The coating process is one of the key processes in the manufacturing of lithium battery electrodes. The stability and consistency of the coating determine the structure of the electrode, which in turn affects the performance and cycle life of the battery. As large-sized power lithium battery products had higher requirements for coating width, one of the core technical issues is how to ensure the flow uniformity of electrode slurry at the outlet of the coating die. Therefore, rational design of the flow channel structure of the coating die is crucial. This study aims to address the issue of inhomogeneous velocity distribution at the outlet of the wide coating die, and constructs die head flow channel models with different sizes and structures for flow field simulation. The flow characteristics of electrode slurry inside the die head are analyzed, and the structural dimensions of the coat-hanger die are optimized by using Box-Behnken design. The coat-hanger die with an inclined variable-diameter homogenizing cavity had a stronger drainage effect on the slurry, helping to improve the flow uniformity in the width direction. Adjustments to the die structure dimensions significantly impacted flow uniformity. The main structural parameters of the coat-hanger die were optimized using Box-Behnken design, resulting in an increase in the uniformity of the slurry to 0.99.

As a key process in manufacturing lithium-ion batteries, coating compliance with process specifications plays a crucial role in the capacity consistency and safety of batteries and involves complex mechanisms such as surface chemistry and rheology theory. Lithium-ion battery manufacturers and coating equipment suppliers are pursuing automated and intelligent coating operation methods, whereas current research on coating is often limited to surface chemistry and other microscopic problems. Based on comprehensive literature research and simulation analysis, four evolution processes of mass-density flow field and film morphology of slurry, namely during slot die coating, mold expansion, wetting film formation, and drying shrinkage, are proposed for the first time. The influence of six key processes and quality indices, namely the consistency of the transverse opposite density, consistency of longitudinal areal density, edge thickness, film width, missing coating defects, and peel strength after calendaring, are summarized in the process of coating evolution. Through the analysis of these active variables and uncontrollable factors affecting the coating process, we can better understand the possible problems and reasons related to these problems in the coating process. Finally, a closed-loop control strategy combining intelligence and manual intervention was devised to manage the six coating quality indices. This strategy provides theoretical guidance and an algorithmic framework for improving intelligent coating and unmanned production. Additionally, this strategy is of great significance for improving the quality and production efficiency of lithium-ion battery coatings.

This study investigates the effects of diffusion-induced stress and thermal stress on lithium-ion batteries during discharge by establishing an electrochemical-mechanical-thermal coupling model for an 18.5 Ah soft-package NCM111 lithium-ion battery using Comsol Multiphysics 6.0. The analysis encompasses the lithium concentration difference between the centers and surfaces of anode particles, diffusion-induced stress, thermal stress, and expansion behavior at different discharge rates. Diffusion-induced stress is simulated using a one-dimensional electrochemical model and its derivative particle dimension, while thermal stress is addressed through a three-dimensional solid mechanics and heat transfer model. The findings indicate that both diffusion-induced and thermal stresses escalate with an increase in discharge rate, and a lower discharge rate mitigates the stress experienced by the battery. Specifically, diffusion-induced stress in the anode particles correlates with the lithium concentration difference between the centers and surfaces of these particles, intensifying progressively during discharge. In the pre-discharge phase, this concentration difference is higher near the separator than near the collector, with the situation reversing post-discharge. A critical turning point occurs at a depth of discharge (DOD) of 60%-70%, suggesting a similar trend for diffusion-induced stresses. Notably, the diffusion-induced stress in the anode particles reaches the order of MPa, substantially exceeding the thermal stress in the cell, which is on the order of kPa. Furthermore, the maximum thermal stress and displacement in the cell exhibit a linear relationship with the cell's temperature difference, escalating with discharge rate. A distinctive observation is the significant thermal stress at the connection points between the tabs and the cell in soft-pack batteries, in contrast to cylindrical batteries. This comparative analysis of diffusion-induced and thermal stresses during the discharge of ternary soft-pack NCM111 lithium-ion batteries aims to provide theoretical insights for the development and stress monitoring of electrodes and cells.

