YPVDF nanofiber membranes coated with yttria-stabilized zirconia (YSZ) nanoparticles on the surface of polyvinylidene fluoride (PVDF) nanofibers were prepared by a coaxial electrospinning process. In this process, 1,3-dioxolane (DOL) was used as the polymer precursor monomer; tris (trifluoromethanesulfonic acid) aluminum (Al(OfT)₃) was used as the initiator; LiTFSI was used as the lithium salt; and FEC was used as the SEI film additive. In situ polymerization was performed on the YPVDF nanofiber base film with a high surface area to generate PDOL. PDOL formed a large number of YSZ/PDOL organic-inorganic rapid-ion-transport interfaces with YSZ on the surface of YPVDF fibers. PDOL@YPVDF-CSE has an ionic conductivity of 0.94 × 10^-4 S/cm at room temperature. The Lewis acid sites (Zr⁴⁺, Y³⁺, and oxygen vacancies) of the YSZ on the YPVDF fibers facilitate the adsorption of anions from LiTFSI, thus promoting the dissociation of Li⁺ from LiTFSI. Therefore, PDOL@YPVDF-CSE YSZ has a lithium ion migration number of 0.78, and the Lewis acid sites provided by YSZ can synergistically promote the ring opening polymerization of DOL with Al (OfT)₃, resulting in a DOL monomer conversion rate of 98.2%. In addition, because of the introduction of YPVDF nanofibers, the heat-resistant decomposition temperature of PDOL@YPVDF-CSE increased to 312 ℃. A symmetric lithium battery assembled as Li| PDOL@YPVDF-CSE |Li was capable of stable cycling for over 1500 h. An assembled LiFeO₄| PDOL@YPVDF-CSE | battery subjected to 800 cycles at a rate of 0.5C had a capacity retention rate of 97.4%, and after 500 cycles at a rate of 2C, the capacity retention rate was 96.8%. Meanwhile, the NCM622 | PDOL @ YPVDF-CSE | Li battery assembled with LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) as the positive electrode had a capacity retention rate of 96.2% after 150 cycles at a rate of 1C.

∶By disassembling lithium ferric manganese phosphate (LMFP) batteries with different cycles, the relationship between the deposition of Mn and Fe in the negative electrode and the cycle was obtained. The results showed that the deposition of Mn in the negative electrode of LMFP increased linearly with the increase of cycles, while the deposition of Fe in the negative electrode remained unchanged and was at the same level as that of lithium iron phosphate (LFP). The properties of positive and negative electrodes at the beginning of the cycle life (BOL) and the end of the cycle life (EOL) were characterized using the capacity recovery test, X-ray diffraction (XRD), and electrochemical impedance spectroscopy (EIS). The results showed that the structure of LMFP cathode material is relatively stable during the cycle, and the capacity loss primarily occurs in the negative electrode. A small amount of Mn²⁺ dissolved from the positive electrode of LMFP migrates and deposits to the negative electrode, catalyzing the decomposition of electrolytes and lithium salt and resulting in a large amount of active lithium loss. Simultaneously, the negative electrode impedance increases and finally shows a significant decline in circulation capacity.

Sodium-ion batteries (SIBs) have attracted immense attention in large-scale electrical energy storage. During the synthesis of layered cathode materials by the high-temperature solid-phase method, sodium salts, and metal oxides form a layered structure by breaking and reorganizing chemical bonds. However, some sodium salts fail to enter the bulk structure of the materials and remain on the surface of the materials to form alkaline substances, such as NaOH, NaHCO₃, and Na₂CO₃, collectively referred to as residual bases. These residual bases accelerate the dissolution of the transition metal layer in the liquid electrolyte, leading to irreversible degradation of the crystal structure of the cathode material. Moreover, the decomposition of sodium carbonate at high voltages produces CO₂ gas, one of the causes of battery pack expansion poses a safety hazard, and leads to severe sodium ion losses in sodium-ion full batteries, thereby limiting their energy density and cycle life. To solve this issue, this study adopted an innovative experimental scheme to successfully convert the harmful residual alkali components on the material surface into the NaMgPO₄ cladding structure. Differential electrochemical mass spectrometry (DEMS), a technique that has gained immense prominence in recent years, was used to confirm the effectiveness of this residual alkali treatment process. The prepared NaMgPO₄ cladding layer was uniformly covered on the surface of the cathode material with a thickness of about 5 nm and good crystallinity. X-ray diffraction (XRD) analysis showed that the diffraction peaks of the cladding material corresponded exactly to those of the original material, demonstrating that a small amount of cladding would not affect the crystal structure of the material. Furthermore, the presence of the capping layer slightly increases the spacing between the sodium layers, improving the multiplicity performance of the cathode material. The NNM-2 material showed excellent multiplicity performance, achieving a specific capacity of 169 mAh g^-1 during the first turn at a current density of 1C and a capacity retention rate of 74% following 100 cycles. The CO₂ emission was almost eliminated during the charging and discharging process, suggesting a substantial reduction in the residual alkali content.

Sodium-ion batteries (SIBs) are considered a highly promising battery technology in the post-lithium era due to their abundant resources, affordability, high safety, and eco-friendliness. However, their use in cold regions and seasons is significantly limited without appropriate thermal protection. Although several factors contribute to the common capacity loss of SIBs at low temperatures, most are associated with the liquid electrolyte. In low-temperature environments, the fluidity of the electrolyte decreases, leading to poor compatibility between the electrolyte and the electrode. Therefore, the transport capacity of the sodium ion(Na⁺)in the electrolyte significantly decreases, resulting in a rapid decline in SIB performance and a substantial reduction in cycle life. This study explores the behavior of Na⁺ in the main electrolyte and at the interface between electrolyte and electrodes, summarizes the strategies to improve the performance of SIBs at low temperatures focusing on electrolyte salts, solvents, and additives, and highlights that the kinetic decline of Na⁺ through the interface is the primary factor affecting the performance of the battery at low temperatures. In addition, this study introduces new insights on solvation structure and systematically examines the design strategies of low-temperature electrolytes based on the regulation of solvation structure to improve the composition of the electrode/electrolyte interface and reduce the potential barrier of desolvation energy. Furthermore, potential approaches are proposed to improve the low-temperature performance of the battery by enhancing interface dynamics, providing valuable guidance for the effective design of low-temperature SIBs.

