Search Results (538)

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

This paper introduces an enhanced bi-directional full-bridge resonant converter designed for Vehicle-to-Grid (V2G) systems. A key innovation lies in the incorporation of an auxiliary LC resonant circuit connected via a tertiary transformer winding. This circuit dynamically modifies the magnetizing inductance based on operating frequency, enabling soft-switching across all primary switches, specifically, Zero-Voltage Switching (ZVS) at turn-on and near Zero-Current Switching (ZCS) at turn-off across the entire load spectrum. Additionally, the converter supports both Constant Current (CC) and Constant Voltage (CV) charging modes at distinct, fixed operating frequencies, thus avoiding wide frequency variations. A 3.3 kW prototype developed for onboard electric vehicle charging applications demonstrates the effectiveness of the proposed topology. Experimental results confirm high efficiency in both charging and discharging operations, achieving up to 98.13% efficiency in charge mode and 98% in discharge mode. Full article

This paper presents the results of laboratory tests on the intensity of mass and heat exchange in a polytunnel, with a focus on the synergy between the polytunnel and a stone accumulator. The subject of study was a standard polytunnel made of double polythene sheathing. In the process of selecting the appropriate working conditions for such a polytunnel, the characteristic operating parameters were modeled and verified. They were related to the process of mass and energy exchange, which takes place in regular controlled-environment agriculture (CEA). Then, experimental tests of a heat accumulator on a fixed stone bed were carried out. The experiments were carried out for various accumulator surfaces ranging from 18.7 m² to 74.8 m², which was measured perpendicularly to the heat medium. To standardize the results obtained, the analysis included the unit area of the accumulator and the unit time of the experiment. In this way, 835 heat and mass exchange events were analyzed, including 437 accumulator charging processes and 398 discharging processes from April to October, which is a standard period of polytunnel use in the Polish climate. During the tests, internal and external parameters of the process were recorded, such as temperature, relative humidity, solar radiation, wind speed and air flow speed in the accumulator system. Based on the parameters, a set of empirical relationships was developed using mathematical modeling. This provided the foundation for calculating heat gains as a result of its storage in a stone accumulator and its discharging process. The research results, including the developed dependencies, not only fill the scientific gap in the field of heat storage, but can also be used in engineering design of polytunnels supported by a heat storage system on a stone bed. In addition, the proposed methodology can be used in the study of other heat accumulators, not only in plant production facilities. Full article

This article proposes a 3D mathematical model of the influence of electrical heterogeneity of the ion exchange membrane surface on the processes of salt ion transfer in membrane systems with axial symmetry; in particular, we investigate an annular membrane disk in the form of a coupled system of Nernst–Planck–Poisson and Navier–Stokes equations in a cylindrical coordinate system. A hybrid numerical–analytical method for solving the boundary value problem is proposed, and a comparison of the results for the annular disk model obtained by the hybrid method and the independent finite element method is carried out. The areas of applicability of each of these methods are determined. The proposed model of an annular disk takes into account electroconvection, which is understood as the movement of an electrolyte solution under the action of an external electric field on an extended region of space charge formed at the solution–membrane boundary under the action of the same electric field. The main regularities and features of the occurrence and development of electroconvection associated with the electrical heterogeneity of the surface of the membrane disk of the annular membrane disk are determined; namely, it is shown that electroconvective vortices arise at the junction of the conductivity and non-conductivity regions at a certain ratio of the potential jump and angular velocity and flow down in the radial direction to the edge of the annular membrane. At a fixed potential jump greater than the limiting one, the formed electroconvective vortices gradually decrease with an increase in the angular velocity of rotation until they disappear. Conversely, at a fixed value of the angular velocity of rotation, electroconvective vortices arise at a certain potential jump, and with its subsequent increase gradually increase in size. Full article

To address the charging demand challenges brought about by the widespread adoption of electric vehicles, integrated photovoltaic–storage–charging stations (PSCSs) enhance energy utilization efficiency and economic viability by combining photovoltaic (PV) power generation with an energy storage system (ESS). This paper proposes a two-stage data-driven holistic optimization model for the siting and capacity allocation of charging stations. In the first stage, the location and number of charging piles are determined by analyzing the spatiotemporal distribution characteristics of charging demand using ST-DBSCAN and K-means clustering methods. In the second stage, charging load results from the first stage, photovoltaic generation forecast, and electricity price are jointly considered to minimize the operator’s total cost determined by the capacity of PV and ESS, which is solved by the genetic algorithm. To validate the model, we leverage large-scale GPS trajectory data from electric taxis in Shenzhen as a data-driven source of spatiotemporal charging demand. The research results indicate that the spatiotemporal distribution characteristics of different charging demands determine whether a charging station can become a PSCS and the optimal capacity of PV and battery within the station, rather than a fixed configuration. Stations with high demand volatility can achieve a balance between economic benefits and user satisfaction by appropriately lowering the peak instantaneous satisfaction rate (set between 70 and 80%). Full article

