Energy Storage最新文献

This work reports the synthesis of Sr₂FeCoO₆, double perovskite, via a wet chemical method. X-ray diffraction (XRD) analysis and Rietveld refinement confirmed the successful formation of pure, single-phase perovskite structure with the $��\mathrm{Pm}�\overline{3}�m�$ space group. The Raman spectrum showed minor peaks, suggesting structural distortions likely caused by defects. Transmission electron microscopy (TEM) revealed irregularly shaped polycrystalline particles, while Brunauer–Emmett–Teller (BET) analysis indicated an average surface area of 3.01 m²/g and a pore diameter of 37.8 nm. Current collectors, namely, carbon Toray paper, carbon cloth, nickel foam, and nickel strip, were selected to evaluate the electrochemical properties of Sr₂FeCoO₆. The morphology of the current collectors was captured using a scanning electron microscope (SEM). The electrochemical performance of bare and loaded (with Sr₂FeCoO₆) current collectors was assessed under similar measurement conditions. The high specific capacitance of the sample is observed over the carbon cloth and nickel foam to be 105.7 and 93.3 F/g, respectively, while bare carbon cloth shows very high capacitance. By comparing the performance of different current collectors, we have identified the key factors influencing the material's performance. This study will enhance our understanding of its potential applications in energy storage and other pertinent areas.

It is essential to forecast the exact rate at which the cell's capacity would decline for practical uses, to comprehend the intricate and non-linear behavior of the cell. Furthermore, the majority of studies provided subpar prediction criteria, making early cell lifetime prediction difficult. Applying reliable and accurate aging models to the dynamic on-road conditions presents additional challenges. In this work, the battery lifetime during its earliest phases of use was accurately predicted using machine learning models. After analyzing the patterns of the parameters, 12 hand-crafted features were selected and the raw data of the first 100 cycles of 126 cells was used for creating the dataset for the features. The dataset was then used to train five machine learning models namely random forest, gradient boosting machine (GBM), light gradient boosting machine (LGBM), extreme gradient boosting machine (XGBoost), and gradient boost with categorical features (CATBoost). The statistical analysis reveals that XGBoost algorithm present the best result with a R² value of 0.95 and root-mean-square-error (RMSE) of 97 cycles. Lastly, in comparison to existing studies, the RMSE significantly reduced from a maximum of 138 to 97 cycles.

Fulfilling the increasing energy demands of the world through renewable energy sources requires the utilization of a highly efficient large-scale electrochemical energy storage device. A hybrid supercapacitor (HSC) that consists of a battery-type electrode coupled with a counter-capacitive electrode, while in principle offering supercapacitor-like power, cyclability, and higher energy density, can be a potential device for large-scale energy storage to cater to the energy needs through renewable energy sources. The KCo_0.5Ni_0.5PO₄ electrode demonstrated notably enhanced electrochemical performance, attributed to the synergistic interaction of Co²⁺ and Ni²⁺ ions in a phosphate framework. The incorporation of redox-mediated diffusive charge storage through the incorporation of Ni²⁺ on the Co²⁺ site resulted in a large-scale charge storage capacity, coupled with capacitive-type surface charge storage on the KCo_1−xNi_xPO₄ electrodes. The KCo_0.5Ni_0.5PO₄ delivers 173 mAh/g (capacitance: 1038 F/g) at a current density of 0.5 A/g in an aqueous 2 M KOH electrolyte, accompanied by cyclic stability up to 5000 cycles. HSC mode consists of activated carbon as the negative electrode along with KNi_0.5Co_0.5PO₄ as the positive electrode, displaying high energy density and power density of 183.7 Wh/kg and 7952 W/kg, respectively, in 2 M aqueous KOH electrolyte. The superior performance in HSC mode makes KCo_0.5Ni_0.5PO₄ a potential positive electrode for the development of high-performing HSCs.