Accurate estimation of the State of Charge (SOC) and internal temperature of a battery is pivotal for enhancing its performance and safety. The precision of the battery model and the efficacy of the estimation algorithm play critical roles in this context. This paper introduces a multiparameter thermoelectric coupling model for cylindrical lithium-ion batteries, considering the interplay between SOC and temperature fluctuations. It employs an enhanced entropy heat coefficient experiment to determine reversible and irreversible heat generated during battery operation. For parameter identification, the Variable Forgetting Factor Recursive Least Squares algorithm is utilized. The accuracy of the proposed multiparameter electrothermal coupling model is corroborated by comparing the SOC and internal temperature estimation results with those of standalone electrical and thermal models. The findings indicate that our model achieves an improvement in estimation accuracy exceeding 70% over the conventional electric heating model. Furthermore, we developed a Singular Value Decomposition-based Adaptive Unscented Kalman Filter algorithm for real-time joint estimation of SOC and internal temperature, which was experimentally validated under dynamic stress test conditions. Comparative analysis with Extended Kalman Filter and Unscented Kalman Filter algorithms demonstrates the superior accuracy of our method in SOC and temperature estimations, with average errors of 5% and 0.2°C, respectively.

This study investigates the surface morphology, tensile strength, puncture resistance, and additional properties of polyethylene (PE)-based separators with varying thicknesses and double-sided ceramic coatings. Among these, three were chosen for constructing high-capacity prismatic batteries to conduct thermal runaway tests. The findings indicate that ceramic coatings of different thicknesses exhibit a dense surface, a broad particle size distribution, and similar morphological characteristics. The tensile and puncture strengths of the ceramic-coated separators, all based on a 12 μm PE film, showed no significant variance across different coating thicknesses or between single-and double-coated films of the same thickness. Under identical testing conditions, the thermal shrinkage rates of the separators were observed in the order: (12+2+2) μm, (12+1.5+1.5) μm<(12+4) μm<(12+3) μm<(12+2) μm. The state of charge at which thermal runaway occurred for batteries using (12+2) μm and (12+4) μm separators were 116.94% and 117.64%, with peak temperatures reaching 530.9 ℃ and 430.7 ℃, respectively. The experiments demonstrate that an increase in ceramic coating thickness delays thermal runaway and reduces peak temperatures. Furthermore, the battery constructed with the (12+2+2) μm double-coated separator experienced thermal runaway during the heating phase postovercharge, reaching a maximum temperature of only 369.5 ℃. An analysis of the experimental outcomes offers insights into battery design optimization, highlighting the critical nature of designing the overhang-where the separator's width exceeds that of the negative electrode, and the dimensions of the negative electrode surpass those of the positive electrode-for battery safety. Comprehensive evaluation of usage scenarios and extreme conditions is essential in battery design, taking into account the thermal shrinkage rates of chosen separators to calculate the contraction ratio, thereby ensuring the overhang design meets safety standards throughout the battery's lifecycle.

Thermal runaway in energy storage batteries poses a significant risk in energy storage power stations, making thermal management crucial for the efficiency, lifespan, and operational safety of batteries. This study presents the design of an energy storage battery module with a rated capacity of 11.52 kWh, utilizing a 60-series large cylindrical battery as the fundamental unit. A numerical model, based on the finite element method, was developed to couple fluid and temperature fields within the battery module. This model facilitates the analysis of air flow rates in the battery module's air ducts and the temperature field distribution. To validate the accuracy of the numerical calculations, a prototype was subjected to a charging/discharging temperature-rise test. The study further optimizes the temperature field distribution of the battery module by adjusting the arrangement of heat dissipation holes. A novel side U-shaped opening structure is introduced, significantly enhancing the temperature uniformity within the battery module and reducing the maximum temperature of the cells. Postoptimization, the maximum temperature difference in the module cells decreased by 2.6 ℃, and the standard deviation of temperature dropped by 1.18. These findings offer valuable insights for estimating temperature rise in energy storage battery modules and designing efficient heat dissipation mechanisms.

