Lithium-ion batteries are regarded as one of the most promising energy storage devices. However, traditional lithium-ion batteries face safety risks due to flammable organic liquid electrolytes and are limited by their energy density. All-solid-state lithium-ion batteries (ASSLiBs) are emerging as a safer and more energy-dense alternative, offering improved electrochemical cycle performance and long-term stability. A critical challenge for ASSLiBs is the cost-effective fabrication of solid electrolytes. NASICON-type Li_1.3Al_0.3Ti_1.7(PO₄)₃ (LATP) material has attracted wide attention due to its high ionic conductivity and stability under humidity conditions. Atmospheric plasma spraying (APS) is a low-cost and high-efficiency coating technology that involves heating and accelerating powder materials with a plasma jet to form continuous deposits on a substrate. This study investigates the preparation of LATP splats and deposits via APS, focusing on how spray parameters and particle size affect P content and the microstructure of the deposits. Results show that plasma arc power has a significant effect on the melting degree of LATP particles, with the fraction of completely melted particles increasing with the increase of the arc power. When the arc power increases from 34 kW to 42 kW, the fraction of fully-molten splats grows from 32.9% to 53.5%, and the maximum particle size increases from 34.3 μm to 48.5 μm. Additionally, P preferentially evaporates during plasma spraying, with a particle size effect on P loss when particle size is smaller than 25 μm. Smaller particles leads to greater P evaporation, with the lowest loss of 5% observed at 34 kW arc power, increasing to 10% at 42 kW. The particle size distribution also has a significant effect on the microstructure of plasma-sprayed LATP deposits. Dense electrolytes are achieved using the powder A with a particle size range of 30—50 μm at an arc power of 42 kW. The phase structure of LATP deposits remains consistent with that of the starting powder, indicating that plasma spraying does not alter its phase structure. These results underscore the potential of APS for large-scale preparation of LATP electrolytes.

Protons carry one positive charge with the smallest ionic radius and mass. Hydrogen is a ubiquitous element on earth, this makes rechargeable proton battery systems the next generation of energy storage batteries. To date, anode materials for proton batteries, such as WO₃, TiO_2, and MXenes, still suffer from low discharge capacity and poor rate performance. α-MoO₃, one of the layered crystalline compounds composed of [MoO]₆ octahedra double sublayers connected by van der Waals interaction. α-MoO₃ possesses a high theoretical specific capacity of 558 mAh/g because of three electron reactions and low proton insertion potential, is recognized as one of the most promising anode materials for proton batteries. Unfortunately, α-MoO₃ suffers severe lattice distortion and damage in aqueous electrolytes during the discharge process, causing rapid decay of the reversible capacity. In this study, we report the synthesis of W-doped α-MoO₃ for the first time. The XRD and RAMAN results show that the substitution of Mo with W results in a stronger W—O bond and enhances the inlayer Mo=O bond. Additionally, the interlayer spacing of α-MoO₃ increases from 13.84 to 13.87 Å after W-doping because the W⁶⁺ has a larger radius (0.60 Å) than Mo⁶⁺(0.59 Å). Moreover, the CV results showed that the redox reactions of the W-doped material were mainly controlled by charge transfer between the electrode surface atoms rather than proton diffusion mass transfer. The reversible discharge capacities of the α-MoO₃ and W_0.035Mo_0.965O₃ were 202.4 and 189.2 mAh/g at 5 C (1 A/g), respectively. After 600 cycles, the capacity retention is 83.0% for W_0.035Mo_0.965O₃ is higher than that of α-MoO₃ (69.6%). Even at 125 C (25 A/g), W_0.035Mo_0.965O₃ delivers a discharge capacity of 144.2 mAh/g which is higher than that of α-MoO₃ (90.7 mAh/g). Subsequently, the full Swagelok cell was assembled using MnO₂ as the cathode, W_0.035Mo_0.965O₃ as the anode, glass-fiber filter paper as the separator, and 2 mol/L H₂SO₄ + 1 mol/L MnSO₄ as the electrolyte. At 15 C (3 A/g), the full cell exhibited a reversible capacity of 177.0 mAh/g and a capacity retention of 83.8% after 400 cycles. These results show that W doping effectively improves the cycling stability and rate performance of α-MoO₃ materials.

The frequent fire incidents involving lithium-ion batteries have significantly impacted the application of distributed energy storage lithium battery packs. Effective fire suppression measures, such as deep cooling and sustained temperature reduction, are critical for mitigating these risks. This study investigates the impact of incorporating porous fire-retardant materials on the efficiency of liquid nitrogen in extinguishing fires within energy storage modules. An experimental system was developed to test the extinguishing performance of liquid nitrogen when combined with various porous fire-retardant materials, including glass wool, nano aerogel, aluminum silicate ceramic fiber, and fire-retardant sponge. Experimental results demonstrate that under the same amount of liquid nitrogen, the incorporation of these materials within the module effectively enhances the extinguishing efficiency. Notably, when combined with nano aerogel, the surface temperature rise of batteries in thermal runaway situations is reduced to 28 ℃, 63 ℃ lower than when using liquid nitrogen alone. Similarly, other materials also reduce temperature rise compared to liquid nitrogen alone. Additionally, the placement of these materials significantly affects their performance; deploying them on the side walls of the module is more effective in extinguishing fires than bottom deployment. These findings provide insights into improving fire extinguishing technologies for lithium battery systems in energy storage applications.

In real-world applications, lithium-ion batteries can occasionally undergo slight overcharging due to inconsistency within the battery pack or malfunctions in the charging system. Prolonged exposure to such overcharge conditions can pose significant safety hazards. To investigate the effects of slight-overcharge cycling on the aging characteristics and failure mechanisms of lithium-ion batteries, experiments were conducted with batteries subjected to different cut-off voltages during overcharging. The aging analysis was analyzed using electrochemical impedance spectroscopy (EIS), distribution of relaxation time (DRT), and incremental capacity analysis (ICA). These techniques were further corroborated by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) on disassembled electrodes. The experimental results show that slight-overcharge cycling significantly accelerates battery aging compared to normal cycling, leading to a more rapid decline in specific capacity. Additionally, as the number of cycles increases, the depletion of lithium ions and active materials within the battery is expedited, leading to a noticeable increase in various types of battery impedance. Notably, the charge transfer resistance of batteries cycled at 4.5 V slight-overcharge increased by 196.15% from their initial state. This overcharging condition also exacerbates battery polarization and deteriorates the cycling stability of the materials. Post-cycling analysis revealed that slight-overcharge cycling causes active materials to detach from the current collectors, with significant fracturing observed in in the cathode's active particles and thickening of the solid electrolyte interface (SEI) film on the anode. Additionally, there was a notable increase in the contents of F and P elements.

