During the global energy system's transition to renewable energy, energy storage technology has emerged as the core regulatory unit of new power systems, yet it faces multifaceted challenges, including inefficient material development, complex system optimization, lagging safety management, and imperfect market mechanisms. The DeepSeek large language model, with its low energy consumption, high efficiency, and exceptional reasoning capabilities, proffers an innovative pathway to address critical bottlenecks in energy storage. Through core technologies such as multi-head latent attention, DeepSeek mixture-of-experts models, and multi-token prediction, DeepSeek significantly reduces energy costs in both model training and inference. Its broad application prospects in energy storage research are expected to drive a paradigm shift from “trial-and-error” to “intelligent design” in materials development, establish multi-scale coupled digital twin frameworks for system optimization, transform safety management from passive response to proactive early warning, and create data-driven dynamic market evaluation systems for policy analysis. The “system symbiosis and energy-efficiency co-evolution” development paradigm provides a technological foundation for the deep integration of artificial intelligence with clean energy technologies, potentially accelerating the construction of carbon-neutral computing infrastructure and ushering energy storage technology into an intelligent new era.

Lithium-ion batteries have the potential risk of thermal runaway, and a single heat spread can result in the burning of the module or whole package, causing casualties and loss of property, which is a thorny problem that currently hinders the adoption and use of electric vehicles. In this study, a design concept of a porous thermal insulation boardis proposed. This concept uses the low thermal conductivity of the static air in the hole as a sandwich layer between the monomers to prevent heat spread. First, two heat transmission paths of porous heat insulation plates were analyzed: solid heat transfer and gas heat transfer. The heat propagation characteristics of the heat insulation plate under different thicknesses and different hole area ratios were simulated, and the delayed effect of the hole area ratio on heat propagation was verified by experiments. The results showed that at the same thickness, the larger the hole area ratio of the heat insulation plate, the better the thermal runaway barrier effect. The heat spread time within a 3-mm-thick heat insulation board with a 42.12% hole area was 51% higher than that of a non-porous heat insulation board with the same thickness. In addition, the porous heat insulation plate exhibited high structural strength, which could not easily crushable when the battery thermal runaway occurs, preventing the direct contact of adjacent monomer heat transfer. The materials for manufacturing porous heat insulation plates are readily available, it has a simple structure and is easy to process, thereby offering valuable guidance for the structural safety design of future battery modules.

Vanadium flow battery (VFB) is imperative long-term energy storage technology for the development of low-carbon power systems. Developing high-power stacks is an important way to promote the scale application of VFB. Electrode, as a crucial material in a VFB power unit, is the key to realize high-power battery technology. To enhance the electrochemical performance of commercial carbon felt electrodes, two thermal treatment activation strategies are proposed in this work: low-temperature long-time treatment and high-temperature short-time treatment. Specifically, in the air atmosphere, pristine carbon felts are activated by adjusting the processing temperature and time. Physical, chemical, and electrochemical properties of activated electrodes are characterized using scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, contact angle measurement, cyclic voltammetry, electrochemical impedance spectroscopy and single-cell charge-discharge tests. Results indicate that low-temperature long-time treatment controllably increases carbon felt fiber roughness, and preserves mechanical stability while introducing oxygen functional groups with minimal impact on the graphite crystal structure. After thermal treatment at 450 ℃ for 6 h, the activated carbon felt exhibits the best electrochemical activity for VO²⁺/VO₂⁺ and V²⁺/V³⁺ reactions. The BET surface area increases to 1.75 m²/g, and the content of oxygen functional groups on the surface increases to 10.38% (atomic percentage). Optimized activated carbon felt electrodes exhibit 77.8% energy efficiency at 300 mA/cm², surpassing pristine carbon felt. This study offers practical guidance for activating commercial carbon felt electrodes. It is of great practical significance for the development of high power VFB.

The rational design of high-performance anode materials is crucial for advancing sodium-ion battery technology. Among potential candidates, two-dimensional transition metal carbides, particularly those incorporating C₂ dimer structures, exhibit superior electrode material properties owing tothe high carbon atomic mass on their surfaces. Consequently, we selected the VC₂ monolayer, featuring a highly stable a C₂ dimer structure, for investigation.Utilizing first-principles calculations, we evaluated its structural and electronic properties, as well as its sodium-ion storage performance on the surface, includingadsorption sites, multilayer adsorption dynamics, diffusion pathways, and open-circuit voltage. Our findings reveal that the VC₂ monolayer exhibits exceptional structural stability and electrical conductivity, strongly suggesting its suitability for sodium-ion storage applications. Theoretical calculations predict a substantial sodium-ion adsorption capacity for multi-layer VC₂, reaching 715 mAh/g. Furthermore, the calculated low-diffusion energy barrier of 0.23 eV ensures swift charging and discharging rates. Notably, as the sodium-ion concentration escalates, the average total adsorption energy remains consistently negative. Furthermore, open-circuit voltage calculations underscore the stability of sodium intercalation voltage. Collectively, these results underscore the potential of VC₂ monolayers as an ideal anode material for sodium-ion batteries. This study not only introduces a novel perspective for the pursuit of high-performance anode materials but also lays a theoretical foundation for the subsequent optimization and design of such materials.

Frequent opening and closing of the refrigerator results in energy consumption problems owing to the large temperature difference within and outside a refrigerator during cold-chain transportation. Latent heat characteristics of phase-change materials for cold energy storage are a reliable and effective solution for the aforementioned challenge. In this study, we describe the construction of a new type of evaporator with half of the coil tube filled with cold-storage phase-change material. The evaporator stores part of the cooling capacity of the phase-change material as the refrigeration unit actively cools and continues to release this part of the cooling capacity after shutdown. By changing the position of the cold-storage coil, we studied the cooling times, heat-transfer change, and entransy dissipation of the built-in and new external evaporators. The results show that the cold storage capacity of the two phase-change coils is 285 kJ, and the cooling release time of the built-in and external storage evaporators are 6.2 min and 7.3 min, respectively, compared to the non-cold-storage evaporator. Based on the actual transportation time and the demand for transporting goods, during long-distance operations with durations of approximately 10 h and a refrigeration temperature range of 10-15 ℃, the conventional evaporator needs to undergo 12 start-stop cycles. In contrast,the built-in and external new evaporators undergo 9.79 and 9.87 start-stop cycles, respectively, with an approximate reduction by 2.2 start-stop cycles, effectively reducing the number of start-stop energy consumption of the system. The total energy consumption under the built-in cold storage form is reduced by about 0.25 kWh, accounting for 1.6% of the total energy consumption. The entransy dissipation increases for the external and built-in evaporators are over 400 J·K and approximately 100 J·K, respectively, compared to the evaporator without cold storage. Overall, the built-in cold storage evaporator exhibits a better energy-saving effect.

