directions, including the development of novel nanomaterial systems, innovative nanostructure designs, scalable manufacturing processes, and enhanced device stability and safety, to further advance next-generation radioisotope batteries. Full article

Accurate and trustworthy prediction of lithium-ion battery aging remains challenging due to multi-mechanistic degradation, cell-to-cell variability, and distribution shift between laboratory calibration and deployment. Fractional-order models have been proposed to capture long-memory effects in electrochemical systems; however, it remains unclear when such memory is empirically identifiable and beneficial within the common prognostics abstraction of state-of-health (SOH) versus cycle index. This work develops a fully reproducible computational pipeline for mechanistic battery aging based on a Caputo fractional differential equation (FDE) and evaluates its cross-cell generalization on open NASA cycling data. Parameters are identified using bounded robust nonlinear least squares and validated under a strict transfer protocol: calibration on cells B0005/B0006 and evaluation on held-out cells B0007/B0018 without refitting. The fractional model is benchmarked against a classical ODE surrogate, an ECM-inspired resistance-proxy baseline, and one-step-ahead machine-learning predictors. Uncertainty quantification is performed via parameter bootstrap and subsequently calibrated using conformal correction to target nominal coverage under transfer. Results show that the fractional order tends to collapse toward the integer-order limit (α → 1) in this dataset, indicating limited evidence of additional long-memory at the SOH-versus-cycle level under the considered protocol, while robust identification remains essential for stability. Calibrated prediction intervals achieve near-nominal coverage on held-out cells, highlighting the importance of UQ calibration under cell-to-cell shift. The proposed scripts and environment specifications enable direct replication and facilitate future extensions to stress-aware fractional models and hybrid physics–ML approaches. Full article

Home Energy Management Systems (HEMS) are increasingly positioned at the center of residential flexibility, particularly as homes integrate photovoltaics, battery storage, electric vehicles, and responsive loads. This systematic review examines recent advances in optimization and multi-asset coordination for HEMS. Searches were conducted in Scopus, Web of Science, IEEE Xplore, and ScienceDirect for studies published between 2020 and 2025; after screening and eligibility assessment, 90 studies were included. The findings indicates that deterministic optimization remains well suited to structured scheduling problems, whereas metaheuristic, hybrid, and learning-based methods are better able to address nonlinearity, uncertainty, and real-time adaptation. Across the reviewed literature, multi-asset integration generally improves cost, peak demand, self-consumption, and, in some cases, user comfort and emissions. Yet the field remains dominated by simulation-based validation. Future progress of HEMS will depend on real-world validation, interoperable system design, explainable control, and stronger alignment with user behavior, communication constraints, and regulatory frameworks. Full article

Cobalt–copper bimetallic oxides (CoCuO_x) show great potential for constructing high-performance micro-supercapacitors (MSCs) for micro-electronic applications. However, their poor conductivity and complex preparation procedures significantly hinder their broad applications. To address these challenges, oxygen-vacancy-modified CoCuO_x-based binder-free electrodes were fabricated using a one-step computer numerical control wire electrical discharge machining (CNCWEDM) strategy. This approach enabled the fabrication of CoCuO_x-based maze-like MSCs (CoCuMMSCs) with designable electrochemical performance, which could be simply controlled by their geometric shape and machining voltage. Subsequently, theoretical simulations were conducted for studying the effect of MSCs geometric shape on their capacitive behavior. Remarkably, the CoCuMMSCs fabricated by a machining voltage of 100 V achieved the maximum capacitance of 32.8 mF cm⁻² at 0.15 mA cm⁻². Furthermore, the CoCuMMSCs demonstrated outstanding performance at ultrahigh scan rates of up to 50,000 mV s⁻¹, exceeding by more than two orders of magnitude the values previously reported in the literature. The obtained results proved that the development of the CNCWEDM technique facilitated manufacturing CoCuMMSCs devices with excellent performance by the comprehensive utilization of oxygen-vacancy incorporation, synergistic effect of cobalt and copper oxides, binder-free electrode design, proper device construction and controllable machining voltage. The advanced CNCWEDM strategy creates a new pathway for the high-efficiency fabrication of high-performance bimetallic-oxide-based micro-electronic devices, such as MSCs, intelligent micro-sensors and micro-batteries. Full article

