Electrochemical impedance spectroscopy (EIS) is a fundamental technique for elucidating dynamic interactions within electrode materials and electrochemical energy storage systems, offering critical insights into the coupled mechanisms of charge transfer and ionic transport in high-frequency supercapacitors. To enable precise quantification of high-frequency capacitance components, this study utilizes a first-order RC equivalent circuit model to extract key electrochemical parameters from impedance spectra that directly correlate with device performance. These parameters are subsequently incorporated into a genetic algorithm-driven global optimization framework to achieve accurate decoupling of distinct capacitance contributions. To validate the robustness and universality of this approach, four representative electrode systems-graphene film, carbon nanotube film, carbonized melamine foam, and commercial YP50F porous carbon-were systematically investigated through high-frequency EIS characterization. Capacitance decomposition analysis based on the acquired impedance data revealed that high-frequency capacitance behavior involves multi-relaxation dynamics, which can be resolved into three distinct contributions: C_Debye, C_HN, and C_RBM relaxation processes. Importantly, the dominance of weak short-range ionic interactions was identified as the critical factor governing the superior high-frequency response characteristics of these supercapacitor systems. These findings clarify the fundamental physical origins underlying capacitance composition in high-frequency supercapacitors. The proposed integrated analysis methodology provides a robust theoretical framework for guiding material optimization and interface engineering strategies aimed at enhancing high-frequency supercapacitor performance.

Lithium ion capacitors (LICs), a new type of energy storage devices, can bridge the performance gap between high-power and high-energy storage systems. In this study, pouch-type LICs were fabricated with different positive/negative (P/N) mass ratios were fabricated using activated carbon (AC) as the cathode and orthorhombic niobium pentoxide (T-Nb₂O₅) as the anode. The effect of the prelithiation ratio was investigated. Results indicate that prelithiation can effectively improve the electrochemical performance of pouch-type LICs. Regardless of the P/N mass ratio, when the prelithiation ratio was increased to 60%, the open-circuit potential increased to >1.5 V; the initial Coulombic efficiency exceeded 80%; the specific capacity of the active material improved, accompanied by reduced internal resistance; the cyclic voltammetry curves exhibited typical capacitive behavior; the voltage retention remained above 75% after 33 d of storage at room temperature; the capacity retention during C-rate testing exceeded 95% at a current of 100C; and the capacity retention during cycle life testing was >90% after 3000 cycles. A higher P/N mass ratio had a more favorable impact on LIC performance due to enhanced initial Coulombic efficiency and improved voltage retention during the self-discharge process. After 60% prelithiation, the LIC with the highest P/N mass ratio (P/N=1.1) exhibited stable energy output, achieving an energy density of 30.4 Wh/kg at a power density of 103.2 W/kg and retaining 29.4 Wh/kg at a power density of 2098.1 W/kg.

Cooling the motor rotors in large-capacity flywheel energy storage systems operating in vacuum environments is a significant challenge. Aiming to reduce the temperature rise of the rotor, a 1.25 MW flywheel energy storage unit is proposed herein to provide an axial internal flow cooling scheme for the hollow shaft of the flywheel motor rotor. A mathematical model of the temperature field in the rotor shaft was developed by integrating heat conduction and convection equations. The predictions of the model reveal that in the absence of any cooling measures, the surface temperature of the rotor rises linearly with the operating time. However, with the proposed hollow shaft internal flow cooling scheme, the rotor temperature can be effectively stabilized, demonstrating the potential for controlling the temperature rise. To validate the feasibility and effectiveness of the proposed cooling scheme, an experimental platform was carefully designed and constructed. The hollow shaft internal flow cooling scheme is highly effective in curbing the temperature rise of the rotor. Detailed analysis shows that increasing the flow rate of the cooling medium (heat-transfer oil) leads to a significant enhancement in the cooling effectiveness. Further research shows that increasing the flow rate of the heat-transfer oil can enhance the heat dissipation effect, raising the rotational speed can effectively control the temperature rise of the rotor, and by increasing the heating power, the rate of the temperature rise can be controlled in a linear manner. This study provides a practical and effective solution to the pressing issue of rotor cooling in such systems.

The flywheel energy storage system converts electrical energy into kinetic energy by accelerating the flywheel through a motor, storing the energy, decelerating and braking the flywheel to generate electricity, and releasing kinetic energy. The system relies on power electronic devices to control the acceleration or deceleration of the motor to achieve energy conversion. Characterized by a fast response, high charging and discharging frequency, high conversion efficiency, and long service life, flywheel energy storage systems are widely used in fields such as power frequency regulation, energy recovery, and uninterrupted power supply. In this study, a scheme for the magnetic bearing of a high-power flywheel energy storage system is designed by utilizing a support method combining radial and axial heavy-load electromagnetic bearings. The finite element method is used to complete the analysis of the electromagnetic performance of the system through simulation. Furthermore, a multi-physics coupled model is established for analysis of the bidirectional coupling between the electromagnetic and thermal fields, enabling comprehensive evaluation of the temperature distribution of the magnetic bearings under different current conditions. The performance of the designed heavy-duty electromagnetic bearing meets the design requirements, and the natural air-cooling method can ensure the safe operation of the system. Although reducing the coil current contributes to improving the thermal safety, it also leads to a decrease in the electromagnetic force. Therefore, the design of magnetic bearings for flywheel energy storage systems must achieve a proper trade-off between thermal management and the electromagnetic performance.