We study the charged black-hole solutions of a 2 + 1 nonlinear electrodynamical theory with a cosmological constant. Considered as a one-parameter group of theories (the exponent of the squared Maxwell tensor), the causal structure of all possible black holes is scrutinized. We analyze the singularity character that each theory delivers, together with their horizons and the plausible limitations in black-hole charges. The investigation demonstrates a rich structure of three different groups of theories according to the qualitative behavior of the singularity, horizons and limitations in the geometric charges. For such groups, we study the effect of a scalar field propagating in the spacetime of fixed black holes. All analyzed geometries are stable to such linear perturbations, evolving as usual quasinormal spectra of the black holes calculated for the different cases. Full article

Electric vehicles (EVs) are central to the decarbonisation of transport systems and achievement of the Sustainable Development Goals (such as SDGs 7 and 13, affordable and clean energy and climate action, respectively). This study adopts a hybrid methodological framework, merging panel econometric models with machine learning (ML), to examine the drivers of EV adoption across Africa, China, and the European Union between 2015 and 2023. We analyse the influence of charging station density (CSD), GDP per capita, renewable energy share (RES), urbanisation, and electricity access using both first-difference and fixed-effects models for causal insight and Random Forest, XGBoost, and neural network algorithms for predictive analytics. While CSD emerges as the most significant driver across models, results reveal a paradox—GDP per capita demonstrates a negative relationship with EV adoption in econometric models yet ranks among the top predictive features in ML models. This divergence highlights the limitations of assuming linear causality in high-income settings and underscores the value of combining causal and predictive approaches. SHAP and PCA analyses further illustrate regional disparities, with Africa showing low feasibility scores due to infrastructure and grid limitations. Sub-regional case studies (Kenya, South Africa, Morocco, Nigeria) emphasise the need for tailored, integrated policies that address both energy infrastructure and transport equity. Findings highlight the value of combining interpretable models with predictive algorithms to inform inclusive and region-specific EV transition strategies. Full article

In order to solve the problem of frequency and voltage stability degradation caused by high proportion of renewable energy grid connection, this paper proposes a multi-energy dynamic coordinated control framework, which integrates the inertia damping characteristics of virtual synchronous generator (VSG) and the flexible load regulation capability of virtual synchronous motor (VSM) to build a two-way interactive mechanism. For the first time, a virtual inertia dynamic compensation algorithm based on VSG is proposed. By introducing the frequency change rate adaptive inertia coefficient adjustment mechanism, the system’s active support capability for wind and solar power fluctuations is improved by 32% compared with the traditional fixed inertia strategy; a breakthrough design of the VSM-driven hydrogen production system dynamic matching control strategy is made, and an electrolyzer efficiency-power dual variable coupling model is established to achieve optimal control of hydrogen production efficiency fluctuation rate ≤ ±2.1% within a wide power range of 10–95%; an innovative mixed integer quadratic programming real-time optimization model considering battery SOC safety constraints is constructed, and the wind and solar consumption efficiency is improved by 28.6% compared with the single energy storage mode through energy storage-hydrogen production complementary scheduling. A simulation platform was built based on Simulink to verify the system performance under three conditions: load mutation, source-grid fluctuation, and simultaneous source-load change. The simulation results show that under different working conditions, the fluctuation range of the system frequency can be stabilized within ±0.15Hz, and the voltage deviation is less than 2%; through the coordinated scheduling of the battery and the hydrogen production system, the battery charge state is always maintained in a safe range of 15–85%, and the hydrogen production power regulation rate reaches 1.5 kW/s. The study shows that the proposed control strategy can significantly enhance the inertia response capability of the system, achieve dynamic power balance and power quality optimization under multiple working conditions, and provide a feasible technical path for the high proportion of renewable energy grid connection and efficient preparation of green hydrogen. Full article

