Battery Energy最新文献

Microgrids (MGs) are a solution to excessive load demand and power grid failure because they provide utility systems with stability and continuous power flow. A controller for a Fuzzy Logic System with neural network that is adaptable (Adaptive Fuzzy Neural Network Inference System) is suggested for a hybrid microgrid that is fueled by renewable energy sources. A modern high-gain Landsman converter is one of the numerous converters in use is employed to increase the solar output and achieve a steady DC-link voltage to provide outputs with high efficiency. The converter control is accomplished via the ANFIS method, a noteworthy substitute that combines two computational techniques: Neural networks and fuzzy set theory (ANN). Using the Crow Search Algorithm (CSA), the ANFIS constraints are reinforced to boost the convergence rate and dependability predictive accuracy rate. PWM-based rectification system controlled by a Proportional-integral control algorithm then links the wind system and microgrid configuration. When power from solar and wind sources is scarce, energy storage battery system (BESS) is used to hold energy for use in the DC connection. The MATLAB platform simulates evaluations of the control strategy. The proposed Landsman converter with high gain demonstrates superior energy efficiency compared to the Super Lift Luo converter, which in turn makes it a more effective solution for stabilizing DC-link voltage and boosting RES outputs in hybrid microgrid systems.

Bacterial cellulose (BC) as a natural polymer possessing ultrafine nanofibrous network and high crystallinity, leading to its remarkable tensile strength, moisture retention and natural degradability. In this study, we revealed that this BC membrane has excellent affinity to organic electrolyte, high ionic conductivity and inherent ion selectivity as well. Due to its ability of migrating lithium ions and suppressing the shuttling of anions across the membranes, it is deemed as available model for iodide-assisted lithium-oxygen batteries (LOBs). The cycle life of the LOBs significantly extends from 74 rounds to 341 rounds at 1.0 A g⁻¹ with a fixed capacity of 1000 mAh g⁻¹, when replacing glass fiber (GF) by BC membrane. More importantly, the rate performance improves significantly from 42 to 36 cycles to 215 and 116 cycles after equipping with the BC membrane at 3.0 and 5.0 A g⁻¹. Surprisingly, the full discharge capacity dramatically enhanced by ca. eight times from 4,163 mAh g⁻¹ (GF) to 32,310 mAh g⁻¹ (BC). Benefited from the convenient biosynthesis, cost-effectiveness and high chemical-thermal stability, these qualities of the BC membrane accelerate the development and make it more viable for application in advancing next-generation environmentally friendly LOBs technology with high energy density.

The growth of lithium dendrites has been regarded as the biggest challenge for lithium metal batteries (LMBs). Vertical graphene (VG) is a promising inhibitor against lithium dendrites. However, there is no research on the effects of various defect types of VG on LMBs. Herein, we grew different defect types of VG on copper foam as LMBs anode and then studied their electrochemical properties in detail. As the synthesis temperature increases, the density of carbon nanosheets (CNS) gradually rises, causing the VG to transition from vacancy-like type to boundary-like type. The cycling test shows that the boundary-like type electrode exhibits the highest coulombic efficiency exceeding 97.9% after 200 cycles at 5 mA cm⁻² among various defect type electrodes. The superior electrochemical performance of the boundary-like type electrodes is attributed to their high defect content and abundant edge defects, which provide numerous nucleation sites for lithium and promote uniform deposition. Additionally, the unique three-dimensional morphology of VG offers sufficient space for lithium deposition, effectively inhibiting the growth of lithium dendrites. This study highlights that boundary-like type VG can effectively enhance the stability of LMBs, and provides a new idea for the application of VG to the anode of LMBs.

High-nickel ternary cathode (HNCM) materials are regarded as the primary choice for lithium-ion batteries (LIBs) due to their high energy density. However, their development is limited by lithium–nickel mixing, microcrack generation, and surface side reactions. Herein, a combined roll-to-roll and vacuum vapor deposition process is used to prepare LiNi_0.9Co_0.05Mn_0.05O₂ (NCM9055) cathode material with a dense, ultrathin, and robust lithium fluoride (LiF) protective layer. Compared with traditional methods, this approach allows precise control over the thickness and rate of the deposited LiF layer, producing a uniform and robust protective layer that enhances surface stability. This approach not only effectively reduces direct contact between the electrolyte and the electrode surface, mitigating corrosion and side reactions, but also strengthens the structural integrity of the cathode, thereby significantly improving cycling stability. The NCM9055 with a 10 nm LiF layer exhibits enhanced electrochemical performance, especially at cut-off voltages of 4.3 and 4.5 V, and also excellent cycling performance at 1 C. Additionally, the introduction of the LiF layer improves the thermal stability of NCM9055, further enhancing the safety of high-nickel batteries. This study not only demonstrates the combination of roll-to-roll processing and vacuum vapor deposition as a fast and effective modification technique but also highlights the advantages of vacuum vapor deposition in forming a homogeneous and robust LiF layer, which is essential for rapid production and for improving the safety and energy density of HNCM materials in advanced LIBs.

This paper introduces an advanced framework to enhance power system flexibility through AI-driven dynamic load management and renewable energy integration. Leveraging a transformer-based predictive model and MATPOWER simulations on the IEEE 14-bus system, the study achieves significant improvements in system efficiency and stability. Key contributions include a 44% reduction in total power losses, enhanced voltage stability validated through the Fast Voltage Stability Index (FVSI), and optimized renewable energy utilization. Comparative analyses demonstrate the superiority of AI-based approaches over traditional models such as ARIMA, with the transformer model achieving significantly lower forecasting errors. The proposed methodology highlights the transformative potential of AI in addressing the challenges of modern power grids, paving the way for more resilient, efficient, and sustainable energy systems.