Lithium metal anode has a high theoretical specific capacity and low electrode potential. It is the ideal anode material for high-energy density secondary batteries. However, the growth of lithium dendrites and volume expansion during the cycling of lithium metal anode limit the commercial application of lithium metal batteries. In this study, the lithiophilic Ag-3D-Cu collector was prepared by electroless silver plating on the surface of foam copper to address dendrite growth and volume change in lithium metal anode during cycling. Silver particles can guide the uniform deposition of Li⁺ and inhibit the growth of lithium dendrites, and the three-dimensional porous structure of foam copper can effectively alleviate the volume expansion. The Ag-3D-Cu-30 s electrode obtained after a chemical silver plating time of 30 s exhibits a lower overpotential for nucleation and polarization compared to 3D-Cu electrodes while achieving a uniform lithium deposition morphology. The Li||Ag-3D-Cu-30 s cell also shows stable cycling for nearly 140 cycles, with an average Coulombic efficiency of 98%, demonstrating excellent cycling performance. The specific discharge capacity of Ag-3D-Cu-30 s/Li||LFP full cell was maintained after 360 cycles at 1.0 C, demonstrating excellent cycling stability. These results indicate that by electroless silver plating on the surface of foam copper to construct lithiophilic Ag-3D-Cu electrode, silver particles can guide the uniform lithium deposition, effectively reducing the nucleation and polarization overpotential of lithium deposition, and improving the Coulombic efficiency and cycle life of lithium metal batteries.

The shortage of lithium resources has spurred a research boom in sodium-ion batteries, with Prussian blue analogs (PBAs) becoming a popular choice for cathode materials due to their low cost, fast ion transport, structural stability, and environmental friendliness. Notably, iron-based PBAs(Fe-PBAs) are promising for industrial applications due to the abundance of iron resources. In this study, the structural characteristics, electrochemical reaction properties, and the existing challenges of Fe-PBAs were analyzed, comprehensively reviewing the latest research achievements in synthesis methods and strategies for enhancing electrochemical performance. Optimizing the electrochemical performance of Fe-PBAs hinges upon effectively inhibiting the generation of [Fe(CN)₆] vacancies defect, the introduction of crystallization water, and activating the electrochemical activity of low-spin Fe. In terms of synthesis methods, the latest advancements in mainstream technological pathways such as coprecipitation, hydrothermal synthesis, and ball milling, showcasing the diversity and development potential of Fe-PBAs preparation techniques were reviewed. To enhance the electrochemical performance of Fe-PBAs, this study systematically summarize the enhancement mechanisms, application effects, and potential limitations of strategies such as ion doping, morphology and structure regulation, surface coating modification, synthesis process optimization, and electrolyte optimization, based on the latest research findings. Finally, this study discuss the future trends and directions for the development of Fe-PBAs, emphasizing the importance of continuous optimization of manufacturing processes, exploring new avenues for in-depth modification and enhancement, leveraging AI technology, and applying novel reactor technologies. This study aims to provide valuable references for research and applications in the field of Fe-PBAs cathode materials and sodium-ion batteries.

Lithium-ion batteries, as a foundational technology in electrochemical energy storage, are playing an increasingly vital role in driving economic and social progress. However, the uneven global distribution of lithium resources presents significant challenges, particularly regarding the security of lithium supplies in China. Sodium-ion batteries have emerged as a promising complementary technology, offering a strategic alternative that capitalizes on abundant sodium reserves and mitigates dependence on foreign resources. The performance of these batteries largely depends on the anode material, with hard carbon being the most industrially advanced option owing to its commendable overall performance. However, its low capacity remains a key limitation to further progress. This review examines four distinct models of sodium storage in hard carbon: "insertion-filling," "adsorption-insertion," "adsorption-filling," and the "three-stage" model. Subsequently, it explores various characterization techniques—such as Raman spectroscopy, pair distribution function analysis, positron annihilation lifetime spectroscopy, extended X-ray absorption fine structure, electron paramagnetic resonance, gas adsorption/desorption, and small-angle X-ray scattering to elucidate the defects and pore structures of hard carbon. Moreover, the review emphasizes various strategies to enhance slope and plateau capacities of hard carbon. These include heteroatom doping, modulating carbonization temperature, modifying pore structures, and refining microcrystalline structures. A comprehensive analysis indicates that increasing the defect concentration in hard carbon significantly enhances its slope capacity, while increasing the volume of closed pores effectively improves its plateau capacity. Finally, the review delineates potential development trajectories and future prospects for hard carbon, aiming to provide valuable insights for advancing sodium-ion battery technology.

Sodium-ion batteries (SIBs), characterized by low cost and abundant resources, are promising candidates to supplement or even replace lithium-ion batteries in the field of large-scale energy storage. Hard carbon is currently the most useful anode material for SIBs, and its closed-cell structure is beneficial for improving the capacity of the low-voltage plateau region. However, the mechanism of the formation of the low potential plateau region in hard carbon is still controversial, and the relationship between the closed pore structure of hard carbon and electrochemical performance is not yet clear, which limits the industrial-scale manufacture of SIBs. In the study, we review the research progress and recent developments on the closed-cell structure of hard carbon in SIBs. First, we summarize the basic definition and sodium-storage mechanism of closed cell structure in hard carbon materials. Subsequently, we discuss the different methods for regulating the closed pore structure of carbon materials, closed pore structure analysis and characterization techniques, as well as the correlation between closed pore structure and sodium-storage electrochemical performance. Finally, we analyze the problems and challenges in the electrochemical performance of closed cell structures in hard carbon materials for SIBs, and we propose the primary development directions for the future.