Insulation resistance detection is crucial for the safe operation of battery energy storage systems. This study addresses the significant and random measurement errors associated with the commonly used balanced-unbalanced bridge method. By establishing a computer simulation model of this method, the research investigates the error-influencing factors in insulation resistance detection. The analysis quantitatively examines the impact of these errors, focusing on voltage measurement error bias and accuracy. To address the engineering challenge of a limited and imprecise measurement range due to reduced positive bus insulation, an improved balanced-unbalanced bridge method is proposed. This improvement includes a theoretical foundation and a detailed methodology for selecting bridge resistance. Furthermore, the study tackles the issue of diminished measurement accuracy and insulation monitoring dead zones when both positive and negative insulations are compromised. A variable resistance switch bridge method, derived from the three-dimensional surface analysis of the simulation model, is suggested to enhance detection accuracy under such conditions. Field tests conducted at an engineering site confirm the validity of the error analysis and the appropriateness of the parameter selection. The findings offer valuable insights and a theoretical framework for designing and selecting parameters for the insulation monitoring function in battery management systems.

In the realm of wound cell quality control, tab alignment accuracy stands as a paramount yet intricate factor influencing multiple stages of the production process. To address the challenges associated with the alignment accuracy of multiple tabs in cells, this paper introduces a tab position model coupled with an edge closed-loop control algorithm. This approach facilitates the detection, correction, and control of various parameters impacting alignment accuracy, thus providing a theoretical foundation for enhancing existing control technologies and ensuring precise control over tab alignment. Furthermore, the tab position model elucidates the effects of diverse parameters on tab positioning and the manifestation of tab misalignment. This enables research and development personnel to gauge the impact of these parameters on misalignment and devise corrective strategies articulated through control methodologies. The synergy of simulation analysis and empirical control evaluations underscores the control method's adaptability in refining tab positioning, with the model accurately depicting the tab's positional dynamics. Additionally, this study delves into a logical framework for realizing a comprehensive closed-loop system in intelligent winding, aiming to optimize the control loop throughout the winding process. The insights gleaned hold substantial relevance for achieving a quality-centric closed-loop in winding operations, thereby enhancing battery performance and production efficiency.

As lithium-ion battery production rapidly expands, manufacturers urgently require high-precision and high-efficiency sorting methods to improve the consistency, lifespan, safety, and energy density of battery packs. Traditional techniques that rely on capacity and internal resistance address static consistency postgrouping but fail to ensure dynamic consistency within the same group. Addressing this, our study focuses on the dynamic characteristics of the charge-discharge voltage curve to propose a next-generation sorting approach. We extract key dynamic features from the voltage curve during the battery capacity grading process, utilizing big data from the production line, and employ K-means clustering for battery sorting. Furthermore, we assess battery performance consistency by analyzing metrics from the recharging stage postcapacity grading, devising an evaluation method based on the standard deviation of these metrics. Our proposed sorting method demonstrates a 15.65% improvement in the overall performance consistency of batteries compared to conventional approaches.

