To address the rapid degradation of high-voltage lithium cobalt oxide (LCO) performance caused by crystal structure deterioration and intensified side reactions, this study introduces a novel multifunctional electrolyte additive, 2,6-pyridinitrile (DCPY), to enhance electrode/electrolyte interface stability. The electrode interface, electrolyte physical properties, and electrochemical characteristics of the battery are analyzed using scanning electron microscopy, transmission electron microscopy, electrochemical techniques, and theoretical calculations. The findings reveal that as a multifunctional electrolyte additive, DCPY forms a highly stable interfacial film on both positive and negative electrodes, effectively suppressing electrode/electrolyte interface side reactions and preventing the dissolution and deposition of transition metals. Additionally, the pyridine functional groups in DCPY interact with phosphorus pentafluoride in the electrolyte, substantially reducing corrosion caused by hydrofluoric acid (HF) at both electrode interfaces. This interaction enhances the high-voltage stability of commercial LCO pouch cells. As a result, adding 0.5% DCPY to the base electrolyte solution (Base) optimizes cycling performance in commercial pouch cells (Base + DCPY), increasing capacity retention from 80% to 90% after 800 cycles at 25 ℃ and from 80% to 85% after 600 cycles at 45 ℃. Furthermore, DCPY substantially mitigates gas generation issues during high-temperature operation. Even at an increased operating voltage of up to 4.55 V, LCO//Gr (graphite) pouch cells maintain excellent cycling stability under elevated temperatures. This study presents a practical strategy for developing high-voltage energy storage batteries with high energy density.

Lithium metal anodes offer a high theoretical specific capacity (3860 mAh/g) and, when combined with high-voltage cathodes, can significantly boost the energy density of lithium metal batteries (LMBs). However, their practical application is challenged by issues such as lithium dendrite growth, unstable solid-electrolyte interphase (SEI), and poor compatibility with high-voltage cathodes in DME ether-based electrolytes. To address these challenges, this work introduces a siloxane electrolyte as a substitute for DME ether-based electrolytes. The high chemical bond energy of siloxane enhances the oxidation stability of the electrolyte, making it compatible with high-voltage cathodes. Furthermore, the strong interaction between Li⁺ ions and FSI^- anions in the siloxane electrolyte promotes the preferential reduction of FSI^- anions on the lithium anode surface. This process forms a LiF-rich SEI film, effectively inhibiting dendrite growth and improving Li⁺ transport kinetics. Compared to DME ether-based electrolytes, cells utilizing the DMS-1 siloxane electrolyte exhibit superior electrochemical performance. The Li||Cu cycle can be stable for 300 cycles at a current density of 1.0 mA/cm². Full cells with Li||LFP and Li||NCM811 cathodes also show excellent electrochemical performance in the DMS-1 electrolyte. The Li||LFP cell maintains its capacity without significant fading after 400 cycles at 2.0 C. The Li||NCM811 cell achieves 83% capacity retention after 300 cycles at 1.0 C, showing excellent cycle stability. The siloxane electrolyte developed in this work provides a promising strategy for constructing long-cycle, high-voltage LMBs.

The detection and safety early warning method for lithium-ion battery aging employs double-layer GeTe thermoelectric materials. This method capitalizes on the relationship between the temperature differential (ΔT) across the thermoelectric material and the induced thermoelectric signal to accurately identify "irreversible reactions" at the microscopic level within the battery. During the early stages of thermal runaway, the thermoelectric sensor's response current can surge to 183.7 μA/μm, approximately an order of magnitude higher than standard operating conditions. This significantly reduces the risk of abnormal aging and thermal runaway in lithium-ion battery energy storage systems. The study also evaluated how external stress affects the sensitivity and reliability of these double-layer GeTe thermoelectric devices. Findings have revealed that their response signals are highly sensitive to ΔT and temperature variations. Specifically, before the battery's temperature differential surpasses 60 K, the sensor's thermoelectric response ratio increases by over 1.2 times for every 10 K increment in the temperature gradient. Moreover, even under curling strain, the strength of the thermal runaway early warning signal remains over five times stronger than during normal operating conditions, highlighting the devices' robust stress stability. The findings suggest that double-layer GeTe thermoelectric materials possess excellent thermoelectric sensing capabilities, sensitivity, and stability. These qualities make them a promising solution for monitoring the aging process and enabling early detection of thermal runaway incidents in the energy storage of lithium-ion batteries.

To meet the market demand for high-capacity lithium-ion battery cathode materials, C/Li_1.2Ni_0.13Co_0.13Mn_0.54O₂ composite precursors were prepared using carbon-containing droplet combustion. Lithium-rich manganese-based cathode materials were then obtained through calcination and carbon removal. The electrochemical performance of these materials was compared with that of samples prepared using the solution combustion method. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were employed to analyze the microstructure and crystal morphology of the prepared samples. Electrochemical properties were evaluated using a battery testing system and an electrochemical workstation. The results indicate that Li_1.2Ni_0.13Co_0.13Mn_0.54O₂ prepared via carbon-containing droplet combustion exhibited an initial discharge specific capacity of 390.89 mAh/g at a 0.1C rate and 185.06 mAh/g at a 5C current density. After 100 cycles at a 1C rate, the discharge capacity remained at 108.78 mAh/g, demonstrating good capacity retention and rate performance. However, its cycling stability was slightly lower than that of samples obtained via the solution combustion method.

Chromium oxides (Cr₈O₂₁) have been extensively studied as cathode materials for lithium primary batteries due to their high specific capacity and stable voltage plateau. However, designing state-of-the-art electrolytes for Li/Cr₈O₂₁ is crucial to ensure applicability under various conditions. In this study, a novel dual-salt electrolyte (FB55-10%) was developed through theoretical calculations and experimental validation. LiBF₄ enhances the low-temperature performance of Li/Cr₈O₂₁, while LiFSI, with its superior stability, compensates for the low ionic conductivity of LiBF₄ at normal temperatures. In addition, methyl butyrate (MB), characterized by low viscosity and a low melting point, was selected as a co-solvent with propylene carbonate (PC) to extend the operating temperature range from the melting point to the boiling point, thereby enabling Li/Cr₈O₂₁ applications across a wide temperature range (-45—65 ℃). Furthermore, the low-temperature (-45 ℃) performance of Li/Cr₈O₂₁ was further improved by incorporating a solid-state electrolyte, Li_1.3Al_0.3Ti_0.7(PO₄)₃ (LATP), into Cr₈O₂₁ composite materials. As a result, the Li/Cr₈O₂₁ coin cell achieved specific capacities of 404 mAh/g, 410 mAh/g, and 223 mAh/g at 25 ℃, 65 ℃, and -45 ℃, respectively, at a current density of 0.02C. In addition, a 3 Ah pouch cell incorporating this novel electrolyte demonstrated excellent electrochemical performance, confirming the superior wide-temperature adaptability of this electrolyte system.

