Search Results (3,082)

Maritime transport is under increasing pressure to cut greenhouse gas and pollutant emissions to meet global decarbonization goals and tighter environmental standards. Ship electric propulsion systems offer a promising solution for short-range maritime operations, particularly for small vessels and coastal activities. Full-electric vessels can significantly reduce operational emissions; however, a key challenge is the extensive charging time for onboard energy storage, which can affect operational continuity and logistical efficiency. This study examines mission planning and energy management for a hybrid multi-source electric mail boat operating in the Aeolian archipelago. It evaluates the viability and performance of a daily inter-island route powered by a high-temperature methanol fuel cell, batteries, and photovoltaic panels. A routing and simulation framework was developed to model the boat’s itinerary among seven islands, accounting for realistic navigation speeds, scheduled stops, solar energy availability, and battery state-of-charge constraints. The study analyzes distance, travel time, energy consumption, solar power generation, and fuel–electric usage with high temporal resolution, enabling detailed analysis of power flows during sailing and docking. Several operational strategies were assessed, including periods of increased speed supported by battery assistance and fuel–electric cell output, combined with coordinated energy management to keep battery levels above a lower acceptable threshold while completing the route in a single day. The methodology provides a practical tool for planning low-emission island networks and supports the integration of innovative energy systems into small electric workboats operating in specific maritime regions. Full article

Purpose: Natural polysaccharides have shown considerable potential in the management of type 2 diabetes mellitus (T2DM) due to their multi-target metabolic regulatory effects. However, their clinical translation is limited by poor oral stability and low intestinal permeability. Snow lotus polysaccharide (SIP), a representative plant-derived polysaccharide, exhibits promising metabolic benefits but suffers from these delivery barriers. This study aimed to develop an oral nanodelivery system to enhance the gastrointestinal stability and intestinal transport of SIP, thereby improving its in vivo efficacy. Methods: SIP-loaded chitosan–tripolyphosphate nanoparticles (SIP@CS-TPP) were prepared via ionic crosslinking and characterized in terms of particle size, surface charge, morphology, and structural features. In vitro release behavior under simulated gastrointestinal conditions was evaluated. Ex vivo intestinal permeation was assessed using an isolated intestinal sac model. The metabolic regulatory effects were further investigated in a high-fat diet/streptozotocin-induced T2DM rat model. Results: SIP@CS-TPP nanoparticles exhibited a uniform particle size of 188.9 ± 12.8 nm, a surface charge of 28.3 ± 5.1 mV, and good stability after freeze-drying. A pH-responsive and diffusion-controlled release profile was observed. Ex vivo studies demonstrated significantly enhanced intestinal transport, with an approximately 3.7-fold increase in apparent permeability compared with free SIP. In vivo, SIP@CS-TPP improved glycemic control, glucose tolerance, insulin resistance, lipid metabolism, oxidative stress, and inflammatory responses more effectively than free SIP at the same dose. Conclusions: The CS-TPP nanodelivery system effectively enhances the oral delivery and metabolic regulatory effects of SIP. This study highlights the potential of a delivery-oriented strategy to improve the in vivo performance of natural polysaccharides and provides a promising approach for their application in metabolic disease management. Full article

Colletotrichum species are among the most destructive phytopathogens worldwide, with appressorium-mediated penetration representing a critical stage in host infection. Targeting this morphogenetic transition offers a promising strategy for sustainable disease control by interfering with the infection process rather than solely inhibiting fungal growth. In this study, chitosan–κ-carrageenan nanoparticles (CS–κ-CRG) without and with lysozyme (CS–κ-CRG/Lz) were synthesized, characterized, and evaluated for their ability to inhibit appressorium formation in Colletotrichum siamense, a strain exhibiting low sensitivity to chitosan. The nanoparticles showed monodisperse size distributions, with hydrodynamic diameters of 503 and 333 nm for CS–κ-CRG and CS–κ-CRG/Lz, respectively, positive surface charges of approximately +26 mV, spherical morphology, and a lysozyme encapsulation efficiency of 63%. Both formulations significantly reduced conidial viability and delayed germination, inducing morphological alterations such as conidial swelling, hyphal deformation, and vacuolization. Fluorescence microscopy using calcofluor white and propidium iodide revealed disturbances in cell wall organization and loss of membrane integrity. Both nanomaterials markedly affected appressorium development in a concentration- and formulation-dependent manner. Notably, CS–κ-CRG/Lz showed stronger suppression of appressorium formation, whereas at 200 µg·mL⁻¹, CS–κ-CRG nanoparticles stimulated appressorium formation, suggesting that sublethal nanoparticle stress may trigger compensatory or hyper-pathogenic responses. These findings highlight the potential and complexity of utilizing chitosan-based nanomaterials for phytopathogen management and emphasize the importance of mechanistic and dose–response evaluations before field application. Full article