Improperly treated municipal sludge can lead to significant ecological damage, making it essential to find effective mitigation strategies. Incineration offers a viable solution to reduce this harm, but the resulting residue may still contain heavy metals that are difficult to stabilize. To effectively address this issue and develop low-cost and environmentally friendly composite phase change thermal storage materials, a novel approach proposes using municipal sludge incineration residue as the skeleton material and potassium nitrate as the phase change thermal storage material. Five different mass ratios of sludge incineration residue to potassium nitrate were prepared using cold-pressing and sintering methods. The materials were evaluated based on their macroscopic and microscopic appearances, compressive strength, thermal stability, chemical compatibility, heat transfer, and thermal storage properties. Economic feasibility and CO₂ emissions were also analyzed. The results show that within the temperature range of 100—380 ℃, the optimal mass ratio of sludge incineration residue to potassium nitrate is 5∶5 (sample SC3). This composition achieves a heat storage density of 322.45 J/g, latent heat of 41.75 J/g, and maximum thermal conductivity of 1.04 W/(m∙K). The compressive strength reaches 153.78 MPa, indicating that the materials exhibit good chemical compatibility and are evenly distributed in sample SC3. Additionally, sample SC3 shows good high-temperature thermal stability, maintaining performance after 1000 heating/cooling cycles. The thermal storage cost is calculated at 63.06 CNY/MJ, and the total CO₂ emissions are 1083.53 kg/t, which are lower than those associated with traditional skeleton material-based composite phase change thermal storage materials. This suggests that the approach offers both environmental benefits and practical feasibility.

To address the issue of poor stability in practical applications and expand the application range of inorganic hydrated salt phase change materials for low-and medium-temperature thermal energy storage, we designed a composite phase change material by coating a dual-network hydrogel composed of polyacrylamide and sodium alginate with Na₂SO₄·10H₂O (sodium sulfate decahydrate, SSD). The resulting inorganic hydrated salt composite phase change material exhibited a phase change temperature range of 30—45 ℃. The microstructure, chemical composition, crystal structure, and thermophysical properties of the phase change hydrogels were characterized using SEM, FT-IR, XRD, and DSC. The results demonstrated the successful encapsulation of SSD within the dual-network hydrogel, forming a composite phase change hydrogel with high thermal conductivity, shape stability, and excellent temperature control properties. At an SSD mass fraction of 70%, the melting enthalpy reached 123.91 J/g for the phase change hydrogel. After 500 thermal cycles, he latent heat of phase change and temperature remained stable for the phase change hydrogel, indicating good thermal cycle stability. This study effectively addressed concerns regarding the poor stability of inorganic hydrated salt-based phase change materials while providing new insights and theoretical data support for future applications in low- to medium-temperature thermal energy storage.

To address the low heat transfer efficiency in shell-and-tube phase change thermal storage units, a new design featuring radial rectangular fins within a composite salt shell-and-tube structure was proposed. Numerical simulations were carried out and verified against experimental data, followed by an analysis using appropriate performance metrics. Three-dimensional transient simulations were conducted using ANSYS FLUENT, where the inlet temperature of the heat transfer fluid and the spacing between fins were varied to model the thermal storage process. The study focused on comparing and analyzing temperature variations of the phase change material, heat transfer processes, and melting conditions. The results indicate that increasing the temperature of the heat transfer fluid and reducing the spacing between the fins are effective methods for enhancing heat transfer. Specifically, for every 5°C increase in the temperature difference between the heat transfer fluid (water) and the phase change material composite salt (CH₃COONa·3H₂O-KCl), the melting rates of the phase change material increased by 54.98%, 34.67%, 23.92%, 18.13%, and 14.45%, respectively, while the latent heat storage rates increased by 61.56%, 45.79%, 35.15%, 27.04%, and 22.31%, respectively, with diminishing returns as the temperature difference grew. Additionally, reducing the fin spacing by 10 mm led to increases in the melting rates of the phase change material by 32.37%, 41.26%, and 38.66%, though it also leads to corresponding reductions in thermal storage capacity of 6.40%, 11.95%, and 6.55%, and in energy storage density of 0.53%, 10.97%, and 1.57%, respectively. In practical applications, the fin spacing should be optimized by considering both heat transfer efficiency and cost. The research provides theoretical support for the design and optimization of thermal storage units in engineering applications.

A plastic film composite current collector (PFCC) is a new battery collector with a sandwich-like structure made of a two metal layer, plastic polymer, and another metal layer. IPFCCs have attracted research attention because they can improve the energy density and safety of lithium-ion batteries (LIBs). However, PFCCs have several disadvantages in the application of LIBs, which limits their industrialization process. This paper summarizes the their disadvantages, including weak polymer-metal adhesion, poor electrical conductivity, and poor corrosion resistance, Furthermore, the study discusses the related mechanisms of their disadvantages, including the weak physical and chemical forces of the interface bonding between polymer and metal layers; shortcomings of the polymers, such as their own insulating properties, the vulnerability of PET to catalytic depolymerization, and poor thermal stability; and the poor bonding force at the polymer-metal interface during electrolyte immersion and cold press processing. This paper systematically summarizes the development history of PFCCs in the LIB application technology, and simultaneously explains how they can be improved through interface engineering, material regulation, processing technology and equipment optimization, and other methods and measures, such as setting up a real-time welding-quality monitoring system, establishing a multifunctional interfacial reinforcement layer, and designing a functional structure inside and outside of the collector to lay the theoretical foundation for PFCC development and to promote the development of PFCCs in LIBs.