All-vanadium flow batteries are essential for long-term energy storage owing to their high power density, intrinsic safety, and the decoupling of capacity and energy. However, commercial applications are hindered by issues such as low energy efficiency and poor cycle stability, which are primarily attributed to the poor ion selectivity of commonly used Nafion membrane materials. To address these limitations, this study prepared a crosslinked sulfonated polybenzimidazole microporous proton exchange membrane (tbt-SP-110) using a star crosslinker. This star crosslinker was introduced into the sulfonated polybenzimidazole membrane to construct an ion transport channel. This channel accelerated proton conduction while containing water molecules to limit the swelling of the membrane material. Combined with the inherently high ion selectivity of polybenzimidazole, this approach facilitates rapid and selective proton transport. Owing to the excellent properties of the developed membrane material, the Coulomb efficiency and energy efficiency reached 99.45% and 79.06%, respectively, at a current density of 250 mA/cm². Under a current density of 200 mA/cm², the capacity retention rate was maintained at 72% after 100 cycles. These results highlight the potential of the membrane for use in high-performance all-vanadium flow batteries.

High-temperature corrosion of molten salt is a critical factor affecting the long-term safe operation of concentrating solar power systems. To investigate the compatibility between molten salt used in the third-generation solar thermal power technology and high-temperature equipment, as well as the impact of flow rate on molten salt corrosion, this study examines the corrosion behavior of ternary nitrocarbonate mixed molten salt with a high decomposition temperature and 347H stainless steel. A dynamic corrosion test platform was established, and a 1000-hour dynamic corrosion test was conducted at 640 ℃ using the weight loss method. The corrosion behavior of 347H stainless steel was analyzed at two flow rates, 0.6 m/s and 2 m/s. The surface morphology and corrosion products were examined using scanning electron microscopy (SEM), X-ray diffraction, and energy-dispersive spectroscopy. The results indicate that oxidation corrosion occurs at both flow rates, with corrosion weight loss (m_mass) increasing as the flow rate increases. After 1000 h, the m_mass values were 17.8407 mg/cm² at 0.6 m/s and 24.6420 mg/cm² and 2 m/s. Additionally, the oxidation layer formed on the stainless steel surface deteriorates more rapidly at higher flow rates, leading to an increased corrosion rate of 347H stainless steel. However, the corrosion rate decreases at a constant flow rate as soaking time increases.

The global energy crisis has driven substantial research efforts to enhance energy utilization efficiency and address supply-demand imbalances. Latent heat storage (LHS) systems, known for their high energy density and flexibility in energy storage and release, serve as a crucial solution to this challenge. Phase change heat exchangers play a vital role in improving the performance of LHS systems. This study aims to enhance the heat transfer efficiency of phase change heat exchangers by modifying conventional straight fins. Three fin structures (triangular, wavy, and square) were designed, along with three levels of eccentricity (10 mm, 15 mm, and 20 mm). The optimal fin structure was then combined with the most effective eccentricity. Using Fluent, a simulation analysis of thermal energy storage and cold storage models was conducted. The energy storage rate was quantitatively assessed based on the average energy storage time required for phase change materials per unit volume. The results indicate that square fins substantially enhance both heat and cold storages. Compared to the original straight fins, the heat storage rate is improved by 12.2%, while the cold storage rate increases by 9.2%. Additionally, introducing a downward eccentricity in straight fins improved the heat storage effect, with a 15 mm eccentricity yielding the best results (a 25% increase in heat storage rate compared to the original straight fins). However, downward eccentricity negatively impacted cold storage performance. By integrating the two optimal configurations (square fins and fins and a downward eccentricity of 15 mm) the heat storage rate was further enhanced by 28.2% compared to the original straight fins.

Phase change material (PCM)-filled bed energy storage systems offer an excellent solution for storing thermal energy, enabling better energy utilization. To further enhance the energy storage efficiency of PCM-filled beds, this study proposes a composite PCM (CPCM) that incorporates expanded graphite (EG) to improve the thermal conductivity of paraffin wax (PW). In addition, a small amount of styrene-ethylene-butylene-styrene block copolymer (SEBS) is added to enhance the stability of the storage material. The heat transfer performance of CPCMs applied in spherical thermal storage units is thoroughly investigated through experimental testing and numerical simulations. Furthermore, the impact of different rotational speeds of the heat transfer fluid on the thermal storage performance of individual spherical units is analyzed.Resultsdemonstrate that when an EG concentration of 5% reduces the time required for complete thermal charging of the storage unit by 67.8% compared to pure paraffin wax. In addition, rotating the heat transfer fluid significantly enhances the heat transfer performance of the spherical storage units. The optimal heat transfer performance is achieved at a rotational speed of 0.5 rad/s. Thus, the results of this study provide a theoretical foundation for improving the efficiency of PCM-filled beds.

Solid-state lithium-sulfur batteries (SLSBs) are among the most promising next-generation energy storage devices due to their high theoretical energy density and low cost. Compared to conventional lithium-sulfur batteries (LSBs) with liquid electrolytes, SLSBs have the potential to eliminate the shuttle effect, thereby extending battery lifespan. However, substantial challenges remain, particularly regarding fundamental mechanisms and manufacturing processes. These include understanding the solid-solid conversion mechanism from S₈ to Li₂S to identify limiting factors and potential solutions, constructing a dynamic and stable charge transfer network for high-loading cathodes, and managing dendrite growth and strain regulation in the lithium metal anode. Addressing these challenges requires innovative electrode material design, interface optimizations, and advanced characterization techniques using in-situ and ex-situ methods. This review highlights recent research advancements in SLSBs, focusing on cathodes, anodes, and characterization methods. Additionally, we summarize key differences between SLSBs and LSBs in terms of cathode material and electrode structures. For the cathode, it is essential to maintain efficient charge percolating pathways for both ions and electrons while regulating electrode deformation. Increasing the critical current density of lithium stripping for the anode is crucial for achieving high specific-energy in SLSBs. Further mechanistic investigations are necessary to design low-strain cell configurations that enable the development of high specific-energy SLSBs.