This paper presents an electromagnetic translational–rotary motion impact energy harvester based on a magnetic cylinder rotated around a fixed magnetic ring. It is beneficial for capturing impact energy generated by natural human motions, such as clapping, boxing, and stomping. The energy harvester consists of a circular housing, twelve coils, a magnetic cylinder, and a magnetic ring. Once activated, the magnetic cylinder revolves and rotates around the magnetic ring, inducing a significantly large electromotive force across the twelve coils. According to Faraday’s law, the output voltage generated by the coils is proportional to the turns, enabling the efficient harvesting of biomechanical waste energy. Moreover, the energy harvester can convert translational motion from any orientation into a multi-circle rotational motion of the low-damping magnetic cylinder, which passes through twelve coils and applies a variable magnetic field across them. During a single excitation event, the prototype harvester was able to charge a 470 μF, 25 V capacitor to over 0.81 V in just 39.5 ms. The energy output and effective average power were calculated to exceed 0.15 mJ and 3.80 mW, respectively. Full article

The surge in electric vehicles (EVs) in Denmark challenges the country’s residential low-voltage (LV) distribution system. In particular, it increases the demand for home EV charging significantly and possibly overloads the LV grid. This study analyzes the impact of EV charging integration on Denmark’s residential distribution networks. A residential grid comprising 67 households powered by a 630 kVA transformer is studied using DiGSILENT PowerFactory. With the assumption of simultaneous charging of all EVs, the transformer can be heavily loaded up to 147.2%. Thus, a voltage-droop based autonomous control approach is adopted, where the EV charging power is dynamically adjusted based on the point-of-connection voltage of each charger instead of the fixed rated power. This strategy eliminates overloading of the transformers and cables, ensuring they operate within a pre-set limit of 80%. Voltage drops are mitigated within the acceptable safety range of ±10% from normal voltage. These results highlight the effectiveness of the droop control strategy in managing EV charging power. Finally, it exemplifies the benefits of intelligent EV charging systems in Horizon 2020 EU Projects like SERENE and SUSTENANCE. The findings underscore the necessity to integrate smart control mechanisms, consider reinforcing grids, and promote active consumer participation to meet the rising demand for a low-carbon future. Full article

Inverted organic light-emitting devices (OLEDs) have been attracting considerable attention due to their advantages such as high stability, low image sticking, and low operating stress in display applications. To address the charge imbalance that has been known as a critical issue of the inverted OLEDs, Li-doped MgZnO nanoparticles were synthesized as an electron-injection layer of the inverted OLEDs. Hexagonal wurtzite-structured Li-doped MgZnO nanoparticles were synthesized at room temperature via a solution precipitation method using LiCl, magnesium acetate tetrahydrate, zinc acetate dihydrate, and tetramethylammonium hydroxide pentahydrate. The Mg concentration was fixed at 10%, while the Li concentration was varied up to 15%. The average particle size decreased with Li doping, exhibiting the particle sizes of 3.6, 3.0, and 2.7 nm for the MgZnO, 10% and 15% Li-doped MgZnO nanoparticles, respectively. The band gap, conduction band minimum and valence band maximum energy levels, and the visible emission spectrum of the Li-doped MgZnO nanoparticles were investigated. The surface roughness and electrical conduction properties of the Li-doped MgZnO nanoparticle films were also analyzed. The inverted phosphorescent OLEDs with Li-doped MgZnO nanoparticles exhibited higher external quantum efficiency (EQE) due to better charge balance resulting from suppressed electron conduction, compared to the undoped MgZnO nanoparticle devices. The maximum EQE of 21.7% was achieved in the 15% Li-doped MgZnO nanoparticle devices. Full article

The prediction of lithium-ion battery capacity degradation is crucial for enhancing the reliability, efficiency, and sustainability of energy storage systems. This study proposes a data-driven approach to model capacity degradation in 18650 lithium-ion cells, supporting the long-term performance and responsible management of battery technologies. A systematic search was conducted to identify publicly available experimental datasets reporting charge/discharge processes, leading to the selection of the MIT-BIT Battery Degradation Dataset (Fixed Current Profiles and Arbitrary Use Profiles). This dataset was chosen for its extensive degradation data, variability, and adaptability to real-world applications. Of the 77 tested cells, 73 were included after filtering data completeness; cells with missing critical information, such as temperature, were excluded. A subset of cells tested under a 1C–2C charge/discharge profile was analyzed, and cell 52 was selected for its comprehensive structure. Using this dataset, a predictive model was developed to estimate the battery capacity based on the current, voltage, and temperature, with capacity as the target variable. A neural network was implemented using TensorFlow and Keras, incorporating ReLU activation, Adam optimization, and multiple loss functions. The dataset was standardized using MinMaxScaler, StandardScaler, and RobustScaler, and the training–test split was 75–25%. The model achieved a prediction error of 3.35% during training and 3.48% during validation, demonstrating robustness and efficiency. These results highlight the potential of data-driven models in accurately predicting lithium-ion battery degradation and underscore their relevance for promoting sustainable energy systems through improved battery health forecasting, optimized second-life use, and extended operational lifetimes of storage technologies. Full article