Vanadium compounds with a variety of crystal structures have high theoretical capacity and are considered a promising positive electrode material for water-based zinc-ion batteries. However, due to the slow diffusion of zinc ions and poor structural stability, further development is limited. In this paper, Mn(VO₃)₂-NaV₈O₂₀ heterogeneous nanoribbons were designed and synthesized using a simple one-step hydrothermal method. A Mn(VO₃)₂-NaV₈O₂₀@PANI (PMNVO) composite material was then created by applying a PANI film on the nanoribbons' surface through an electrostatic self-assembly method. By combining these two strategies, the cathode material for AZIBs demonstrates efficient ion-electron cooperative transport, rapid zinc ion transport rate, excellent zinc storage performance, and a stable crystal structure. Experimental results demonstrate that the heterogeneous interface between the two crystals enhances the charge transfer kinetics of zinc, improves zinc diffusion kinetics, and the nanoribbon morphology provides more reactive sites. Additionally, when combined with PANI, the material's electrical conductivity is enhanced, and the crystal structure is more stable, resulting in PMNVO exhibiting excellent Zn storage properties and high electrochemical kinetics. Specific test results indicate that at a current density of 0.2 A/g, the specific capacity of PMNVO is 417.6 mAh/g. At 1 A/g, the initial specific capacity is 360.3 mAh/g, with a capacity retention rate of 91.0% after 200 cycles. At 8 A/g, the initial specific capacity is 189.3 mAh/g, and the capacity retention rate after 3000 cycles is 99.3%, demonstrating excellent rate performance and long cycle life. The apparent diffusion coefficient ranges from 1.87×10^-10 to 4.97×10^-11 cm²/s, indicating good electrochemical kinetics. This study serves as an example for the design of AZIBs cathode materials.

Lithium-ion batteries have become the most widely used energy storage devices, with energy density and power density as critical parameters for assessing their performance. However, high energy density and high-power density represent a contradictory pair, exhibiting a "trade-off" phenomenon. The development of lithium-ion batteries with both high energy density and high-power density (referred to as "dual-high" lithium-ion batteries) is crucial for addressing the demands of high-efficiency modern equipment (such as special equipment, electric drones, etc.). Essential new materials are the primary factors determining the performance of dual-high lithium-ion batteries, and the advancement in battery performance requires a focus on energy storage mechanisms and innovative material preparation technologies. This paper first introduces the definition and key performance indicators of dual-high lithium-ion batteries and subsequently reviews the recent advancements in critical cathode and anode materials for dual-high lithium-ion batteries and their modification strategies. It also evaluates the influence of various electrolytes on the performance of lithium-ion batteries and discusses the design and fabrication of dual-high lithium-ion batteries. A summary of the current research status, challenges, and future development trends is presented, offering innovative concepts for designing and developing the next generation of dual-high lithium-ion batteries.

The formation of a solid electrolyte interface (SEI) on the surface of anodes leads to the irreversible consumption of the active lithium in lithium-ion battery systems during the first charge. This results in capacity fading and cycle life decay, particularly in the case of high-energy-density battery designs. Compensation of active lithium can be an effective way to solve such problems. The recent prelithiation strategies can be mainly divided into cathode and anode prelithiation based on the process method. Notably, cathode prelithiation technology has attracted wide attention because of its safety and high compatibility with battery-manufacturing processes. Cathode prelithiation technology mainly refers to adding lithium-containing compounds with high irreversible capacity into cathodes; in other words, cathode lithium additives are used. The main cathode lithium supplement additives are binary lithium-containing compounds, ternary lithium-containing compounds, and organic lithium-containing compounds. In addition, the active lithium replenishment of cathodes can be achieved by designing over-lithiated cathode materials. This study summarizes the research progress of cathode prelithiation strategies for improving the initial efficiency, energy density, and cycle life of lithium ion batteries. Different strategies are compared. The prospect for further commercialization of cathode prelithiation strategies is also predicted.

Lithium-ion battery electrode coating process is a crucial component in battery manufacturing, significantly affecting energy density, cycle life, and safety of the batteries. As the demand for batteries in the new energy sector increases, ensuring high-quality coatings while enhancing production efficiency has become a key priority for battery manufacturers. In the context of the ongoing trends of digitalization and industrialization in battery manufacturing, this study provides an in-depth analysis of advancements in lithium-ion battery wafer coating techniques; it comparatively examines the characteristics of common coating techniques and identifies critical factors influencing coating quality and efficiency from the perspectives of internal and external flow fields. In addition, this study systematically investigates the optimization strategies for slit extrusion coating using process simulation. Furthermore, it compares and examines the characteristics of various online inspection approaches used in production lines, assessing their impacts on production efficiency and energy density. This study also discusses the practicality and limitations of these inspection approaches, identifies existing research gaps, and outlines future development trends. The aim is to provide theoretical support and practical guidance for optimizing the lithium-ion battery coating process.

Lithium-ion batteries offer several advantages, including high specific energy, extended cycle life, high power output, and low environmental impact, making them widely used in applications such as new energy vehicles, aviation, and energy storage. However, as the energy density of lithium-ion batteries increases, safety concerns have become more pronounced, attracting significant attention. To address these challenges and enhance battery safety, researchers have explored various strategies to mitigate thermal runaway. This study reviews relevant articles on the thermal runaway mechanism of Li-ion batteries and the optimization and improvement of battery materials to reduce the degree of thermal runaway. First, the potential mechanism of thermal runaway triggering and various stages of reaction are reviewed, including solid electrolyte interface decomposition, negative electrode and electrolyte reaction, electrolyte decomposition, and positive and negative electrode redox reaction, which generate substantial heat and combustible gases. Second, based on the thermal runaway trigger mechanism, the improvement measures and defects at the material level are summarized, such as the use of metalized plastic current collector collectors, the addition of flame retardants or self-destructors, and the use of high-safety electrolyte and multi-functional separators. The challenges associated with the development of high-safety batteries, such as improving safety while accompanied by electrical performance reduction, are also outlined. The aim is to provide a better understanding of the thermal runaway triggering mechanism of lithium-ion batteries and strategies to improve battery safety, offering insights for future research focused on developing safer lithium-ion batteries and advancing the field.