To satisfy the demand for ultra-high-rate pulse discharge performance (over 100 C) in special applications, short-duration, high-frequency power storage technology has become crucial for resolving specific power supply challenges. This study investigated multidimensional optimization approaches, including separator selection, conductive additive comparison, current collector design, slurry mixing processes, and anode composite systems, to reduce ohmic polarization in lithium-ion batteries under ultra-high-rate pulse discharge conditions and achieve rapid response during the initial stage of pulse discharge. It was found that high-porosity wet-process separators can combine the high mechanical strength of wet-process base membranes with the fast ion transport characteristics of dry-process membranes. Vapor-grown carbon fiber demonstrated a faster response to high-rate discharge compared to carbon nanotubes. In anode system optimization, hard carbon/graphite and SiO₂/graphite composites exhibited contrasting performance: the former showed superior polarization suppression (with a 0.1 V increase in the 150 C pulse plateau) at the expense of energy density, while the latter enabled 19.4% electrode thinning and a 4% energy density improvement, albeit with reduced pulse rate capability. A comparison of mixing processes revealed that different mixing methods showed no significant difference in the power performance of batteries with ultra-thin electrodes containing a high content of conductive additives. Analysis of different current collector terminal designs indicated that a dual-side terminal design provided shorter and more uniform electron transport pathways than a top-terminal design, effectively mitigating ohmic voltage drop. The developed ultra-high-power battery maintained a discharge plateau of 3.4 V at 150 C (1 s pulse).

While nano-sized materials offer notable advantages over micron-sized particles in enhancing rapid charge-discharge capability and optimizing power density, they suffer from several limitations, including low initial Coulombic efficiency, low volumetric energy density, insufficient mass loading, inferior cycling stability, complex manufacturing processes, and high production costs, collectively restricting their practical applications. In this study, nano-scale LiCoO₂ (N-LCO) was synthesized via a facile wet ball-milling method and subsequently combined with micron-scale LiCoO₂ (M-LCO) to form particle-graded composites. The effects of varying mass ratios of nano-sized particles (x% N-LCO) on the electrochemical performance were systematically investigated. Through X-ray diffraction, scanning electron microscopy, and electrochemical characterization, a comparative analysis of the structure, morphology, and electrochemical properties of M-LCO and x% N-LCO materials was conducted. The results demonstrate that the 10% N-LCO composite exhibits outstanding electrochemical properties: a high initial discharge specific capacity (170.1 mAh/g) with an initial Coulombic efficiency of 93.83%, remarkable rate capability (79.3% capacity retention at 10 C), and excellent cycling stability (96.33% capacity retention after 100 cycles at 1 C). A 1.4 Ah pouch-type lithium-ion battery assembled with the 10% N-LCO composite cathode and commercial hard carbon anode achieved a specific energy of 116.78 Wh/kg. The battery maintained 78.57% capacity retention at a 200 C discharge rate and sustained ultra-high pulse discharges at 350 C (second-level) and 1000 C (millisecond-level). Notably, a power density of 88.44 kW/kg was achieved during 1000 C pulse discharge. This work significantly enhances the rate capability of cathode materials through nano/microstructural particle grading of LiCoO₂, offering valuable insights into the design and engineering of short-duration, high-frequency, and ultrahigh-power lithium-ion batteries.

Lithium titanium oxide (LTO) batteries offer high safety, rapid charge-discharge capabilities, and long cycle life, presenting significant potential for application in energy storage and frequency modulation markets. However, research on high-rate cyclic aging under different state-of-charge (SOC) ranges remains limited. This study addresses the requirements of short-term, high-frequency energy storage scenarios by investigating commercial LTO batteries. The evolution of electrochemical performance in LTO batteries cycled under different SOC upper and lower limit conditions at a rate of 4 C was examined. Using non-destructive analysis methods such as incremental capacity curves and differential voltage curves, the dominant mechanisms of thermodynamic capacity decay were analyzed. Experimental results indicate that as the upper SOC limit increases or the lower SOC limit decreases, the cycling capacity of LTO batteries significantly decreases. Meanwhile, the compensation of lithium insertion depth in the negative electrode within high SOC ranges and the repair effect during constant voltage charging improve cycling stability in high SOC ranges compared with low SOC ranges. Analysis of the charge curves reveals a strong correlation between capacity degradation and SOC interval conditions. The loss of active lithium is the primary factor contributing to capacity degradation during cycling in 0%—100% SOC, 0—80% SOC, and 80%—100% SOC ranges. In contrast, in the 20%—100% SOC range, capacity degradation is mainly caused by the loss of active material in the positive electrode. When cycled in the low SOC range (0—20%), the contributions of active lithium loss and positive electrode active material loss to capacity decay are comparable. These findings elucidate the influence of SOC range on high-rate cycling capacity degradation in LTO batteries, providing theoretical guidance for their application in short-term, high-frequency energy storage scenarios.