Silicon-based materials are among the most promising anode materials for solid-state batteries owing to their high specific capacity. However, interface failures between silicon-based electrode materials and solid-state electrolytes disrupt ion and electron transport pathways, leading to increased internal impedance, uneven current-density distribution, and eventual degradation of battery capacity and cycle life. This issue presents a major challenge in designing high-energy-density and long-cycle silicon-based solid-state batteries. First, we evaluate the reasons for interface failures between silicon-based materials and solid-state electrolytes, focusing on crystal structures, critical dimensions, and electrochemical sintering. We also discuss the impact of lithium concentration on the electronic conductivity, ionic diffusion coefficient, and Young's modulus of pure silicon materials. Furthermore, we summarize various strategies to address the interface failures, including the application of binders, buffer layers, electrode-material structure design, and particle-size matching between electrode materials and electrolytes. Additionally, we emphasize the potential influence of applying equal and constant stacking pressure on battery performance during the cycling process. This study aims to elucidate the scientific challenges associated with silicon-based material and electrolyte-interface failures in solid-state batteries, resulting in capacity decay and decreased cycle life. Further, this work proposes strategies to address these challenges considering silicon-based material design, electrode material preparation, and electrode-electrolyte matching, thereby guiding further advancements in this field.

As the significance of clean energy grows, there is an increased and diverse demand for energy-storage technologies. Zinc-bromine flow batteries (ZBFBs) are efficient and sustainable medium and long-term energy storage technologies that have attracted attention owing to their high energy density, long life, and low cost. The system uses zinc and bromine as active materials to store and release energy in electrolyte solutions. In this study, we summarize the basic working principle and application background of ZBFBs, the optimization strategy, and the latest development potential of diaphragm and electrolyte. First, we introduce the charge-discharge mechanism and electrochemical behavior of zinc-bromine batteries. Subsequently, we analyze the key factors that affect the performance of the battery, including the composition and concentration of electrolytes, the type and structure of the diaphragm, and the development status of modification technology of the diaphragm. Specifically, we discuss how these modifications alleviate the zinc dendrite phenomenon, as well as improve bromine capture, mechanical properties, ion-exchange rates, and conductivity. We also discuss the optimization of electrolytes in alleviating zinc dendrite and improving conductivity and flow rate. Finally, we summarize the challenges in current ZBFB research and future development directions. We emphasize the importance of material innovation, system integration, and large-scale application in achieving high-performance and low-cost ZBFBs. This study aims to present researchers with the latest progress in ZBFB technology, thereby guiding future research directions and facilitating technological breakthroughs.

The vanadium redox flow battery (VRFB) holds significant promise for large-scale energy storage applications. A key strategy for reducing the overall cost of these liquid flow batteries lies in enhancing their power density and operational efficiency. The electrode serves as the core site for the mutual conversion of electrical and chemical energy, with its structural characteristics and surface properties directly impacting electrochemical reaction rates, internal battery resistance, and electrolyte transport processes, thereby influencing overall battery performance. The synergistic enhancement of electrode transport and electrochemical performances can be achieved by developing macroscopic and microscopic ordered electrode structures. In this study, the structural design of electrodes from macro to micro scales and the research progress in VRFB. At the macro scale, we summarize and analyze how structural parameters such as electrode compression ratio, electrode flow field structure, and electrode geometric shape influence battery performance was analyzed. At the micro scale, single-layer electrodes with multilevel porous distributions as well as multilayered electrodes with gradient distributions using physical and chemical methods was constructed. These approaches can increase the specific surface area of the electrode, promote the electrochemical reaction, and improve the diffusion of electrolytes on the electrode surface. Finally, this study discusses the existing problems and propose future research directions for structured electrode design.

The insulation failure of an energy storage system can generate high voltage between the battery casing and the electrode, posing significant safety risks. This study experimentally investigates the thermal runaway characteristics of a 52 Ah lithium iron phosphate battery with an aluminum-plastic film casing under high voltage caused by insulation failure. Thermal runaway is induced by applying high-voltage direct current at varying amplitudes between the battery's positive electrode and the aluminum-plastic film shell. The resulting fault phenomena and the electrothermal behavior of the battery under these conditions are analyzed. Experimental findings reveal that when a 500 V voltage is applied, thermal runaway occurs. This process unfolds in four stages: high-voltage breakdown aluminum-plastic film stage, molten aluminum and negative electrode transition short circuit stage, overcharge stage, and trigger thermal runaway stage. The battery's condition after experiencing thermal runaway is examined using industrial computed tomography (CT) and scanning electron microscopy (SEM).The analysis reveals that the most severe thermal runaway occurs at the location where the aluminum-plastic film shell is electrically compromised. When a voltage of 100 V or 300 V is applied between the positive electrode of the battery and the aluminum-plastic film shell, no breakdown occurs in the film. However, when the voltage exceeds 400 V, the aluminum-plastic film breaks down, leading to thermal runaway. The higher the voltage amplitude, the more severe the thermal runaway degree. The research results are crucial for improving the safety of energy storage systems and electrical insulation designs.

Through investigation of thermal runaway processes in lithium batteries and the timely issuance of early warnings are crucial for ensuring the safe operation of energy storage systems. However, most current research on thermal runaway smoke and gas warnings for lithium batteries remains theoretical, with limited exploration into the practicality and reliability of these methods in real-world scenarios. In this study, the relationship between temperature changes during overcharging and thermal runaway of lithium iron phosphate batteries in energy storage cabinets and battery packs, and the subsequent changes in smoke and gas concentrations is examined. This was achieved by building experimental platforms to explore these scenarios. The study also compare the effectiveness of various industrial smoke and gas sensors within energy storage cabinets in warning of lithium battery thermal runaway. The experimental results indicate that, by placing the sensor at the top of the energy storage cabinet and the vent hole of the waterproof-breathable valve aligned with the sensor, the smoke and gas sensors detect the values sequentially after the safety valve opens. The smoke sensor is the most sensitive response, capable of issuing an early warning signal immediately after the safety valve opens. Hydrocarbon gas sensors provide a precise indication of the moment of thermal runaway in lithium batteries. Subsequently, Ansys Fluent was employed to simulate the dispersion of gas and smoke within the energy storage cabinet following the activation of the lithium battery's safety valve in the battery pack. The simulation results reveal that the diffusion of smoke and gas was primarily influenced by the momentum of the gas and the positioning of ventilation holes on the waterproof, breathable valve housing. The top of the energy storage cabinet allows for timely and accurate detection of escaping gas and smoke. The findings of this study offer guidance for thermal runaway warning strategies in energy storage cabinets for lithium batteries and the placement of gas and smoke sensors.