Internal resistance is a crucial parameter for assessing both the lifespan and battery operation state, serving as a key indicator of the challenges associated with electron and ion migration or diffusion within the electrodes. However, accurately measuring internal resistance can be challenging due to its sensitivity to environmental factors such as temperature and pressure. Precise detection of internal resistance is essential for enhancing the accuracy of battery management systems. Given the current challenges in internal resistance measurement, such as the influence of multiple variables, significant errors, and limited application scope, this paper reviews and analyzes recent research on five typical methods for measuring the internal resistance of lithium batteries, namely, the mixed pulse power characteristic method, DC internal resistance testing method, AC injection method, DC discharge method and electrochemical impedance spectroscopy method. The study focuses on the specific influence of internal and external environments on internal resistance and innovatively explore the relationship between internal resistance and battery life, operational status, and safety alerts. These insights offer a pathway to improve the accuracy of chemical power performance evaluations, predict chemical power life, and optimize chemical power use. Finally, the strategies for improving internal resistance measurement methods, including the integration of machine learning models, are examined and discussed. It proposes quantitative metrics, such as short test time, high test consistency, and superior accuracy, to further refine measurement methods and expand their applications. These advancements are expected to significantly enhance the accuracy of the internal resistance measurement in chemical power systems, improving the monitoring and analysis of battery modules, and provide new ideas for optimizing battery performance across various types of chemical power systems.

High speed permanent magnet machines can fulfill the requirements of flywheel energy storage systems by providing high efficiency and high power density. Currently, there are two main challenges: rotor strength and heat dissipation. The rotor structure must endure the centrifugal forces generated by high-speed rotation, while the vacuum environment exacerbates the issue by increasing thermal resistance and complicating heat dissipation This article analyzes the classification and structural characteristics of permanent magnet machines used in flywheel energy storage systems. It compares key parameters across various examples of these machines, examines methods for calculating and reducing electromagnetic losses, and summarizes the loss ratios for different flywheel permanent magnet motors. The ratio of electromagnetic losses to the rated power of permanent magnet motors is typically below 5%, with rotor losses in high-power permanent magnet motors often less than 0.4% of the rated power. The article provides a brief review of the current research on thermal management for permanent magnet machines. Key areas for future development in flywheel energy storage permanent magnet machines include the exploration of new permanent magnet materials, rotor direct cooling methods, and integrated rotor structures.

The different high-power energy storage devices have different characteristics, such as energy density, power, and sustained release time, owing to their energy storage mechanisms, leading to the disequilibrium of the development level and different application scenarios. There is a lack of systematic arrangements for typical high-power energy storage devices based on a single technical featurethat helps customers have a clearer understanding of high-power energy storage devices. This study outlines the mechanisms and application scenarios of typical high-power energy storage devices and compares different characteristics of high-power energy storage devices, such as energy density, power, and sustained release time. The research progress of high-power energy storage devices is categorized and summarized based on sustained release time. Moreover, an outlook on the development of high-power energy storage devices is presented.

With the increasing energy crisis and worsening greenhouse effect, the development of Li-CO₂ batteries holds significant promise as a next-generation energy storage device with high efficiency and CO₂ utilization. The operation of Li-CO₂ batteries involves complex multiphase reactions coupled with electron/substance transfer, primarily occurring at the cathode. Therefore, the design and synthesis of effective cathode catalysts are crucial for enhancing battery performance. This study reviews the advantages of Li-CO₂ batteries as new energy storage devices and examines their electrochemical reaction mechanisms. The key challenges, such as the large potential gap, rapid capacity degradation, and poor cycling stability are discussed. This review focuses on cathode material research and outlines the key conditions for high-efficiency catalysts. Among the conventional catalysts, such as carbon-based non-metallic, noble metal, and transition metal, the emerging single-atom catalysts (SACs) and redox mediators are highlighted, demonstrating excellent catalytic performance as proven via characterization and theoretical calculations. Considering the future challenges in Li-CO₂ batteries, more research should be done in SACs to promote continuous technological progress in this field.

With the growing focus on developing portable devices and electric vehicles, lithium-ion batteries (LIBs) have gained widespread commercial use owing to their high energy density, long cycle life, and small self-discharge. However, LIBs are prone to various failure modes during operation, such as lithium plating, short circuit, thermal failure, and gas generation. These issues can lead to capacity degradation, battery expansion, thermal runaway, and other safety concerns. Therefore, understanding and addressing these failure mechanisms are crucial for advancing the safety and longevity of LIBs. This study explores the failure mechanism of graphite negative electrodes, which are widely used in LIBs, under various conditions such as lithium plating, high and low temperature, overcharging, and other conditions. It also highlights advanced characterization techniques used to analyze these failure mechanisms. By examining the graphite structure, phase transition during lithium insertion, graphite surface morphology, heat released by the negative electrode, and gas generated by the reaction, the four primary causes of these failures are discussed, which mainly affect the failure mechanisms, such as graphite layer spacing, phase transition during lithium insertion of graphite, loss of active lithium, additional interface film generation, and other side reactions. Finally, the characterization methods for various failure causes are summarized. The standardization and normalization of battery failure analyses are further discussed, which play a key role in advancing future research and development efforts aimed at improving the safety and performance of LIBs.

In this study, we conducted vapor explosion experiments on the typical lithium-ion electrolyte solvent ethyl methyl carbonate (EMC) using an explosion limit and ignition energy test platform. Consequently, we measured for the first time the maximum explosion pressure (P_max), lower explosion limit (LFL), and other basic parameters of EMC's explosion risk at different initial temperatures and equivalent ratios. We also analyzed the kinetic mechanism of the combustion chemical reaction by numerical simulation. The results show that the P_max of the EMC/air mixed gas first increases and then decreases with increasing equivalent ratio, reaching a maximum near the equivalent ratio ϕ = 1.2. Additionally, as the temperature increases, P_max decreases and exhibits a linear relationship with the reciprocal of the initial temperature 1/T₀ because of the thermal loss, P_max,exp is smaller than the maximum adiabatic explosion pressure P_max,ad under the same working conditions. The LFL of EMC/air mixed gas decreases with increasing T₀, showing a linear relationship, and the parameters of the classical model for fuel LFL are modified, with the new formula's predicted values agreeing well with the experimental values. The analysis of the reaction mechanism shows that the effect of different T₀ on LFL is mainly due to the effect of the generation rate of OH radicals. The findings of this study provide insights for quantitatively assessing the explosion risk of EMC and provide references for developing corresponding safety standards for its practical use.

