Search Results (9,438)

Rapid and accurate prediction of the maximum remaining life of lithium-ion batteries is a critical technical challenge for enhancing battery management system reliability and enabling the efficient secondary utilization of retired batteries. Traditional approaches that rely on full charge–discharge cycles or complex electrochemical models often suffer from long detection time and limited adaptability, making them unsuitable for fast testing scenarios. To address these limitations, this study proposes a novel capacity prediction method that integrates charging segment feature extraction with a back-propagation neural network (BPNN) co-optimized using the genetic algorithm (GA) and dung beetle optimizer (DBO). Leveraging the public CALCE datasets, key degradation-related features were extracted from partial charging segments to serve as inputs to the prediction framework. The hybrid GA_DBO algorithm is employed to jointly optimize the BPNN’s weights, learning rate, and activation thresholds. A comparative analysis is conducted across various charging durations (900 s, 1800 s, and 2700 s) to evaluate performance under different input lengths. Results reveal that the model using 1800 s charging segment features achieves the best overall accuracy, with a test set mean squared error (MSE) of 0.0001 Ah², mean absolute error (MAE) of 0.0092 Ah, root mean square error (RMSE) of 0.0122 Ah, and a coefficient of determination (R²) of 99.66%, demonstrating strong robustness and predictive capability. This research overcomes the traditional reliance on full cycles, demonstrating the effectiveness of short charging segments combined with intelligent optimization algorithms. The proposed method offers a high-precision, low-cost solution for online battery health monitoring and rapid sorting of retired batteries, highlighting its significant engineering application potential. Full article

Background: The rapid aging of China’s population poses significant challenges, particularly in public health and medical services. Frailty, a reversible geriatric syndrome, is a critical intervention target for disability prevention among older adults. Objective: We hypothesize that both intervention groups will demonstrate significant improvements in Short Physical Performance Battery (SPPB) scores compared to the control group, and that these improvements will be accompanied by parallel reductions in inflammatory markers and beneficial alterations in the gut microbiota. Methods: The MultIfaceted NUtrition inTervention for frail Elderly (MINUTE) trial is a randomized, parallel-group controlled trial. In Beijing, China, 315 frail older adults were recruited and randomly assigned to 3 groups: a control group receiving routine community health management only, multifaceted nutrition intervention group, and a multifaceted nutrition and exercise combined intervention group, each comprising 105 participants. The study consists of a three-month intervention period followed by a nine-month follow-up. During the three-month intervention period, the control group receives routine community health management, while the multifaceted nutrition intervention group receives daily dietary guidance, personalized nutrition consultations, and health education. Additionally, the combined intervention group receives exercise interventions in addition to the nutritional components. After the three-month intervention, all three groups will be followed up for nine months to assess the sustainability of the study. Results: The primary outcomes are the changes in the SPPB scores. The secondary outcomes include frailty scores, intrinsic capacity, malnutrition risk, frailty recovery rates, serum differential metabolites, inflammatory factors, and gut microbiota changes. This study aims to establish a scalable and sustainable pathway for frailty prevention among community-dwelling older adults in China and provide valuable insights to inform strategies for healthy aging. Trial registration: This study is conducted in accordance with the Declaration of Helsinki and approved by the Peking University Institutional Review Board (IRB00001052-23178), with all amendments subject to prior review and approval. Informed consent is obtained from participants, and findings will be disseminated through peer-reviewed publications, conference presentations, and summaries for school staff and participants. ClinicalTrials.gov (NCT06547593) registered 30 July 2024. Full article