Dry electrode technology is a key innovation in developing high-performance energy storage devices due to its solvent-free process, low manufacturing cost, high mechanical strength, and environmental benefits. This study analyzes the principles of dry electrode technology, summarizes the properties and applications of commonly used binders in dry electrode fabrication, highlights its advantages, reviews its origin and development history, and examines recent research progress in the fields of supercapacitors and lithium-ion batteries. Focusing on six dry electrode process technologies—polymer fibrillation, dry spray deposition, vapor deposition, hot-melt extrusion, direct pressing, and 3D printing—this study discusses their process principles, research advancements, key equipment, critical process parameters, and comparative advantages and disadvantages. The findings indicate that the current large-scale manufacturing of dry electrodes faces challenges, including limited production capacity, special raw material treatment requirements, and incompatibility with existing production lines. Finally, future research directions for dry electrode technology in lithium-ion batteries and supercapacitors are outlined, including developing new binders, optimizing dry mixing processes, adjusting electrode mass loading, refining production routes, and exploring novel fabrication methods. This study is a valuable reference for researchers and engineers, offering guidance for advancing dry electrode technology in supercapacitors and lithium-ion batteries.

The integration of energy storage technology with thermal power plant retrofitting enables stable grid connection of renewable energy and flexible peak shaving of coal-fired units. This study proposes a molten salt Carnot battery energy storage system integrated with a thermal power plant to enhance peak-shaving flexibility. Using a typical 600 MW subcritical coal-fired power plant as a reference, a coupled system model—including the coal-fired power plant, a heat pump thermal energy storage unit, and a steam thermal energy storage unit—is developed using Aspen Plus. The study analyzes the efficiency, peak-shaving capacity, and depth of three thermal energy storage modes- steam thermal energy storage (STES), steam combined with heat pump thermal energy storage (SHPTES), and heat pump thermal energy storage (HPTES) across different operational stages. Additionally, the efficiency and peak-shaving performance of the coupled system under various energy storage and release loads and schemes are evaluated. The results indicate that the STES approach achieves higher coupled system efficiencies when the coal-fired power plant operates at low loads. In contrast, HPTES demonstrates superior peak-shaving performance, with its maximum peak-shaving capacity per unit of heat storage load increasing by 69% compared to STES. The addition of thermal energy storage devices substantially enhances the peak-shaving performance of the coal-fired power plant while causing only a minimal efficiency loss. When operating at a rated load with a heat storage load of 90 MW, the SHPTES approach increases the peak-shaving capacity and depth by 78.29 MW and 13.04%, respectively, with an efficiency loss of only 0.16%. Furthermore, coupling schemes for STES are examined. During energy storage, reheated steam is extracted and returned to the deaerator after heat release. In the energy release process, a portion of the feedwater is heated and directed to the boiler via a bypass. This coupling scheme effectively balances heat storage capacity, peak-shaving capacity and depth, and system efficiency. This study provides valuable guidance for retrofitting coal-fired power plants with molten salt Carnot batteries to improve operational flexibility.

To recover waste heat from liquid-cooled data centers and advance the development of green data centers utilizing renewable energy, this study proposes a Carnot battery system driven by waste heat from liquid-cooled data centers. The system consists of a heat pump and a transcritical CO₂ power cycle. A thermodynamic model is developed to evaluate the system's thermal performance under various operating conditions. The results indicate that under design conditions, with an energy storage capacity of 100 kW × 5 h and R245fa as the heat pump working fluid, the heat pump achieves a COP of 5.23. Additionally, the power generation efficiency based on heat pump heating reaches 13.28%, and the round-trip efficiency is 67.64%. The study further investigates the effects of key operating parameters—including heat pump evaporator superheat, high and low water tank temperatures, power generation cycle pump outlet pressure, and condensing temperature—on system performance. The findings reveal that round-trip efficiency increases with higher superheat in the heat pump evaporator but decreases as the power generation cycle's hot and cold water tank temperatures, pump outlet pressure, and condensing temperature rise. Notably, when the condensing temperature of the power generation cycle falls below 18 ℃, round-trip efficiency can exceed 100%. A sensitivity analysis of the system's operating parameters further highlights that the condensing temperature of the power generation cycle has the most substantial impact on round-trip efficiency.

Mechanical pump energy storage systems play a crucial role in modern energy management, particularly in enhancing energy storage efficiency and optimizing the use of renewable energy sources. This paper presents an in-depth analysis of the working principles and thermodynamic models of mechanical pump energy storage systems, revealing the key factors that influence system efficiency. It proposes strategies for screening based on operational characteristics and designing thermodynamic processes, thereby improving performance during both compression and expansion stages. The paper also addresses system integration and the optimization of thermal energy management pathways. The goal is to enhance the overall performance of mechanical pump energy storage systems, promote their application in renewable energy and smart grid contexts, and provide robust support for the widespread adoption of clean energy.

Because NCM lithium-ion batteries are currently the primary energy source for electric vehicles, overcharging them could trigger thermal runaway and lead to serious vehicle fire accidents. In this study, a fire test of a NCM lithium-ion battery electric vehicle was performed by overcharging the battery. During the test, the temperatures inside the battery pack, under the chassis, on the car surface, inside the car, and the radiative heat flux intensities all around the car were measured. In addition, the fire heat release rate was measured using the oxygen consumption principle. The results show that after the thermal runaway of the battery pack, combustible gases were released and ignited, forming a jet flame. The horizontal distance of the flame jet was approximately 4 m. The flame quickly ignited the tires of the vehicle and other combustible materials around the chassis. The resulting fire broke the window, igniting the interior and seats within the vehicle compartments. The vehicle was engulfed in an intense flame. The fire growth rate (α = 0.98 kW/s²) of the tested vehicle was significantly higher than that of an ultra-fast fire (α = 0.1875 kW/s²), and the peak heat release rate was approximately 8.0 MW. The peak radiative heat flux intensities at 0.5 and 1.0 m from the edge of the vehicle were 60—80 and 30—35 kW/m², respectively, which could ignite nearby vehicles and cause fire to spread.