Internal short circuits (ISC) present significant challenges in lithium-ion batteries, underscoring the need for accurate simulation models to understand battery failure mechanisms. This study examines NCM/graphite batteries and develops a three-dimensional single-cell ISC model that includes exothermic side reactions with thermal runaway. By utilizing electrochemical-thermal coupling, the study investigates the triggers of thermal runaway and the progression of ISC-induced thermal runaway. Initially, the heat generation and reaction rates of four exothermic side reactions are calculated using the Arrhenius equation. The results indicate that the highest heat is produced by reactions between the negative electrode and the electrolyte. Additionally, an analysis of the thermal runaway triggering characteristics of four typical ISC forms within a single cell reveals that an internal short circuit involving an aluminum anode poses the greatest danger. The resistance value of the short circuit is positively correlated with the time it takes for thermal runaway to be triggered. Additionally, the area of the high-temperature hotspot at the critical short-circuit resistance is determined to be 30 mm². This simulation identifies the critical short-circuit resistance values for four different ISC forms and reveals the internal lithium-ion concentration and temperature distribution when thermal runaway is triggered. These findings offer valuable theoretical insights for investigating ISC failure mechanisms and designing safe lithium-ion batteries.

High nickel lithium-ion batteries offer high energy density and power density, making them widely used today. However, capacity loss due to aging remains a challenge for their efficient utilization. This study investigates the aging mechanism of high-nickel lithium-ion batteries, conducting cyclic aging experiments on high-nickel/silicon carbon-based lithium batteries. The aging modes at different life stages of high-nickel ternary lithium-ion batteries are comprehensively analyzed and verified using both nondestructive and destructive tests. The results indicate that the capacity of Li-ion batteries decreases in two distinct stages. In the first stage, the capacity loss is linear, primarily due to the loss of lithium ions, with the main aging mechanisms being the growth of the SEI and the degradation of anode materials. In the second stage, the capacity decreases abruptly, driven by both the loss of lithium ions and a decline in electrical conductivity. The primary aging mechanisms during this stage include the dissolution of cathode materials, irreversible changes in cathode crystal structures, and the degradation of the battery separator. The main aging mechanisms include the dissolution of anode material, irreversible changes in anode crystal structure, and blockage of the battery separator. Computed tomography analysis of the overall morphology of lithium-ion batteries at different life stages reveals that the initial battery production process significantly impacts the areas where aging occurs. XPS test results indicate that the passivation layer on the surfaces of both the anode and cathode continues to thicken throughout the battery's cycling experiments. Additionally, the deposition of transition metal Ni on the surface of the cathode material is observed, which significantly affects the battery's energy storage capacity. This paper reveals the aging characteristics and mechanism of high-nickel lithium battery, offering valuable theoretical insights for the graded utilization of these batteries.

The lithium-ion battery is one of the most widely used new energy batteries today. With the advantages of light weight, long life, high capacity and low pollution, it has been widely promoted and popularized, and especially plays a non-negligible role in electric vehicles, mobile communications, military equipment, drones and other fields. However, the lithium-ion battery poses significant safety hazards. The substance composition and improper use can easily lead to the thermal chemical reaction inside the lithium-ion battery, and cause the battery thermal runaway, which may cause the battery fire, or may even cause the equipment explosion, seriously affecting the users' life and property safety. Therefore, people attach great importance to the safety status assessment of the lithium-ion battery, and hope to comprehensively grasp the law of thermal runaway of the lithium-ion battery by analyzing the causes of its safety problems, and propose reliable early warning methods, in order to provide effective support for the safety application of the lithium ion battery in electric vehicles, energy storage power stations and other fields.

In the lithium-ion battery energy storage systems, the cell experiences increased force fluctuations over prolonged cycles, which can impact cell lifespan and system reliability. Numerical simulation is an effective tool for predicting the stress state of the cell. By developing a constitutive model that accurately reflects the mechanical properties of the cell and applying it to numerical simulations, we can precisely predict the cell's stress state and provide valuable insights for engineering design. Uniaxial compression tests of the battery reveal distinct nonlinear plastic behavior during loading and nonlinear elastic behavior during unloading. This indicates that a single constitutive model is insufficient to accurately characterize the mechanical response of the cell during both loading and unloading processes. This paper employs two types of constitutive models to accurately represent the cell's behavior, the PE (porous elasticity) model for nonlinear elastic behavior and the CFP (crushable foam plasticity) model for nonlinear plasticity and strain hardening. By using these models, the material parameters from the stress-strain curve obtained from testing are inversed. The core of the cell is treated as a laminated composite, and the PE and CFP models, along with their respective material parameters, are alternately applied to develop the numerical model of the cell. A simulation model is established and numerically solved to match the conditions of the uniaxial compression test of the battery. The stress-strain curve data from the numerical simulation results are compared with the actual test results. The findings indicate that the modeling method accurately characterizes the mechanical behavior of the cell during both loading and unloading processes, with a degree of agreement that meets the requirements for engineering simulation applications.

By leveraging the advantages of methanol steam reforming (MSR) for hydrogen production and tubular solid oxide fuel cells (SOFC), a portable hydrogen and power generation device was developed by integrating MSR with micro-tubular SOFC (μT-SOFC). A numerical model of μT-SOFC with a double-convex current collector was established and validated (error rate less than 5%) using COMSOL Multiphysics. The simulation results show that the double-convex platform current collector enhances the current collection efficiency while maintaining a small temperature difference at different voltages. MSR catalysts were prepared using the impregnation method, whereas the anode-supported μT-SOFC was fabricated using an extrusion forming-leaching process. The morphologies of the MSR catalysts and μT-SOFC were investigated using scanning electron microscopy and energy-dispersive X-ray spectroscopy. Gas chromatography of the MSR product revealed a hydrogen gas volume fraction approaching 70%. The portable device utilizes a stepper motor to control the inlet flow rate of methanol-water solution, achieving different volume flow rates of MSR gas products, with an average flow rate reaching up to 1163 mL/min. The μT-SOFC demonstrated an open circuit voltage of 0.96 V and a maximum output power density of 190 mW/cm². After a simulated 4-hour operational test, there was no significant decline in electrochemical performance. Further simulations under these operating conditions showed that the cell performance was primarily limited by the MSR conversion efficiency. Additionally, adjusting the air inlet direction was found to enhance the output power. Currently, there are few studies on the application of μT-SOFC and related devices. This study provides guidance for the application of μT-SOFC in portable devices.