Solid-solid phase change materials (SSPCMs) are a new class of materials capable of efficiently absorbing and releasing thermal energy through a reversible phase change process while maintaining solid-state properties. These materials have attracted extensive attention from the scientific research community owing to their small volume change during phase change, absence of any leakage phenomenon at high temperatures, and excellent mechanical properties. Polymeric SSPCMs with polyethylene glycol (PEG) as the phase-transition unit have shown great potential for applications in various key areas such as energy storage, temperature regulation, and thermal management. This is because they possess unique advantages such as high energy storage density, excellent cycling stability, and tunable phase transition temperature. This study focuses on the research progress of PEG-based SSPCMs in recent years, summarizing their classification and chemical preparation methods. Considering their defects such as thermal conductivity, flame retardancy, and poor recyclability, we focus on optimizing and modifying their properties to broaden their applications. This study also summarizes the research progress on their potential applications in the fields of thermal management of batteries, electronic devices, and flexible wearables, as well as the thermal energy storage of solar energy. Finally, the current challenges affecting PEG-based SSPCMs were analyzed. Future research should be devoted to the research and development of environmentally friendly and multifunctional SSPCMs, as well as to improving the comprehensive performance, reducing the cost, and enhancing the feasibility of SSPCMs. Another research avenue is to explore the potential of their application in a wider range of fields to further promote their practical applications.

In order to explore the suppression effect of composite water extinguishing agents on lithium-ion battery fires, fire extinguishing experiments were conducted using water-soluble salts and surfactants at varying concentrations of a single component. Based on the results, four complex solutions (CS) were synthesized. The study focused on the surface tension and pyrolysis characteristics of fire extinguishing agents to determine the mechanism behind fire suppression. Simultaneously, various experiments were conducted to investigate characteristics of fine water mist parameters in extinguishing lithium-ion battery fires, focusing on the impact of working pressure and atomization cone angle on the effect of fire suppression. The results showed that applying four compound extinguishing agents can effectively inhibit the thermal runaway propagation of lithium-ion batteries. The results of the comprehensive evaluation of various extinguishing parameters and the effectiveness of extinguishing agents indicated that the CS-4 extinguishing agent, which is formulated with 0.15% FC-4 (nonafluorobutanesulfonyl fluoride), 1% APG0810 (alkyl glucoside), 0.32% CH₄N₂O (urea), and 2% NaHCO₃ (sodium bicarbonate), demonstrated an outstanding comprehensive extinguishing effect. High-pressure fine water mist had a stronger flame suppression ability, whereas the flame under low-pressure fine water mist appeared to be intensified after being initially suppressed. The fire suppression efficiency of the fire extinguishing agent showed a V-shaped trend with the increase of water mist atomization cone angle, and the best effect was achieved when the atomization cone angle was 60°. The comprehensive research on the characteristic parameters of composite water extinguishing agents and water mist extinguishing systems can provide references for developing fire control strategies and extinguishing agents for lithium-ion battery fires and the safety protection of lithium battery energy storage power stations.

With the continuous iteration and innovation of the thermal management technology of energy storage batteries, direct immersing liquid cooling technology has shown immense application potential. Compared with the indirect cold plate liquid cooling technology, the direct immersing liquid cooling technology has significantly improved battery heat transfer and temperature equalization. Although there is a continuous emergence of relevant research on immersing battery pack cooling, systematic induction and comprehensive studies on the impact of key influencing factors on immersing battery systems remain insufficient. Therefore, the effects of coolant type, cell heat value, and flow channel on the temperature characteristics were investigated using numerical simulations based on a self-developed novel immersing battery pack system. The research indicated that the fluorinated solution exhibits the best temperature performance as immersing coolant, followed by silicone and mineral oils. The key parameter that determines the performance of the coolant is the fluid kinematic viscosity. Compared with the cold plate technology, the immersing technology showed better cooling performance at low, medium, and high charge and discharge ratios, more significantly in medium and high cell heat value conditions. The maximum temperature, maximum temperature difference, and maximum temperature difference between the top and bottom surfaces of the immersing battery pack decreased by 27.26 ℃, 1.76 ℃, and 32.03 ℃, respectively. The flow channel significantly improves the temperature performance of the battery pack. Compared with the battery pack without a flow channel, the maximum temperature, maximum temperature difference, and maximum temperature difference between the top and bottom surfaces of the battery pack with a flow channel decreased by 3.34 ℃, 2.65 ℃, and 1.54 ℃, respectively. It improves the uniformity of the coolant distribution by changing the spatial distribution of the flow lines in the battery pack. This research offers a reference and summarizes the heat flow field for the thermal design of immersing battery packs and the selections of cells and coolants for future applications.

Grid-forming energy storage plays an important role in new power systems; for instance, such storage participates in the frequency modulation and inertia control of power systems. However, whether such grid-forming energy storage can fully function in a regulatory role depends on the correct configuration of its capacity and location. To solve the problem of optimal allocation of grid-forming energy storage access to the power grid, this work examined the optimal allocation method of fixed volume location of grid-forming energy storage participating in the primary frequency modulation of a power system with a high proportion of new energy. First, the control strategy of the storage, including its active power frequency droop control strategy and the virtual synchronous machine control strategy, was examined. A comprehensive frequency regulation method combining the two control strategies was proposed. The primary frequency regulation effect index, power grid vulnerability index, and energy storage economy were considered as multiple objectives. Accordingly, a combination of the cross-mutation multi-objective particle swarm optimization algorithm with the TOPSIS algorithm was adopted. Based on the information entropy method weight from the Pareto solution set, an optimization plan was devised for determining the location and capacity of the grid-forming energy storage power stations. Finally, validation and optimization analyses were conducted for an actual calculation example of a regional power grid.

This article aims to explore in depth the energy characteristics of the vehicle electromechanical integrated composite energy storage system. By analyzing its composition, working principle, performance of each energy storage component, and energy management strategy, it elaborates on the application advantages and future development directions of the system in the field of electric vehicles. Through literature review and theoretical analysis, this article reveals the significant role of composite energy storage systems in improving the range, acceleration performance, and energy recovery efficiency of electric vehicles, providing valuable references for the development of electric vehicle technology.