This study focuses on 100 Ah lithium iron phosphate pouch cells, triggering thermal runaway by side heating. By utilizing industry characterization tools such as Industrial Computer Tomography, Scanning Electron Microscopy, and Gas Chromatography, the thermal runaway characteristics and gas evolution patterns at 40%, 60%, 80%, and 100% SOC were systematically analyzed. The results show that the overheating trigger of battery thermal runaway can be subdivided into four stages: increased overheating temperature; by-product reaction gas expansion; separator shrinkage and cracking with smoke emission; and thermal runaway caused by severe temperature rise and gas production. Further calculation of heat generation energy revealed that at 100%, 80%, 60%, and 40% SOC, the peak heat generation rates reached 140.34, 115.44, 14.76, and 3.91 kW, respectively, and the energy released at 100% SOC is equivalent to 104.63 grams of TNT, with a destructive radius reaching 5.90 meters, representing nearly a 64.3% increase in hazard compared to that at 40% SOC. Characterization of battery materials post-thermal runaway indicated that the LFP cathode material transformed from a block shape to aggregated irregular spheres, and the graphite anode structure was transformed from layered to aggregated spherical particles due to accentuated internal side reactions. A comparison of gas evolution characteristics showed that with increased SOC, the amount of H₂ produced by the battery increased, while the amount of CO₂ decreased. The explosion risk of the gases produced at various SOC levels is higher than that of Common hydrocarbon gases, with the explosion upper limit first decreasing and then increasing. The findings from this study provide a theoretical basis and practical guidance for the safety design of follow-up energy storage systems.

With the rapidly increasing demand for energy storage, single batteries are increasingly designed for larger capacities. Consequently, large-capacity batteries are gradually becoming mainstream electrochemical energy storage systems. However, existing research on battery pack cooling systems primarily focuses on the small-capacity battery systems. In this study, we investigate a submerged liquid cooling system for 280 Ah large-capacity battery packs. We discuss the effects of various parameters on cooling performance, including battery spacing, coolant import and export methods, inlet and outlet flow rates, and types. Furthermore, we analyze the influence of coolant thermophysical parameters on the cooling effect. The results show that increasing the cell spacing appropriately has a positive cooling effect on submerged liquid-cooled battery packs. When the cell spacing is increased from 0 mm to 5 mm, the maximum temperature difference ΔT_max and the maximum temperature T_max of the battery packs are reduced by 1.57 ℃ and 1.84 ℃, respectively. The coolant inlet position has a greater effect on ΔT_max and T_max than the outlet position, and the inlet position has a greater effect on the flow field inside the battery box than the outlet position. ΔT_max and T_max decrease with increase in the inlet flow rate. When the inlet flow rate increased from 0.2 m/s to 0.4 m/s, ΔT_max and T_max decreased by 21.1% and 8.0%. Deionized water exhibits the best cooling effect, whereas silicone oil exhibits the worst cooling effect. Compared to silicone oil, deionized water reduced ΔT_max and T_max by 5.17 ℃ and 5.99 ℃. Among the thermophysical parameters of the coolant, the order of importance and influence on the battery pack's cooling performance is as follows: density, specific heat capacity, thermal conductivity, and power viscosity. The findings of this study offer valuable insights into designing large-capacity battery pack-submerged liquid cooling system.

The widespread application of lithium-ion batteries in electric vehicles, renewable energy, and other fields necessitates accurate prediction of their remaining useful life (RUL). Such predictions enable real-time monitoring of the battery's internal performance degradation, thereby reducing the risk associated with battery usage. We propose a combined prediction algorithm utilizing variational mode decomposition, sparrow search algorithm (SSA), and long short-term memory (LSTM) for predicting the remaining life of lithium-ion batteries. Initially, indirect health indicators for predicting RUL were extracted from the current, voltage, and temperature curves of the batteries. These indicators included isobaric charging time, isobaric charging energy, peak discharge temperature, and constant-current charging time. Subsequently, the VMD method was employed to decompose the capacity, aiming to avoid local fluctuations in capacity recovery and interference from test noise that could affect RUL prediction results. To address the susceptibility of traditional LSTM model hyperparameter settings toward experience and randomness, an SSA was proposed to optimize the parameters of the LSTM model, thereby enhancing the model's predictive capabilities. Ultimately, by utilizing NASA and CALCE datasets, a comparison was conducted between the proposed model and other models. Experimental results demonstrate that the proposed method achieves high predictive performance, with the root mean square error for RUL prediction of lithium-ion batteries consistently maintained within 2%.

The skyrocketing demand for AI computing has fueled a surge in energy consumption within internet data centers (IDCs), surpassing expectations. However, the unpredictable nature of renewable energy poses significant challenges for stable grid operation, which demand response from IDCs alone cannot fully solve. Configurable energy storage enhances flexibility. This paper presents a two-stage robust model for IDCs and battery energy storage (BES) planning, minimizing operational costs amidst wind power uncertainty. A lifespan constraint ensures realistic planning. The traditional C&CG algorithm's speed-accuracy dilemma is tackled with an Inexact C&CG (i-C&CG) algorithm, avoiding exact column/constraint generation. Simulations on IEEE 30-node and 118-node systems highlight the benefits of this approach. The proposed energy storage configuration reduces annual costs by 39785 CNY for storage systems and 289080 CNY for IDCs. The i-C&CG algorithm shortens iteration time by 3632 seconds, achieving a 0.18 precision and 0.46 relative error. These results match the traditional C&CG's convergence and optimality gaps.