Currently, the rapidly growing population is producing hazardous waste materials at an unprecedented rate, which seriously affects the global environment. Additionally, increasing population and pollution have amplified the need for renewable energy and efficient energy-storage technologies. One strategy is to implement greener processes for efficiency and/or utilize the waste generated for useful domestic and industrial applications. In this context, here, we harnessed the most littered environmental pollutant, cigarette filter waste (CFW), to synthesize carbon nanomaterials (CNM) via a single-step pyrolysis process, devoid of any catalyst or activating agent, possessing optimal characteristics for serving as an active electrode material in the fabrication of cutting-edge supercapacitors, thereby addressing the issue of waste recycling and the need for energy storage devices among the populace. Supercapacitors, namely SC-1 to SC-4 matching electrolytes, 1M H₂SO₄, 2M H₂SO₄, 1M KOH, and 2M KOH, fabricated using CNM electrodes were evaluated. Among these, SC-2 exhibits superior performance, demonstrating a remarkable capacitance of 240 Fg^–1 at low scan rates (2 mVs^–1), an enhanced energy density (22.4 Whkg^–1), and commendable power density (399.43 Wkg^–1). Furthermore, SC-2 maintained 5000 cycles of outstanding stability with 97.8% capacitance retention. This study unveils the potential of CFW-derived CNMs as an electrode material for the realization of state-of-the-art supercapacitors.

This study introduces a novel composite cathode for aqueous zinc-ion batteries (ZIBs), leveraging porous basil-derived activated carbon (BAC) and nanostructured manganese dioxide (MnO₂) synthesized through a one-step hydrothermal process. For the first time, basil-derived carbon is integrated with MnO₂, resulting in enhanced electrochemical performance. The MnO₂/BAC composite delivers a remarkable specific capacity of 237 mAh/g at 0.5 A/g, along with an energy density of 314 Wh/kg and a power density of 0.66 kW/kg, outperforming cathodes made from pristine MnO₂ or BAC. These improvements stem from reduced particle size and a synergistic balance of capacitive and diffusive charge storage mechanisms. Density functional theory calculations corroborate the experimental results, revealing the composite's superior quantum capacity (158.7 µC/cm²) and quantum capacitance (80.4 µF/cm²). Stability assessments highlight excellent cycle life, with > 90% capacity retention and 100% Coulombic efficiency over 300 cycles. The exceptional performance is attributed to the material's unique nanostructure, high surface area (1090 m²/g), and optimized porosity. Additionally, practical applications of ZIBs in pouch cell form using the MnO₂/BAC cathode are demonstrated, showcasing its capability to power a toy car over a satisfactory distance. This study establishes a new benchmark for sustainable and cost-effective cathode materials, significantly advancing ZIB technology for high-efficiency energy storage applications.

MXene materials exhibit outstanding pseudocapacitive performance, holding great potential for application in zinc-ion hybrid supercapacitors (Zn-HSCs). Exploring the effect of the surface terminal regulation on the performance of MXene is crucial yet challenging. In this study, the phosphorus-terminal groups (P─C and P─O) with a P concentration of 2.71 at% are successfully tailored and interlayer spacing is enhanced during the ultraviolet light irradiation process of Ti₃C₂T_x MXene, which is the first report of photoinduced P-doped MXene modification. Density functional theory calculations show that P doping is more likely to be adsorbed by ─O groups than to replace Ti vacancy, and the stability of the MXene electrode can be improved by the introduction of a phosphorus terminal. The resulting P-doped Ti₃C₂T_x MXene shows a significant increased pseudocapacitance performance, achieving superior results compared with traditional resistance furnace heating methods. The specific capacitance achieves 500.5 F g⁻¹, due to the ─P functional group and Ti atom double reoxidation sites. Furthermore, a Zn-HSC device of P-doped Ti₃C₂T_x exhibits a specific capacitance of 207.4 F g⁻¹ and energy densities of 56.5 Wh kg⁻¹. This study also provides valuable insights and a reference for the realization of phosphorus doping in other MXene materials.

Accurately estimating the battery's capacity over its cycle life is essential for ensuring its safety in applications, including transportation and the medical field, where specific power delivery is a key component for optimal output. Most research concerning lithium-ion health prediction utilizes current-voltage data or techniques that rely on modeling microscopic degradation. Acquisition of current-voltage data directly builds up degradation within the cell, and physics-based methods require high computational power. Recent research pivoted to using electrochemical impedance spectroscopy (EIS) for battery health prediction since it provides information-rich data while non-destructive to the cell. One major drawback of using EIS is the time it takes to acquire data, especially at lower frequencies where diffusion within the cell is prevalent. To address this, this investigation focuses on feature extraction, which creates a subset of data from a publicly available data set to contain the frequencies that are mostly correlated with degradation. Analysis shows that a simulated cell's state of health (SOH) can get as low as 0.94% MAPE using the two most correlated frequencies in the charge transfer (CT) region. This study provides a methodology to accurately predict the capacity and SOH while reducing the time needed to acquire EIS data by 93% for this case. This method also highlights the usefulness of a single-cell model for battery test bench applications.

Glass fiber (GF) is a widely used separator in zinc-metal batteries, and its geometrical configuration significantly affects the battery's cycle performance. However, the underlying mechanisms remain unclear. In this study, we examine how the separator's geometry influences battery cycle life, which is determined by the competition between internal short circuits caused by Zn dendrite growth and the deteriorated charge transfer during repeated electrochemical cycling—both of which are affected by the separator's configuration. As the dominance of these failure mechanisms varies with battery processing parameters (e.g., cycling current density and capacity), the systematic study presented here offers guidance for separator choice in Zn metal batteries.