To address the poor stability and difficulty in rapid frequency regulation for a large number of renewable new energy sources connected to the power grid system, a method of achieving a fast frequency response and controlling the stability of multi-port energy routers under frequency regulation conditions was developed. First, to address the low-frequency instability caused by the strategy used to control the frequency modulation in existing multi-port energy routers (small hydro power, photovoltaic, energy storage, and grid connected ports), methods of controlling energy routers were analyzed by combining small signal impedance modeling methods to derive impedance models for each port of multi-port energy routers. Based on the equivalent impedance model of each port of the energy router, the instability and mechanism of achieving stable operation of the multi-port energy router were studied. The existing strategy for controlling the frequency modulation in energy routers is susceptible to interaction of the inductive impedance, capacitive impedance, and negative damping impedance. The phase difference at the intersection of port impedances is greater than 180°, and the Nyquist plot surrounds the (-1, 0j) point in a clockwise manner, making the system prone to instability during low-frequency oscillation. However, the new, fast-frequency-response control strategy proposed herein for the energy router can ensure that the phase difference at the intersection of port impedances is less than 180° and the Nyquist plot does not surround the (-1, 0j) point, ensuring stable operation of the system. The data provide theoretical guidance and technical support for achieving a fast frequency response and stable control of multi-port energy routers under frequency modulation conditions. Finally, the effectiveness of the proposed method for achieving a fast-frequency response and stable control of multi-port energy routers was verified by simulation.

Energy storage systems (ESS) that operate solely in either grid-following (GFL) or grid-forming (GFM) control modes are insufficient for the demands of complex and dynamic power grids. This study proposes a seamless GFL/GFM switching control method for energy storage converters, enabling smooth transitions between GFM and GFL modes in response to environmental changes. The proposed method leverages the steady-state vector relationships of the two control modes and integrates key modules, including power angle observation, coordinate system rotation, and proportional-integral (PI) regulator initialization. Through impedance-based analysis, the correlation between system stability and the grid short-circuit ratio (SCR) under both modes is examined. It is demonstrated that dynamic variations in SCR can be effectively managed via hybrid GFL/GFM control and seamless switching, thereby improving system stability. The proposed approach is validated through simulation studies conducted using a MATLAB/Simulink model of an ESS. The results confirm that the converter achieves seamless transitions between GFL and GFM control, with enhanced responsiveness and stability. This research enhances the operational flexibility of high-power, high-frequency grid-forming ESS, enabling reliable performance in diverse scenarios such as grid-connected/off-grid operation and strong/weak grid conditions, and ensuring system safety and stability under varying external conditions.

With the transformation of China's energy structure, the demand for energy storage devices with rapid response, high-frequency regulation, and intrinsic safety for the new power systems is increasing. Supercapacitors have attracted considerable attention owing to their advantages as energy storage devices, including their high power density, long cycle life, wide-temperature range of operation, and no safety hazards due to dendrite growth. This review systematically evaluates the technical systems and application progress of supercapacitors. In terms of cell research and development, the typical technical routes and performance of two types of electric double-layer supercapacitors and hybrid supercapacitors are analyzed. In terms of system applications, this review discusses the implementation of supercapacitors in several applications such as wind turbine pitch systems, energy storage in new energy systems, frequency modulation in thermal power plants, independent energy storage systems, and transportation. In addition, this review briefly discusses the use of supercapacitors in other applications such as kinetic energy recovery of power machinery (e.g., cranes), data center backup power systems, and power equipment. Finally, the challenges still facing supercapacitors in energy density, life cycle cost, and application scenarios are analyzed. Future research is discussed, focusing on its prioritization of three areas: developing new material systems, promoting the diversification of application scenarios, and innovating a "supercapacitor+" hybrid energy storage model. Finally, the review discusses the necessity of broadening the commercial application of supercapacitors in new power systems through system and scenario innovation with differentiated products.

Pseudocapacitors are highly attractive for energy storage applications due to their ability to deliver both high energy and power densities. Over the past decade, significant progress has been made in developing and optimizing pseudocapacitive materials. Nevertheless, the intrinsic complexity of pseudocapacitive interfaces and their rapid charge-discharge dynamics pose considerable challenges for conventional experimental techniques to elucidate the coupled ion transport and charge transfer mechanisms. A comprehensive understanding of the microscopic processes underlying pseudocapacitance remains a major challenge. This review systematically traces the evolution of pseudocapacitance theory, emphasizing its fundamental distinctions from electric double-layer capacitance and battery-type behavior. By integrating recent advances in computational modeling, we critically evaluate the essential role of simulations in unraveling pseudocapacitive mechanisms. Key methodologies discussed include first-principles calculations, molecular dynamics simulations, implicit solvation models, ab initio molecular dynamics, continuum transport models, and multiscale simulation strategies. These approaches provide valuable theoretical insights into interfacial reaction kinetics, ion transport pathways, and structure-property relationships, thereby informing the rational design of high-performance pseudocapacitors.

