Battery Energy最新文献

Advancements in industry and technology, along with population growth and increasing demands for a comfortable lifestyle, have continuously driven up energy consumption. The depletion of conventional energy sources has increased interest in renewable and alternative energy sources. Renewable energy sources are types of energy that are continuously replenished in nature and are produced sustainably through natural processes. In recent years, PV technology has become one of the most preferred renewable energy systems due to its modular structure, easy installation, advanced technological level, and low operating costs. Energy storage units are crucial for ensuring that energy needs are met under all circumstances. Hybrid energy storage systems play a significant role in energy storage and enable the efficient use of resources. This paper discusses the development of a Hybrid Energy Storage System (HESS), consisting of a lithium-ion (Li-ion) battery and supercapacitor (SC). The designed system is integrated with a PV system to meet the energy requirements of a Brushless DC motor (BLDC).

Back Cover: Aqueous electrolytes play a key role in determining the electrochemical performance of aqueous energy storage devices. In article number BTE.20240089, Yibing Yang, Min Liu, Dongliang Zhang, Shuilin Wu, and Wenjun Zhang reported a ‘water in ionic liquid’ electrolyte simultaneously featured with high ionic conductivity, broad temperature compatibility, and wide electrochemical stability window. Such electrolyte enables an aqueous supercapacitor with outstanding electrochemical performance.

Front Cover: Transition metal molybdates have emerged as promising electrode materials for energy storage applications. In the article number BTE.20240073, D. S. Sawant, S. B. Kulkarni, D. P. Dubal, and G. M. Lohar present an innovative approach combining machine learning (ML) techniques to predict and analyze how structural, compositional, and synthesis parameters influence the electrochemical performance of molybdates. By identifying the critical factors that govern their energy storage behavior, the study offers valuable insights into the rational design of molybdate-based composites. The authors also review morphology-dependent supercapacitor performance, highlighting how the integration of experimental data with ML-driven optimization can accelerate the development of next-generation energy storage systems.

With the expansion of off-grid hydrogen production systems, the randomness and volatility of renewable energy sources place higher demands on the power supply reliability of energy storage systems (ESS). This paper presents an adaptive hierarchical control (AHC) strategy for parallel energy storage units (ESUs) in electrolytic hydrogen production systems to improve the reliability of power supply. In this strategy, each ESU is considered an agent, and a dynamic average consensus algorithm is used to obtain the average value of the observed quantities. In the primary control layer, a sigmoid function is proposed to improve the droop coefficient, enabling the state of charge (SoC) of each ESU to converge to the average value. On this basis, a novel acceleration factor based on a normal distribution function is designed to accelerate the speed of SoC balancing in the later stage. In the secondary control layer, a unit virtual voltage drop balancing term and an average voltage compensation term are used to distribute the output current of ESUs proportionally according to their capacity and restore the average bus voltage deviation. The stability analysis confirms that the proposed method is strongly stable. Finally, a photovoltaic hydrogen production simulation model and a StarSim HIL experimental platform are established. The results show that the proposed control strategy can achieve rapid SoC balancing and accurate load current distribution with excellent average bus voltage compensation under various complex operating conditions.

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.