A method to improve the cycle stability of silicon-carbon anode materials for new energy batteries

Abstract

This application relates to a method for improving the cycle stability of silicon-carbon anode materials in new energy batteries. The method includes: first, acquiring initial microstructure data of the silicon-carbon material; then, constructing a finite element mesh model based on the data to simulate the stress distribution during lithiation and determine the critical stress value of structurally weak regions; subsequently, designing a porous composite buffer structure based on the obtained critical stress value to construct a three-dimensional conductive network, obtaining the resistivity distribution matrix and coverage topology map of the conductive network; subsequently, adjusting the ratio of silicon-carbon material to conductive additives according to the resistivity distribution matrix and coverage topology map to obtain the final material ratio scheme; finally, conducting cycle performance tests according to the final material ratio scheme to obtain the process parameter combination data of the anode material. The above method effectively alleviates the volume change problem of silicon-carbon anode materials during charge and discharge processes, improves the structural stability of the material, and thus significantly enhances the cycle stability of the battery.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery technology, and in particular relates to a method for improving the cycle stability of silicon-carbon anode materials in new energy batteries. Background Technology

[0002] In the current era of booming new energy technologies, the performance improvement of new energy batteries, as core energy supply components in many fields, is crucial. Among them, silicon-carbon anode materials, due to silicon's theoretical specific capacity of up to 4200 mAh / g, far exceeding that of traditional graphite anode materials (approximately 372 mAh / g), have become a key research direction for improving the energy density of lithium-ion batteries. However, silicon undergoes a huge volume change (up to 300%) during charging and discharging, which leads to electrode structure damage and particle pulverization, resulting in poorer contact between active materials, conductive agents, and current collectors, causing increased internal resistance and rapid capacity decay in the battery. In existing solutions, carbon coating technology can provide physical buffering, but insufficient interfacial bonding strength leads to easy peeling of the coating layer. For example, in the patent CN112349876A "Hollow porous tin dioxide-cuprous oxide-copper and hollow porous tin dioxide-copper integrated lithium battery anode and its preparation method", the capacity retention rate of traditional carbon-coated silicon anode is only 70% after 50 cycles. Although porous structure design can provide buffer space, the pore structure is highly random and cannot accurately match the stress distribution, resulting in insufficient buffering efficiency. For example, in a journal article published in the Journal of Power Sources in 2021, the crack density in the stress concentration area of ​​the porous carbon buffer structure reached 0.3 cracks / μm². Traditional conductive agent dispersion technology cannot construct a three-dimensional through network, resulting in a significant decrease in conductivity with the number of cycles. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for improving the cycle stability of silicon-carbon anode materials in new energy batteries, which can effectively alleviate the volume change problem of silicon-carbon anode materials during charging and discharging and significantly enhance the cycle stability of batteries.

[0004] In a first aspect, this application provides a method for improving the cycle stability of silicon-carbon anode materials for new energy batteries, including:

[0005] Obtain initial microstructure data of silicon-carbon materials; initial microstructure data includes particle size, porosity, and crystal structure parameters.

[0006] A finite element mesh model was established based on the initial microstructure data to simulate the stress distribution during the lithiation process, and the critical stress value of the weak structural region was obtained.

[0007] A porous composite buffer structure was designed based on the critical stress value, and a three-dimensional conductive network was constructed using atomic layer deposition and carbon nanotube doping processes. The resistivity distribution matrix and coverage topology of the conductive network were obtained.

[0008] The ratio of silicon-carbon material to conductive additives is adjusted based on the resistivity distribution matrix and coverage topology diagram to obtain the final material ratio scheme.

[0009] Based on the final material formulation, the cycle performance was tested using roll-to-roll deposition and continuous heat treatment processes to obtain the process parameter combination data for the anode material.

[0010] In one embodiment, a finite element mesh model is established based on initial microstructure data to simulate the stress distribution during the lithiation process, obtaining the critical stress values ​​of the structurally weak regions, including:

[0011] Obtain grain boundary distribution parameters and phase transformation characteristic parameters from the initial microstructure data; the grain boundary distribution parameters include grain size and orientation information.

[0012] The anisotropic elastic tensor is calculated based on the phase transition characteristic parameters, and a finite element mesh model including grain boundary topology is generated.

[0013] The lithium-ion diffusion path data in the finite element mesh model is extracted, and the local strain increment is calculated by combining it with the preset concentration gradient field.

[0014] The stress gradient distribution map is obtained by iteratively solving the local strain increment and the anisotropic elastic tensor.

[0015] Regions exceeding the phase transition threshold are identified based on stress gradient distribution maps, and the interfacial bonding strength parameters of these regions are extracted.

[0016] By matching the interface with strength parameters and a preset fracture toughness database, the critical stress value of the weak area of ​​the structure is obtained.

[0017] In one embodiment, a porous composite buffer structure is designed based on a critical stress value, and a three-dimensional conductive network is constructed using atomic layer deposition and carbon nanotube doping processes. This yields the resistivity distribution matrix and coverage topology map of the conductive network, including:

[0018] The stress distribution gradient data of the porous composite buffer structure were calculated based on the critical stress value.