Flywheel energy storage systems (FESSs) offer outstanding advantages in grid frequency regulation, inertia support, high-frequency peak shaving, and other applications due to their high power density, long service life, rapid responses, and environmental friendliness. However, FESSs face challenges related to achieving cost effectiveness and design reliability, stability of high-speed permanent magnet motors, control of magnetic suspension control, online prediction of faults, and control of multimachine parallel arrays. This paper reviews recent literature on the application of artificial intelligence (AI) technologies in key areas of FESSs, including design optimization, motor control, magnetic suspension control, grid-connected control, and fault diagnosis. Particular focus is given to the use of algorithms such as neural networks in several technical domains: modeling and analysis of composite material rotors, multiparameter collaborative optimization of permanent magnet synchronous motors (PMSMs), efficiency optimization and speed estimation of PMSMs under varying conditions, electromagnetic bearing control algorithms, grid-connected system robustness and distributed collaborative control, frequency regulation strategies, and bearing fault diagnosis and early warning. Future development directions such as the integration of large AI models and multitechnology collaborative optimization are also discussed. These insights aim to support intelligent research and development in FESSs.

With the widespread application of lithium-ion batteries, state-of-power (SOP) prediction has become increasingly crucial as a key technology to ensure efficient and safe battery operation. This study provides a comprehensive review of SOP prediction methods, analyzing four main approaches: map-based methods, mechanism model methods, equivalent circuit model (ECM) methods and data-driven methods. In addition, the prediction of module-level SOP is discussed. The map-based method is straightforward. However, it requires multiple charge and discharge experiments, which are time-consuming and limit its applicability to single operating conditions. The mechanism model method, based on porous electrode theory and concentrated solution theory, can accurately describe internal battery reactions through partial differential equations. This method can account for the impact of internal battery parameters on power performance, but it comes with a relatively high computational complexity. The equivalent circuit model method employs circuit components to simulate the dynamic responses of batteries. This method can integrate multiple constraints, including voltage, current, and SOC, to optimize performance while balancing accuracy and computational efficiency. The data-driven method leverages machine learning techniques, such as neural networks and support vector machines, to construct SOP prediction models directly from running data. It can also integrate with traditional mechanistic models to form hybrid architectures, where the prediction performance is contingent upon both data quality and quantity. For module-level SOP prediction, this paper highlights the impact of cell inconsistency on module power and discusses potential solutions. Finally, existing challenges and future development directions are summarized. Current SOP prediction technologies still face four major challenges: (1) Existing methods struggle to meet the specific requirements of energy storage scenarios. (2) The prediction accuracy and computational efficiency fail to meet the requirements of practical applications. (3) Battery aging introduces time-varying parameters, leading to model mismatches. (4) inconsistencies among battery modules exacerbate prediction difficulties. To address these challenges, future SOP prediction technologies will advance in several key areas, including high-precision modeling and optimization of solution strategies, dynamic updating of model parameters and constraint boundaries, and the development of approaches such as "weak cell identification-characteristic cell modeling-dynamic model parameter updates". These advancements will provide safer and more efficient battery management solutions for energy storage systems.

Amidst the rapid global energy transition, the safe operation of large-scale energy storage stations faces severe challenges. Thermal runaway (TR)-induced arc faults—characterized by ultrahigh temperatures and energy density—have emerged as a critical factor exacerbating fire and explosion risks. This review examines recent advances on the arc formation mechanisms in energy storage batteries and the impact of arcing on TR propagation. The relationship between the TR characteristics and different types of arc ignition pathways is summarized. Three multipath coupling mechanisms driving arc formation are methodically analyzed. Insulation failure caused by thermal/mechanical stress reduces the safe clearance below the critical breakdown distance, triggering direct contact of or gaseous dielectric breakdown. Compared to air, TR-ejected particulates and electrolyte vapors degrade the insulation strength by 50%—80%. Structural degradation during prolonged operation evolves into persistent arcing through deterioration of the insulation. Current research on arc initiation mechanisms still has limitations: the primary mode of occurrence is triggered by ejected materials, with the initiation location predominantly at the battery safety vent and terminal posts. There is insufficient research on the arc initiation mechanism and on triggering mediated by electrolyte. Although magnetohydrodynamic (MHD) simulations characterize the coupled features of thetemperature, magnetic, and flow fields during stable arcing, they inadequately resolve dynamic arc initiation during TR cascades. Therefore, an integrated "thermal-electrical-mechanical-chemical" multi-physical field model is required for simulating transient arcing behavior under TR conditions to establish a theoretical foundation for mitigating arcing hazards in energy storage systems. This paper aims to deepen the understanding of the arc occurrence characteristics in energy storage battery systems, provide ideas for improving the electrical safety of the systems, and promote the high-safety development of energy storage systems.