To study the energy efficiency and the loss proportion of each link in the charging and discharging process of the transmission chain slope gravity energy storage system (TCS-GESS), the mathematical expression of each link loss and the corresponding energy efficiency calculation method were derived for the mass block movement, mechanical transmission, and electrical driving links of the system. Furthermore, an energy efficiency analysis model for TCS-GESS using MATLAB/Simulink was established. An experimental scheme was designed under charging and discharging conditions and measured the energy efficiency of each link of the system under different load conditions using a 2.2 kW gravity energy storage prototype. The accuracy and practicability of the energy efficiency analysis model were verified by comparing the results of the energy efficiency calculated with the five dimensions of speed, mechanical power, charging and discharging power, transmission loss, and motor loss. The results show that the system efficiency gradually increases with an increase in load. Among them, the chain loss accounts for a large proportion of each loss link. The loss of gearbox and gear plate was not significantly altered and accounts for a small proportion. The motor loss accounts for a medium proportion and increases as the load increases under charging and discharging conditions. Under rated load conditions, the charge and discharge efficiencies were 59.5% and 37.4%, respectively, and the system efficiency was 23.2%. Furthermore, the charging and discharging efficiency of the GESS with the same transmission mechanism and different rated powers was predicted. The results indicate that the charging and discharging efficiency was <68% when the system capacity was <1 MW, and the energy efficiency improvement potential of the system was limited when the capacity was >10 MW. Based on the findings from this study, this paper recommends selecting the optimal power range for a GESS utilizing the same transmission mechanism within the range of 1—10 MW.

Energy storage technology is the key technology of the parallel operation of renewable energy, and can ensure the stability and security of power system supply. Physical energy storage technology has broad application space in the parallel operation of high proportional renewable energy because of its high efficiency, long life and environment-friendly characteristics. By analyzing the relevant policies of energy storage technology in power system, this paper understands the social background of the application of energy storage technology, clarifies the application value of energy storage technology in power system, and analyzes the characteristics and application scenarios of different types of physical energy storage technology, in order to provide effective reference for the application and promotion of energy storage technology.

The thermal management of electric vehicles predominantly relies on liquid cooling. Recognizing the limitations of traditional serpentine liquid cold plate, characterized by poor temperature uniformity and high voltage drop, this study explores the application of topology optimization technology to design the flow channels. The objective is to develop a cooling system that effectively addresses the critical requirements of high-temperature safety and uniform temperature distribution within the battery pack. First, based on the 2D simulation of Comsol variable density topology optimization, by taking the lowest average temperature in the design domain as the objective function and the flow channel volume fraction as the constraint condition, the flow channel distribution law in the design domain was obtained using a variable control method. Sensitivity filtering was implemented using the Helmholtz filter, resulting in the design of a novel tree-like topology optimization channel. Furthermore, the 2D topology simulation results were transformed into the actual channel geometry, and the tree-like topology runner heat sink was prepared using 3D printing technology. The experimental design of the response surface was carried out using the simulation technology of heat flux coupling, and the interaction effect of the volume fraction A, inlet temperature B, and flow rate C on the heat dissipation performance of the runner was studied. The actual temperature control ability of the topological flow channel was verified by repeated group experiments, and this confirmed the high prediction accuracy of the simulation. The optimal Pareto front solution was obtained using the optimization iterative analysis of the non-dominant genetic algorithm: A = 0.3, B = 20 ℃, and C = 10 L/min had the best heat dissipation performance. Compared with the serpentine channel, the optimal topological flow channel reduces the inlet and outlet pressure drop from 4863 Pa to 822 Pa, a decrease of 490%. The maximum temperature of the battery module decreased from 27.88℃ to 27.21 ℃, a decrease of 2.4%; The temperature difference decreased from 5.7 ℃ to 4.95 ℃, a decrease of 13.2%; The above results meet the experimental test requirements under the driven-durability condition of the battery module. In this study, the advantages of tree-topology optimized flow channels for battery module thermal management are proposed and verified, and an effective scheme is provided for the design of a battery thermal management system.

With the continuous development of power grid technology, the application of composite energy storage electronic transformer in source containing power grids is becoming increasingly widespread. However, in practical operation, energy storage power electronic transformers face challenges such as voltage drops or interruptions in the power grid, which puts higher demands on their stability and power quality. This article reviews the coordinated control and monitoring strategies of transformers based on voiceprint recognition. Firstly, the principle, structure, and current technical characteristics of composite energy storage electronic transformers are analyzed; Based on this, the application of voiceprint recognition technology in energy storage electronic transformers was further explored, and finally, the coordinated control and monitoring strategies of transformers under voiceprint recognition technology were summarized. I hope to provide some theoretical reference for future research.

The thermal management system of electric vehicle batteries is crucial in cooling the battery. In this study, we propose a wet cooling system for lithium-ion batteries. The wet cooling system consists of two cooling stages. The first stage involves precooling the air via spray evaporation, and the second stage involves the formation of a liquid film on the battery surface via spraying. The liquid films evaporate under air flow which then cools the battery. To study the influence of various factors on spray evaporation and liquid-film evaporation, computational fluid dynamics analysis of the wet cooling system was carried out using Fluent software. The effects of spray mass flow, wind speed, air humidity, droplet size, and air pressure on the cooling performance of both stages were explored. The simulation results show that the effects of spray mass flow rate and wind speed are different for both stages, whereas the effects of air humidity, droplet size, and air pressure on both stages are identical. The effectiveness of air precooling in the first stage increases with higher spray mass flow rates and decreases with increasing wind speeds. Lower humidity, smaller particle size, and reduced air pressure favor both cooling stages. The position of the nozzle has an impact on the spray evaporation, and an adequate distance between the nozzle and the battery is more effective for the air precooling of the spray. To provide better cooling conditions for both stages, the spray mass flow rate and wind speed were improved in stages. After the improvement, the air precooling capacity of the first stage was augmented through a reduction in both wind velocity and spray mass flow rate. In the second stage, the battery was subjected to increased wind velocity and a more uniform liquid-film distribution. This resulted in a 1.9 ℃ decrease in the battery's average temperature and a 1.7 ℃ reduction in its maximum temperature differential.