Among the various cooling methods for lithium battery energy storage systems, liquid-cooled cooling systems have significant advantages. However, the performance of liquid-cooled cooling systems are significantly influenced by the type and structure of liquid-cooled plates. To address the thermal safety problem of lithium-ion batteries, a 280 Ah lithium-ion battery was numerically analyzed using ANSYS FLUENT. In addition, a three-dimensional transient heat generation model was constructed based on Bernardi's heat generation rate, fluid momentum conservation equations, and energy conservation equation to simulate the change in temperature field of lithium battery pack at high rate of operation, and to evaluate the cooling effect of three connection methods of dual-channel parallel series liquid cooling plate scheme. The effects of coolant flow rate, initial temperature of coolant and channel height of the liquid cooling plate on the cooling effect of the lithium battery pack were analyzed. The optimal cooling scheme of the liquid cooling plate was thus obtained. The results demonstrated that among the three connection schemes examined, the third scheme demonstrated the best heat dissipation performance, ensuring a uniform temperature distribution across the battery pack. Based on the third connection scheme, as the coolant flow rate increased, the maximum temperature of the lithium battery pack changed from a rapid decline in the initial stage to a slow decline. Lowering the inlet temperature of the coolant significantly decreased the maximum temperature of the lithium battery pack. By analyzing and comparing coolant channels with different heights, an appropriate increase in the channel height can effectively improve the heat dissipation performance of the liquid cooling system. Based on this study, the best cooling scheme can be achieved by increasing the coolant flow rate, reducing the coolant inlet temperature, and increasing the flow channel height of the liquid cooling plate structure.

The configuration of multiple shared energy storages (SESs) in a new distribution network (NDN) has become a trend, providing multi-resource cooperative effects. However, maximizing the efficiency of SESs requires more than just evaluating their value. The multifaceted and comprehensive impacts of each SES on the NDN must also be considered. To quantify the marginal operational contribution of a single SES under the joint effect of multiple SESs in the NDN, a method for allocating the operational efficiency of multi-SESs based on the improved Shapley value method is proposed. An optimized scheduling model was established to minimize operating costs in the NDN while considering new energy consumption and carbon emission reduction. The model includes operational efficiency evaluation indicators that evaluate the economic, reliability, and environmental impacts of SESs on the NDN. In addition, an operational efficiency evaluation model is established on the basis of a subjective-objective empowerment method, combining improved hierarchical analysis and inter-criteria correlation. To consider the differences in the unilateral efficiency of different SESs, correction factors were introduced to improve the Shapley value method. These factors consider each SES's economic, reliability, and environmental contributions to allocate operational efficiencies across multiple SES combinations. The proposed method was validated via simulations on an improved IEEE 33-node NDN with three SESs. The results demonstrate that the proposed method effectively quantifies and allocates the comprehensive utility of SESs at different nodes using the NDN. In particular, the operational efficiency of SES1 and SES2, which positively affect the NDN, is increased. The proposed method provides a new framework for the efficient scheduling and potential utilization of SESs in distribution networks.

With the acceleration of global energy transition, the installed capacity of renewable energy sources such as wind power and photovoltaics is rapidly increasing. However, the randomness, volatility, and intermittency of new energy generation pose significant challenges to the stable operation of the power grid, leading to increasingly prominent issues in the consumption of new energy. This article aims to explore methods to enhance the consumption effect of new energy storage by optimizing the configuration and operation of energy logistics systems, especially energy storage systems. Through intelligent route planning, multimodal transportation, optimization of storage networks, use of clean energy, and optimization of energy storage systems, efficient consumption of new energy electricity can be achieved, providing strong support for the sustainable and healthy development of the new energy industry.

Compressed air energy storage (CAES) systems are recognized as one of the most promising large-scale physical energy storage technologies. At the heart of these systems lies the axial flow compressor, whose safety and operational stability are critical for ensuring the economic viability and safe use of advanced CAES. Within the compressor, the blades play a key role in energy conversion but are vulnerable to fatigue damage during operation. The stacking line of the blade can effectively adjust the stress distribution characteristics. This study focuses on the structural optimization of a 1.5-stage axial flow compressor within a CAES system. Latin hypercube planning (LHS) is employed for parameter selection, a radial basis function neural network (RBFNN) is used to establish the agent model, and the non-dominated sorting genetic algorithm-II (NSGA-II) is applied to capture the target value. Together, these approaches establish an integrated parametric optimization framework for three-dimensional bending and sweeping structure modeling of axial flow blades. The optimization results show that the maximum equivalent stress of the optimized blade decreases from 376.8 MPa to 255.9 MPa, achieving a stress reduction of 32.1%. The blade stress distribution is primarily influenced by centrifugal forces, while appropriate bending and sweeping modifications can effectively adjust the center of gravity and the centrifugal bending moment for each blade section. Notably, the optimization method does not significantly impact the flow field distribution, rotor surface load distribution, and tip clearance energy dissipation and achieve the decoupling of aerodynamic performance and structural stress distribution.

This paper focuses on the computer network control technology in the compressed air energy storage (CAES) system. Firstly, it expounds on the research status and development process of the CAES system. Then, it delves into the key roles of computer network control in this system, such as data acquisition and monitoring, instruction transmission and execution. By analyzing the existing problems in control technology, a series of improvement strategies are proposed, including optimizing network topology and enhancing data transmission efficiency. Finally, it looks ahead to the future research directions in this field, providing a reference for further promoting the integrated development of CAES and computer network control technology.