Ammonia decomposition for hydrogen production is critical in ammonia-hydrogen energy storage systems. Electrically heated hydrogen production, catalyzed for high efficiency and flexibility, has emerged as the most widely adopted method. This paper proposes a new heating strategy, i.e., segmented heating of the ammonia decomposer, to enhance the ammonia decomposition rate and energy utilization efficiency. The decomposer is subjected to simulation analysis based on the Temkin-Phyzev reaction kinetics and the plug flow reactor models. The parameters of the decomposer under uniform heating are analyzed to determine the temperature distribution and molar fractions of each component within the reactor, thereby clarifying the factors influencing energy utilization efficiency. Compared with the ammonia decomposition rate and energy utilization efficiency across the five heating coverage rates, we optimized the optimal configuration parameters, verified the principle of segment heating, and analyzed the energy utilization efficiency to confirm the rationality of segment heating. The results indicated that the decomposition rate of ammonia decomposition is faster, and the energy requirement is greater in the inlet section of the decomposer. Under a constant total energy supply, segmented heating significantly enhances the ammonia decomposition rate and energy utilization efficiency. The concentration of energy supply at the front end enhances ammonia decomposition efficiency and reduces the discharge gas temperature at the outlet. The rate of ammonia decomposition remains relatively stable when the energy supply segment ratio is less than 20%. This study is crucial for improving the ammonia decomposition rate and energy utilization efficiency, offering significant insights for developing ammonia-hydrogen energy storage technology.

With the continuous development and improvement of wind power generation technology in China, its significance in power system operation and scheduling is becoming increasingly prominent. To predict wind power efficiently and accurately and reduce the negative impact of a large number of wind power on the grid, this paper focuses on a backpropagation (BP) neural network combined with ERA5 data for wind power prediction of a wind farm in the northern part of China and uses particle swarm optimization (PSO) algorithm to optimize the model, combined with the average absolute error, root mean square error and Pearson correlation coefficient to analyze the effect of wind power prediction. The results showed that the predicted and measured wind power changes in the model training set are the same, exhibiting a similar trend of increase and decrease. The average absolute error of the BP model is 702.12 W, with a root mean square error of 1000.18 W and the correlation coefficient is 0.91, whereas the average absolute error of the PSO-BP model is 700.75 W, with a root mean square error of 995.16 W, and the correlation coefficient is 0.94. The ERA5 data in the test set overestimates the wind power to a certain extent; however, the overall trend remains the same. The average absolute error of the BP model is 861.09W, with a root mean square error of 1150.86W, and the correlation coefficient is 0.81, whereas the average absolute error of the PSO-BP model of 829.55W, with a root mean square error of 1117.39 W, and the correlation coefficient is 0.83, indicating effective modeling. The prediction effect of the PSO-BP model is relatively good, and the PSO-BP model has a certain degree of improvement compared with the BP model, which has good applicability in predicting wind power in this region. The results of this study can provide a reference for the prediction of wind power in areas with limited observational data or where the quality of observational data is not high.

The development of power systems and the popularization of distributed generation have made the stability and operational efficiency of distribution networks a research focus. Distribution network communication technologies enable information transfer and data exchange among devices, supporting grid monitoring, control, and management; distributed energy storage technologies are vital for peak shaving and frequency modulation, significantly impacting grid stability. The collaborative application of these two technologies is an essential development direction in the power system domain, bearing significant importance for enhancing grid stability, reliability, optimizing energy allocation, and improving power quality. This study provides an overview, including technological progress, principles, advantages, and the significance and development of their collaborative applications. The synergistic application of distribution network communication and distributed energy storage technologies represents an important development trend in the current power system sector, with substantial implications for improving grid stability, reliability, energy allocation optimization, and power quality enhancement.

To address the challenge of synchronously and rapidly quantifying the increase in system reliability during the site selection and sizing process of energy storage in distribution networks, this study proposes an optimization configuration model and solution approach that integrates system reliability constraints. First, the proposed model evaluates the impact of energy storage configuration on system reliability using the expected power shortage index as a metric. This study develops a dual-layer planning model for energy storage optimization in distribution networks, considering economic and reliability objectives. The upper layer focuses on minimizing the net investment cost of energy storage, whereas the lower layer aims to minimize load reduction under N-1 fault conditions in the distribution system. Second, based on the KKT condition and composite function differentiation rule in the process of solving optimization problems using the interior point approach, analytical expressions for the sensitivity analysis of the system reliability index to the upper limit of energy storage output and capacity are derived. These expressions were integrated into the linearized system reliability inequality constraints, facilitating parameter transfer between the upper and lower layers of the model. Finally, the effectiveness and rationality of the proposed dual-layer planning model and the derived sensitivity analysis expressions are validated using the improved IEEE RTS-79 test system.

The radial chamber structure of a turbine in a compressed air energy storage (CAES) system leads to circumferential nonuniformity of the airflow at the guide vane inlet, resulting in the degradation of turbine performance. This paper presents a numerical simulation of a single-stage axial turbine coupled with a radial inlet chamber in a CAES system and examines the impact mechanism of the nonuniform distribution of the guide vane circumferential direction on the turbine efficiency and its internal flow field. A large number of sampling schemes are obtained by the Latin hypercube sampling method, while dynamic updating and simulation of the 3D model and mesh are achieved by parametric processing and software interaction, facilitating the automated simulation of the entire process when the nonuniform guide vane is circumferentially arranged by the sinusoidal modulation function. The results indicated that when the difference between the circumferential angle of the adjacent guide vanes and the uniform distribution angle is less than 10%, the overall performance parameters, such as turbine isentropic efficiency, expansion ratio, and mass flow rate, vary by less than 0.1% compared with the initial uniform distribution scheme. If the difference between the circumferential angle of the adjacent guide vanes and the uniform distribution angle exceeds 15%, it will have an obvious negative impact on the internal flow field of the turbine. This discrepancy will lead to an increased pressure loss of the guide vane section, a decrease in the expansion ratio, an increase in the mass flow rate, and a downward trend in the isentropic efficiency, with a maximum decrease of about 1%.

As a crucial component of the modern energy system, the development of smart grids is inherently linked to the synergistic application of automation control and energy storage technologies. Computer-driven automation control and energy storage technologies play a significant role in enhancing the efficiency, stability, and reliability of power grid operations. This article focuses on the integrated application of automation control and energy storage technologies within smart grids, proposing an optimization strategy to facilitate the efficient operation and stable development of the power grid. It aims to foster continuous innovation and application of smart grid technologies, ultimately providing robust technical support for the sustainable development of the power grid.