Indirect liquid cold plate cooling technology has become the most prevalent method for thermal management in energy storage battery systems, offering significant improvements in heat transfer and temperature uniformity compared to air cooling. However, challenges such as excessive temperature gradients between the top and bottom of battery cells, high circulation resistance, and elevated power consumption in the cooling pipeline remain unresolved. In order to solve these problems, this study focuses on a novel direct immersing liquid cooling system, where the battery pack is fully submerged in a cooling liquid. Numerical simulations were conducted to evaluate the temperature distribution and flow characteristics of this immersive cooling system and compare them with a traditional cold plate system. The study further explores the effects of variables such as immersing cooling liquid flow rate, cell distance, and the number of ejection holes on the thermal performance of the immersing battery pack. The research shows that, in comparison with cold plate cooling, the direct immersion system significantly reduces both the maximum temperature and temperature gradients on the top surface of the battery pack, therefore enhancing overall cooling efficiency. At the same time, the temperature difference between the top and bottom of the battery cells significantly decreases, effectively addressing the thermal gradient issue inherent in cold plate systems. With the increase of cooling liquid flow rate and cell distance, the maximum temperature and temperature difference on the top surface of the battery pack decrease to varying degrees, albeit at a diminishing rate. While increasing the number of ejection holes decreases the maximum temperature, it also noticeably increases the temperature gradient across the pack.

A liquid cooling system offers high thermal conductivity and specific heat capacity, making the liquid cooling plate is a crucial component within the system. The design and structural of the internal flow channels within the cooling plate directly influence the maximum temperature, temperature uniformity, and overall temperature consistency of the lithium-ion battery. To ensure that the maximum temperature and temperature difference of the battery remain within the safe operating range under the a 5C discharge rate and an ambient temperature of 299.15 K, this study designs a double-helix shaped flow channel for a liquid cooling plate based on a cosine function. The evaluation indexes include the battery's maximum temperature and temperature difference. Using Ansys Fluent finite element analysis software, the study investigates the effects of various parameters—such as function amplitude, inlet flow rates, function periods, and channel widths—on the heat dissipation performance of the cooling plate. The four influencing factors are then ranked in order of importance through orthogonal tests and range analysis. The results indicate that the inlet flow rate and runner function period significantly impact the maximum temperature, temperature uniformity, and temperature consistency of lithium-ion batteries. By increasing the inlet flow rate, runner width, and function period, the maximum temperature and temperature difference are reduced, leading to improved temperature uniformity. The function amplitude has the least effect on battery temperature. The optimal structure was determined to have a function amplitude of 25 mm, an inlet flow rate of 0.2 m/s, a period of 2, and a runner width of 5 mm. Compared to the initial structure, the optimized design reduced the battery pack's maximum temperature by 2.36 K and temperature difference by 1.27 K.

As a novel energy storage technology that has emerged in recent years, vertical gravity energy storage offers benefits such as flexible site selection and environmental sustainability. However, research on its internal system remains limited, and studies on key technical indicators like system efficiency and power stabilization are still underdeveloped. This paper addresses these gaps by developing physical models for vertical gravity energy storage systems, including an efficiency model and a power model. For the efficiency model, the study identifies sources of loss and examines how efficiency varies with parameters such as the mass of heavy objects, maximum velocity, acceleration through simulation. The results show that maximum velocity, acceleration, and shaft height have a significant impact on the efficiency of the system, while the mass of heavy objects has a minimal impact. Reducing the maximum velocity and shaft height can notably enhance system efficiency. The power model introduces a multi-channel power superposition method to realize stable power output through power compensation. This method employs staggered start-up techniques to realize power superposition. The study simulates power output under various control strategies and evaluates performance based on power fluctuation rate and power loss rate. The results show that increasing the number of channels effectively reduces power fluctuations, with fluctuations dropping to only 2.5% when the number of channels reaches 8. Additionally, power loss rate decreases with more channels and stabilizes beyond 4 channels. Increasing the number of channels can effectively improve the external output power performance of the system.

With the rapid development of renewable energy and the transformation of energy structure, wind and solar energy storage technology has become an important means to achieve sustainable energy utilization. Smart energy, as a new form of energy that integrates modern information technologies such as the Internet of Things, big data, and cloud computing, has gradually shown enormous potential and value in the field of wind and solar energy storage. This paper summarizes and analyzes the smart energy design of wind and solar energy storage under the Internet background in detail. On the basis of fully describing the development process of smart energy and wind and solar energy storage technology, this paper discusses the integrated energy design mode of wind and solar energy storage from three aspects of optimal scheduling, energy storage management and grid connection control of smart energy in wind and solar energy storage, in order to provide a useful reference for the further development of wind and solar energy storage technology.

This paper addresses the urgent need for primary frequency regulation technology in new energy power stations. It explores the innovative use of megawatt (MW)-scale flywheel arrays, designs an integration scheme for these flywheel energy storage systems, and proposes a control strategy for their application in primary frequency regulation within renewable energy power stations. A 5 MW/175 kWh flywheel array system has been independently connected to the low-voltage side (35 kV) of the main transformer in a renewable energy power station. To thoroughly validate the feasibility and performance advantages of this setup, a series of experimental tests have been conducted. These tests cover various aspects, including rapid switching between charging and discharging for a single unit, response to frequency step disturbances, anti-disturbance performance, and primary frequency regulation dead-zone testing. Furthermore, an analysis of long-term real-time monitoring data from the site reveals that the implementation of MW-scale flywheel arrays significantly improves the primary frequency regulation capability of renewable energy power stations, leading to a faster system response.

The optimization of civil engineering and architectural structure design for large-scale compressed air energy storage systems is a key link to ensure the safe, stable, and efficient operation of the system. This article focuses on the theme of modeling large-scale compressed air energy storage systems from three aspects: thermodynamic energy storage analysis, design objectives, and subsystem equipment selection. By sorting and analyzing the design methods, the shortcomings were pointed out, such as limited gas storage capacity in building structures, inadequate energy storage standards, and inflexible site selection. A series of optimization design measures have been proposed to address these issues, including structural design, material selection, construction technology, and environmental adaptability.

The heat pump system's high electric heating conversion efficiency, convenient motor speed control, and strong flexibility makes it an effective solution for integrating thermal power systems in electrothermal coupling applications. When equipped with a high-speed flywheel motor, the electrothermal coupling heat pump system benefits from large inertia and long delay characteristics and has enhanced support for coordinated electrothermal operations. However, existing flywheel energy storage motors are mostly optimized based on the rated working points, and it is difficult to achieve an optimal comprehensive efficiency during the entire working cycle. Therefore, based on the actual working scenario of electric heating cooperation, this study obtained the changes in theoperating conditions of the flywheel motor under a single complete working cycle and proposed a multi-operating efficiency optimization method for the flywheel motor based on a genetic algorithm. First, an efficiency calculation model for a high-speed motor was established, and the speed curve of a flywheel motor under a single operation was calculated based on the running scene of a heat pump. Second, the variables to be optimized were determined, and the parameters with lower degrees of correlation were removed using parameter sensitivity variables to reduce the amount of computation. A comprehensive efficiency index of the motor was proposed based on actual working conditions to quantify the efficiency of the motor over the entire working cycle. Finally, the optimal working point was obtained using a genetic algorithm. After optimization, the overall efficiency increased by 0.34%, the motor efficiency improved within the full speed range, and the energy loss in a single working cycle decreased by 38.7 kJ, which is 14.8% lower than that before optimization. The optimization results showed that the proposed method could improve the operating efficiency of the flywheel motor during the entire working cycle, reduce the energy loss during heat pump operation, and enhance the efficiency of the flywheel energy storage system.