The large-scale integration of new energy sources into the power grid has led to increased frequency fluctuations, posing challenges to system stability. The participation of a flywheel energy storage array in primary frequency regulation can effectively enhance frequency stability and improve grid security. To address power distribution within the flywheel energy storage system and among individual units in the array, a two-layer control strategy is proposed. This strategy considers flywheel array losses, reduces system frequency deviation, and mitigates output fluctuations in thermal power units. The upper-layer control incorporates a dynamic droop-inertia coordinated strategy. A mathematical model is developed to quantify flywheel array losses, and an inequality constraint on flywheel output is established using the Logistic function. The particle swarm optimization algorithm is employed to determine the optimal power distribution among flywheel units, ensuring system requirements are met while minimizing overall power losses. Simulation results demonstrate that the proposed control strategy enhances system frequency stability, reduces wear on thermal power units, and lowers the power losses of the flywheel array.

Flywheel energy storage technology, as an efficient and environmentally friendly energy storage method, has broad application prospects in fields such as power system frequency regulation, transportation, and industrial production. However, optimizing its control strategy is crucial for achieving efficient energy storage. This article deeply explores the application of data mining technology in the automatic control strategy of flywheel energy storage. The article first outlines the research process of flywheel energy storage technology, and then analyzes in detail the flywheel energy storage automatic control technology under data mining, including the principles and types of data mining technology, as well as the actual control process. In order to provide reference and ideas for subsequent related research.

With the intensification of global climate change and the increasing awareness of environmental protection, upgrading the new energy technology industry has become a key way to alleviate the energy crisis and achieve sustainable development. The rapid development and widespread application of big data technology provide unprecedented opportunities and challenges for the research and industrial upgrading of new energy technologies. This article aims to explore the technological upgrading and development of the new energy industry under the influence of big data, analyze the specific applications and future development trends of big data in the field of new energy and even the energy storage industry, in order to provide reference for the innovative development of new energy technology.

The choice of coolants is particularly crucial, and immersion cooling systems offer outstanding advantages in thermal management technology, with high potential and application value. To investigate the actual performance of different coolants and their effectiveness in suppressing battery thermal runaway, this study conducted thermal runaway experiments on 86 Ah lithium iron phosphate cells immersed in six coolants: thermal oil (L-QD350), 10# transformer oil, vegetable oil (DS3 natural ester), silicone oil (50 cSt), ethylene glycol stock solution (99.9%, polyester grade), and electronic fluoride liquid (Novec-7200). The performance of these coolants was compared based on experimental phenomena, voltage and temperature curves, time-domain and thermal evaluation indicators of each link, and the influence of vegetable oil immersion level on thermal runaway suppression. The experimental results demonstrate that all coolants produced significant white smoke during the thermal runaway process. However, white smoke ceased after prolonged immersion in vegetable oil, whereas thermal oil and electronic fluoride liquid exhibited spontaneous ignition during thermal runaway. Vegetable oil and ethylene glycol stock solutions performed well in various time-domain and thermal evaluation indicators, whereas silicone oil performed poorly in time-domain indicators, and electronic fluoride liquid performed poorly in thermal indicators. In summary, vegetable oil and ethylene glycol stock solutions outperformed the other coolants in terms of cooling efficiency and thermal runaway suppression, whereas silicone oil and electronic fluoride liquid exhibited relatively weaker performance. In addition, the amount of vegetable oil added has a significant impact on thermal runaway. The higher the immersion amount, the slower the development of thermal runaway, and the lower the temperature on the battery surface, the less severe the thermal runaway. However, the optimal amount of coolants should be determined based on the application conditions, cost, and specific coolant parameters. These findings provide effective data support for battery thermal management systems and provide a reference for selecting coolants for immersion cooling systems.

Electrolytes are a critical component of lithium ion batteries. Among their key constituents, solvents significantly influence the ion transport rate in the liquid phase, the solvation structure of lithium ions, and the composition and structure of the solid electrolyte interphase. Solvent detection plays a vital role in scientific research, public testing, and industrial production. However, as the range of electrolytes continues to expand, their increasingly complex compositions demand more advanced detection methods. Electrolyte detection analysis generally involves two stages: qualitative and quantitative analysis. Comprehensive identification of components during the qualitative stage provides a solid foundation for subsequent analysis. For solvent detection, gas chromatography-mass spectrometry technology is utilized to identify as many different types of solvents and additives as possible in a single injection. This technology also accommodates the separation requirements of diverse solvents such as esters, ethers, and benzene rings. This articles introduces a gas chromatography-mass spectrometry detection method for electrolyte solvents and additives. The method achieves effective separation by optimizing key parameters such as injection port temperature, heating rate, column temperature, and column flow rate. These adjustments could change the adsorption capacity of the chromatographic column for different components and utilize the velocity differences of each compound in an inert gas stream. Using this approach, the simultaneous and effective separation of 19 distinct components was achieved. These include15 ester solvents such as ethylene carbonate, propylene carbonate, and methyl ethyl carbonate; 2 ether solvents such as diethylene glycol dimethyl ether and ethylene glycol dimethyl ether; aldehyde solvent 1,3-dioxolane; and the benzene ring solvent cyclohexylbenzene. This comprehensive and efficient method offers a universal solution for the qualitative detection of electrolytes containing multiple solvents and additives.

Lithium-ion batteries used for energy storage face significant risks. These include overcurrent induced by conduction and induction, and surge currents caused by device failures when integrated into the grid or hybrid systems. However, the temperature rise response of lithium-ion cells under surge current impulses remains poorly understood. This study investigates the temperature behavior of cylindrical ternary lithium-ion cells subjected to surge current impulses of varying amplitudes. Both typical multipoint temperature measurements and three-dimensional infrared observations were employed to analyze the dynamic temperature rise response of the cells. In addition, an electrochemical coupled finite element simulation model was developed to clarify the temperature rise characteristics of the cells. The results indicate that surge currents with waveforms of 8/20 μs and peak values of 7.4, 10.6, and 13.0 kA lead to temperature rises of 0.7 ℃, 1.8 ℃, and 4.2 ℃ at the cell surface, respectively. Notably, surge currents with higher peak values (≥10.6 kA) cause the cathode region to exhibit a higher temperature compared to the anode region, while the separator experiences the lowest temperature rise.