The reduction in the proportion of synchronous machines in the new power system brings challenges to maintaining the stable operation of the system. Consequently, the constructed-grid energy storage system has received immense attention due to its voltage source external characteristics and good weak-grid stability. Nevertheless, the nature of grid-constructed energy storage is still a power electronic device with multiple control loops and control parameters; thus, the stability of grid-constructed energy storage needs to be considered. Numerous studies have focused on the stability of multi-loop controlled energy storage, while there are fewer studies on the stability of structural network type. This paper examines the main circuit and power ring of grid-constructed energy storage using a virtual synchronous generator (VSG). We analyzed the voltage, current, and virtual impedance loops from the perspective of actual control demand, selected the dq coordinate system, and established the multi-loop small-signal and the equivalent output impedance models. Subsequently, the influence of the control loop parameters on the impedance characteristics of different frequency bands is analyzed, followed by a grid-connected small-signal stability analysis using the impedance method. Finally, these results were confirmed by simulation. The findings indicated that the VSG-type grid-connected energy storage exhibits negative damping characteristics in the low-frequency band (1—10 Hz), with the power loop inducing an elevation in the low-frequency negatively damped resonance frequency. Furthermore, the voltage loop induces negative damping characteristics in the middle-frequency band of the system (10—100 Hz), whereas the current loop induces negative damping characteristics in the high-frequency band of the system (100—10000 Hz). Under a weak grid condition, the reduction in power ring damping and the increase of virtual inertia, along with a decrease in voltage ring proportionality coefficient and an increase in integral coefficient, can induce low-frequency oscillations that may lead to destabilizing the system, while the current ring has a small effect on system stability. With a short circuit ratio (SCR) of 2, introducing the virtual impedance loop enlarges the area of the stabilization region of the power loop control parameter by about 1.67 times and the area of the stabilization region of the voltage loop control parameter by about 4.5 times.

As a new type of compressed energy storage technology, compressed carbon dioxide(CO₂) energy storage has received widespread attention from the academic and business communities in recent years. This technology can meet the demand for large-scale, long-term energy storage in China and has good development prospects. In this regard, this study outlines the research status of this technology from two aspects: compressed CO₂ energy storage systems and CO₂ storage devices. The results of this study show that the existing compressed CO₂ energy storage systems can be mainly classified into five categories: energy storage systems with supercritical CO₂ in low-pressure and high-pressure tanks, systems with liquid CO₂ in a low-pressure tank and supercritical CO₂ in a high-pressure tank, systems with gas in the low-pressure tank and supercritical CO₂ in the high-pressure tank, systems with liquid CO₂ in the low-pressure and high-pressure tanks, and systems with gas in the low-pressure tank and liquid CO₂ in the high-pressure tank. The existing theoretical researches mainly focus on the steady-state analysis of system performance. Few analyze the dynamic characteristics of the system under full operating conditions. Demonstrative projects mostly adopt an energy storage system with high-pressure, liquid-low-pressure atmospheric flexible storage. CO₂ storage devices mainly comprise underground saline aquifers, underground salt caverns, flexible gas storage sheds, adsorption gas storage beds, gas tanks, and liquid tanks. Among them, flexible gas storage sheds and gas/liquid tanks have been used in engineering applications. However, a gas storage shed has a large volume, and further research is required to understand the thermodynamic characteristics of CO₂ during charging/discharging processes. Along this line of research, future development trends of compressed CO₂ energy storage systems are stated. On the one hand, compressed CO₂ energy storage involves various forms of energy and can be coupled with external cold/heat sources and other thermodynamic systems to meet the demands of cold, heat, and electricity storage on the load side. Such coupling improves the overall system performance. However, organic working fluids can be mixed with CO₂ to solve the problems of dry ice formation and low system pressure ratio in low-pressure CO₂ liquid storage. Thus, high- and low-pressure liquid storage can be realized to increase the compressed energy storage density drastically.

Innovative energy storage promotes energy transformation and establishes a new "source network storage" power system, serving as an essential infrastructure and emerging as a key strategic industry in China. In recent years, driven by national policy, technological research, and innovative development, the trend of new energy storage scale development in China has gradually emerged. Gravity energy storage is recognized as a novel strategy for its high efficiency, environmental sustainability, exceptional stability, and large-scale energy storage capacity, as confirmed by countless experts and researchers. Simultaneously, the depletion of underground resources leads to the abandonment of certain mines. This paper reviews the development of shaft-type gravity energy storage systems, explains the potential of reusing abandoned mines as a resource in the development of gravity energy storage technology, constructs a mathematical model of shaft-type gravity energy storage system based on linear motors, and emphasizes the value of the linear motors in the special application scenario of abandoned mines, where slide rails replace cables in the operation of rotating motors, effectively addressing the challenges posed by abandoned mines. The use of slide rails instead of cables during rotating motors enhances the stability of the shaft-type gravity energy storage system, analyses the possible configurations of the system, compares the advantages and disadvantages of the different linear motors, elucidates the control technology and the objectives of the linear motors under diverse operating conditions, summarizes the existing multi-motor control technology, and anticipates future applications of linear motors in the shaft-type gravity energy storage system.

The performance of turbine compressors plays a crucial role in the overall efficiency of large-scale compressed gas energy storage systems. Surge, an inherent characteristic of turbo-compressors, cannot be eliminated but can be suppressed, significantly affecting the operational efficiency, safety, and stability of compressors. Thus, anti-surge technology is essential for ensuring the safe and stable operation of turbo-compressors, particularly in compressed gas energy storage systems, where compressors frequently start and stop. This study reviews recent developments in turbo-compressor anti-surge technologies, focusing on four key areas: First, it discusses the physical characteristics of turbo-compressor surge, including the mechanism behind surge generation, identification techniques, and flow field characteristics when a surge occurs. Second, it provides an overview of the progress in three anti-surge control approaches and their respective advantages and disadvantages, including passive control to prevent surge by limiting the compressor inlet flow, active control to prevent surge by changing the compressor performance, and active/passive control that combines active control and passive control. Third, it examines compressor surge detection technologies based on signal analysis and processing. Finally, it explores future trends in turbo-compressor anti-surge technologies. A comprehensive analysis indicates that a combination of an in-depth understanding of surge characteristics, a well-designed anti-surge control strategy, and advanced surge detection technologies can effectively suppress compressor surges.