This study addresses the optimal scheduling of a proton exchange membrane fuel cell (PEMFC)-based building combined cooling, heating, and power (CCHP) system, aiming to improve operational efficiency and flexibility under coupled electricity–thermal–cooling demands and load uncertainty. A physics-informed scheduling environment was developed using component models and multi-energy balance constraints, including a PEMFC with waste-heat recovery, a lithium bromide absorption chiller, a reversible heat pump with condenser heat recovery to thermal storage, a battery energy storage system, and a hot-water thermal storage tank. The dispatch problem was formulated as a Markov decision process and solved using deep reinforcement learning with TD3; performance was evaluated on typical summer and winter days, and robustness was tested by generating 100 scenarios with 30% demand perturbations. The results show that TD3 learns coordinated multi-energy dispatch patterns consistent with seasonal operation and reduces hydrogen consumption relative to a rule-based strategy under uncertainty while requiring millisecond-level inference time. Dynamic programming achieved slightly lower hydrogen consumption but incurred orders-of-magnitude higher computation time. Overall, TD3 provides a practical trade-off between near-optimal performance, robustness, and real-time applicability for PEMFC-based building CCHP scheduling. Full article

A novel procedure is proposed in this paper to investigate the capacity of parking structures to resist additional live loads that could come from many cars, potentially from heavier or driverless cars. In recent decades, the typical operating weight of passenger vehicles has risen significantly. The anticipated widespread adoption of electric vehicles (EVs), which contain heavy battery systems, may further increase live load demands. As a result, a new robust procedure is needed to assess the live load effects on parking structures. Hence, using the proposed innovative approach based on 3D influence surfaces, tributary areas (AT) and three-dimensional influence surfaces (AI) were calculated (for the first time) to examine the equivalent uniformly distributed load corresponding to selected column axial loads and beam midspan moments that are expected to be experienced during the lifetime of parking structures. As case studies, the responses of two existing multistory parking garages on the Ohio State University campus were investigated under different arrangements of two car types—standard cars and sports utility vehicles (SUVs)—and the calculated maximum live loads were compared with the current code requirements. The results show that the maximum live load for the midspan moment is conservative; however, the maximum axial column loading in the extreme scenarios presented in this paper can be larger than the specified (original) design limit of the selected parking garages. The novel methodology proposed in this paper is based on 3D influence line analysis and can be applied for any vehicle configuration and weight, and different parking arrangements or loading scenarios to investigate the performance of parking garages. Full article

Electric vehicles are becoming more common daily because countries are moving towards net-zero emissions. Different generations of NMC battery cells are used for EV applications. This work investigates the degradation behavior of high-energy 75 Ah prismatic NMC631 lithium-ion cells using a combined incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) framework under different conditions. Cells are cycled at an identical C-rates and depths of discharge (DoD), and at different temperatures to systematically evaluate the impact of temperature on electrochemical aging. ICA results revealed that cells cycled at low temperatures maintain stable peaks and a high SoH (>90%) after completing 1600 full equivalent cycles (FECs). EIS analysis confirms the distinct impedance evolution patterns. Degradation mode analysis is performed using the ICA, and EIS highlights the combined evolution of conductivity loss, loss of lithium inventory, and loss of active material. It also highlights different degradation path trajectories under identical operating conditions stem from the progressive amplification of internal cell heterogeneities during aging. The results demonstrate that combining ICA and EIS provides complementary insights into degradation evolution and enables clear differentiation between gradual aging and sudden failure pathways in high-energy NMC cells. Full article

With the accelerated transformation of green shipping and the advancement of ship electrification, lithium-ion batteries have become the core solution for ship propulsion due to their advantages of high energy density and zero emission. Efficient thermal management serves as a key technical support to ensure the safe and stable operation of batteries, extend their service life, and mitigate the risk of thermal runaway. Lithium-ion batteries accumulate heat during discharge, and pure phase change material (PCM) cooling systems are limited by low thermal conductivity, leading to excessive battery temperature rise and poor temperature uniformity. To address this problem, RT42 (a paraffin-based PCM with a melting temperature range of 311.15–316.15 K) was selected as the PCM in this study. The battery thermal management system (BTMS) coupling RT42 with a three-dimensional fin network structure was designed. Numerical simulations were conducted via ANSYS Fluent, and the enthalpy-porosity method was adopted to simulate the PCM phase change process. The effects of fin distribution, spacing and layer number on BTMS performance were systematically investigated and compared. Results show that the heat transfer process in the PCM can be significantly improved due to the three-dimensional fin network, and the battery maximum temperature can be reduced by 7.53 K compared with the pure PCM system. This study provides theoretical support for the design and optimization of high-efficiency BTMS. Full article