As the global energy crisis approaches, the development and utilization of new energy has become an important field for people to explore today. Solar energy, wind power, hydrogen energy, etc. have become the important representatives of the new energy development by their advantages of clean and pollution-free. Among them, wind power storage is an important part of the current power system, which can be connected to the grid system to provide important help to meet the power supply needs of people in a region, and has a very bright development prospect. However, the wind power generation is seriously affected by climate, and its power supply output has randomness and instability. Therefore, energy storage devices need to be configured in wind power system to realize the energy storage capacity optimization, so as to ensure the balance and stability of power supply in the grid. Based on the development status of wind power system, this paper analyzes its hybrid energy storage capacity optimization model, and proposes a collaborative optimal configuration scheme considering different conditions for hybrid energy storage capacity of wind power system, which can provide a reference for effectively improving the operation efficiency and quality of the grid with the wind power integration.

With advancements in lithium-ion battery technology and decreasing costs, large-scale lithium-ion battery energy storage systems are transitioning from demonstration phases to commercial applications. Optimizing the design of battery thermal management systems is crucial for enhancing the overall performance of energy storage systems. Effective temperature control not only extends the lifespan and discharge capacity of energy storage batteries but also plays a vital role in ensuring the safe operation of power plants. As large-scale electrochemical energy storage power stations increasingly rely on lithium-ion batteries, addressing thermal safety concerns has become urgent. The study compares four cooling technologies—air cooling, liquid cooling, phase change material cooling, and heat pipe cooling—assessing their effectiveness in terms of temperature reduction, temperature uniformity, system structure, and technology maturity. The findings indicate that liquid cooling systems offer significant advantages for large-capacity lithium-ion battery energy storage systems. Key design considerations for liquid cooling heat dissipation systems include parameters such as coolant channels, cold plate shapes, and types of coolant used. Furthermore, the liquid cooling system can be optimized in conjunction with other cooling methods to enhance the thermal performance of the system. By refining control targets and algorithms, intelligent and precise management of battery module temperature can be achieved, thereby improving the overall efficiency of the thermal management system. Liquid cooling technology requires ongoing optimization in several areas, including key system parameter design, control strategy development, and application requirements, to achieve effective temperature control and meet economic and efficiency goals.

Battery energy storage management systems play a key role in modern energy networks. With the increasing requirements for energy efficiency and reliability of energy storage systems, the application of deep learning techniques in battery energy storage management has received widespread attention. The purpose of this paper is to discuss the innovative application and optimisation strategy of Transformer network technology in battery energy storage management. Firstly, this paper introduces the basic concept of battery energy storage management system and the main challenges faced at this stage. Then, it comprehensively analyses the current status of the application of deep learning technology in battery energy storage management, focusing on the performance of various network models and their effectiveness in practical applications. Finally, a battery energy storage management strategy based on the optimisation of Transformer architecture is proposed, which has significant advantages in enhancing system stability. The research in this paper not only provides new technical means for battery energy storage management, but also provides theoretical support and practical reference for the further development of related technologies in the future.

The network security issue of chemical energy storage related control systems has always been a hot research topic in the field of energy applications. The application of big data technology provides a new perspective and method for the safety monitoring and protection of chemical energy storage systems. This article will review the innovation of chemical energy storage network security technology under big data. The article first analyzes the current categories of energy storage network security technologies, including physical security protection, network security protection, data encryption, and intrusion detection defense; Then objectively analyzed the current conventional security issues of chemical energy storage networks and their insufficient impact; Finally, Zongsu discussed the improvement of chemical energy storage network security technology by big data and the future development trends of both. To provide some inspiration and suggestions for the development of chemical energy storage network security technology.

With the increasing global attention to environmental protection and sustainable development, the new energy industry is gradually becoming a new driving force for economic growth. The continuous development of artificial intelligence technology has led to its widespread application in the field of new energy. In this regard, the article analyzes and summarizes the application of artificial intelligence in the field of new energy in China, based on the actual situation of new energy development in China. The article first elaborates on the various applications of artificial intelligence in the field of new energy, including smart grid management, smart photovoltaic power generation systems, and smart energy storage systems. Then, it conducts a prospect analysis, including the current challenges and development prospects. Through this in-depth study, it can be determined that the combination of artificial intelligence and China's new energy field is closely related, and the mutual promotion of the two has broad development prospects.

Simulation of lithium-ion batteries plays an important role in battery research and evaluation. The classical pseudo-two-dimensional model used in battery simulations typically assumes a constant electrode particle radius, which limits simulation accuracy. In this study, we enhance the pseudo-two-dimensional model by integrating the cumulative distribution function of electrode particle radius distribution through inverse transformation sampling. This process corrects the active specific surface area of the electrode particles, effectively incorporating the electrode particle radius distribution while maintaining the homogeneity characteristics of the pseudo-two-dimensional model, thereby improving simulation accuracy. Firstly, we derive a theoretical method to incorporate the electrode particle radius distribution into the pseudo-two-dimensional model. We use a pseudo-random function to generate uniform distribution numbers based on electrode spatial coordinates, which are then substituted into the cumulative distribution function to determine the electrode particle radius at different electrode spatial coordinates through inverse transformation sampling. Then, we present a method for correcting the active specific surface area of the electrode particles after introducing the radius distribution, along with expansion equations for common electrode particle radius distributions within the pseudo-two-dimensional model, each with different combinations of expanded radius distribution for the positive and negative electrodes, under the same conditions. Simulations comparing the pseudo-two-dimensional model with and without the expanded radius distribution are conducted and validated against experimental data. The results show that incorporating the radius distribution significantly improves the simulation of polarization changes inside the battery, especially during the relaxation process, leading to enhanced simulation accuracy.