Faced with the escalating global energy crisis and environmental pollution, new energy vehicles are gradually replacing traditional fuel vehicles and becoming the main trend in the development of the automotive industry. This article aims to explore the latest research and innovation in the manufacturing technology of new energy vehicles, with a focus on analyzing the application of intelligent manufacturing technology in the production of new energy vehicles, including the latest progress in battery technology, motor technology, electronic control systems, lightweight design, intelligent driving technology, and other aspects. By reviewing domestic and international research results, this article aims to provide theoretical support and technical guidance for the sustainable development of the new energy vehicle industry.

Lithium-ion battery failure analysis is a critical area in battery research and development, aging mechanism studies, and battery cascade utilization. The reliability of analysis results depends on the accurate testing and characterization of materials and device performance parameters. Currently, numerous methods are available for characterizing the physical properties of battery materials and devices. However, variations in test items, test cycles, sample preparation, and equipment popularization influence the frequency and selection of these techniques. For instance, scanning electron microscope and X-ray diffraction have become fundamental methods for analyzing material morphology and structure. Advanced characterization technologies such as synchrotron radiation, secondary ion mass spectrometry, and nuclear magnetic resonance are increasingly applied to investigate lithium-ion battery testing and failure research. This study reviews several mainstream and advanced physical characterization methods in lithium-ion battery analysis. In addition, this study highlights the best characterization directions of various technologies based on their working principles to address the specific issues related to failure mechanisms, providing valuable guidance for researchers in selecting the appropriate characterization methods to enhance their analysis and offer stronger evidence for failure analysis.

Lithium-ion batteries (LIBs) are widely used in energy storage due to their high energy density, long lifespan, and low cost. The intercalation and deintercalation of lithium ions in the positive and negative electrodes cause volume changes, accumulating stress and strain within the battery. These stress and strain are closely related to the battery's state of charge (SOC) and state of health. Therefore, real-time monitoring of the battery's strain changes is crucial for ensuring its long-term safety and stability during operation. In this study, Fiber Bragg Grating (FBG) sensors were used to monitor the strain of graphite-lithium iron phosphate (AG||LFP) pouch cells. A method was developed to integrate optical fibers with the battery and electrode sheets. This allowed for in situ monitoring of the strain development in each electrode and the entire battery, and the sources of stress in pouch cells were analyzed. Furthermore, using the strain data, the SOC of AG||LFP pouch cells was estimated, demonstrating the potential of FBG sensors in monitoring the battery charge levels.

The prediction of lithium-ion battery capacity using data-driven methods involves many challenges, such as the difficulty in obtaining complete charging data, low data sampling precision, and poor quality of feature extraction. To overcome these challenges, this study proposes a lithium-ion battery capacity prediction method based on short-term charging data and an enhanced whale optimization algorithm. First, to improve data precision, the charging data were supplemented by cubic spline interpolation. Next, by exploring the relationship between the charging voltage curve and capacity degradation, the voltage increment over a specific charging time interval was identified as the feature factor. An enhanced whale optimization algorithm was then utilized to extract aging features effectively from the short-term charging data. Subsequently, a Gaussian process regression model was constructed for capacity prediction. After determining the amount of training data, the predictive results of different algorithms were compared to verify the effectiveness of the proposed model. Finally, the method was tested on different batteries to validate its prediction accuracy and generalization capability. The results demonstrate that for the laboratory dataset, by using the first 15% of aging features as the training set, the maximum error of this type of battery can be controlled within 2.49%, with 97% of the prediction errors being within 1.5%. For a publicly available dataset, by using only 12 groups of training data, the maximum error of this type of battery can be controlled within 1%, achieving accurate capacity prediction using low-precision and short-term charging data.

To ensure the safe and stable operation of energy storage systems, the accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is crucial. This study presents an integrated forecasting model that combines the artificial bee colony (ABC) algorithm with a long short-term memory (LSTM) network enhanced by dropout techniques. This combination effectively improves the accuracy of RUL predictions for lithium-ion batteries. First, the dropout regularization method is utilized to effectively mitigate overfitting, thereby enhancing the generalization capability of the predictive model. Subsequently, an activation layer network structure is introduced to address capacity recovery and data noise issues, significantly enhancing the ability of the model to handle complex nonlinear data. Thereafter, the hyperparameters of the LSTM-based comprehensive forecasting model are optimized using the ABC algorithm to avoid local optima and improve the precision of RUL predictions. Finally, the predictive accuracy and robustness of the proposed model are verified using a public dataset from the NASA Research Center and the CALCE. The paper conducts an experimental analysis and verification of The predictive performance of various algorithms were experimentally analyzed and verified using training data at 40% and 60% levels. The performance of swarm optimization algorithms, such as the Sparrow Search Algorithm and the Humpback Whale Optimization Algorithm, were also compared. The experimental results demonstrate that the proposed ABC-LSTM integrated forecasting model can capture the global trends and local characteristics of the capacity degradation of the lithium-ion battery more accurately than the compared models. The root-mean-squared error of the RUL prediction results obtained with a 60% proportion of training data remained consistently within 1.02%; the mean absolute error remained consistently within 0.86%, and the fitting coefficient exceeded 97%.

The degradation of lithium battery state of health (SOH) is to some extent a nonsmooth stochastic process, which makes most of the current point estimation machine learning approaches limited in practical applications. In recent years, Gaussian process regression (GPR), which is based on the Bayesian theory, has been widely used in lithium battery SOH interval estimation due to its ability to quantify uncertainty in the estimation results; however, the performance of GPR significantly depends on the selection of its kernel function. Current studies typically rely on empirically selecting a fixed single kernel function, which may not be suitable for diverse datasets. To address this limitation, this study introduces an SOH interval estimation method for lithium batteries based on an adaptive optimal combination of kernel functions in GPR. The proposed method first extracts multiple health factors from the battery's charge/discharge data and uses the Pearson correlation coefficient method to optimize six health factors that are strongly correlated with SOH as inputs to the model. Subsequently, with a set of seven commonly used kernel functions, new kernel function combinations were created by two-by-two random combinations. Cross-validation was then used to adaptively optimize the optimal kernel function combinations. The proposed approach was validated using three different datasets, and the results indicate its excellent performance in SOH interval estimation. For the three publicly available datasets, the average interval width index is within 0.0530, the average interval score is greater than -0.0004, and the root mean square error is less than 0.0181.