This paper proposes a 1000 W high-frequency three-phase power inversion underwater capacitive wireless power transfer (UCWPT) system for power delivery to autonomous underwater vehicles (AUVs). The multi-phase coupling structure is designed as a columnar configuration that conforms to the shape of AUVs. This paper innovatively presents a curved coupling coupler composed of six metal plates. This design significantly enhances the mutual capacitance of the coupling structure and the power transfer capacity of the UCWPT system. Utilizing the columnar structure, the receiver of the capacitive wireless power transfer system can be easily integrated into AUVs, reducing the installation space. Furthermore, the cylindrical dock-transmitter terminal structure of the system greatly improves the anti-misalignment capability. This addresses issues such as charging voltage and current fluctuations caused by vehicle rolling in dynamic ocean environments. Additionally, the wireless power transfer capacity is notably enhanced. An experimental platform was constructed, and tests were conducted in both air and water media. A 1000 W experimental setup was developed to validate the theoretical analysis and simulations. The experimental results align closely with the theoretical predictions. At a fixed distance of 3 cm between transmitter and receiver, peak power transfer efficiencies of 80% in air and 74% in water were achieved with stable operational performance. The cylindrical structure demonstrates robust anti-misalignment properties. Full article

To address the frequency regulation requirements of hybrid energy storage (HES) in renewable-dominated power grids, this paper proposes an asymmetric droop control strategy based on an improved backpropagation (BP) neural network. First, a simulation model of HES (comprising supercapacitors for the power support and batteries for the energy balance) participating in primary frequency regulation is established, with a stepwise frequency regulation dead zone designed to optimize multi-device coordination. Second, an enhanced Sigmoid activation function (with controllable parameters a, b, m, and n) is introduced to dynamically adjust the power regulation coefficients of energy storage units, achieving co-optimization of frequency stability and State of Charge (SOC). Simulation results demonstrate that under a step load disturbance (0.05 p.u.), the proposed strategy reduces the maximum frequency deviation by 79.47% compared to scenarios without energy storage (from 1.7587 × 10⁻³ to 0.0555 × 10⁻³) and outperforms fixed-droop strategies by 44.33%. During 6-min continuous random disturbances, the root mean square (RMS) of system frequency deviations decreases by 4.91% compared to conventional methods, while SOC fluctuations of supercapacitors and batteries are reduced by 49.28% and 45.49%, respectively. The parameterized asymmetric regulation mechanism significantly extends the lifespan of energy storage devices, offering a novel solution for real-time frequency control in high-renewable penetration grids. Full article

The present work examines pure and palladium photofixed TiO₂ and binary (TiO₂/ZnO) photocatalysts for breaking down tartrazine, a food coloring agent, in distilled water. Powders with the following compositions are obtained using the sol-gel process: 100TiO₂, 10TiO₂/90ZnO, 50TiO₂/50ZnO, and 90TiO₂/10ZnO. The composite materials are analyzed using SEM-EDS, UV-Vis, DTA-TG, and X-ray diffraction. The synthesized gels are then photo-fixed with UV light to incorporate palladium ions and are also examined for tartrazine (E102) degradation. The photocatalytic tests were carried out in a cylindrical glass reactor illuminated by ultraviolet light. Compared to mixed binary catalysts, the prepared pure TiO₂ catalyst demonstrated greater activity in the photodegradation of tartrazine (E102). The further of a specific quantity of zinc oxide reduced the catalytic properties of TiO₂. The recombination of photoinduced electron-hole pairs in ZnO may account for this. In comparison to the pure samples, the co-catalytic palladium-modified gels exhibited higher photocatalytic efficiency. Heterojunction and palladium modification of the composites partially captured and transferred the electrons. Consequently, the longer lifetime of the photogenerated charges improved the catalytic activity of the palladium titanium dioxide and binary gels. Additionally, under UV light, pure and palladium photofixed TiO₂ and binary sol-gel samples displayed excellent stability for tartrazine photodegradation. Full article