Sodium and lithium exhibit similar physicochemical properties, and sodium resources are abundant and widely distributed. Accordingly, sodium-ion batteries (SIBs) are regarded as promising complements to lithium-ion battery energy storage systems, with broad potential for large-scale and short-term high-frequency energy storage applications. However, the initial coulombic efficiency (ICE) of sodium-storage anode materials is generally low, preventing them from realizing their theoretical capacity. Pre-sodiation technology, as one of the most effective active sodium compensation strategies, can effectively mitigate active sodium loss. This paper comprehensively analyzes the major challenges facing pre-sodiation technology in recent years and summarizes novel approaches proposed to address these issues. Based on the redox properties of various sodium sources, current pre-sodiation techniques are categorized into reductive and oxidative pre-sodiation. The advantages, disadvantages, and industrial feasibility of different pre-sodiation methods are compared, with a focus on elucidating their mechanisms and research progress. Furthermore, the development prospects of pre-sodiation technology are discussed. This review aims to deepen understanding of pre-sodiation technology and provide theoretical guidance and innovative insights for optimizing and developing novel pre-sodiation techniques suitable for high-power applications at scale. Based on existing research, we propose that solid-state oxidative pre-sodiation materials hold promise for enabling multiple sodium replenishments throughout the battery's lifecycle, thereby establishing a technical foundation for high-power, high-energy-density sodium-ion batteries.

Against the backdrop of numerous challenges in integrating wind and photovoltaic power into the grid, the development of energy storage technology has emerged as a key means of enhancing renewable energy utilization and strengthening the operational flexibility of power systems. Power curve decomposition methods are increasingly being used to optimize hybrid energy storage configurations. This report explores various power curve decomposition techniques for energy storage and their applications in the energy storage field, including traditional decomposition methods and those based on square-wave foundations. First, four traditional power curve decomposition methods are reviewed: Fourier-transform, wavelet packet decomposition, empirical mode decomposition, and low-pass filtering. The applications of these methods in the energy storage field, as explored by relevant researchers, are also introduced and the current research landscape is analyzed at both the national and international levels. The report highlights existing gaps in adapting these methods to the characteristics of hybrid energy storage systems. It concludes that configurations must be designed to enable energy storage devices to rapidly store and release electrical energy during charging and discharging, and that the power curve of a single storage device often exhibits a waveform with matrix-like characteristics. A method for decomposing, transforming, and analyzing energy storage power curves is proposed based on three transformations with a square-wave base, and the application in energy storage configuration is discussed. Finally, the current challenges in the decomposition of energy storage power curves are summarized and prospects for future research are proposed. The findings provide theoretical support for optimizing energy storage configurations, improving the accuracy of power curve decomposition, and optimizing the performance of energy storage systems in various operational scenarios.

As the penetration of renewable energy resources keeps increasing, the frequency stability of the power system is becoming a major concern due to the intermittency and uncertainty associated with such energy resources. Among various energy storage technologies, supercapacitors feature a high power density, a long cycle life, and a wide operating temperature range, which make them a competitive candidate for the frequency regulation applications in power systems. This paper reviews the supercapacitor energy storage systems for such applications. First, this paper analyzes the frequency regulation requirements of power systems and the potential benefits of supercapacitor energy storage systems in this context. Next, this paper summarizes the control strategies and component sizing methods for the supercapacitor energy storage systems. Specifically, classical control methods such as droop control and advanced control strategies such as model predictive control are covered. As for component sizing, rule-based methods and optimization-based methods are discussed. Then, this paper analyzes the demonstration projects using supercapacitor energy storage systems for frequency regulation applications. In particular, this paper elaborates on the architectures, operation modes, and economies of hybrid energy storage systems incorporating supercapacitors and lithium-ion batteries. Finally, this paper outlines several research and development issues that the academia and the industry need to address to accelerate the adoption of supercapacitor energy storage systems for frequency regulation applications in power systems.

Local mechanical compression is one of the major contributors to thermal runaway in lithium-ion batteries (LiBs) during vehicle collisions. To investigate the failure mechanism of capacitive LiBs under localized indentation, ball-head indentation experiments were performed on 18650 capacitive LiBs. These batteries featured a positive electrode composed of Ni-Co-Mn oxide and activated carbon composite, and a negative electrode made of an extremely soft carbon/graphite composite. The failure process and temperature evolution behavior were analyzed. The effects of state of charge (SOC), indentation location, and secondary use on battery safety were systematically examined. The results show that the peak load decreases with increasing SOC, and the internal short-circuit deformation also diminishes as SOC increases. Damage near the positive terminal is more likely to induce thermal runaway, and elevated temperatures are observed as the damaged area expands. Once the indentation depth reaches a critical threshold, interlayer bending and radial cracking occur, axial electrode plates are damaged, the battery's lithium intercalation and deintercalation capability is considerably reduced, and severe self-discharge is observed. This study offers valuable insights into identifying short-circuit locations and vulnerable regions within the battery, providing a theoretical basis for improving the safety design of battery packs.

