Search Results (5,059)

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, incorporating both continuous and discrete decision variables—such as charging power and cooling modes—into a unified optimization framework. An environment-adaptive optimization strategy is also developed. To enhance learning efficiency and policy safety, a contrastive learning–enhanced policy gradient (CLPG) algorithm is proposed to distinguish between high-quality and unsafe charging trajectories. A manifold-aware action generation network (MAN) is further introduced to enforce dynamic safety constraints under varying environmental and battery conditions. Simulation results demonstrate that the proposed framework reduces charging time to 18.3 min—47.7% faster than the conventional CC–CV method—while achieving 96.2% energy efficiency, 99.7% capacity retention, and zero safety violations. The framework also exhibits strong adaptability across wide temperature (−20 °C to 45 °C) and aging (SOH down to 70%) conditions, with real-time inference speed (6.76 ms) satisfying deployment requirements. This study provides a safe, efficient, and adaptive solution for intelligent high-power EV fast-charging. Full article

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across various charging contexts and control scenarios. This paper categorizes RL methodologies from value-based to actor–critic and hybrid frameworks, and explores their integration with optimization techniques, forecasting models, and multi-agent coordination strategies. By examining key design aspects—including agent structures, training schemes, coordination mechanisms, reward formulation, data usage, and evaluation protocols—this review identifies broader trends across central control dimensions such as scalability, uncertainty management, interpretability, and adaptability. In addition, the review assesses common baselines, performance metrics, and validation settings used in the literature, linking algorithmic developments with real-world deployment needs. By bridging theoretical principles with practical insights, this work provides comprehensive directions for future RL applications in EVCS control, while identifying methodological gaps and opportunities for safer, more efficient, and sustainable operation. Full article

The development of advanced photocatalysts that are efficient, recyclable and sustainable represents a significant challenge in the face of the growing presence of persistent organic contaminants in industrial wastewaters. This paper presents a novel approach based on the design of new heterostructures synthesized from BiOI and magnetic materials, using not only synthetic magnetite, but also magnetic compounds extracted from mine tailings, transforming environmental liabilities in active supporting materials through valorization strategies in line with the circular economy. Through precise control of composition, it was established that a proportion of 6% by mass of the magnetic phase allows the formation of a heterostructure that is highly photocatalytically efficient. These compounds were evaluated using caffeic acid, an organic contaminant of agroindustrial origin, as a target compound. Experiments were carried out under simulated solar radiation for 120 min. Among the materials synthesized, the BiOI/MMA heterostructure, derived from industrial tailing A, displayed an outstanding photodegradation efficiency of over 89.4 ± 0.25%, attributed to an effective separation of photoinduced charges, a broad active surface and a synergic interface interaction between its constituent phases. Furthermore, BiOI/MMA exhibited excellent structural stability and magnetic recovery capacity, which allowed for its reuse through two consecutive cycles without any significant losses to its photocatalytic performance. Thus, this study constitutes a significant contribution to the design of functional photocatalysts derived from industrial tailings, thus promoting clean, technological solutions for the treatment of wastewater and reinforcing the link between environmental remediation and circular economy. Full article

Hybrid systems have recently become widespread in motorsports due to advantages such as increased power through the use of electric motors and reduced fuel consumption thanks to regenerative braking. Achieving high performance from a hybrid powertrain requires a highly efficient control system for managing power flows between the internal combustion engine (ICE) and the electric motor. The goal of this study is to develop a control algorithm for a hybrid powertrain aimed at minimizing lap times compared to traditional vehicles equipped with an ICE. To achieve this objective, a mathematical vehicle model based on the tractive balance equation was used. Lap time simulations were conducted for both a traditional ICE vehicle and a hybrid system. The results showed that the hybrid vehicle has a significant advantage in lap time; however, the energy from a fully charged battery would only be sufficient for two laps. To address this issue, a hybrid system control algorithm is proposed, which maintains the energy balance of the battery throughout the entire lap while still providing better lap times compared to a vehicle equipped with a traditional ICE. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

We report combined scanning probe microscopy and electrical measurements to investigate local electronic transport in reduced graphene oxide (rGO) devices. We demonstrate that quantum transport in these materials can be significantly tuned by the electrostatic potential applied with a conducting atomic force microscope (AFM) tip. Scanning gate microscopy (SGM) reveals a clear p-type response in which local gating modulates the source–drain current, while scanning impedance microscopy (SIM) indicates corresponding shifts of the Fermi level under different gating conditions. The observed transport behavior arises from the combined effects of AFM tip-induced Fermi-level shifts and defect-mediated scattering. These results show that resonant scattering associated with impurities or structural defects plays a central role and highlight the strong influence of local electrostatic potentials on rGO conduction. Consistent with this electrostatic control, the device also exhibits chemical gating and sensing: during exposure to electron-withdrawing molecules (acetone), the source–drain current increases reversibly and returns to baseline upon purging with air. Repeated cycles over 15 min show reproducible amplitudes and recovery. Using a simple transport model, we estimate an increase of about $40\%$ in carrier density during exposure, consistent with p-type doping by electron-accepting analytes. These findings link nanoscale electrostatic control to macroscopic sensing performance, advancing the understanding of charge transport in rGO and underscoring its promise for nanoscale electronics, flexible chemical sensors, and tunable optoelectronic devices. Full article