The rapid growth of electric vehicle (EV) adoption in Malaysia is projected to generate substantial volumes of end-of-life lithium-ion batteries, creating both environmental challenges and opportunities for repurposing into second-life batteries (SLBs). This study investigates the technical, economic, and regulatory feasibility of deploying SLBs for photovoltaic (PV) energy storage in petrol stations, an application aligned with the nation’s energy transition goals. Laboratory testing of Nissan Leaf ZE0 battery modules over a 120-day operation period demonstrated stable cycling performance with approximately 7% capacity fade, maintaining state-of-health (SOH) above 47%. A case study of a 12 kWp PV–SLB hybrid system for a typical Malaysian petrol station shows 45 kWh of usable storage, capable of offsetting a daily electricity demand of 45 kWh, reducing capital cost by 30–50% compared to new lithium-ion systems, and achieving 70–80% lifecycle CO₂ emission reductions. The proposed architecture leverages SLBs’ suitability for slower, steady discharge to provide reliable nighttime operation and grid load relief, particularly in semi-urban and rural stations. Beyond technical validation, the paper evaluates economic benefits, environmental impacts, and Malaysia’s regulatory readiness, identifying gaps in certification standards, reverse logistics, and workforce skills. Strategic recommendations are proposed to enable large-scale SLB deployment and integration into hybrid PV–petrol station systems. Findings indicate that SLBs can serve as a cost-effective, sustainable energy storage solution, supporting Malaysia’s National Energy Transition Roadmap (NETR), advancing circular economy practices, and positioning the country as a potential ASEAN leader in battery repurposing. Full article

The decarbonization of maritime transport has primarily targeted large vessels, leaving small craft largely dependent on fossil fuel despite their inherent use of wind propulsion. This study addresses that gap by designing and simulating a zero-emission propulsion system for a 12.5 m sailing yacht based on integrated renewable energy. The retrofit replaces the diesel engine with an electric drivetrain supported by static solar panels and wind turbines, as well as dynamic sources, including hydro-generators and a regenerative propeller. In addition to performance under typical weather profiles, we conducted a lifecycle environmental impact estimation and evaluated system resilience under low renewable input. Simulations used real mid-latitude meteorological data to assess operational and environmental sustainability. The results show that during two representative 24 h voyages, propulsion and hotel loads were sustained solely by onboard renewables, with battery state of charge remaining above 28–46%. In an emergency calm scenario, the yacht motored for four hours at 5–6 knots using only stored energy, with solar input extending range. The findings demonstrate that integrated multi-source renewables can provide complete energy autonomy for sailing yachts. The approach illustrates practical feasibility under real conditions, scalability to eco-tour boats and ferries, and alignment with international decarbonization targets. Full article

Biomass-derived hard carbon (BHC) has attracted considerable attention as a sustainable and cost-effective anode material for sodium-ion batteries (SIBs), owing to its natural abundance, environmental friendliness, and promising electrochemical performance. This review provides a detailed overview of recent progress in the synthesis, structural design, and performance optimization of BHC materials. It encompasses key fabrication routes, such as high-temperature pyrolysis, hydrothermal pretreatment, chemical and physical activation, heteroatom doping, and templating techniques, that have been employed to control pore architecture, defect density, and interlayer spacing. Among these strategies, activation-assisted pyrolysis and heteroatom doping have shown the most significant improvements in sodium (Na) storage capacity and long-term cycling stability. The review further explores the correlations between microstructure and electrochemical behavior, outlines the main challenges limiting large-scale application, and proposes future research directions toward scalable production and integration of BHC anodes in practical SIB systems. Overall, these advancements highlight the strong potential of BHC as a next-generation anode for grid-level and renewable energy storage technologies. Full article