The rapid integration of electric vehicles (EVs) and decentralized renewable energy sources is transforming urban power systems, while simultaneously increasing the complexity of real-time coordination across charging infrastructure, distributed energy resources, and grid-support devices. This systematic review synthesizes recent research on multi-agent reinforcement learning (MARL) for EV–grid coordination, with emphasis on four emerging dimensions: forecasting-informed control, safety-constrained learning, explainability and interpretability, and trustworthy decentralized coordination. A systematic literature search was conducted in IEEE Xplore, Scopus, Web of Science, ScienceDirect, MDPI, and arXiv, covering primarily the period 2016–2025, with selected early-2026 studies retained where relevant, with selected earlier foundational studies retained for context. The review was conducted and reported in accordance with the PRISMA 2020 framework. A total of 412 records were identified through database searching; after duplicate removal and screening, 58 studies were included in the final qualitative synthesis. The reviewed literature shows that MARL is increasingly being applied to EV charging coordination, demand-side management, community energy systems, transactive energy, and ancillary grid services. The evidence further indicates that forecasting integration improves anticipatory control, safety-aware formulations enhance operational reliability, and explainability-oriented designs help address transparency and trust barriers in safety-critical grid environments. However, the literature remains limited by heterogeneous benchmarks, inconsistent evaluation metrics, and a lack of real-world deployment evidence. This review provides a structured synthesis of current methodologies, identifies critical research gaps, and outlines future directions for the development of safe, interpretable, and deployment-ready MARL frameworks for urban energy systems. Full article

Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high computational resources and behave as black-box models with limited explainability. To overcome these limitations, the present work proposes a SoC estimation approach based on the Light Gradient Boosting Machine (LightGBM). The proposed model provides a balanced trade-off between prediction accuracy and computational efficiency. Furthermore, feature engineering is performed to derive additional informative features, improving the model’s ability to learn driving conditions and battery dynamics. In addition, the study incorporates a seasonal analysis by evaluating the model under both summer and winter conditions, allowing the impact of environmental variations on SoC estimation performance to be investigated. Moreover, Explainable Artificial Intelligence (XAI) techniques are employed to interpret the model predictions. Evaluation on real-world on-road data demonstrated that the proposed model achieved substantial improvements in estimation performance compared to recent studies. Full article

The electrification of regional railway lines using battery-electric trains requires accurate simulation tools to support energy management and thermal control design. This paper presents an integrated dynamic simulation model of the traction system of a Hitachi Caravaggio ETR 521 regional train operating in battery-electric mode, developed in MATLAB/Simulink 2024b. The model incorporates all key drivetrain components, including a train reference generator, speed controller, motor controller, three-phase inverter, induction motor, a Kokam Co., Ltd. lithium-ion battery pack, and a detailed battery thermal management system. The proposed framework enables simultaneous evaluation of traction performance, battery state of charge (SOC) evolution, and thermal behavior under realistic conditions. To validate the model, simulations of the Treviso–Vicenza route were conducted under two scenarios: traction-only operation and operation with a 160 kW auxiliary load. Simulation results demonstrate that auxiliary loads significantly affect energy consumption and battery thermal behavior, with energy consumption increased by 50%. The results highlight the importance of integrating thermal effects into energy management and sizing decisions for battery-electric regional trains. The developed model provides a practical tool for optimizing battery sizing, thermal management strategies, and overall energy performance, supporting the planning and design of sustainable electric railway solutions. The modular MATLAB/Simulink architecture is designed to be route-agnostic; extension to other regional lines with different gradients, speed profiles, or extreme climate conditions (e.g., alpine routes or high-temperature regions) requires only updated route data and adjusted ambient boundary conditions, demonstrating the model’s broad applicability beyond the Treviso–Vicenza case study. Full article