The reliability of various energy sources can be increased and distributed production and renewable energy can be fully integrated into the power grid on a wide scale through the growth and development of the microgrid (MG). Global energy difficulties are brought about by the finite supply of fossil fuels and the world's expanding energy consumption. Due to these challenges, the electric power system has to convert to a renewable energy-based power generation system to produce clean, green energy. However, because of the unpredictable nature of the environment, the shift toward the use of renewable energy sources raises uncertainty in the production, control, and power system operation. This manuscript proposes a renewable energy-based energy management system for electric vehicles and AC–DC MGs. The proposed method is Hermit Crab Optimizer (HCO). The major goal of the proposed strategy is to supply steady power regardless of generation disparity, which should stop the storage devices from degrading too quickly. The HCO approach provides a stable power balance for MG operation. The proposed technique efficiently strikes a power balance to meet load requirements and recharge electric cars. By then, the proposed strategy is implemented in the MATLAB platform and the execution is computed with the existing procedure. The proposed technique displays better outcomes in all existing systems like biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and artificial neural network (ANN). The existing technique shows the cost of 25$, 30$, 35$, 40$, and the proposed technique displays the cost of 20$ which is lower than the other existing techniques.

The hypothesis of the energy adsorption phenomenon was confirmed by density distributions of CDD, TDOS, and LOL for GaN and ternary alloys of AlGaN and InGaN. Based on TDOS, the excessive growth technique on doping manganese is a potential approach to designing high-efficiency hybrid semipolar gallium nitride–based devices in a long wavelength zone. A vaster jointed area engaged by an isosurface map for Mn doping GaN, AlGaN, and InGaN toward formation of nanocomposites of Mn@GaN–H, Mn@AlGaN–H, and Mn@InGaN–H after hydrogen adsorption due to labeling atoms of N(4), Mn(5), and H (18), respectively. Therefore, it can be considered that manganese in the functionalized Mn@GaN, Mn@AlGaN, or Mn@InGaN might have more impressive sensitivity for admitting the electrons in the status of hydrogen adsorption. Furthermore, Mn@GaN, Mn@AlGaN, or Mn@InGaN are potentially advantageous for certain high-frequency applications requiring batteries for energy storage. The advantages of manganese over GaN, AlGaN, or InGaN include its higher electron and hole mobility, allowing manganese doping devices to operate at higher frequencies than nondoping devices. A comprehensive investigation on hydrogen grabbing by heteroclusters of Mn-doped GaN, AlGaN, and InGaN was carried out using DFT computations. The position of the Mn-doped energy states was evaluated via the spectra obtained from the bipolar devices with the Mn-doped GaN/AlGaN/InGaN as an active layer.

In this study, we report the synthesis, characterization, and thermal behavior of sodium hydroxide based two manganese-phosphate nanocrystallite composites, MnPNa₁ = Mn₂(PO₄)OH. 0.2H₃PO₄. 0.1NaOH.H₂O (calcined) and MnPNa₂ = Mn₂(PO₄)OH. 2H₂O. 0.8H₃PO₄. 0.1NaOH. H₂O (non-calcined), and the molecular weights of the composites are estimated to be 247.40 and 360.20 g/mol, respectively. Comprehensive characterization was carried out, which includes elemental analysis, X-ray powder diffraction, thermogravimetric analysis, derivative thermogravimetry, Fourier Transform Infrared (FT-IR) Spectrometry, and scanning electron microscopy. Confirmation of the different functional groups within the composites was done through FT-IR analysis. Differential scanning calorimetry analyses revealed distinct thermal behaviors: MnPNa₁ exhibited consistent exothermic properties, making it suitable as a heat dissipation material (HDM) with high stability across a broad temperature range. In contrast, MnPNa₂ displayed a high specific heat capacity (Cp) of 1.23 J/g·K, highlighting its potential as a sensible heat storage material. The crystallinity of MnPNa₁ (89.83%) further supports its stability and application in heat dissipation technologies, while MnPNa₂'s smaller crystallite size enhances its surface interactions for efficient heat storage. The crystallite sizes of MnPNa₁ and MnPNa₂ are found to be 25.5 and 18.8 nm, respectively.