During the long-term cycling of lithium-ion batteries (LIBs), performance degradation is inevitable, which directly impacts the stable operation of the battery system. To address this, this paper proposes a novel for predicting the remaining useful life of LIBs based on multi-scale features derived from recurrence plots. This approach aims to overcome the limitations of extracting key degradation features from one-dimensional (1D) signals, such as voltage data. By leveraging the rich spatiotemporal degradation features captured in recurrence plots, we first develop a deep learning architecture for effective multi-scale feature extraction. This architecture detects temporal variations within the same voltage region across multiple cycles and examines the spatial evolution of recurrence plots between adjacent voltage regions using variable-sized receptive fields. This approach enables the extraction of deep multi-scale features, which are then mapped to RUL modeling. Comprehensive evaluation experiments were performed to systematically validate the predictive effectiveness of the proposed method. The results suggest that by using a limited number of charge process recurrence plots as input, the model achieves rapid convergence and accurate predictions. Additionally, in cross-rate prediction scenarios, the proposed method improves performance metrics, with absolute error (MAE) and root mean squared error (RMSE) reduced by approximately 7-fold and 5.7-fold, respectively, at a 2C rate compared to shallow indicators. Finally, comparative experiments with 1D sequence inputs further validate the effectiveness of predicting the RUL of LIBs using multiscale features of recurrence plots. This approach achieves performance improvements of approximately 50% and 43% in various evaluation metrics, while requiring relatively minimal time-series voltage sampling data for imaging.

With the rapid development of the new energy vehicle industry, the safety of power lithium-ion batteries—one of the core components of these vehicles—has garnered significant attention. Understanding the mechanical response and thermal runaway characteristics of lithium-ion batteries under impact load is critical for effectively preventing and managing fire accidents resulting from collisions in new energy vehicles. This article investigates the safety performance of 21700 cylindrical lithium-ion batteries under planar and cylindrical impacts using a custom-built battery impact experimental platform. The study records data on temperature, voltage, and impact load, analyzing how impact height and state of charge (SOC) influence the mechanical response and thermal runaway behavior of the batteries. The results indicate that as SOC of the battery increases, its impact resistance improves; In planar impact experiments, the ultimate strain of the battery is -0.206, and the ultimate impact stress is 13.49 MPa. In cylindrical impact experiments, the ultimate strain of the battery is -0.253, and the ultimate impact stress is 33.58 MPa. The severity of battery thermal runaway is significantly influenced by the shape of the impactor, the impact height, and the battery's SOC. Cylindrical impacts cause more severe damage to the battery, and both increased impact height and higher battery SOC exacerbate the thermal runaway reaction. This study provides valuable data to support the safety design of batteries and the development of fire prevention and control measures for new energy vehicles.

With the rapid development of energy storage grid technology and new energy electric vehicle technology, the global demand for energy storage systems is increasing. However, the complexity of the application environment and the large-scale battery composition increase the probability of failure of the energy storage system. This paper describes the research on big data technology and artificial intelligence technology in energy storage system fault prediction and diagnosis from two perspectives. Big data technology can analyze a large amount of energy data, thereby improving the production and utilization efficiency of energy storage systems and reducing energy waste and loss. Artificial intelligence technology can mine the valuable information hidden behind big data, train energy data, and predict and diagnose energy storage systems. The integration of big data technology and artificial intelligence technology can process and analyze a large amount of energy data, thereby improving the efficiency of energy storage systems, predicting and diagnosing whether energy storage systems have failed, and promoting the monitoring and management of energy storage systems.

During the low-temperature charging and discharging of lithium-ion batteries, lithium plating and stripping occur, with irreversible lithium plating leading to the formation of "dead lithium". This phenomenon adversely affects the battery's electrochemical performance. This study investigates the lithium plating-stripping behavior of commercial batteries, using LiFePO₄ as the cathode and graphite as the anode, during 0.1C charge-discharge cycles at temperatures from 5 to -12 ℃. A correlation between total and reversible lithium plating was established at different charging and discharging temperatures. Analysis of disassembled batteries, including negative electrode samples analyzed by SEM, EDX and XPS, revealed insights into the morphology, elemental distribution, and surface composition, clarifying the distribution of "dead lithium" on and within the negative electrodes. The study also assessed the impact of low-temperature charging and discharging on battery performance. It was found that the batteries' discharge capacities decreased following low-temperature cycling. At 0.5C, capacity degradation was more rapid after cycling at 5 ℃ compared to the primary battery, but slower at temperatures lower than 5 ℃. We conclude that with lower temperatures increase both the total amount of lithium plating and the irreversible portion. The irreversible plating leads to a loss of active lithium, reducing capacity. However, at temperatures below 5℃, the increased loss of active lithium results in a lower negative electrode potential during lithiation, slowing the following capacity fading. The worse cycling performance at 5℃ is attributed to changes in the negative electrode's element distribution, pore structure, and surface composition.

This article summarizes the innovative application of AI assisted dynamic voiceprint analysis strategy in battery pack anomaly recognition. The article first outlines the foundation of voiceprint recognition theory, and then provides a detailed introduction to the theoretical framework of artificial intelligence (AI), including key technologies such as machine learning and deep learning, as well as how these technologies demonstrate powerful capabilities in complex data processing, pattern recognition, and other areas. The article focuses on exploring battery pack anomaly detection strategies based on AI and voiceprint technology. By integrating high-precision sound collection equipment, advanced signal processing technology, and optimized AI algorithms, this strategy can monitor the sound changes during the operation of the battery pack in real time, and use dynamic voiceprint analysis technology to extract key sound features for abnormal pattern recognition and classification. The new technology not only improves the accuracy and real-time performance of anomaly recognition, but also effectively addresses complex and variable abnormal situations that may occur during the operation of battery packs.

With the rapid development of the new energy industry, chemical batteries, as its core components, are crucial for the stability and safety of the overall system in terms of their performance and quality. As an important component of batteries, the defects in the preparation process of chemical battery electrodes directly affect the electrochemical performance and safety of the battery. This article reviews the research on defect recognition methods for chemical battery electrodes using voiceprint and AI analysis techniques. By combining these two technologies, the accuracy and efficiency of defect detection can be improved, providing strong support for quality control in battery production processes.