Copper foam, owing to its high thermal conductivity, enhances the conductive heat transfer of phase change materials (PCMs); however, its porous structure can inhibit convective heat transfer. To optimize the overall heat transfer enhancement, this study proposes the use of copper foam with a gradient-porosity structure to improve the thermal performance of PCMs. Numerical simulations were conducted to investigate the influence of gradient direction and porosity span on the heat transfer behavior. The results indicate that one-dimensional horizontal gradient porosity improves heat transfer, with the heat transfer rate increasing with porosity span for positive gradients and decreasing for negative gradients. The maximum improvement observed was 5.8% for a negative gradient span of 4%. For one-dimensional vertical gradients, positive gradients yielded superior enhancement, with a maximum increase of 11.5% at a 10% span. In the case of two-dimensional gradients, placing lower porosity at the bottom led to improved heat transfer performance, with a maximum increase of 5.5%. Overall, vertically oriented one-dimensional positive gradient copper foam with a 10% porosity span demonstrated the most effective heat transfer enhancement.

Vanadium nitride (VN) is considered an ideal electrode material for supercapacitors due to its extremely high theoretical specific capacity, good electronic conductivity, and wide operating voltage window. However, VN materials obtained through existing preparation methods often exhibit a small specific surface area, dense surface structure, and poor electrochemical activity, resulting in low actual specific capacity, poor rate performance, and short cycle life. This article summarizes the energy storage mechanisms and preparation methods of VN based on recent literature. In addition, the effects of surface composition, structure, and morphology on the specific capacity, rate performance, and cycling stability of VN are discussed. Strategies for improving the electrochemical performance of VN are presented, with a focus on constructing nano-microstructures and nanocomposites. Approaches such as fabricating nanocrystals, nanobelts, nanofibers, and nanorods are introduced to enhance specific capacity and rate performance. The construction of nanocomposites, including VN/porous carbon, VN@carbon, VN/carbon nanotubes, VN/graphene, and VN/other transition metal nitrides, is described as a means to improve the conductivity and overall performance of VN. Moreover, the impact mechanisms of these nano-microstructures and nanocomposites on specific capacity, rate performance, and cycling stability are analyzed. Existing challenges in current enhancement strategies are also discussed. Finally, future research directions and development trends for VN-based electrode materials are proposed.

Due to their high specific capacity and low cost, lithium-rich oxide materials have been regarded as next-generation cathodes to overcome the bottleneck of energy density in lithium-ion batteries. However, traditional polycrystalline agglomerated lithium-rich materials suffer from intrinsic defects such as particle pulverization and irreversible lattice oxygen release caused by structural reconstruction during long-term cycling, leading to continuous deterioration of the electrode-electrolyte interface and capacity fading. The single-crystal approach has been demonstrated as an effective strategy to mitigate these degradation mechanisms by eliminating grain boundary stress. This paper reviews the unique advantages of lithium-rich single-crystal materials with high nickel content in terms of structural and electrochemical properties. It systematically compares the differences between lithium-rich single-crystal and polycrystalline materials across key performance metrics, including initial Coulombic efficiency, structural stability, morphological evolution after cycling, and gas generation behavior induced by interfacial side reactions. Furthermore, it elaborates on the influence of critical parameters in mainstream synthesis processes, such as high-temperature solid-state methods, molten salt-assisted approaches, and hydrothermal/solvothermal methods, on crystal morphology. Modification strategies and research advancements in lithium-rich single-crystal materials are comprehensively summarized, demonstrating that optimization approaches, including elemental doping, surface coating, and structural engineering, can significantly enhance both structural stability and electrochemical performance. Finally, future development directions for lithium-rich single-crystal materials are discussed, providing theoretical foundations and technical support for the development and application of high energy density lithium-ion batteries.

To enhance the frequency characteristics of power grids and fully leverage the rapid response advantages of distributed energy storage systems (DESSs), a cooperative primary frequency control method based on reinforcement learning-model predictive control (RL-MPC) is proposed. First, a primary frequency control model incorporating DESSs is established based on frequency response characteristics, state of charge (SOC), and power control strategies. Then, a hierarchical mixed control architecture is designed: the upper layer employs a deep Q-network (DQN) to dynamically optimize the MPC weight matrix while sensing frequency deviation, rate of change, and SOC distribution entropy in real time. The lower layer utilizes distributed MPC to determine the output sequences of multi-node energy storage units and introduces a graph attention network (GAT) to achieve adaptive optimization of the communication topology. This approach reduces computational complexity in coordinated control and enhances the strategy's generalization capability. Finally, simulations conducted in Matlab/Simulink verify that the proposed method effectively improves the primary frequency response speed and control accuracy of DESSs, thereby strengthening overall power system stability.