Driven by increasingly stringent requirements for energy saving and emission reduction in non-road machinery, hybrid wheel loaders have attracted growing attention as a practical pathway toward cleaner construction equipment. However, conventional energy management strategies often show limited adaptability to highly transient operating cycles and struggle to balance fuel economy, real-time applicability, and battery charge sustainability. To address these issues, this study proposes an improved sparrow-search-algorithm-based equivalent consumption minimization strategy (ISSA-ECMS) for a series hybrid wheel loader. A quasi-static powertrain model was established, while ISSA was used to optimize both the hyperparameters of a Convolutional Neural Network-Long Short-Term Memory (CNN–LSTM) stage-recognition model and the stage-dependent ECMS parameters. A hidden Markov model (HMM)-based post-processing framework was further introduced to improve temporal consistency in operating-stage recognition. The results show that the optimized ISSA-CNN–LSTM achieved 93.22% accuracy, 93.08% Macro-F1, and 93.21% Weighted-F1, while HMM refinement further improved recognition accuracy from 94.02% to 97.92%. In energy management simulations, ISSA-ECMS maintained the terminal state of charge (SOC) at 50.0069%, reduced fuel consumption by 2.1% and 1.4% compared with conventional ECMS and A-ECMS, respectively, and increased the proportion of engine operating points in the economical region to 77.549%. Compared with dynamic programming, its fuel-consumption increase was only 0.28%, while retaining online applicability. These results demonstrate that the proposed method provides an effective and practical solution for real-time energy management of series hybrid wheel loaders. Full article

As an important state parameter in battery management systems, accurate state of charge (SOC) estimation is of great significance for the safe and reliable use of batteries. In this paper, a Temporal Convolutional Network–Transformer (TCN–Transformer) model is proposed for achieving accurate estimation of SOC. First, the TCN is integrated in series with the Transformer model. This integration not only extracts the local characteristics of time-series data but also captures broader spatiotemporal correlations, thereby enhancing the feature representation and achieving highly accurate estimation. However, since the hyperparameter settings of neural networks have a significant impact on model performance, this study employs the advanced hippo optimization (HO) algorithm to determine the optimal values for the number of filters, filter size, number of residual blocks, and number of encoder layers, ultimately improving the model’s stability and efficiency. Finally, the proposed model was tested under various dynamic driving conditions at different temperatures. Experimental validation on the CALCE dataset demonstrates that the proposed HO–TCN–Transformer achieves RMSE and MAE both under 0.7%, representing an approximately 50% overall error reduction compared to the standalone TCN. Cross-validation across five folds confirms robust performance with <7% standard deviation. Full article

To improve the state-of-charge (SOC) estimation accuracy of sodium-ion batteries under complex operating conditions, this paper proposes a particle swarm optimization-based heterogeneous adaptive extended Kalman filter. A hardware-in-the-loop (HIL) validation platform is also established to reproduce the sampling-chain constraints of a practical battery management system. In addition, a second-order equivalent circuit model (ECM) serves to characterize battery dynamics and generate validation data. Within this framework, the degradation in estimation performance from the theoretical environment to practical hardware execution is quantitatively analyzed. The feasibility of using ECM-generated data for SOC estimation algorithm validation is also evaluated. Using measured Federal Urban Driving Schedule data at 25 °C, the proposed method achieves high estimation accuracy and stable convergence in both environments. Specifically, the mean absolute error and root-mean-square error in the theoretical environment are 0.11% and 0.25%, respectively. Under HIL conditions, the corresponding values are 0.60% and 0.63%. Additional tests under different temperatures and composite disturbance conditions further verify the adaptability and robustness of the proposed algorithm. The results also show that practical hardware constraints introduce non-negligible performance degradation. In addition, ECM-generated data remain highly consistent with measured data in terms of error-evolution trends. Therefore, ECM-generated data can serve as a feasible validation data source for SOC estimation algorithm performance evaluation and rapid validation. Full article

In response to the influence of extreme disasters, damage to distribution lines and user outages, a parallel implementation strategy is proposed for emergency repair of disaster-damaged distribution networks and rapid restoration of power supply for users, considering the collaboration of “human–vehicle–road–pile” resources. This strategy constructs a hierarchical optimization framework, with the upper-level model aiming to minimize the repair time for disaster damage. It adopts a collaborative optimization approach between repair resources and transportation routes to quickly repair the connection between the distribution network and the main power network. In the lower-level model, a model predictive control mechanism is adopted to schedule electric vehicles (EVs) in Real-time as mobile energy storage systems, and vehicle-to-grid (V2G) service technology is used to provide an emergency power supply for key loads during the repair period, achieving parallel optimization of “repair–restoration”. Considering constraints such as emergency repair resources, time-varying transportation, electric vehicle scheduling and power management, charging pile capacity, power flow safety of the distribution network, and topology of the distribution network, second-order cone relaxation technology is adopted to improve solving efficiency. The simulation results show that compared with the traditional serial restoration strategy, the proposed strategy delivers a dual benefit: it significantly eliminates the power supply vacuum period without compromising the efficiency of emergency repair operations. Specifically, it increases weighted load restoration by 57.2% compared with traditional sequential methods and reduces the average outage time for key loads from 3.22 h to 0.5 h, effectively enhancing the resilience and restoration ability of the power supply guarantee of the distribution network. Full article