Micro-combustion-powered thermoelectric generators (μ-CPTEGs) combine the high energy density of hydrocarbons with solid-state conversion, offering compact and refuelable power for long-endurance electronics. Such characteristics make μ-CPTEGs particularly promising for aerospace systems, where conventional batteries face serious limitations. Their achievable performance hinges on how a swirl-stabilized flame transfers heat into the hot ends of thermoelectric modules. This study uses a conjugate CFD framework coupled with a lumped parameter model to examine how input power and equivalence ratio shape the flame/flow structure, temperature fields, and hot-end heating in a swirl combustor-powered TEG. Three-dimensional numerical simulations were performed for the swirl combustor-powered TEG, varying the input power from 1269 to 1854 W and the equivalence ratio from φ = 0.6 to 1.1. Results indicate that the combustor exit forms a robust “annular jet with central recirculation” structure that organizes a V-shaped region of high modeled heat release responsible for flame stabilization and preheating. At φ = 1.0, increasing Q_in from 1269 to 1854 W strengthens the V-shaped hot band and warms the wall-attached recirculation. Heating penetrates deeper into the finned cavity, and the central-plane peak temperature rises from 2281 to 2339 K (≈2.5%). Consistent with these field changes, the lower TEM pair near the outlet heats more strongly than the upper module (517 K to 629 K vs. 451 K to 543 K); the inter-row gap widens from 66 K to 86 K, and the incremental temperature gains taper at the highest power, while the axial organization of the field remains essentially unchanged. At fixed Q_in = 1854 W, raising φ from 0.6 to 1.0 compacts and retracts the reaction band toward the exit and weakens axial penetration; the main-zone temperature increases up to φ = 0.9 and then declines for richer mixtures (peak 2482 K at φ = 0.9 to 2289 K at φ = 1.1), cooling the fin section due to reduced transport, thereby identifying φ = 0.9 as the operating point that best balances axial penetration against dilution/convective-cooling losses and maximizes the TEM hot-end temperature at the fixed power. Full article

Background: Euthanasia and physician-assisted suicide (EPAS) are highly contentious topics with significant medical, legal, and cultural implications. Previous research suggests that various sociodemographic, health, and psychosocial factors determine attitudes toward EPAS. This study set out to identify psychosocial predictors of support for EPAS. We hypothesized that perceived control, self-efficacy, and social support would each predict support for EPAS after controlling for sociodemographic and health-related variables. Methods: For this study, we recruited 446 Jewish Israeli adults who were 82.32 years of age on average (SD = 5.99; range 65–101 years). Participants completed a battery of questionnaires including a series of vignettes featuring hypothetical family members with a terminal illness (i.e., cancer, dementia, Parkinson’s disease). We performed a three-step hierarchical regression equation, controlling for demographic factors (age, gender, education, relationship status, economic status, and religiosity) as well as perceived and relative physical health. Results: As hypothesized, both self-efficacy and (the absence of) social support predicted support for EPAS; perceived control did not. Religiosity was the strongest predictor of opposition to EPAS. Fully 31% of variance in support for EPAS was predicted by this regression model. Conclusion: Support for EPAS does not appear to reflect a pervasive need for control over all aspects of life (i.e., perceived control) but a more specific need for personal autonomy (i.e., self-efficacy). Longitudinal research is required over multiple points of data collection to ascertain how change in social support affects support for EPAS in late life. Policy makers should embrace these findings when formulating end-of-life care policies, ensuring that both social support and personal autonomy are prioritized in the care of older adults. Full article

Inkjet printing enables contactless deposition onto fragile substrates for printed energy-storage devices and supports flexible batteries and supercapacitors with reduced material use. This review examines multilayer and interdigital architectures and analyzes how ink rheology, droplet formation, colloidal interactions, and the printability window govern performance. For batteries, reported inkjet-printed electrodes commonly deliver capacities of ~110–150 mAh g⁻¹ for oxide cathodes at C/2–1 C, with coulombic efficiency ≥98% and stability over 10²–10³ cycles; silicon anodes reach ~1.0–2.0 Ah g⁻¹ with efficiency approaching 99% under stepwise formation. Typical current densities are ~0.5–5 mA cm⁻² depending on areal loading, and multilayer designs with optimized drying and parameter tuning can yield rate and discharge behavior comparable to cast films. For supercapacitors, inkjet-printed microdevices report volumetric capacitances in the mid-hundreds of F cm⁻³, translating to ~9–34 mWh cm⁻³ and ~0.25–0.41 W cm⁻³, with 80–95% retention after 10,000 cycles and coulombic efficiency near 99%. In solid-state configurations, stability is enhanced, although often accompanied by reduced areal capacitance. Although solids loading is lower than in screen printing, precise material placement together with thermal or photonic sintering enables competitive capacity, rate capability, and cycle life while minimizing waste. The review consolidates practical guidance on ink formulation, printability, and defect control and outlines opportunities in greener chemistries, oxidation-resistant metallic systems, and scalable high-throughput printing. Full article