In practical applications, complete charge-discharge curves are often unavailable. To address this issue, this study proposes a lithium-ion battery state of health (SOH) estimation method using short-term stochastic charging data and an optimized convolutional neural network (CNN). The goal is to develop an efficient technique that accommodates the random and disordered nature of charging processes in real-world scenarios. The proposed method segments the original charging voltage time-series data of lithium batteries to generate randomized charging data. A shallow CNN comprising four convolutional layers is then constructed to adaptively extract aging-related features from the data. In addition, the dung beetle optimization algorithm is employed to optimize the model parameters, resulting in a multistage model. The experimental results demonstrate that the proposed method can accurately estimate the SOH of Li-ion batteries during stochastic charging, even when using only five consecutive seconds (100 data points) of raw voltage time-series data. The practicality and accuracy of the proposed model were validated under various charging conditions and rates. The results demonstrate that the model continues to exhibit a low prediction error. The average absolute error of the SOH estimation is less than 2.07% under constant current-voltage charging mode and less than 1.22% under multistage constant current charging mode. In the model comparison, the proposed CNN-based method achieved an average mean absolute error of 1.17%, outperforming integrated prediction models, such as the CNN-Long Short-Term Memory Network and CNN-Gated Recurrent Unit, in terms of estimation accuracy and stability. In addition, the randomized voltage segment used in the CNN accounted for 88.9% of the total charging time, is higher than the other two prediction models. The experimental results demonstrate the effectiveness of the proposed method in addressing the stochastic nature of battery charging data, demonstrating excellent accuracy and high adaptability in the context of charging rates, charging modes, and random charging voltage data over short periods. This results of this study provide a crucial technical reference for future advancement in battery health monitoring and battery management systems.

Specific heat capacity and thermal conductivity are critical parameters for assessing the thermal safety of lithium-ion batteries. Although previous studies have examined the factors affecting these thermal properties, most have concentrated on the batteries' internal characteristics. Studies on the impact of external testing conditions on these parameters are scarce. This study introduces a method for testing the thermal properties of lithium-ion batteries using an adiabatic calorimeter. The approach facilitates the simultaneous determination of specific heat capacity and vertical thermal conductivity. The effects of varying the heating sheet size and the battery's temperature rise rate on test results were analyzed. Findings indicate that increasing the battery's temperature rise rate from 0.2 ℃/min to 0.8 ℃/min slightly reduces the average specific heat capacity by less than 1%, while significantly lowering the vertical thermal conductivity. Additionally, reducing the heating sheet size leads to a notable reduction in the average specific heat capacity, which affects the measured heat generation power. Although it has minimal impact on vertical thermal conductivity. Based on the experimental results, recommendations for optimizing testing conditions of lithium-ion battery thermal parameters are proposed. The maintenance of a constant heating sheet size and a moderate increase in the battery's temperature rise rate can improve testing efficiency without significantly compromising the accuracy of heat generation power measurements. To improve the reliability of thermal property testing, utilizing a larger heating sheet size and maintaining a controlled temperature rise rate is recommended, balancing accuracy and testing efficiency.

To address the challenges of unstable predictions and accuracy when using extreme learning machines (ELMs) to predict the remaining useful life of lithium-ion batteries, this study proposes a cheetah optimization (CO) algorithm to optimize the ELM model performance. The equal voltage drop discharge time, extracted from the lithium-ion battery dataset, is employed as an indirect health factor. Furthermore, the CO algorithm is introduced to optimize the ELM parameters. This initial population of the CO algorithm is improved using sine chaotic mapping. The effectiveness and accuracy of the proposed model are verified using the battery dataset provided by the NASA Center for Excellence Prediction and the Oxford Battery Degradation Dataset from Oxford University. The optimal amount of training data and the ideal number of neurons are obtained through multiple experiments with the original ELM model. The residual service life of batteries is predicted using the proposed SCO-ELM model. Compared with the original ELM and the genetic algorithm-optimized ELM model, the proposed SCO-ELM model achieves a root mean square error below 0.004 and significantly faster prediction times. The prediction accuracy improves by 40% on average and the prediction speed is improved by more than 78%. Using the training results of battery B0005 to predict the performance of similar battery packs, the prediction accuracy improves by 25% on average, and the prediction speed increases by more than 75%. Thus, the experimental results confirm that the proposed method offers high prediction accuracy, fast computation speed, low operation complexity, and a stable model.

The characteristics of lithium-ion battery thermal runaway and its potential to trigger fires in electrochemical energy storage power stations remain poorly understood. Furthermore, there is a lack of robust disaster hazard evaluation methods that can address these risks. Thus, this study focused on a lithium-titanate battery storage power station battery and conducted both experimental research and theoretical analysis. The thermal runaway and fire hazards of lithium-titanate batteries were investigated under various abuse conditions to reveal the evolution of thermal runaway characteristic parameters. Results revealed that both the battery's state of charge and the location of the applied heat significantly affect the thermal runaway behaviors. Consequently, the date were used to develop two worst-case scenarios were to characterize the hazards associated with a single battery's thermal runaway. Using these assumptions, a three-dimensional hazard evaluation method was designed, assessing "fire"(heat hazard), "poison" (gas toxicity hazard), and "explosion" (explosion hazard). Thus, the study results indicate that under the worst extreme thermal runaway conditions, the safe zone distance for lithium-titanate batteries is 96 m.

To address the issue of inaccurate battery health state estimation caused by strong interference in the lithium battery capacity increment curve, such as sensor measurement noise or varying operational conditions, this paper proposes an innovative solution using triple variational mode decomposition (VMD) to improve the accuracy of state of health (SOH) estimation. First, a dual VMD technique is utilized to denoise the distorted battery capacity increment curves. These interference sources include global voltage noise, local voltage noise, and local current mutations. Peak features were then extracted from the denoised curves. To further enhance the ability of these peak features to represent the battery's health state, a second VMD decomposition is applied to the extracted peak features. Using Pearson correlation analysis, the mode components are reconstructed into two sub-components: the main degradation trend that reflects the overall attenuation of the feature over time, and the fluctuation trend that captures short-term variations in the feature. These two components are used together as health indicators for SOH estimation. Finally, based on the NASA dataset, battery SOH estimation validation experiments were conducted using algorithms such as long short-term memory networks. The experimental results show that the proposed method effectively estimates the lithium-ion battery SOH under strong interference conditions, achieving high estimation accuracy and demonstrating significant advantages.