Defects arising during the battery manufacturing process can significantly compromise the safety of battery production. Among these, the intrusion of metal foreign bodies into the production line poses a risk of spontaneous internal short circuits (ISCs) or even thermal runaway. Despite the critical nature of this issue, research on the evolution mechanism of battery defects, particularly those involving small metal foreign bodies, remains scant. This study introduces hundred-micron diameter copper particles into batteries to simulate the intrusion of metal foreign bodies in the production line, thereby generating defective batteries. It then analyzes the ISC current characteristics of these batteries, disassembles them to examine the microstructure of the ISC regions, and develops a simulation model to assess the potential distribution within these regions. The study comprehensively elucidates the influence and mechanisms of defects on the critical detection indicator K-value (voltage drop rate) in the production line, with verification conducted on high-capacity batteries in an actual pilot line. The findings contribute to enhancing defect detection accuracy and preventing potential safety hazards. The results demonstrate that the intrusion of metal foreign bodies, such as copper particles, can precipitate ISC in two distinct modes: cathode-particle-anode and cathode-anode, each indicating different current paths and potential distributions. The ISC current generates a potential gradient in the cathode, reducing the particle's potential and hindering further dissolution. Consequently, the expansion of the ISC region ceases, stabilizing both ISC modes under K-value test conditions. The ISC severity is comparable across both modes, with current levels ranging between 0.1～1 mA. Given the stability of the ISC current, its impact on the K-value can be quantified via the differential voltage slope. The K-value increase attributable to ISC is inversely proportional to battery capacity; in a 100 Ah battery, a 1 mA ISC current has a limited effect on the K-value, increasing it by only 0.01 mV/h. To maintain defect detection precision on the production line, both the K-value detection threshold and the reference values for normal batteries should be adjusted downward as battery capacity increases.

This study proposes a state of charge (SOC) estimation method based on backpropagation neural network (BPNN) and adaptive unscented Kalman filter (AUKF). Firstly, a series of temperature compensation strategies were studied and designed to improve the estimation accuracy under low temperature and low SOC conditions, focusing on the relationship between battery SOC and terminal voltage at different temperatures. Secondly, a battery model coupled with temperature compensation strategy was established using backpropagation neural network (BPNN). This model can better adapt to battery state changes under low temperature and low SOC conditions, improving the accuracy of SOC estimation. Finally, a SOC estimation framework for BPNN-AUKF was established based on the BPNN battery model. By utilizing the information and residual sequences between measured and predicted values, the system process and measurement noise covariance were estimated and corrected. Through experimental verification, it was found that this method has significant advantages in low-temperature environments. Compared with traditional methods, it can more accurately estimate the SOC of batteries and has good generalization ability. This SOC estimator based on BPNN-AUKF method is not only suitable for autonomous unmanned underwater vehicles (AUV), but also has broad application value for other vehicles working in complex environments.

The advent of ChatGPT signifies the birth of a new scientific research paradigm centered around "pretraining+fine-tuning". Companies such as OpenAI will lead the path toward artificial general intelligence (AGI) models. This indicates that artificial intelligence can surpass human intelligence and solve universal problems. AGI represents a model that is not designed for solving specific problems and even has the ability of self-learning. However, ChatGPT and other models still use texts combined with images as inputs. For a battery system, text information is less and most data as input is multimodal, such as temperature and voltage-current curve. The results related to a battery include the state of the battery including its charge and health, remaining useful life, whether there is a turning point in battery performance diving, and even the assessment of secondary (gradient) use of the battery without previous data. This means that ChatGPT can also help solve the battery system problem; however, its method can involve extra and complex solutions for minor problems even if AGI may solve the current battery problems in the future. Simultaneously, AGI can have huge parameters that are not suitable for offline operation of electric vehicles. We anticipate that AGI for a battery must have its own language and understand the physical and chemical processes during the operation of the battery. If AGI for batteries can understand why batteries become bad for example the lithium dendrites, they should predict all types of battery including all solid state battery. This review discusses how to redesign a battery model, including character representation, data distribution, pretrained methods and strategies, and fine-tuning for various tasks. In addition, minor parameters for the model should be concentrated for offline prediction and under international conditions. We will introduce the stages, problems, and evaluation indexes for developing a pretrained multimodal battery general model with minor parameters based on semantic detection (PBGM). We also present the three-step development strategy in PBGM by the Institute of Physics, Chinese Academy of Sciences (PBGM-IOPCAS).