Decarbonizing heavy-duty road freight transportation requires efficient energy management in zero-emission powertrains. This study investigates energy management strategies (EMSs) for a heavy-duty Fuel Cell Plug-in Hybrid Electric Vehicle (FC-PHEV). Rather than the typical charge-sustaining operation, these strategies are designed for charge-depleting operation, in which each route begins with a charged battery and ends at a lower state of charge (SOC), leveraging the vehicle’s plug-in capability. The EMSs are evaluated primarily in terms of energy consumption, while battery C-rate and fuel cell ramp rate are used as simple stress indicators for comparative analysis. A backward-facing vehicle model is developed to test several EMSs, including both optimization- and rule-based strategies. The Equivalent Consumption Minimization Strategy (ECMS) emerged as a promising option, motivating further testing with a forward-facing model and additional drive cycles. The simulation results show that ECMS consumed only 1.1% more energy than the global optimal solution found by Pontryagin’s Minimum Principle (PMP) and 7.5% less energy than a simple rule-based strategy, on average across five drive cycles. These results show that ECMS can be effective for a heavy-duty FC-PHEV operating in charge-depleting mode, extending its demonstrated applicability beyond charge-sustaining and light-duty vehicles. Full article

Effective operation and maintenance (O&M) of deep-sea observation networks are challenged by complex environments and energy limitations. While digital twin (DT) technology offers promising solutions, existing frameworks struggle with high-fidelity, multi-platform orchestration and predictions of electrical energy state. This study proposes a DT framework for a deep-sea observation network (DSON-DT), encompassing telemetry acquisition, predictive analytics, and feedback control to realize a closed-loop workflow for monitoring and managing platform states within virtual scenes. Powered by real-time Internet of underwater things (IoUT) data, a high-fidelity virtual environment is constructed in the Unreal Engine 5 game engine, accurately mapping ambient marine environments and reconstructing platform dynamic behaviors via data-driven approaches and geometric constraints. An improved auto-regressive long short-term memory (AR-LSTM) network is proposed to forecast the battery state of charge (SoC). Experimental results show that this algorithm effectively mitigates the impacts of severe deep-sea noise and the flat open-circuit voltage plateau, suppressing state oscillations to provide reliable references for proactive endurance management. The Vue.js-based web prototype, deployed via pixel streaming, offers seamless interfaces for interactive visualization, analysis, and remote operation. This research achieves comprehensive situational awareness for deep-sea platforms, providing validated technical support for the holistic evaluation and intelligent O&M of heterogeneous marine infrastructures. Full article

Background: Conducting research in emergency departments and critical care units is crucial for improving patient management through evidence-based practices. Healthcare professionals and researchers in the field of traumatic brain injury (TBI) have a moral and legal obligation to inform patients before conducting any diagnostic test or therapy as part of a clinical study. However, challenges and barriers to conducting research in these high-pressure environments must be acknowledged. Shall the pathway to obtain informed consent in TBI-related research be revisited? We sought to map literature, identify gaps, and clarify the bioethics that should be followed in TBI-related research. Methods: A Scoping review was conducted to identify the obstacles and challenges investigators encounter in clinical and translational TBI research, with a specific emphasis on informed consent and regulatory impediments that often serve as bottlenecks or rate-limiting steps for participant enrollment and overall study success. This review used google scholar and Midline from inception to 2025. Results: Patients with TBI or their surrogates may be unable to provide informed consent within limited therapeutic windows. Despite international regulations and national laws, restrictions on obtaining consent are often criticized as ambiguous in certain situations. Furthermore, the fast-paced, emotionally charged atmosphere in emergency settings poses a risk of delaying crucial research interventions. There are accepted alternatives to informed consent, such as proxy consent, deferred consent, exceptions from consent, and waivers of consent, which are ethically and socially acceptable and compliant with regulations. However, these alternatives are underutilized or may be abused in some cases. Conclusions: This review calls for clarifying and modifying arbitrary regulatory restrictions on research and streamlining the Common Rule. Scientists should also share their innovative solutions to strike a balance between ethical considerations and the minimization of research barriers. Full article

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a /Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems. Full article

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK^® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems. Full article