Accurate and reliable prediction of remaining useful life (RUL) of power batteries is crucial for mitigating user concerns regarding mileage and safety. To improve the accuracy of RUL prediction, we propose an improved grey wolf algorithm to optimize the GPR model, based on the NASA dataset. This study focuses on three aspects. First, five indirect health factors were extracted based on battery charging and discharging data including charging voltage saturation interval (CVSI, HI1), charging peak temperature interval (CPTI, HI2), constant current charging interval (CCCI, HI3), discharging peak temperature interval (DPTI, HI4), and discharging constant current interval (DCCI, HI5). The grey correlation method was used to analyze the correlation between health factors and capacity. Second, GPR method was selected as the RUL prediction model for power batteries. In response to the problem of traditional model parameter identification falling into local optima, an improved grey wolf algorithm based on a differential algorithm is proposed to enhance the model's prediction ability. Finally, the proposed method was validated using the NASA dataset. The experimental results showed that the proposed algorithm can control the RUL prediction error within 2%.

Modern vehicle networking technology plays an important role in the energy storage control of new energy vehicle batteries, and further analysis and research are needed. This article summarizes the specific application of vehicle networking related technologies in the energy storage of new energy vehicle batteries. Firstly, the latest development of vehicle networking technology was analyzed, including in vehicle sensor communication, cloud computing, and intelligent traffic management; Then, the application of vehicle networking technology in electric vehicle energy storage control was analyzed from multiple aspects, including real-time data monitoring and analysis, remote monitoring and management, charge and discharge control, and collaborative management. Research has shown that vehicle networking technology can upgrade the energy storage control of new energy vehicles in multiple aspects, and has broad research prospects in the future. It is an important guarantee for the research of new energy vehicle energy storage technology.

In secondary batteries, the evolution of electrode/electrolyte interfaces plays a crucial role in battery performance and stability. In this study, we review several advanced representative in-situ characterization techniques based on their working mechanisms, including atomic force microscopy, three-dimensional laser confocal microscopy, electrochemical quartz crystal microbalance, electrochemical differential mass spectrometry, Raman spectroscopy, and Fourier transform infrared spectroscopy. Based on the evolution of interfaces in secondary batteries, we categorize these phenomena into the evolution of intermediate phases at the electrode/electrolyte interface from liquid to solid, the electrodeposition process of deposition-type metal anodes, and the evolution of the three-phase interface in metal-gas batteries, as well as the electrochemical decomposition and dissolution of solid components from solid to liquid. Several examples of applying these advanced in-situ characterization techniques to complex systems are provided in this work, demonstrating the multi-scale, three-dimensional analytical capabilities of multi-modal in-situ interface characterization. The combined use of these techniques not only offers a deeper understanding of the dynamic evolution of electrode/electrolyte interfaces under actual battery operating conditions but also reveals key factors that affect battery performance and stability. We also discuss the challenges and progress in current research and propose future research directions. The applications and development of in-situ interface characterization techniques will contribute to a deeper understanding of interface evolution mechanisms, improving battery performance and stability, and promoting advancements in battery technologies.

The performance of lithium-ion batteries is significantly influenced by the characteristics and changes occurring at their surfaces and interfaces. Using appropriate surface analysis technologies to examine the composition, structure, and distribution of these interfaces is essential. Such analyses provide critical insights for optimizing interface performance, studying ion transport behaviors, and understanding battery failure mechanisms. Auger electron spectroscopy (AES) is a surface analysis technique that utilizes an electron beam probe, offering high spatial resolution. AES is capable of performing qualitative and semi-quantitative analyses of most elements, except H and He, and their oxidation states, along with providing two-dimensional imaging. This work introduces the technical principles, main functions, and analysis methods of Auger electron spectroscopy, while also reviews its application cases in lithium batteries. It summarizes the experience gained from employing AES in this field and explores potential advancements for its application in lithium battery technology.

Retired batteries should undergo rigorous testing and evaluation to ascertain their performance before they are deployed in echelons utilization to ensure that appropriate application scenarios are selected based on the performance of the batteries. An accurate assessment of the State of Health (SOH) is fundamental to determine whether a power battery possesses the value of echelons utilization. In view of the low accuracy of SOH evaluation of retired power batteries, the relaxation time distribution method was used to analyze the electrochemical impedance spectroscopy in this study. This was done to obtain the characteristic frequency that can accurately reflect the health state of the battery. The impedance data corresponding to the characteristic frequency was used as the characteristic input parameters, and the extreme learning machine model optimized using the sparrow algorithm served as input to realize the SOH evaluation of decommissioned power batteries. To verify the effectiveness of the evaluation method, seven retired prismatic lithium-iron-phosphate batteries were subjected to cyclic aging experiments, and electrochemical impedance spectroscopy tests were performed on the batteries after each cycle. Actual electrochemical impedance spectroscopy of the decommissioned power batteries was used for analysis and modeling to evaluate the SOH, and the results were compared with actual SOH data obtained using traditional SOH evaluation methods. Our findings demonstrate that the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) associated with the relaxation time distribution method are lower than those of other methods. Compared with the unoptimized extreme learning machine model, the values of the MSE and MAPE reduced by 47.1% and 60.5%, respectively, indicating that the SOH evaluation method employed in this study is relatively highly accurate and less prone to error, indicating its applicability in practical echelons utilization.