This paper presents a novel open-loop modulation and control strategy for bidirectional, multi-source/load parallel AC-link power converters. While these converters offer advantages such as high-frequency operation and flexible power conversion capabilities, their application to complex systems such as nanogrids presents significant control challenges. Traditional control methods often struggle to efficiently manage power flow and charging/discharging processes, especially when dealing with multiple sources and loads of varying characteristics. To address these issues, this paper proposes a new control strategy that enables intelligent source and load selection while maintaining fast charging and discharging times. Simulation results demonstrate the effectiveness of the proposed approach. This research contributes to advancing the state-of-the-art in power electronics by providing a foundation for improved control of complex power conversion systems for renewable energy applications. Full article

Wireless sensor networks (WSNs) are an advanced solution for data collection in Internet of Things (IoT) applications and remote and harsh environments. These networks rely on a collection of distributed sensors equipped with wireless communication capabilities to collect low-cost and small-scale data. WSNs face numerous challenges, including network congestion, slow speeds, high energy consumption, and a short network lifetime due to their need for a constant and stable power supply. Therefore, improving the energy efficiency of sensor nodes through solar energy harvesting (SEH) would be the best option for charging batteries to avoid excessive energy consumption and battery replacement. In this context, modern wireless communication technologies, such as Wi-Fi and Li-Fi, emerge as promising solutions. Wi-Fi provides internet connectivity via radio frequencies (RF), making it suitable for use in open environments. Li-Fi, on the other hand, relies on data transmission via light, offering higher speeds and better energy efficiency, making it ideal for indoor applications requiring fast and reliable data transmission. This paper aims to integrate Wi-Fi and Li-Fi technologies into the SEH-WSN architecture to improve performance and efficiency when used in all applications. To achieve reliable, efficient, and high-speed bidirectional communication for multiple devices, the paper utilizes a Markov model, sleep scheduling, and smart switching algorithms to reduce power consumption, increase signal-to-noise ratio (SNR) and throughput, and reduce bit error rate (BER) and latency by controlling the technology and power supply used appropriately for the mode, sleep, and active states of nodes. Full article

Amorphous carbon (a-C) materials have attracted significant attention for environmental remediation due to their chemical stability and high surface area; however, their photocatalytic activity remains limited by rapid electron–hole recombination. In this study, ZnO-doped amorphous carbon (a-C@ZnO) composites were synthesized via a one-pot hydrothermal method to enhance charge separation and photocatalytic performance. The synthesis involved the carbonization of glucose and the incorporation of zinc species under controlled conditions, resulting in composites with varying ZnO contents. The physical and chemical properties of the materials were thoroughly characterized by SEM, Raman spectroscopy, and X-ray photoelectron spectroscopy, confirming the successful integration of ZnO within the carbon matrix and the formation of Zn–O–C chemical bonds. Photocatalytic tests, evaluated through the degradation of rhodamine 6G under UV irradiation, demonstrated that ZnO doping significantly improved photocatalytic efficiency, with the a-C@ZnO_0.75 sample achieving a 72% degradation rate and the highest kinetic rate constant. The enhancement was attributed to improved charge transfer and reactive oxygen species generation facilitated by the ZnO–a-C interface. These findings highlight the potential of ZnO-doped amorphous carbon composites as effective, low-cost photocatalysts for water purification applications. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

Silicon-doped C₆₀ fullerenes represent a distinctive class of heterofullerenes with tunable structural, electronic, and chemical properties arising from substitutional incorporation of Si atoms into the carbon cage. This review provides a comprehensive analysis of substitutional Si–C₆₀ systems and their endohedral and exohedral complexes, with emphasis on synthesis strategies, structural features, and theoretical investigations. Experimental methods, including laser vaporization and arc discharge of Si-containing graphite targets, have enabled the preparation of Si-doped fullerenes, although challenges remain in controlling the dopant number, position, and distribution. Computational studies, dominated by density functional theory and molecular dynamics simulations, elucidate the effects of Si substitution on cage geometry, HOMO–LUMO modulation, charge localization, aromaticity, and finite-temperature stability. Exohedral functionalization and endohedral encapsulation of Si-doped cages significantly enhance their potential for applications in sensing, catalysis, energy storage, and nanomedicine. Si incorporation consistently strengthens adsorption of small molecules, pharmaceuticals, biomolecules, and environmental pollutants, often transforming weak physisorption into strong chemisorption with pronounced electronic and spectroscopic changes. The synergistic insights from experimental and theoretical work establish Si-doped fullerenes as versatile, electronically responsive nanoplatforms, offering a balance between stability, tunability, and reactivity, and highlighting future opportunities for targeted synthesis and application-specific design. Full article

This article investigates the zonotope-based state estimation for boost converter system with Markov jump process. DC-DC boost converters are pivotal in modern power electronics, enabling renewable energy integration, electric vehicle charging, and microgrid operations by elevating low input voltages from sources like photovoltaics to stable high outputs. However, their nonlinear dynamics and sensitivity to uncertainties/disturbances degrade control precision, driving research into robust state estimation. To address these challenges, the boost converter is modeled as a Markov jump system to characterize stochastic switching, with time delays, disturbances, and noises integrated for a generalized discrete-time model. An adaptive event-triggered mechanism is adopted to administrate the data transmission to conserve communication resources. A zonotopic set-membership estimation design is proposed, which involves designing an observer for the augmented system to ensure $H_{\infty }$ performance and developing an algorithm to construct zonotopes that enclose all system states. Finally, numerical simulations are performed to verify the effectiveness of the proposed approach. Full article