Weak-grid operation, with a low short-circuit ratio (SCR), degrades voltage and frequency regulation and impacts the power control performance of inverter-based resources, triggering oscillations. This paper proposes a community-scale battery energy storage system (BESS)-supported grid-forming control scheme, where the grid-forming inverter acts a virtual synchronous generator (VSG). A grid-connected BESS-powered VSG model with cascaded voltage-current dual-loop control is developed to assess the impacts of line impedance and P-Q coupling on weak-grid connection and stability. In addition to the conventional VSG, dq-axis decoupling, virtual impedance, and adaptive inertia-damping (J-D) are incorporated and evaluated through multi-scenario MATLAB/Simulink simulations. The results indicate that virtual impedance effectively suppresses coupled oscillations, and the coordinated J-D adaptation yields the most pronounced peak mitigation during edge disturbances (e.g., fault clearance and load shedding). In particular, under a 50% three-phase voltage sag, the coordinated strategy reduces the post-clearance peaks of $v_{pcc,rm\mathrm{s \; \; }}$ and $i_{pcc,rm\mathrm{s \; }}$ by approximately 79.9% and 93.5%, respectively, and decreases the intensity of frequency fluctuations by approximately 97.6%. Overall, the proposed community-scale BESS-VSG scheme enhances the dynamic stability of voltage and frequency under weak-grid conditions and provides a practical control framework for engineeringoriented weak-grid support studies. Full article

As power-electronic-interfaced renewable generation displaces synchronous machines, modern power systems face coupled day-ahead challenges: net-load variability demands peak shaving, while declining inertia necessitates explicit frequency-regulation scheduling. In sequential security-constrained unit commitment (SCUC) and Security-Constrained Economic Dispatch (SCED), the reserve procured in SCUC may lose deliverability after redispatch because the same storage bandwidth is reassigned to energy service. This paper proposes a two-stage day-ahead framework that addresses both challenges for low-inertia systems with high inverter-based resource (IBR) penetration. Stage I embeds Rate-of-Change of Frequency (RoCoF), frequency nadir, and quasi-steady-state (QSS) constraints in SCUC, with a piecewise-linear outer approximation for the non-convex nadir limit. Stage II strictly inherits the SCUC commitment and reserve reservation, and it applies bandwidth deduction to prevent peak-shaving redispatch from consuming committed frequency reserve. A technology-aware partition further assigns fast-response Lithium Iron Phosphate (LFP) batteries to sub-second frequency support and long-duration Vanadium Redox Flow Batteries (VRFBs) to energy shifting. Evaluated under the adopted reduced-order frequency-response framework and disturbance representation, tests on a modified IEEE 39-bus system under an extreme-wind scenario demonstrate that explicit frequency constraints eliminate all post-contingency violations, the inheritance mechanism closes a 23.85 MW reserve gap after redispatch, and heterogeneous storage partitioning preserves essentially the same disturbance sensitivity while increasing the peak-shaving ratio to 45.85%, lowering the day-ahead cost to CNY $10.483\times {10}^6$ and reducing the average system price to 209.33 CNY/MWh. Full article

Reliable state-of-health (SOH) estimation is a key prerequisite for the safe and effective reuse of second-life lithium-ion batteries. However, practical assessment during early-stage screening is often constrained by extremely limited cycling data, where only a few discharge cycles are available due to time and cost limitations. This study investigates SOH estimation under an extreme sparse-cycling scenario in which only three discharge cycles per battery are available, reflecting realistic constraints in early-stage second-life battery screening. Under such severe data limitations, conventional data-driven models become unreliable, motivating the need for data-efficient and interpretable approaches. To address this challenge, a physics-aware and explainable machine learning framework is proposed, integrating physically interpretable feature extraction with lightweight regression models and Shapley Additive exPlanations SHAP-based interpretability analysis. Electrochemically motivated and mathematically derived features are extracted from voltage, current, and capacity measurements to ensure robustness under severe data scarcity. Multiple model classes, including linear regression, support vector regression, tree-based ensembles, and deep learning architectures, are systematically evaluated to assess their suitability in this constrained regime. Experimental results on real second-life battery datasets demonstrate that physics-aware linear models provide stable and interpretable SOH estimates under extreme data sparsity, whereas more complex nonlinear and deep learning models exhibit higher variability due to insufficient training data. These findings highlight that model suitability is strongly dependent on data availability and support the adoption of interpretable, physics-aware approaches for early-stage second-life battery screening rather than long-term degradation modeling. Full article

We present a parametrised charging infrastructure model developed to support the design of a hybrid electric zero-emission vessel with corresponding charging infrastructure for operation along the Norwegian Coastal Express route. The charging model includes functionalities to analyse the required battery storage capacity and power ratings and locations of charging facilities for achieving battery-electric operation. We demonstrate the use of the charging model to analyse different zero-emission scenarios for the Norwegian Coastal Express route. In the presented example scenarios, the model takes as input the estimated energy demand for a new zero-emission vessel design for the Coastal Express in different weather conditions, and includes functionality to consider realistic port stays based on existing timetables and historical data of delays. The analyses show minimal required battery capacities and illustrate a trade-off between charging power and battery capacity, as well as exemplifying the impact of different timetables and historic deviations on charging and energy delivered from the battery. The charging model presented is general and can be used for other routes than the Norwegian Coastal Express, as a tool for decision-makers to optimize for battery-electric operation whilst keeping the need for onboard storage capacity and charging infrastructure installations at a minimum. Full article