Lithium-ion batteries are extensively used in electric vehicles and other applications due to their excellent performance. A charging strategy offering good charging performance is crucial for lithium-ion batteries. However, the limitations of conventional charging methods pose challenges to their large-scale adoption in electric vehicles. Therefore, we propose an optimal charging strategy for lithium-ion batteries considering various application scenarios. First, we establish the electric, thermal, and aging models of lithium-ion batteries to simulate the charging process and collect data. Then, the state-of-charge based multistage constant current (SMCC) charging strategy is developed based on the battery internal resistance profile of the battery. An objective function incorporating charging speed and state-of-health (SOH) attenuation is established, and the improved Bat Algorithm is used to optimize the SMCC current. The resulting optimal charging strategy was derived under varying weighting coefficients. For specific battery application scenarios, we proposed a balanced SMCC charging strategy based on Pareto frontier, a fast-charging SMCC charging strategy aimed at increasing cycle times. Finally, the three proposed optimal charging strategies are compared with constant current-constant voltage method. Furthermore, we proved that the proposed optimal charging strategies can better adapt to their corresponding application scenarios, shorten the charging time of lithium-ion batteries, and reduce SOH attenuation.

In a low-temperature environment, the heating of batteries represents a crucial technical means of enhancing the performance of energy-storage systems, extending the lifespan of batteries, and ensuring their safety. The objective of this study is to address the issue of low-temperature heating in high-capacity lithium-ion batteries used for energy storage. Consequently, we propose a method, that makes use of an electric heating film, for rapid heating of the battery module. This approach takes into account the size effect of the battery and its anisotropic heat transport characteristics and is supported by numerical simulation and experimental testing. This study examines the impact of battery heating power, heating sites, and the multidimensional staggered synergistic heating method of the module on the temperature field of the battery and its warming rates. Results demonstrate a linear relationship between the rate of battery temperature increase and the input power applied to the heating film. When the heating power of the larger side of the battery is 350 W, the average temperature of the battery increased from -20 ℃ to 0 ℃ in 118 s, with a maximum temperature of 39.4 ℃. The battery pack reached equilibrium after 291 s. Double-side heating increased the temperature by 18 % compared to large-side heating but reduced material costs by approximately 38 %. The experimental results corroborated the precision of the simulation model and demonstrated that the scheme exhibits a brief heating time, high reliability, and the capacity to rapidly elevate the temperature of the battery pack. These research findings hold significant implications for the development and optimization of thermal management technology for household energy-storage devices.

The estimation of state-of-health (SOH) in lithium-ion batteries is crucial for ensuring the reliability and safety of energy storage systems. However, existing SOH estimation methods are limited by single-feature extraction and dependency on fixed charge-discharge conditions, which hinders adaptability to dynamic operational environments. To address these challenges, we propose an SOH estimation approach based on relaxation voltage, integrating a parallel multiscale feature fusion convolution model (MSFFCM) with XGBoost. The MSFFCM model leverages multilayer stacked convolutional modules to extract deep features from relaxation voltage data while utilizing a parallel multiscale attention mechanism to enhance the capture of multiscale features. These features are then fused with statistical features to improve the model's feature extraction and integration capabilities. Bayesian optimization is applied to the XGBoost model for parameter tuning, enabling high-accuracy SOH estimation based on multi-source fused features. Validation experiments were conducted on datasets from two commercial 18650 lithium-ion battery types under varied temperature and charge-discharge strategies. Results indicate that the proposed method achieves a root mean square error and mean absolute error of less than 0.5%, significantly outperforming conventional methods. This study provides an effective estimation tool for lithium-ion battery health management without reliance on specific charge-discharge conditions, demonstrating promising potential for complex real-world applications.

Proton exchange membrane fuel cells (PEMFCs) are a type of highly efficient ideal zero carbon emission power generation devices. The performance, cost, and durability of PEMFCs are tightly linked to the cathode catalyst layer (CCL). The CCL primarily comprises of Pt/C, ionomer, and porous regions, where the carbon support conducts electrons, the ionomer conducts protons, and the pores transport oxygen. This complexity introduces challenges in precisely characterizing the microstructure of CCL. This study provides an overview of the advancements in the microstructure characterization of CCL. It delves into distinct characterization techniques for the different components within CCL, along with highlighting the importance of combining multiple approaches to ensure a comprehensive understanding of CCL's intricate structure across various scales. Advancements in characterizing the microstructure of CCL offer deeper insights into interactions among catalysts, reactant gases, and ionomers during PEMFCs operation. The dissection of CCL microstructure also provides insight into the mass and heat transfer, as well as proton and electron conduction within CCL. Such efforts can provide valuable data for refined computer modeling, thus supporting the enhancement of PEMFC performance and addressing technological challenges.

To effectively reduce the lifetime loss of retired power batteries and improve the consistency of functional states during battery module operation, this study proposes a multistate coupled battery module screening method that considers the state of energy (SOE), state of health (SOH), and state of power (SOP). First, key electrical performance parameters of retired power batteries, such as capacity, voltage, and internal resistance, are extracted. Using this data, a multistate coupling characterization model is developed to represent the SOE, SOH, and SOP of battery modules. Next, the SOE characteristics of the battery modules are estimated, and the consistency of SOH across the modules is predicted to enable hierarchical utilization. An improved K-means clustering algorithm is used for the first stage of dynamic sorting of battery modules. Finally, a multiparameter SOP characterization model is constructed to estimate the SOP deviation among batteries within each module. The battery module is dynamically sorted in the second stage. Simulation case analysis demonstrates the effectiveness of this method. It significantly improves the consistency of battery modules during their second use, reduces system operational life loss, and establishes a theoretical foundation for large-scale secondary utilization in energy storage systems.

With the continuous advancement of intelligent technologies, data mining plays a crucial role in anomaly detection for thermal energy storage systems, serving as a key technological means to enhance energy management and system security. This article first analyzes potential anomaly patterns within large datasets through in-depth mining, thereby improving the intelligent adaptive capabilities and early fault warning systems of thermal energy storage systems. It introduces several anomaly detection methods based on supervised learning, unsupervised learning, and deep learning techniques. Furthermore, it proposes specific applications of anomaly detection in industrial thermal energy storage systems, explores practices for anomaly detection and optimization using big data, and discusses practical applications of cloud computing-based monitoring and early warning systems for thermal energy storage anomalies. The aim is to promote the comprehensive development of intelligent, precise, and adaptive thermal energy storage systems, thereby enhancing the reliability and security of energy systems.