The remarkable energy density of lithium-oxygen batteries has gained considerable attention among researchers. Nevertheless, the insufficient stability of the lithium metal anode and the high charging overpotential pose substantial obstacles to the advancement of lithium-oxygen batteries, negatively impacting cycling performance and round-trip efficiency. With the evolving techniques, an expanding array of advanced insitu characterization methods is employed for mechanism research and battery structure optimization in lithium-oxygen batteries. Insitu characterization techniques offer static insights into various components of lithium-oxygen batteries while enabling precise monitoring of the dynamic electrochemical behavior and structural evolution of the battery throughout the cycling process with remarkable accuracy. Thus, insitu characterization techniques play a pivotal role in driving the progress of lithium-oxygen batteries. The study comprehensively reviews recent advancements in insitu characterization techniques for lithium-oxygen batteries, encompassing insitu microscopic, insitu X-ray, and insitu mass spectrometry characterizations. Through the analysis of specific cases, we elucidate the functionalities of various insitu characterization techniques, outline their specific applications in lithium-oxygen batteries, and unveils the deeper reaction mechanisms of lithium-oxygen batteries. Furthermore, we explore and anticipate advanced insitu characterization techniques for future research on lithium-oxygen batteries.

The inability of commercial lithium-ion batteries to meet the burgeoning demands of electric vehicles and electronic devices, owing to their low energy density, has propelled lithium-sulfur batteries (LSBs) to the forefront of energy storage research. Characterized by their high energy density, environmental friendliness, and cost-effectiveness, LSBs have emerged as a significant area of interest. Despite these advantages, challenges such as slow redox kinetics, pronounced shuttle effects, electrolyte depletion, and degradation of the lithium anode impede their commercial viability. Understanding the fundamental reaction mechanisms within the LSB system is essential for addressing these issues and enhancing the battery's overall performance. In situ characterization techniques offer the ability to observe the structural and reactional changes in battery components during operation, thereby shedding light on the mechanisms of LSBs and potentially leading to significant performance improvements through material design. This paper reviews recent research on overcoming the challenges faced by LSBs in achieving extended cycle life and high energy density. It also highlights the application of various in situ characterization techniques, including in situ Raman spectroscopy, in situ transmission electron microscopy, in situ resonant inelastic X-ray scattering, in situ infrared spectroscopy, and in situ NMR spectroscopy. Special attention is given to the advancements in in situ characterization technology for monitoring the polysulfide conversion process and elucidating the internal reaction mechanisms of LSBs, particularly in the context of redox reactions, polysulfide dissolution, electrolyte-induced polysulfide inhibition, and lithium anode degradation. The pivotal role of in situ characterization technology in enhancing the understanding of LSB mechanisms is underscored.

Lithium-ion batteries are extensively used in portable electronics, energy storage systems, and electric vehicles. However, with the increasing capacity of these batteries, the risk of thermal runaway and associated safety concerns have escalated. Traditional battery management systems primarily focus on monitoring surface temperature and terminal voltage to assess battery health. Yet, the multilayer structure and poor thermal conductivity of battery modules make it challenging to effectively monitor internal temperature and gas distribution, leading to delayed detection of critical signals such as surface temperature variations. Consequently, there is a growing emphasis on monitoring changes in internal temperature, pressure, strain, and gas signals to provide timely warnings of battery thermal runaway and enhance safety across various applications. This review offers a comprehensive examination of the mechanisms of thermal runaway in lithium-ion batteries and the techniques for monitoring internal battery signals. It highlights a series of exothermic reactions associated with thermal runaway, along with the resulting changes in internal temperature, pressure, and gas signals. Moreover, the review discusses monitoring techniques that directly assess internal battery signals, such as electrochemical impedance spectroscopy and embedded sensor monitoring, offering insights for the optimization of future monitoring methods. The practical applications and potential of embedded sensors within batteries are emphasized, along with the prospects for further enhancing the safety of lithium-ion battery systems.