With the large-scale integration of renewable energy into power grids, system frequency regulation faces unprecedented challenges. This study proposes an adaptive frequency regulation method for hybrid energy storage systems based on quantum-enhanced deep reinforcement learning and spatiotemporal graph neural networks (QE-DRL-ST-GNN), aiming to improve grid frequency regulation performance across multiple timescales. By integrating quantum computing with deep reinforcement learning and graph neural networks, the method overcomes limitations of traditional approaches in handling high-dimensional state spaces and complex spatiotemporal dependencies. QE-DRL-ST-GNN utilizes quantum state encoding to represent system states, extracts spatiotemporal features via convolutional operations on quantum graphs, and optimizes the reinforcement learning strategy through quantum variational algorithms. Furthermore, an adaptive quantum circuit generation mechanism is designed to automatically adjust the quantum circuit structure in response to dynamic system characteristics. Case analyses show that, compared with the traditional quantum-enhanced deep reinforcement learning (QE-DRL) method, QE-DRL-ST-GNN maintains frequency deviation within 0.05 Hz under extreme conditions, whereas the traditional deep reinforcement learning (DRL) method allows a deviation of 0.15 Hz, indicating an improvement of 66.67%. In terms of regulation time, QE-DRL-ST-GNN requires only 1.67 s in complex scenarios, representing a 47% reduction compared with the traditional DRL method. Additionally, in extreme conditions, the performance improves by 13 percentage points over the 83% achieved by the traditional DRL method.

A distributed energy storage unit state-of-charge (SOC)-balancing droop control strategy based on secondary voltage compensation is proposed for islanded direct current microgrids to address issues of inaccurate power allocation and SOC imbalance in energy storage units, which can reduce battery lifespan. The control structure used to implement this strategy consists of two layers: a primary control layer and a secondary control layer. The primary layer ensures voltage stability through traditional droop control, while the secondary layer introduces a voltage compensation term derived from an arctangent function with balanced regulation. This strategy aims to achieve accurate current sharing based on each battery's SOC and capacity. Ultimately, the SOC of all batteries gradually converges to an average value, while the average output voltage remains consistent with the nominal voltage of the microgrid. The simulation and experimental results demonstrate that the strategy features a simple control structure, low communication burden, and improves the SOC balancing accuracy and efficiency of the energy storage units. In addition, it effectively protects batteries from overcharging and overdischarging under varying charge and discharge currents. The simulation and experimental results confirm the validity and effectiveness of the proposed control strategy.

Electric vehicles (EVs), as flexible loads in the optimal scheduling of new power systems, can enhance the utilization of new energy and offer a new way to accelerating the development of next-generation power infrastructure. To address the low-carbon optimal scheduling challenge involving sources and loads, where EVs participate in price- and incentive-based demand response, this study proposes a new day-ahead-intraday low-carbon optimal scheduling method. The approach leverages the demand response characteristics of EVs and employs the sparrow search algorithm (SSA) to optimize a convolutional long short-term memory neural network (ConvLSTM NN). First, the historical data of new energy generation and base load are predicted using the SSA-optimized ConvLSTM NN, thereby reducing the influence of uncertainties on the supply and demand sides in the day-ahead-intraday scheduling of new power systems. Next, three types of EV charging modes are defined based on their charging characteristics observed under demand response participation. A two-stage low-carbon environmental-economic dispatch model for source-load interaction is then developed, incorporating the total system cost under ladder-type carbon trading and the optimal control of pollutant emissions. Finally, an improved multiobjective gray wolf algorithm is employed to solve the model. For validation, typical daily data on photovoltaic power, wind power, and loads are used in the case analysis, which also fully considers the demand response characteristics of the various EV charging modes. The optimal scheduling results across four operating scenarios show that, compared with Scenario 1, Scenario 4 achieves a 10.3% reduction in total cost, a 10.9% decrease in pollutant emissions, and a 4.2% increase in new energy consumption. Overall, the proposed day-ahead-intraday low-carbon optimal scheduling method effectively enhances the environmental and economic benefits of new energy utilization in modern power systems.

To improve the low utilization efficiency of industrial flue gas waste heat caused by high dust content and significant temperature fluctuations, a novel horizontal flue gas–solid sensible heat storage device employing high-temperature concrete as the thermal storage medium was developed. A pilot-scale system was designed and constructed for the recovery and storage of waste heat from steel sintering ring-cooled flue gas. Experimental investigations were conducted to analyze the temperature distribution, flow resistance characteristics, instantaneous energy efficiency, thermal efficiency, and exergy efficiency of the storage device. The results revealed the formation of thermoclines along the axial direction during charging and discharging, with higher temperatures near the charging inlet and a relatively uniform radial temperature distribution. The device exhibited a low pressure drop, with a gradual pressure increase during charging and a corresponding decrease during discharging. Both the instantaneous energy efficiency and heat transfer power declined over time. Lower flue gas flow rates yielded more stable instantaneous efficiency, extended heat exchange duration, and reduced pressure drop, though at the cost of decreased heat transfer power. Therefore, the optimal flow velocity should be selected according to application requirements. During stable operation, the system achieved a heat storage capacity of 1376 kWh, with thermal and exergy efficiencies of 93.02% and 91.7%, respectively. The parallel-plate heat exchange structure enabled efficient heat transfer between the solid storage unit and dusty flue gas while effectively mitigating ash deposition and channel blockage. These findings provide an experimental foundation for the scale-up and application of flue gas-solid sensible heat storage systems.