Given the climate change observed in the past few decades, sustainable development and the use of renewable energy sources are urgent. In this scenario, hydrogen production through electrolyzers is a promising renewable source and energy vector because of its ultralow greenhouse emissions and high energy content. Hydrogen can be used in a variety of applications, from transportation to electricity generation, contributing to the diversification of the energy matrix. In this context, this paper presents an autonomous isolated DC microgrid system for generating and storing electrical energy to be exclusively used for feeding an electrolyzer hydrogen production plant, which has been retrofitted for green hydrogen production. Experimental verification was performed at Itaipu Parquetec, which consists of an alkaline electrolysis unit directly integrated with a battery energy storage system and renewable sources (e.g., photovoltaic and wind) by using an isolated DC microgrid concept based on DC/DC and AC/DC converters. Experimental results revealed that the new electrolyzer DC microgrid increases the system’s overall efficiency in comparison to the legacy thyristor-based power supply system by 26%, and it autonomously controls the energy supply to the electrolyzer under optimized conditions with an extremely low output current ripple. Another advantage of the proposed DC microgrid is its ability to properly manage the startup and shutdown process of the electrolyzer plant under power generation outages. This paper is the result of activities carried out under the R&D project of ANEEL program No. PD-10381-0221/2021, entitled “Multiport DC-DC Converter and IoT System for Intelligent Energy Management”, which was conducted in partnership with CTG-Brazil. Full article

This paper presents an optimization-based scheduling strategy for battery energy storage systems (BESS) in alternating current microgrids, considering both grid-connected and islanded operation. The study addresses two independent objectives: minimizing energy losses in the distribution network and reducing carbon dioxide emissions from dispatchable power sources. The problem is formulated using a full AC power flow model that simultaneously manages active and reactive power flows in BESS located in the microgrid, while enforcing detailed operational constraints for network components, generation units, and storage systems. To solve it, a parallel implementation of the Particle Swarm Optimization (PPSO) algorithm is applied. The PPSO is integrated into the objective functions and evaluated through a 24-h scheduling horizon, incorporating a strict penalty scheme to guarantee compliance with technical and operational limits. The proposed method generates coordinated charging and discharging plans for multiple BESS units, ensuring voltage stability, current limits, and optimal reactive power support in both operating modes. Tests are conducted on a 33-node benchmark microgrid that represents the power demand and generation from Medellín, Colombia. This is compared with two methodologies reported in the literature: Parallel Crow Search and Parallel JAYA optimizer. The results demonstrate that the strategy produces robust schedules across objectives, identifies the most critical network elements for monitoring, and maintains safe operation without compromising performance. This framework offers a practical and adaptable tool for microgrid energy management, capable of aligning technical reliability with environmental goals in diverse operational scenarios. Full article

To gain a comprehensive understanding of the current research landscape in the field of new energy vehicle (NEV) fires and to explore its knowledge base and emerging trends, bibliometric methods—such as co-occurrence, clustering, and co-citation analyses—were employed to examine the relevant literature. A research knowledge framework was established, encompassing four primary themes: thermal management and performance optimization of power batteries, battery materials and their safety characteristics, thermal runaway (TR) and fire risk assessment, and fire prevention and control strategies. The key research frontiers in this domain could be classified into five categories: mechanisms and propagation of TR, development of high-safety battery materials and flame-retardant technologies, thermal management and thermal safety control, intelligent early warning and fault diagnosis, and fire suppression and firefighting techniques. The focus of research has gradually shifted from passive identification of causes and failure mechanisms to proactive approaches involving thermal control, predictive alerts, and integrated system-level fire safety solutions. As the field advances, increasing complexity and interdisciplinary integration have emerged as defining trends. Future research is expected to benefit from broader cross-disciplinary collaboration. These findings provide a valuable reference for researchers seeking a rapid overview of the evolving landscape of NEV fire-related studies. Full article