In recent years, China's new energy vehicle and traction battery industries have experienced rapid development. Some existing standards have not kept pace with these advancements. To address the current needs of China's new energy vehicles and power batteries, GB/T 31467—2023 has been revised and released. This updated standard incorporates China's experience with traction battery electrical performance testing and aligns with relevant international standards. The standard outlines electrical performance testing methods for lithium-ion traction battery packs and systems. The paper compares the test items and technical content of GB/T 31467—2023 with those of GB/T 31467.1—2015, GB/T 31467.2—2015, and ISO 12405-4:2018. It details the revisions made to general test conditions, general tests, and basic performance tests, offering valuable insights for the development and verification of related traction battery products. The standard revision process took into account domestic technical advancements and specific demands of vehicle's for traction battery packs and systems. The revised standard introduces test methods for appearance, polarity, energy density, charging performance, and discharge under operating conditions. Adjustments have been made to the charging and discharging currents for all test items to reflect the actual conditions experienced during vehicle operation, aligning with real-world the charging and discharging currents and operating environments. The standard standardizes test temperatures for no-load state of charge loss, energy efficiency, and cranking power at both low and high temperature. This harmonization supports the development and testing of lithium-ion traction battery packs and systems for electric vehicles.

Energy storage is an important supporting technology for building the new power system and achieving dual carbon goals. Green energy storage embodies the principles of environmental protection, resource efficiency, and energy optimization throughout the product lifecycle, making it a key trend in energy storage technology amidst efforts to peak carbon emissions and achieve carbon neutrality. This paper analyzes relevant regulations and standards that promote the development of green industries and proposes the construction of a green energy storage standard evaluation system through multi-dimensional analysis, such as energy storage technology routes, industrial chain compositions, and life cycle assessments. In addition to focusing on green products, green energy storage standards should encompass green design, manufacturing, operation, and recycling to address the whole lifecycle of energy storage systems. According to the standard of green energy storage products, the selection of evaluation indicators and the determination of benchmark values are examined. It is proposes primary indicators such as resource attributes, energy attributes, environmental attributes, and quality attributes, with secondary indicators including recycling rates, comprehensive energy consumption, and carbon footprint. Quality attributes should serve as the basis for the evaluation of other attributes. The paper also analyzes the formulation of green energy storage product standards, using lithium-ion battery modules as a case study, and summarizes the development of other green energy storage standards. The goal is to promote the development of green energy storage technology, provide a reference for creating and refining green energy storage standards, and support the sustainable development of the new energy storage industry.

The integration of thermal power plants with heat storage technology can enhance the decoupling capability of the units, thereby reducing the impact of deep peak shaving on the safety and economic viability of the system. This study proposes a coupled system comprising thermal power plants and packed-bed heat storage. Considering the dynamic time series of unit variable operating conditions and packed-bed heat storage/release processes, a simulation model of the coupling system under variable operating conditions was established using Ebsilon. We analyzed the impact of deep peak shaving on the thermal dynamics and carbon emissions of the coupling system. The effect of deep peak shaving on the boiler's life loss rate was investigated by calculating the stress variation of the steam-water separator cylinder. The impact of deep peak shaving on turbine consumption was also examined by analyzing the rotor life loss rate curve. Consequently, an efficient model for the scheduling operation of the coupling system was established to conduct a comprehensive economic analysis. The results show that when the boiler operating status and system power generation remain constant and the power supply structure comprises a renewable energy and energy storage ratio of 5∶1, the coupling system reduces carbon emissions by 7418 t/a (with only wind power) to 9216 t/a (with only photovoltaics) compared with self-variable conditions. The impact of deep peak shaving on the boiler lifespan loss is greater than that on the turbine; however, the coupling system can improve the system lifespan. With deep peak shaving occurring 318 times/a, it can increase by 13.3%—15.3% compared to the unit's self-variable conditions (30%—20% rated load). The economic benefits of deep peak shaving in the coupling system are greater than those of the thermal power plant's self-variable conditions. When the heat released by the packed bed is used for power generation or heating, the benefits of the coupling system increase by 400000 and 720000 RMB/year, respectively, compared to its self-variable conditions.

Henan Province is committed to implementing the 20^th National Congress report. For this, Henan province plans to introduce new development areas and tracks in addition to developing new forms and modes of the energy storage industry to advance growth of various types of energy storage grow sequentially. These efforts lay a good foundation for attaining a steady economic growth based on innovation to achieve the "dual carbon" goal in Henan Province. However, to seize the historical development opportunity in the new energy storage industry, the challenges and constraints of industrial development should be overcome and steps to promote a high-quality sustainable development of the energy storage industry should be taken. The challenges faced by the industry development are highlighted by comprehensively summarizing and analyzing the current status, industrial policies, disciplines, and innovation platforms of the new energy storage industry in Henan Province. The synergistic mechanism between the energy sources in the province and the driving forces of the industrial cluster development is investigated in detail, and a four-in-one model (i.e., the government, association, enterprise, university model) is proposed to collaboratively build an innovation-driven industrial cluster. By focusing on the policy leadership, production chain layout, cluster development, technological innovation, talent construction, positive mutual promotion, and efficient execution of the four main driving forces of the model as well as strengthening the participation of associations and universities and promoting the integrated layout, the total factor productivity and core competitiveness of the industry can be enhanced and a benign development environment can be build. This development is led by the new quality productive forces to accelerate the high-quality cluster development of the new energy storage industry in Henan Province.

Building energy consumption represents a significant portion of the energy sector, and integrated energy systems offer a way to optimize various energy sources. In order to realize the low-carbon and economic operation in building integrated energy systems, this study explores optimization strategies. Two different types of buildings are examined, incorporating clean energy sources such as solar and geothermal energy. A matrix-based energy hub model is proposed to guide the research. Based on the data from typical summer and winter days, an optimal scheduling model was established with the goal of minimizing comprehensive operational costs for these typical days. The model is solved using CPLEX software combined with an improved NSGA-II algorithm. The results show that the optimization effect in winter is about 10% higher than that in summer. The optimized building demonstrates a comprehensive operation cost reduction ranging from 15% to 35%, while also limiting pollutant emissions, thereby providing significant economic and environmental benefits.

Against the backdrop of increasingly complex and volatile global energy markets, fluctuations in energy prices have become one of the key factors affecting the economic performance of various industries. The energy storage industry, as a bridge connecting energy production and consumption, its economic performance is deeply affected by fluctuations in energy prices. This article provides a research review on the economic relationship between energy price fluctuations and the energy storage industry. It analyzes in detail how energy price fluctuations affect the economic performance of the energy storage industry from multiple dimensions such as investment decisions, cost control, and expected returns. Based on this, a series of forward-looking and operable response strategies are proposed, aiming to provide theoretical support and practical guidance for the healthy development of the energy storage industry.