Accurate estimation of state of health (SOH) is crucial for ensuring the safe and reliable operation of lithium-ion batteries and extending their service life. To address the challenges posed by many health features that fail to characterize the aging mechanism of batteries and the inability to accurately track SOH trends under abnormal working conditions, this study introduces an SOH estimation approach that combines empirical models with data-driven techniques. The lithium-ion battery anode SEI film thickening mechanism is incorporated into Arrhenius' law to develop an empirical model. Parameters are identified using the least-squares method, and Spearman's correlation coefficients are calculated for each parameter and capacity. The results indicate strong correlations between these parameters and capacity decline, confirming their viability as reliable health indicators for estimating SOH. Furthermore, to address the issue of the bidirectional long and short term memory (BiLSTM) network having excessive parameters and being prone to overfitting, this study employs the subtraction average based optimizer (SABO) algorithm to optimize the hyperparameters of the BiLSTM and develop the SOH estimation model. The proposed approach is validated using experimental test data and data from the National Aeronautics and Space Administration data. In addition, the performance of the method is compared with the estimation results from three algorithms: long and short-term memory (LSTM) network, BiLSTM network, and BiLSTM network with Particle Swarm Optimization (PSO). The results reveal that the SABO-BiLSTM model achieves mean absolute percentage errors of 0.043%, 0.053%, 0.259%, and 0.230% for the SOH estimation of four batteries. These values represent reductions of 94.71%, 92.62%, 88.75%, and 90.13% compared to the LSTM model, reductions of 89.11%, 91.60%, 77.90%, and 76.41% compared to the BiLSTM model, and reductions of 58.65%, 58.91%, 65.37%, and 69.29% compared to the PSO-BiLSTM model.

To address the challenges posed by the large scale and shape differences of defects on the end face of lithium battery casings, which complicate the detection of small target defects, we introduce a novel lithium battery surface defect detection algorithm based on battery defects detection-detection transformer (BDD-DETR). The BDD-DETR framework introduces a new feature perception and fusion network (FPFN) module between the general feature extraction and detection head modules. Through the adaptive feature perception module and the feature fusion path in FPFN, the deep and shallow features of this network from multiple directions are merged, the response of crucial feature information is enhanced, and redundant features are suppressed, which further improves the ability of the model to fuse multi-scale features and its capability to detect small objects. In addition, to minimize distance and shape deviations during defect bounding box regression, the shape intersection over union loss function is employed to train the network model. Experimental results indicate that on a constructed lithium battery end surface defect dataset, compared to the collaborative-detection transformer, BDD-DETR improves average precision by 3.7%, small-scale object detection precision by 8.9%, and average recall rate by 1.1%. Furthermore, BDD-DETR outperforms several advanced object detection approaches in detecting small defects in lithium batteries.

As global demand for sustainable energy increases, ensuring the safety of energy storage batteries has become crucial. Accurate prediction of battery temperature is essential for preventing overheating and reducing the risk of battery failure, fire, or explosion due to high temperatures, thereby improving device safety. This study introduces a combined prediction approach based on ensemble empirical mode decomposition, gated recurrent units, and a basic neural network (NN). Initially, lithium battery temperature data was decomposed into periodic and trend components, which serve as target values for offline supervised learning training. Next, suitable feature parameters based on the temperature characteristics of the battery were selected as input features for the model to create a real-time online prediction model. Finally, the outputs of the two models were superimposed to obtain the final prediction result. We demonstrated the accuracy of the proposed method by comparing it with common NN models. Experimental results indicate that under normal temperature conditions, the proposed method outperforms traditional models in all evaluation metrics, achieving a root mean square error of 0.10℃, an average absolute error of 0.075℃, and a maximum error of 0.34℃. Although the prediction capability of the model decreases under extreme conditions, the error remains within a reasonable range, confirming the robustness of the model under extreme conditions.

This bimonthly review paper highlights 100 recent published papers on lithium batteries. We searched the Web of Science and found 6602 papers online from Oct. 1, 2024 to Nov. 30, 2024. 100 of them were selected to be highlighted. Ni-rich oxides are still under extensive investigations for the modification of doping and coating. For alloying mechanism anode materials, beside 3D structure design, many researchers pay attention to the binders. The studies of solid-state electrolytes mainly focused on synthesis, doping, structure design and stability of pre-existing materials and developing new materials. For liquid electrolytes, the work is mainly focused on the optimal design of solvents and lithium salts for different battery systems and the test of new functional additives. While more research papers related to solid state Li-S batteries appeared, the design of composite cathode and the modification of interfaces of solid state batteries are continually drawn large attentions. There are also a number of papers on the model, measurement and analysis of battery heat production and gas composition, failure mechanisms, thermal runaway, and interfacial stability, etc. Theoretical simulations are devoted to the study of ion transport in solid state electrolytes.

The number of papers published in the field of lithium batteries is increasing rapidly, and increasingly diverse research topics are being explored. Against this backdrop, accurately monitoring the development trends and understanding the latest research directions in this field has become an increasingly complex task. By applying big data and machine-learning technologies, the BERTopic model was used to conduct text analysis on more than 180000 papers on the lithium battery from the Web of Science database. The topic landscape of this field of study was mapped, and emerging research topics and highly cited topics were identified. The results show that research on lithium batteries is significantly accelerating, and the emerging topics are lithium–sulfur batteries, dendrite growth inhibition, battery recycling, and metal recycling rapidly developing. Meanwhile, material research topics such as molybdenum disulfide nanomaterials and iron oxide electrode materials have significant academic influence. The study also discusses the monitoring of current lithium battery research topics by the "Reviews of selected 100 recent papers for lithium batteries", which has good coverage of most scientific and technological topics. This research provides a new method for topic monitoring in the field of lithium batteries, offering intelligence support for policy-making and technological development and providing references for subsequent research in the "Reviews of selected 100 recent papers for lithium batteries."