Battery Electric Vehicles (BEVs) technology is rapidly emerging as the cornerstone of sustainable transportation, driven by advancements in battery technology, power electronics, and modern drivetrains. This paper presents a comprehensive review of current and next-generation BEV powertrain architectures, focusing on five key subsystems: battery energy storage system, electric propulsion motors, energy management systems, power electronic converters, and charging infrastructure. The review traces the evolution of battery technology from conventional lithium-ion to solid-state chemistries and highlights the critical role of battery management systems in ensuring optimal state of charge, health, and safety. Recent innovations by leading automakers are examined, showcasing advancements in cell formats, motor designs, and thermal management for enhanced range and performance. The role of power electronics and the integration of AI-driven strategies for vehicle control and vehicle-to-grid (V2G) are analyzed. Finally, the paper identifies ongoing research gaps in system integration, standardization, and advanced BMS solutions. This review provides a comprehensive roadmap for innovation, aiming to guide researchers and industry stakeholders in accelerating the adoption and sustainable advancement of BEV technologies. Full article

Accurate prediction of the state of charge (SOC) of a battery pack is essential to improve the operational efficiency and safety of energy storage systems. In this paper, we propose a novel lithium-ion battery (Lib) pack SOC prediction framework that combines redundant control correlation downscaling with Adaptive Error Variation Weighting Mechanism (AVM) fusion mechanisms. By integrating redundancy feature selection based on correlation analysis with global sensitivity analysis, the dimensionality of the input features was reduced by 81.25%. The AVM merges BiGRU’s ability to model short-term dynamics with Informer’s ability to capture long-term dependencies. This approach allows for complementary information exchange between multiple models. Experimental results indicate that on both monthly and quarterly slice datasets, the RMSE and MAE of the fusion model are significantly lower than those of the single model. In particular, the proposed model shows higher robustness and generalization ability in seasonal generalization tests. Its performance is significantly better than the traditional linear and classical filtering methods. The method provides reliable technical support for accurate estimation of SOC in battery management systems under complex environmental conditions. Full article

With the rise in traffic congestion, time has become an increasingly critical factor for electric vehicle (EV) users, leading to a surge in demand for fast and convenient charging services at locations of their choosing. Mobile Charging Stations (MCSs) have emerged as a new and practical solution to meet this growing need. However, the limited energy capacity of MCSs combined with the increasing volume of charging requests underscores the necessity for intelligent and efficient management. This study introduces a comprehensive mathematical framework aimed at optimizing both the deployment of MCSs and the scheduling of their battery recharging using battery swapping technology, while considering grid constraints, using the Deep Q-Network (DQN) algorithm. The proposed model is applied to real-world data from Chattanooga to evaluate its performance under practical conditions. The key goals of the proposed approach are to maximize the profit from fulfilling private EV charging requests, optimize the utilization of MCS battery packages, manage MCS scheduling without causing stress on the power grid, and manage recharging operations efficiently by incorporating photovoltaic (PV) sources at battery charging stations. Full article

The accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for ensuring the safety, reliability, and efficiency of modern energy storage systems. Traditional model-based and data-driven methods often struggle to capture complex spatiotemporal degradation patterns, leading to reduced accuracy and robustness. To address these limitations, this paper proposes a novel dynamic graph pruning neural network with self-attention mechanism (DynaGPNN-SAM) for SOH estimation. The method transforms sequential battery features into graph-structured representations, enabling the explicit modeling of spatial dependencies among operational variables. A self-attention-guided pruning strategy is introduced to dynamically preserve informative nodes while filtering redundant ones, thereby enhancing interpretability and computational efficiency. The framework is validated on the NASA lithium-ion battery dataset, with extensive experiments and ablation studies demonstrating superior performance compared to conventional approaches. Results show that DynaGPNN-SAM achieves lower root mean square error (RMSE) and mean absolute error (MAE) values across multiple batteries, particularly excelling during rapid degradation phases. Overall, the proposed approach provides an accurate, robust, and scalable solution for real-world battery management systems. Full article