With the rapid growth in the electric vehicle (EV) adoption, accurately predicting the remaining useful life (RUL) of lithium-ion batteries has become critical for the sustained development of the EV industry. This paper proposes an innovative approach that integrates ensemble mode decomposition (EEMD) and deep learning to improve RUL prediction accuracy for lithium-ion batteries. The proposed method begins with EEMD, which performs multiscale decomposition of battery capacity data. This process separates the global degradation trend from local random fluctuation components. To mitigate the impact of noise on model prediction accuracy, a denoising autoencoder (DAE) is introduced to remove noise from the random fluctuation components. Subsequently, long short-term memory (LSTM) networks and the transformer model are applied to model the global degradation trend and the denoised random fluctuations, respectively. To further refine predictions, a random forest (RF) algorithm calculates the importance weights of each mode component, enabling a weighted reconstruction of the prediction results. Experiments were conducted on a public battery dataset provided by the National Aeronautics and Space Administration (NASA), leveraging 40% and 60% of the historical battery data. The results demonstrate that the proposed method outperforms existing approaches in both accuracy and effectiveness, validating its potential for application in lithium-ion battery RUL prediction.

As an indispensable driving equipment in modern industry, the stable operation of electric motors is crucial for production efficiency and safety. However, electric motors are often affected by load fluctuations during operation, which may lead to equipment failures and performance degradation. In order to improve the accuracy of motor fault prediction, this article will explore the application strategy of energy storage technology under motor load fluctuations. By collecting and analyzing operational data of electric motors, combined with the characteristics of energy storage technology, a series of fault prediction models and optimization methods have been proposed, aiming to achieve early warning and accurate prediction of electric motor faults.

Sodium-ion batteries (SIBs) hold significant promise for electrochemical energy storage owing to their abundant raw materials and cost-effectiveness. However, as SIBs become more widely adopted in energy storage systems, safety concerns are becoming more pronounced, attracting significant research attention globally. This study systematically investigates the thermal runaway (TR) behavior of SIBs under overheating conditions, with a comparative focus on the effects of different cathode materials on battery safety. The findings reveal that the NaNi_1/3Fe_1/3Mn_1/3O₂ batteries exhibit a lower TR trigger temperature, shorter trigger time, and higher surface temperatures compared to Na₃V₂(PO₄)₃ batteries, indicating a higher risk of TR. Constant-capacity tank tests further reveal that SIBs produce substantial gas during TR. Specifically, with NaNi_1/3Fe_1/3Mn_1/3O₂ and Na₃V₂(PO₄)₃ batteries produce 14.58 and 13.16 mol of gas, respectively, corresponding to lower explosion limits of 7.5% and 8.0%. These results provide crucial theoretical insights and technical support for improving the safety design and risk management strategies of sodium-based energy storage systems.

Electric vehicles that rely on lithium-ion batteries for energy face dual challenges of safety and cost. An emerging alternative, sodium-ion batteries, share similar working principles with lithium-ion batteries, while being more economical and inherently safer. To achieve the rapid charging of sodium-ion batteries, this study proposes an optimized nine-step voltage cutoff charging strategy, developed based on the analysis of DC internal resistance changes and differential voltage analysis to monitor characteristic peak variations. The proposed strategy adjusts current limitations across three charging phases (early, middle, and late) to address specific issues, namely high DC internal resistance at low states of charge (SOC) during early charging, increased risk of damage at medium and high SOC regions, caused by high-rate charging and graphite characteristic peak activity. This strategy is compared against existing methods, including the constant current constant voltage (CCCV) charging method and a three-capacity cutoff tiered charging method, by analyzing 150 charging cycles. Experimental results show that, under the same constraints, the proposed battery aging strategies outperform the CCCV charging method. Specifically, compared with the optimized CCCV standard charging, the proposed strategies reduce charging time by 19.04%, improve battery health state by 7.4%, and decrease internal resistance attenuation by 17.8%.

In this work, NaNi_0.4Mn_0.4Fe_0.2O₂ (NMF) was used as the sodium-ion source for insertion into a hard carbon (HC) anode material. Various amounts of sodium ions were pre-doped into the HC electrode to achieve different pre-sodiation ratios. AC//HC sodium-ion capacitors were fabricated using an activated carbon (AC) cathode and a pre-sodiated HC anode. The influence of different pre-sodiation ratios on electrochemical performance was analyzed through electrochemical tests. Results indicated that sodium-ion capacitors with a pre-sodiation ratio above 50% exhibited significantly improved performance, including higher voltage, lower direct current internal resistance, enhanced capacity, superior rate performance, and increased power and cycle stability. With a pre-sodiation ratio of 80%, the capacitor demonstrated a linear charge-discharge profile over the voltage range. Additionally, the discharge capacity increased to 83.73 mAh, which was three times that of a capacitor with 0 pre-sodiation. In terms of rate performance, the discharge energy retention at 3 A (44C) improved significantly to 79.35% compared to that at 0.1 A (1.5C), whereas the capacitor with 0 pre-sodiation retained only 26.35% under the same conditions. Furthermore, as the pre-sodiation ratio increased from 0 to 80%, the power density rose from 9807.89 W/kg to 24498.51 W/kg. Capacitors with a 50% pre-sodiation ratio retained more than 90% of their initial energy after 3000 cycles, and their gas expansion degree was lower than that of capacitors with a pre-sodiation ratio below 30%. The lower open-circuit voltage (OCV) of the HC anode due to pre-sodiation increased the operating voltage range of the AC cathode, effectively enhancing the capacity and energy density of the AC//HC sodium-ion capacitor. Additionally, the lower OCV facilitated the formation of a dense solid electrolyte interphase (SEI) layer, improving its stability and cycle performance. However, controlling the pre-sodiation ratio within an optimal range is crucial to achieving the best electrochemical performance. This research provides a scientific basis for the industrial implementation of sodium-ion capacitor pre-sodiation.