As the energy and power density of lithium-ion batteries have gradually increased in recent years, the safety performance and prediction of remaining service life have become increasingly crucial. This review offers a comprehensive analysis of the current research status of predicting the remaining useful life of lithium batteries. It systematically introduces the existing forecast algorithms, focusing on the application of machine learning methods in this field. Model-based methods encompass electrochemical, equivalent circuits, and empirical models. In contrast, data-driven methods involve machine learning techniques such as support vector machines, Gaussian process regression, extreme learning machines, convolutional neural networks, recurrent neural networks, and transformers. We meticulously examine the advantages and disadvantages of each method, emphasizing on the application and evolution of machine learning methods in feature extraction and fusion techniques. This study summarizes and analyzes current-voltage-temperature, IC, and EIS curves regarding feature extraction. It subdivides and analyzes fusion methods into model-model, data-model, and data-data fusion methods. Finally, addressing the existing research challenges, this review proposes research suggestions for predicting remaining service life from three perspectives: early, online, and multioperating condition predictions. These suggestions provide insights into enhancing the accuracy and practicability of remaining service life prediction algorithms for Li-ion batteries.

Solid oxide fuel cells (SOFCs) efficiently convert chemical energy into electric energy via electrochemical oxidation reactions, holding promise for various engineering applications in large-scale power generation, cogeneration, and integrated fuel upgrading. Direct internal reforming (DIR) technology, catalyzing alkanes like CH₄ at the anode to produce H₂, enhances SOFC application scenarios and reduces operational costs, thereby reducing fuel pretreatment requirements and improving conversion efficiency, which represents a hotspot in SOFC research. Model simulation studies aid in optimizing system design and operational conditions, reducing experimental work, and providing theoretical data support and guidance suggestions. Through the model simulation of the DIR–SOFC system, combined with the field distribution and kinetic parameters, the system reactions can be quantitatively evaluated to understand the complexity of physical and chemical processes. This paper summarizes the current situation of DIR-SOFC modeling work, introducing volume average and microstructural models. It discusses the multiscale mathematical model and reviews the description of the reaction dynamics process "energy-mass-momentum" balance equations as well as the description of the "1D-2D-3D" DIR-SOFC unit. This can be used to evaluate the variables on the DIR. Furthermore, it summarizes the reforming reactions of different liquid fuels in the DIR-SOFC model and the related reaction kinetic parameters. This study highlights existing model limitations and prospects for the future development of the DIR-SOFC system model to increase model accuracy.

Silicene, a two-dimensional material, is a promising anode material for lithium-ion batteries. However, it struggles to exist stably alone. Its structural stability can be effectively improved by elemental doping. Germanium (Ge) not only shares the same valence electron configuration as Silicon (Si), but germanene also boasts higher electronic conductivity and better electrochemical properties. This study investigates the impact of Ge doping on the lithium storage behavior of silicene through first-principles calculations based on density functional theory (DFT). The structural stability, adsorption capacity, diffusion behavior, theoretical specific capacity, open-circuit voltage (OCV), and electronic conductivity of Si₁₇Ge were calculated and analyzed. These results demonstrate that Si17Ge maintains good structural stability after Ge doping without exhibiting structural protrusions, depressions, or planar states. This indicates that Ge doping does not alter the two-dimensional warped structure of silicene, distinguishing it from Ge-doped graphene. The adsorption and diffusion energy barriers indicate that Ge doping enhances the lithium adsorption and diffusion capacity of silicone in horizontal and vertical directions. In the horizontal direction, the diffusion step spans 4.63 ? and requires overcoming an energy barrier of 0.18 eV, which is substantially lower than that of Li atoms on graphene (0.31 eV) and silicene (0.22 eV). Conversely, in the vertical direction, the diffusion energy barrier is 1.14 eV, higher than that on the surface, indicating increased difficulty in vertical diffusion of Li atoms in Si₁₇Ge. Nevertheless, this value is lower than the vertical diffusion barrier of Li atoms in pure silicene (1.67 eV) and graphene (10.02 eV). Through OCV and adsorption energy calculations, it is estimated that Si₁₇Ge can adsorb a maximum of 18 Li atoms and has a theoretical specific capacity as high as 876.85 mAh/g. It exhibits a higher theoretical specific capacity and lower diffusion energy barrier than existing two-dimensional materials. Density of state (DOS) analysis reveals that when Si₁₇Ge adsorbs a lower Li concentration, the Fermi level DOS is enhanced, and the system exhibits metallicity. When Si₁₇Ge absorbs a higher concentration of Li, a noticeable bandgap appears at the Fermi level, causing the system to transition from a conductor to a semiconductor. During the process of Si₁₇Ge adsorbing Li atoms, the density of states near the Fermi level is primarily attributed to Si orbitals. In contrast, the Ge orbitals contribute minimally to the density of states. This study offers crucial theoretical guidance for the design of two-dimensional Si-based anode materials and other two-dimensional materials.