Variations in the characteristic parameters of individual cells within a lithium-ion (Li-ion) battery pack-such as state of charge (SOC), capacity, and internal resistance-can lead to nonuniform thermal distribution due to electrothermal coupling, thereby affecting the overall performance and safety of the pack. This study investigates the dynamic behavior of lithium batteries at different temperatures and depths of discharge, and establishes a second-order RC equivalent circuit-thermal coupling model to examine how inconsistencies in SOC, capacity, and internal resistance influence the thermal behavior of series- and parallel-connected battery packs through numerical simulations. The study quantifies disparities in energy release, heat generation, and temperature distribution across various connection configurations. Results show that, under SOC inconsistency, the parallel-connected pack releases 379.575 Ah due to the self-balancing effect-higher than the 366.024 Ah released by the series-connected pack. However, the standard deviation of the heat generation rate and maximum temperature deviation in the parallel configuration are 2.265 W and 0.62 ℃, respectively, which are significantly greater than those in the series configuration (0.475 W and 0.275 ℃), indicating superior thermal consistency in the series-connected arrangement. For capacity inconsistency, the parallel configuration exhibits greater fluctuations in heating rate due to uneven branch currents, with a temperature standard deviation of 0.421—0.188 ℃ higher than that of the series-connected pack (0.233 ℃). The maximum temperature difference reaches 1.222 ℃ in the parallel configuration, compared to 0.670 ℃ in the series, further highlighting the enhanced thermal uniformity of the series layout. Under internal resistance inconsistency, the average temperature of the series-connected pack is marginally higher (33.233 ℃) than that of the parallel configuration (33.204 ℃), yet the standard deviations of temperature and heat generation rate in the series-connected pack (0.19 ℃ and 0.097 W) remain lower than those in the parallel-connected one (0.215 ℃ and 0.405 W). This suggests that the series configuration effectively mitigates thermal imbalance induced by internal resistance variations. Further quantitative comparison reveals that SOC inconsistency has the most pronounced effect on thermal consistency, with maximum differences in heating rate reaching 6.499 W in the parallel configuration and 1.261 W in the series. Capacity inconsistency leads to the largest temperature difference in the parallel pack (1.222 ℃), which is 1.8 times greater than that in the series. For internal resistance inconsistency, the temperature standard deviation in the series configuration is only 88% of that in the parallel. In conclusion, series-connected battery packs exhibit better thermal consistency under parameter inconsistencies, while parallel-connected packs offer greater energy output but demand more robust thermal management to mitigate temperature fluctuations. These findings provide a quantitative foundation for optimizing thermal models and designing cooling strategies in electric vehicle battery systems, thereby enhancing both safety and operational longevity.

The influence of intermolecular interactions on Li⁺ transport in electrolytes remains insufficiently understood. In this study, molecular dynamics simulations were conducted to investigate the heterogeneous structure of localized high-concentration electrolytes, using 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether (D2) as a diluent. The interactions among solvent molecules and the diluent were analyzed to evaluate their effects on Li⁺ coordination structure and migration behavior. The results show that Li⁺ migrates via repeated ion dissociation/association hopping and exhibits accelerated migration across the D2-CIP (contact ion pair) interface, with D2 molecules serving as carriers along fast transport pathways. Additionally, an electrolyte with a molar ratio of LiFSI∶DME∶D2 = 1∶1.2∶2 exhibited an ion migration rate extremum, significantly enhancing both the reduction resistance of the lithium salt and the ion migration rate. This work offers important theoretical insights into the development of advanced dilution strategies for high-concentration electrolytes.

The development of shared-energy-storage operations has gradually become a key approach for prosumers to promote peer-to-peer (P2P) energy trading and enhance economic benefits. However, the uncertainty in prosumers' transaction behaviors and their complex interactions with shared-energy-storage systems complicate the optimization of P2P trading and shared-energy-storage operations. To address this challenge, this study proposes a collaborative optimization method for shared energy storage based on prosumers' decision-making within a stochastic game framework. To manage the notable uncertainty in multiprosumer trading interactions and maximize prosumers' economic benefits, a P2P trading decision-making model grounded in stochastic game theory is developed. The model employs a Markov decision process to represent the multistage trading behaviors of prosumers within the stochastic game, thereby reducing the influence of uncertainty on P2P trading. The optimal trading price is determined by incorporating a supply-demand ratio pricing mechanism, enabling the optimization of trading strategies. The optimized prosumer group is treated as a coalition that trades with shared energy storage. By considering the operational characteristics of shared energy storage, the model further optimizes its charging and discharging strategies to maximize economic benefits. This collaborative optimization between prosumers and shared energy storage resolves the coordination problem, ultimately maximizing economic benefits for both parties. Based on the proposed model, simulations are conducted and compared with traditional noncooperative game methods. The simulation results validate the advantages of the proposed method.