With the commercial adoption of sodium-ion batteries entering a fast-paced era, attention has shifted to their application in large-scale energy storage systems. Sodium-ion batteries generate heat during charging and discharging owing to a combination of reversible and irreversible electrochemical reactions, especially at high charge and discharge rates. This heat buildup raises battery temperatures, which can accelerate material aging, degrade performance, and even lead to safety concerns such as thermal runaway. This study investigates the electrochemical and thermal behaviors of two types of sodium-ion batteries, namely energy-type and power-type, during their charging and discharging processes. The capacity and energy retention ability of the two types of batteries decreased under high-rate discharge conditions. However, performance degradation was notably less pronounced in power-type batteries compared to energy-type batteries. Furthermore, power-type batteries exhibited a significantly smaller temperature rise and slower temperature increase across various discharge rates. A key observation was that the energy-type battery's temperature exceeded the safe operating limit when discharged at a 1.5 C rate, reaching 51.8 ℃. The results of this research not only provide a theoretical basis for selecting sodium-ion battery types for specific applications but also contribute valuable data for developing thermal management models.

With the development of new energy industry and energy storage power system, the market demand for energy storage system is rapidly increasing. However, the enlargement of energy storage cells and the increasingly complex working environment of energy storage system lead to a sharp increase in the failure rate of energy storage system. The application of artificial intelligence to the fault detection of energy storage system can effectively improve the fault detection efficiency of energy storage system, reduce the manual intervention, and minimize the loss caused by the failure of energy storage system. In practice, through raw data input, feature extraction, model building and fault detection, the fault detection mechanism of the energy storage system based on artificial intelligence can find the rule of the energy storage system failure from the massive data, provide early warning for the energy storage system failure, accurately identify the fault location and type, and predict the development trend of the fault, so as to greatly improve the efficiency of the energy storage system, and promote the intelligentization of the energy storage system.

The integration of a high proportion of wind and solar energy into the power grid poses challenges for grid peak regulation owing to the inherent volatility of these renewable energy sources. However, energy storage systems, with their bidirectional charging and discharging capabilities, offer an excellent solution for peak regulation. Therefore, addressing the dynamic peak regulation capacity of energy storage to accommodate wind and solar fluctuations has become a critical issue in developing the new power system. To tackle this challenge, a collaborative low-carbon economic dispatch model combining wind, solar, thermal power, and energy storage has been proposed. This helps energy storage systems to adapt to the volatility of these energy sources. First, the output distribution function and active output scenarios were developed using the optimal Copula joint probability method. This modeling provides a strong foundation for enabling energy storage systems to adapt to the volatility of wind and solar energy. Second, the peak shaving cost function, the wind and solar power curtailment cost function, the dynamic peak shaving cost function of energy storage, and the operating costs of thermal power units were defined. These were integrated into a joint optimization objective function, accompanied by a set of constraint conditions. Third, a revised particle swarm optimization algorithm was employed to efficiently solve the joint optimization objective function. Finally, a practical example was used to simulate and validate the proposed algorithm. The results demonstrated that the low-carbon economic dispatch method significantly improves the power grid's peak shaving capacity and economic performance, while reducing wind and solar power curtailment.

As one of the key market mechanisms for achieving China's "dual carbon goals," the official relaunch of the national voluntary greenhouse gas emission reduction trading market presents new opportunities for methane mitigation projects. Against this backdrop, this paper reviews the development history of the CCER mechanism, highlighting its evolution from initial exploration to a more standardized framework as a crucial component of China's voluntary emission reduction system. The relaunch of CCER has significantly increased the activity of the carbon market, encouraged greater participation from oil and gas enterprises in methane reduction, and promoted improvements in relevant policies and technical standards.Based on this, the paper conducts an in-depth analysis of the prerequisites for developing methane mitigation methodologies. First, it identifies and categorizes the current status of methane mitigation methodologies in the oil and gas sector at both international and domestic levels. Second, it evaluates the conditions for methodology development in terms of policy support, development benefits, and technical feasibility. Finally, it defines the critical elements of project design documents, focusing on key aspects such as project boundary delineation, baseline emission calculations, additionality demonstration, and monitoring plan formulation.Combining the feasibility analysis of methane reduction methodology development, the paper provides targeted recommendations for stakeholders involved in the process. At present, the domestic oil and gas industry faces multiple challenges in methodology development, including insufficient technical and monitoring capabilities, an incomplete methodological system, the lack of unified standards, and inadequate policy support. To promote the green transition of the oil and gas sector, it is essential to continuously optimize technical guidelines and policy frameworks, improve the development and application of methodologies, and effectively contribute to achieving China's "dual carbon goals."

In the context of the global energy transition and the strategy for carbon peaking and carbon neutrality, cultivating energy storage professionals is crucial for ensuring future national energy security and advancing new energy storage technologies. As an emerging interdisciplinary field, energy storage science and engineering plays a key role in developing high-level professionals capable of driving technological and industrial advancements. Since 2020, 84 universities in China have established energy storage science and engineering as a major. However, talent cultivation in this field remains in an exploratory phase, particularly in the development of experimental teaching methods. This discipline integrates knowledge from diverse fields, including chemical engineering, electrical engineering, materials science, management science, and energy and power engineering. Effectively incorporating these disciplines into practical teaching poses a significant challenge in energy storage education. In response to the strong demand and rapid evolution of the energy storage industry, there is an urgent need to develop a practical experimental teaching system that aligns with industrial advancements and meets sectoral requirements. Such a system would accelerate the training of top-tier, innovative professionals equipped with interdisciplinary knowledge, technical expertise, and practical skills. Leveraging Tianjin University's strengths in "Emerging Engineering Education" initiatives and the superior resources of the National Industry-Education Platform for Energy Storage (Tianjin University), this paper proposes a three-tiered, four-stage experimental teaching system based on the energy storage science and engineering major at Tianjin University. This innovative system emphasizes interdisciplinary integration, the synergy between scientific principles and educational practices, and active collaboration between academia and industry. It incorporates multidisciplinary knowledge through a structured sequence of foundational experiments, specialized experiments, project-based learning, and comprehensive capstone projects. These approaches aim not only to enhance the quality of talent cultivation but also to provide a scalable model for broader adoption in the energy storage sector.