Regulation of the surface morphology and pore structure of silicon carbon particles holds substantial potential for enhancing the performance of lithium-ion battery anodes, which is a crucial advancement for next-generation high-ratio lithium-ion power batteries. In this study, silica-carbon particles with dented surfaces were fabricated using a process involving spray-drying, liquid-phase encapsulation, and low-temperature pyrolysis. Photovoltaic industrial silicon waste served as the silicon source, whereas chitosan and phenol-formaldehyde resin provided the carbon source, with calcium chloride acting as the morphology modifier. By employing thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and electrochemical measurements, we investigated the impact of surface denting on the electrochemical performance of the negative electrode. Our findings indicate that electrodes prepared from nonspherical particles with large pore volumes and dented surfaces exhibit enhanced conductivity and ion transport capabilities owing to the small particle gaps, increased contact area, and adequate filling of conductive additives. These characteristics improve the capacity performance. Specifically, the discharge-specific capacity of the anode prepared from surface-dented particles remained at approximately 680 mAh/g after 400 cycles, with a capacity recovery of 97.8% when the current density reverted from 0.1 C to 1 C. These results underscore the stable charging and discharging performance of the anode, which bodes well for advancing the application of silicon-based lithium-ion batteries in environments with high vibration density.

The vanadium flow battery (VFB), boasting the highest technological maturity, is a prime candidate for large-scale, long-term energy storage, facilitating the seamless integration of renewable energy into grid-connected applications. Bipolar plates are pivotal components of the VFB system. This study comprehensively summarizes the merits, limitations, and research advancements in metal, graphite, and carbon-plastic composite bipolar plates, focusing on their corrosion resistance, conductivity, mechanical properties, and battery characteristics. Moreover, it outlines the application prospects of these three types of bipolar plates in the VFB field, considering the processing technology and manufacturing costs. Furthermore, in conjunction with structural optimization efforts for flow battery bipolar plates, this study analyzes the applicability of flow channel structure designs under various experimental conditions, ranging from flat structures to flow channels, and explores the electrode-bipolar plate integrated structure. It evaluates the potential application prospects of the electrode-bipolar plate integrated structure in the VFB domain, examining aspects such as the preparation processes and battery performance. Finally, considering the current research status of the VFB, this study proposes key focal points for technological breakthroughs in the VFB bipolar plates and structural design. These proposals aim to serve as a reference and foundation for future developments in bipolar plates for VFBs, contributing to the advancement and optimization of the VFB technology for large-scale energy storage applications.

Sharp object intrusion, particularly during automobile collisions, represents a significant risk to power batteries, potentially causing serious damage including ignition or explosion of lithium-ion batteries. This can lead to catastrophic outcomes for electric vehicles and pose risks to personal safety. This study aims to elucidate the safety performance of lithium-ion batteries under nail penetration conditions. Utilizing a custom-built experimental platform, we examined the effects of four parameters on battery safety: state of charge, penetration speed, penetration depth, and penetration location. Cylindrical 18650 lithium-ion batteries were subjected to penetration tests using a 5 mm diameter flat tungsten steel nail. The thermal runaway phenomenon was monitored using an infrared camera, and data such as temperature, open-circuit voltage, and load were recorded before and after the tests. Our findings indicate a clear pattern in the batteries' response to nail penetration; they do not immediately undergo thermal runaway but exhibit a delayed reaction. Factors such as higher states of charge and greater penetration depths significantly increase the likelihood of thermal runaway, which is also more severe when penetration occurs closer to the battery's positive and negative terminals. However, the speed of penetration does not have a significant correlation with the occurrence of thermal runaway. Based on these results, we offer recommendations for the transport, safe usage, and early warning algorithm design of lithium-ion battery packs.