Energy storage technology is a crucial pathway to increasing the absorption ratio of renewable energy, thereby promoting the transformation of the energy structure and the achievement of dual carbon goals. With the rapid development of the energy storage industry and the swift improvement of storage technologies, the field is currently facing a significant talent shortage. The establishment and development of the Energy Storage Science and Engineering program is the key to cultivating top-tier innovative talent in this domain. At present, 84 universities have been approved to develop this program, leveraging their disciplinary characteristics and advantages to form diversified talent cultivation plans. Institutions such as Xi'an Jiaotong University have achieved remarkable results in nurturing talent in the field of energy storage, providing valuable references for the development and improvement of the program. However, given the relatively short period of establishment of this program, an in-depth understanding of the status of talent cultivation and the influence of the program is lacking. This research is based on an analysis of typical training programs from various universities. It employs a mixed-method approach combining questionnaires and interviews to investigate and analyze the status of talent cultivation in the Energy Storage Science and Engineering program. The results reveal the accomplishments and future optimization directions of the program in areas such as interdisciplinary collaboration, platform construction, resource allocation, and influence building. This study provides valuable insights and recommendations for the future development of the program in China.

Against the background of continuous and accelerated promotion of low-carbon energy transition, energy storage plays a pivotal role, serving as flexible regulating resources. The development of the energy storage industry has, thus, ushered in a major opportunity. To fill the gap created by the lack of evaluation standards for the energy storage industry, this study constructed the first comprehensive evaluation system for the high-quality development of the energy storage industry. This evaluation system is based on a comprehensive analysis of the connotation of the high-quality development of the energy storage industry. The aim was to provide a set of scientific and systematic evaluation tools for advancing the energy storage industry to promote its healthy development and to enhance the overall competitiveness of the industry. The evaluation result shows that the level of high-quality development of the industry improved at a relatively fast pace between 2021 and 2023. However, there remains a phased imbalance between the basic goals and the derivative goals, as they are not coordinated in terms of advancement. The main driving force comes from "promoting the construction of a modern industrial system" and "promoting the sustainable development of economy and society." The results of the obstacle model further confirm that "serving the transition and upgrading of the power system" is the core dimension constraining the high-quality development of the industry, and the obstacle degree of this dimension has been increasing. This dimension has always been ranked first in this regard. Other dimensions too suffer from key issues, such as the declining safety and stability of energy storage power plants, the weakening of the industry chain, the declining enterprise total factor productivity. These dimensions, too, warrant attention in the energy storage industry and must be solved. Finally, to further promote the high-quality development of the energy storage industry, a number of relevant suggestions are made.

Energy storage is crucial for large-scale electricity storage in modern power systems, playing a significant role in the stability and flexibility of power supply networks. With the widespread adoption of clean energy, the power system will face a series of fluctuations, and the development of the energy storage industry undoubtedly can effectively alleviate the pressure on electricity application and is important for the development of energy technology. This article provides a research overview of the development of the energy storage industry in the context of the "dual carbon" environmental protection goals. Firstly, it elaborates on the development prospects of the energy storage industry, including the current development layout and future trends. Then, it analyzes the core development issues and challenges faced by China's energy storage industry at this stage. Finally, it proposes a series of development suggestions, including improving policies for the development of the energy storage industry, strengthening research on key technologies, promoting the development of the industrial chain, establishing a comprehensive standardization system for energy storage, and enhancing the independence of the energy storage industry.

The existing technical routes and application scenarios of new energy storage projects are relatively simple. In the future, with the gradual expansion of new energy storage technology types and scenario applications, it will be necessary to establish a scenario applicability evaluation system considering various technology types and to compare the maturity of various scenarios and the comprehensive performance of various types of energy storage in different scenarios. First, to meet the power system regulation requirements of active power balance at different time scales under typical scenarios, the technical requirements of energy storage in different scenarios were compared from the viewpoints of power, duration, and response time. Based on the set energy-based scenarios, power-based scenarios, and comprehensive scenarios, typical configuration schemes commonly used in practice were given. Second, an energy storage suitability evaluation method that combined BWM and TOPSIS was proposed. A comprehensive evaluation index system comprising 11 indicators in four categories (technology, economy, potential, and sustainability) was established. Finally, three scenarios were weighted by expert scoring and the BMW weighting method, and 11 typical configuration schemes were evaluated by the TOPSIS method. The research results provide guidance for the planning and layout of new energy storage technologies.

As the demonstration scale of fuel cell vehicles expands in China, the industry-driven effect has become increasingly obvious. Except for the five major demonstration cities, the overall scale of fuel cell vehicle promotion in China is relatively limited, and the demonstration effect has not been effectively replicated. Local governments face challenges in deciding whether to prioritize the purchase of fuel cell vehicles or the construction of hydrogen refueling stations when promoting industrial development. By comparing international practices, this study examines Japan's approach to promoting fuel cell vehicles and constructing hydrogen refueling stations. In the early stages of the large-scale promotion of fuel cell vehicles and hydrogen refueling stations in Japan, the government initially guided the construction of hydrogen refueling stations after the fuel cell vehicles were ready for promotion. This process exhibited a dynamic of mutual promotion or mutual constraints between vehicles and stations at different stages of development. Once the marketization phase began, demand became the key driving force, with vehicles and stations promoting each other. However, focusing solely on increasing the number of vehicles or stations, without considering the actual service capacity of hydrogen refueling stations, made it difficult to support scale development when coordination between vehicles and stations was insufficient. Furthermore, in the construction of hydrogen refueling stations, the construction of alliance organizations that are more focused on addressing the challenges of industrial development helped reduce cost and enhance the overall economy from the perspective of the whole industrial chain, thereby facilitating the wide application of fuel cell vehicles. Therefore, in promoting the demonstration of fuel cell vehicles, China should fully leverage the international best practices and the role of industry organizations to accelerate the promotion of fuel cell vehicles based on a reasonable matching scheme between fuel cell vehicles and hydrogen refueling stations.

With the transformation of the global energy structure and the rapid development of renewable energy, the importance of new energy storage technologies as an important support in the energy field is becoming increasingly prominent. However, based on market feedback, there are still certain legal issues in the new energy industry that require continuous optimization and improvement of the legal and regulatory system. This article aims to conduct in-depth research on the laws and regulations of the new energy storage industry, analyze its key issues, and propose corresponding suggestions and countermeasures.