The current study aims to predict the thermal runaway in lithium-ion batteries using five artificial intelligence algorithms, considering the environmental factors and various design parameters. Multiple linear regression, k-nearest neighbors, decision tree, and random forest are used as machine learning algorithms, while artificial neural networks are used as deep learning algorithms. Nineteen experimental datasets are used to train the models. First, Pearson's correlation matrix is used to investigate the effects of input parameters on the thermal runaway onset time. The dataset is then updated to include only tests with thermal runaway produced by an external heat source. As a result of comparison among model performance prediction, it is determined that the decision tree model is the best-performing model with a coefficient of determination (R²) score of 0.9881, followed by random forest, k-nearest neighbors, artificial neural networks, and multiple linear regression models. The dataset is modified when the thermal runaway is triggered by external heating and compression forces. Results show that in this case, the performance of the decision tree model has an R² of 0.9742. Finally, the force range in which the model has the best performance is predicted, which is helpful in conducting tests to obtain reliable results.

The development and integration of autonomous power sources (APSs) for electric vehicle (EV) charging infrastructure are essential for reducing dependency on centralized power grids and advancing sustainable transportation. This study presents a novel APS model that integrates hybrid inverters, photovoltaic (PV) panels, and battery storage to create a reliable, cost-effective, and environmentally friendly EV charging solution. The proposed system achieves a 30% increase in charging efficiency compared to traditional grid-dependent models. Furthermore, the APS model reduces operational costs by 40%–60% for EV fleet operators and demonstrates a potential CO₂ emissions reduction of 70%–90% by relying on renewable solar energy. The results highlight the APS's adaptability across various environmental conditions, making it suitable for deployment in both urban and remote areas. This work contributes to the field by providing a scalable and sustainable approach to EV charging that supports green urban infrastructure and promotes resilience in energy supply.

Hydrogen is one of the most promising alternatives to fossil fuels for energy as it is abundant, clean and efficient. Storage and transportation of hydrogen are two key challenges faced in utilizing it as a fuel. Storing H₂ in the form of metal hydrides is safe and cost effective when compared to its compression and liquefaction. Metal hydrides leverage the ability of metals to absorb H₂ and the stored H₂ can be released from the hydride by applying heat when needed. A multi-step methodology is proposed to identify intermetallic compounds that are thermodynamically stable and have high hydrogen storage capacity (HSC). It combines compound generation, thermodynamic stability analysis, prediction of properties of the metal hydride and ranking of discovered materials based on predicted properties. The US Department of Energy (DoE) Hydrogen Storage Materials Database and the Open Quantum Materials Database (OQMD) are utilized for building and testing machine learning (ML) models for enthalpy of formation of the intermetallic compounds, stability analysis, and enthalpy of formation, equilibrium pressure and HSC of metal hydrides. The models proposed here require only attributes of elements involved and compositional information as inputs and do no need any experimental data. Random forest algorithm was found to be the most accurate amongst the ML algorithms explored for predicting all the properties of interest. A total of 349 772 hypothetical intermetallic compounds were generated initially, out of which, only 8568 compounds were found to be stable. The highest predicted HSC of these stable compounds was found to be 3.6. Magnesium, Lithium and Germanium constitute majority of the high HSC compounds. The predictions of HSC using the present models for metal hydrides that are not in the DoE database were reasonably close to the experimental data published recently but there is scope for improvement in prediction accuracy for metal hydrides with high HSC. The findings of this study will be useful in reducing the time required for development and discovery of new hydrogen storage materials and can be used to check the practical applicability of the hydride compound using the predicted properties.

Temperature significantly affects the operation of lithium-ion batteries in electric vehicles (EVs). A battery temperature management system (BTMS) is necessary for battery safety and extended lifespan. This study proposes an innovative flow circulation technique to achieve uniform airflow distribution throughout the 26 650 cylindrical cells arranged in a 5P5S configuration. The 3D models of nine aluminum perforated plates with varying topologies have been developed to identify a more effective cooling method for rectangular battery packs. The CFD simulations examine the effects of air velocities, air inlet temperatures, C rate, and cell spacing (L) on the nine-plate structure. Optimal cooling is achieved with 2 mm cell spacing, evenly dispersing airflow and enhancing heat dissipation. An investigation has been conducted for various C rates. The best thermal performance is obtained at air speeds of 0.8 m/s for 0.5 C, 5 m/s for 1C, and 30 m/s for 2C. The outcome shows that altering the flow distribution layout is a practical way to improve the BP's cooling capacity.