It is difficult for independent energy storage to recover costs by only participating in the spot electricity market. Participation in both the spot and frequency regulation ancillary service markets will become a future trend. Due to the coupling of the two markets, independent energy-storage operators need to develop a reasonable market participation strategy to maximize returns. Considering the current price mechanisms and settlement mechanisms of power consumption of independent energy storage, based on the market rules of spot and primary frequency modulation, a joint optimization model of independent energy storage participating in spot and primary frequency modulation auxiliary service market at the same time is proposed and solved using mixed integer linear programming. Using the spot market price of a node in PJM and the data of Shanxi independent energy storage participating in a frequency modulation trial operation, an example is designed to analyze the cost and income composition of independent energy storage participating in different markets and different frequency modulation power ceilings, analyze the changing trend of daily cycle times of energy storage under different frequency modulation declared power, and compare the influence of transmission and distribution price and fund surcharge caused by power loss on the cost. Under the current market rules, independent energy storage power stations that use more than 2 h can significantly improve their income level and reduce life loss by simultaneously participating in spot and primary frequency modulation markets. The transmission and distribution price, government funds, and additional electricity charges costs caused by the loss of electricity can account for more than 20% of the operating cost of energy storage.

With rapid development in wind power, photovoltaic, and other clean energy industries, demand for container energy-storage power stations is growing. Conventional thermal management systems for container energy storage power stations typically rely on air conditioning units for cooling, resulting in significant annual energy consumption. We propose a heat-pipe natural cooling module assisted by evaporative and sky-radiation cooling. Furthermore, we describe the module structure and working principle. We calculate the applicable time, annual cooling capacity, and energy efficiency ratio of the module based on the annual meteorological parameters of typical cities and analyze the energy-saving benefits of the module. The results indicate that evaporative and sky-radiation cooling can effectively extend the applicable time of the heat-pipe natural cooling module. If the module is applied to Beijing, Xi'an, and Shanghai, the applicable time of the module can be increased by 22.95%, 28.56%, and 13.06%, respectively, on applying evaporative and sky-radiation cooling. The applicable times of the heat-pipe natural cooling module in typical cities were given and can be used to analyze the applicability the cooling technology in different regions of China. Both evaporative and sky-radiation cooling can enhance the cooling capacity of the module, and the improvement by the former is more obvious. When the heat-pipe natural cooling module assisted by evaporative and sky-radiation cooling was applied in Beijing, Xi'an, and Shanghai, the average coefficient of performance was 24.2, 22.3, and 19.7 respectively, which are much higher than the annual energy efficiency ratio of a unit air conditioner with first-level energy efficiency.

This study focuses on the application of scaled mechanical energy storage (MES) technologies in power systems with a high share of renewable energy sources. Technologies such as pumped hydro storage (PHS), compressed air energy storage (CAES), flywheel energy storage (FES), and gravity energy storage (GES) are analyzed in detail. Based on the technical features and strengths of each type of scaled MES, their application scenarios in power systems are analyzed across various time scales. A comprehensive, multidimensional comparison of the technical performance and economic feasibility of different MES technologies is then performed. Finally, the latest MES engineering cases in China are briefly introduced. The results of this paper provide a valuable reference for developing scaled MES in power systems with a high share of renewable energy sources.

Energy Internet is an important component of the modern energy system, which achieves the efficient flow and optimal allocation of energy through high levels of networking and intelligence. Based on this concept, Distributed Energy Storage System (DESS) serves as an important means for peak shaving and frequency modulation, and discussing its application and economic benefits holds significant practical importance. In light of this, this study provides an overview of the application and economic benefits of distributed energy storage systems based on energy internet, including the progress of research on distributed energy storage systems, that is, the current working principles and advantages of distributed energy storage systems; then, based on this, it focuses on the application of distributed energy storage systems in energy internet. The distributed energy storage systems in Energy Internet not only have a wide range of technological applications and significant economic benefits, but also will play an increasingly important role in the future energy system.

In 2023, the EU Battery and Waste Battery Law was passed, which has a profound impact on the battery industry in Europe and China. This article introduces the new regulations made by the law in terms of the scope of application of batteries, production, use, recycling, and extended producer responsibility. Through in-depth research on the law, it is found that the law is more in line with the requirements of green environmental protection and sets higher standards for the production and recycling of batteries and waste batteries. The introduction of this law brings new challenges and opportunities to the market access of the EU battery and waste battery market and the battery and waste battery legislation of other countries and regions. By comparing and analyzing China's battery and waste battery law with this law, it is found that there are certain gaps between China's battery and waste battery legislation and the EU's battery and waste battery legislation. China's battery and waste battery legislation mainly has problems such as no basic law on batteries and waste batteries, the low level of existing relevant laws and regulations (mostly departmental rules), relatively rough and non-specific provisions, lack of operability, and many legislative blanks. China can draw on the advanced experience of the EU's battery and waste battery legislation, especially some cutting-edge and latest provisions, to formulate its own battery and waste battery law or administrative regulations, thereby improving China's battery and waste battery legislation and promoting the sustainable development of China's battery and waste battery industry and enhancing its international competitiveness through the improvement of laws.

Energy storage technology is closely linked with the power system, jointly supporting the transformation and development of the modern energy system. In the context of the current global push for the "dual carbon" goals—namely, peaking carbon emissions and achieving carbon neutrality—energy storage technology has become an indispensable part of the power system. Based on this concept, analyzing the optimization design of energy storage technology and power systems from a cost perspective is of great practical significance. Accordingly, this study provides an overview focused on cost-based analysis of the optimization design of energy storage technology and power systems. This includes research progress in energy storage technology and power systems, namely their current working principles and advantages; then, based on this foundation, it elaborates on how to optimize the design of energy storage technology and power systems from a cost perspective.

Large-scale grid-connected operation of renewable energy has brought challenges to the stability and power supply quality of the power system. The application of energy storage technology has specifically solved the operation problems of the renewable energy power system. This paper summarizes the application status and value of energy storage technology in the renewable energy grid-connected operation, discusses the application scenarios from the power side, the grid side and the user side, and explores the types and problems of common energy storage technology. It forecasts the development trend of energy storage technology in the future, in order to provide an effective reference for the optimal allocation of energy storage in renewale energy side.