To control the power consumption of new energy vehicle batteries and conduct in-situ analysis of their energy consumption behavior, research is conducted on online monitoring and in-situ analysis technologies for new energy vehicle batteries. Improve the structural model of power lithium battery packs, derive monitoring kernel functions based on the thermal coupling characteristics of automotive batteries, and achieve online monitoring of new energy vehicle batteries. On this basis, the electrode materials for automotive batteries were determined, and the in-situ characteristics of their battery components were studied from the perspectives of characterization characteristics and Raman spectroscopy.

Lithium-ion batteries are susceptible to failure during extended use, manifesting as increased internal resistance, capacity decay, lithium plating, and gas generation, among other issues. The challenge of monitoring these failure processes can significantly compromise the safety, reliability, and lifespan of these batteries. Investigating the causes of battery failure under various service conditions, such as calendar aging, extensive cycling, and floating charge, is crucial for understanding the failure mechanisms and effectively monitoring the battery's health and lifespan. This paper reviews existing research on battery failure under different conditions and summarizes the failure mechanisms within the internal components of lithium-ion batteries-cathode, anode, separator, and electrolyte-under various temperature, voltage, and state of charge conditions. It highlights the effects of voltage and temperature on calendar aging, models of failure due to calendar aging, alterations in cathode and anode materials after prolonged cycling, failure mechanisms following high-temperature float charging, and the mechanisms of battery gas generation. Additionally, it proposes targeted optimization strategies for anode materials, separators, electrolytes, and cathode materials in lithium batteries. Comprehensive analysis indicates that failure in lithium-ion batteries can result from lithium loss in electrodes, active material loss, particle breakdown, transition metal dissolution, and solid electrolyte interface decomposition. By minimizing particle size, incorporating electrolyte film-forming additives, and enhancing separator permeability, the failure rate of lithium-ion batteries during long-term service can be reduced, ensuring their safe and stable operation.

The inherent characteristics of fuel cells have led to their increasing presence in hybrid energy storage systems. In order to achieve energy allocation control, further reduce energy costs, and improve application scope, this article carefully analyzes and studies fuel cell hybrid energy storage systems under computer software. The overall framework of fuel cell hybrid energy storage technology, as well as its core technology architecture, includes two major parts: fuel cell power system and control system. The research results indicate that effective energy control systems can improve the rationality of energy storage coordination and reduce application costs, and fuel cell hybrid energy storage systems also have good application and development prospects.

Traditional power systems are constantly developing towards intelligence. The application of large-scale energy storage battery management systems can effectively and timely analyze the working conditions of energy storage units and batteries to improve work efficiency. Faced with the pressure of massive data analysis, more advanced technological upgrades are needed. This article analyzes the battery management system under big data technology. After clarifying the conventional framework of the current battery energy storage management system, big data technology is introduced, and a SOC data prediction method is proposed to construct a data set. Through the Hadoop data management architecture, high-speed data analysis and organization are achieved. It has been proven that big data technology can be effectively applied to energy storage battery management systems, improving their intelligence and achieving faster replacement and expansion of data modules.

The whole process digital intelligent manufacturing technology is a crucial technical means in the manufacturing process of energy batteries. The higher its safety, the higher the quality level of energy signals that energy batteries can store. To promote the high quality storage of energy signals in energy batteries, the whole process digital intelligent manufacturing technology in the manufacturing process of energy batteries was studied. The specific implementation process of the energy battery production process is analyzed, and on this basis, the core energy storage components are designed. The application of the whole process digital intelligent manufacturing technology in energy batteries is studied from the aspects of energy conversion efficiency and energy storage quality.