[0019] The path optimization parameters for atomic layer deposition are determined based on stress distribution gradient data; the path optimization parameters include deposition angle and coverage density.

[0020] Based on path optimization parameters and carbon nanotube doping concentration gradient, the connectivity threshold of the three-dimensional conductive network is calculated; the connectivity threshold includes node contact probability and cross-layer conductivity.

[0021] By inputting the connectivity threshold into the deposition cycle control model, the number of cycles and residence time for atomic layer deposition are obtained.

[0022] The deposition process is performed based on the number of cycles and residence time to obtain the resistivity distribution matrix and coverage topology map of the conductive network.

[0023] In one embodiment, the connectivity threshold is calculated using the following formula:

[0024] ;

[0025] in, Indicates the connectivity threshold. Represents structural factors. Indicates the number of conductive channels. Indicates the volume of a single channel. Indicates the total volume. Indicates the characteristic length. Indicates the association length.

[0026] In one embodiment, the ratio of silicon-carbon material to conductive additives is adjusted according to the resistivity distribution matrix and coverage topology map to obtain a final material ratio scheme, including:

[0027] Obtain the initial parameters for the resistivity distribution matrix and coverage topology map; the initial parameters include the original ratio of silicon carbide material to conductive additives.

[0028] The particle dispersion parameters for determining the distribution density of the conductive network in the material are calculated based on the original proportions.

[0029] If the distribution density is lower than a preset dispersion threshold, a ratio correction coefficient including silicon-carbon materials and conductive additives is generated.

[0030] The material composition parameters are updated using a proportional correction factor to generate capacity decay rate data.

[0031] The capacity decay rate data and the interface impedance spectrum are coupled for analysis to obtain the coupling analysis results for optimizing the topology of the conductive network model.

[0032] Based on the results of the coupling analysis, the particle dispersion parameters were corrected and the optimal component parameters were extracted. The corresponding mapping relationship was then constructed by combining the capacity decay rate curve.

[0033] The final material proportioning scheme is determined based on the mapping relationship; the final material proportioning scheme includes a combination of parameters that meet the requirements of dispersion threshold and conductive network stability.

[0034] In one embodiment, capacity decay rate data is coupled with interface impedance spectrum for analysis to obtain coupled analysis results for optimizing the topology of the conductive network model, including:

[0035] Feature extraction was performed on the capacity decay rate data and interface impedance spectrum to obtain the time-series features of decay rate and the frequency domain features of impedance spectrum; the time-series features of decay rate include the trend of capacity decay rate with the number of cycles.

[0036] Multi-source data fusion is performed on the attenuation rate time-series characteristics and impedance spectrum frequency domain characteristics to generate a dynamic correlation matrix.

[0037] The topological parameters of the conductive network model are determined based on the dynamic correlation matrix; the topological parameters include quantitative indicators of node connectivity and path conduction efficiency.

[0038] The topology parameters are combined with preset optimization weight coefficients to generate a structure optimization strategy; the optimization weight coefficients are obtained by iterative calculation using the gradient descent algorithm.

[0039] The node connection relationships of the conductive network model are updated based on the structural optimization strategy to obtain the optimized topology data.

[0040] The optimized topology data is input into the conductive network model to generate coupling analysis results with improved conduction paths.

[0041] In one embodiment, based on the final material formulation, roll-to-roll deposition and continuous heat treatment processes are used to perform cycle performance testing, obtaining process parameter combination data for the negative electrode material, including:

[0042] Obtain the deposition rate parameters of the roll-to-roll deposition process, adjust the vacuum level of the deposition chamber according to the deposition rate parameters, and obtain real-time deposition thickness data.

[0043] Real-time deposition thickness data is input into the thickness deviation prediction model to obtain the predicted thickness deviation value.

[0044] Deposition rate compensation instructions are generated based on the thickness deviation prediction.

[0045] The heat treatment temperature parameters of the continuous heat treatment process are adjusted according to the deposition rate compensation command to obtain heat treatment temperature adjustment data.

[0046] Input the heat treatment temperature adjustment data into the composition deviation analysis model to generate composition deviation correction coefficients and update the heat treatment temperature parameters.

[0047] Process parameter combination data is generated based on heat treatment temperature parameters and deposition rate parameters; the process parameter combination data is used to control the coordinated operation of roll-to-roll deposition and continuous heat treatment equipment.

[0048] Secondly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0049] Thirdly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method.

[0050] The aforementioned method, computer equipment, and storage medium for improving the cycle stability of silicon-carbon anode materials in new energy batteries firstly acquire initial microstructure data of the silicon-carbon material. Then, based on this initial microstructure data, a finite element mesh model is constructed to simulate the stress distribution during lithiation, thereby determining the critical stress values ​​in structurally weak regions. Next, based on the obtained critical stress values, a porous composite buffer structure is designed, and a three-dimensional conductive network is constructed using atomic layer deposition and carbon nanotube doping processes. The resistivity distribution matrix and coverage topology of the conductive network are then obtained. Subsequently, the ratio of silicon-carbon material to conductive additives is adjusted according to the resistivity distribution matrix and coverage topology to obtain the final material ratio scheme. Finally, cycle performance tests are conducted using roll-to-roll deposition and continuous heat treatment processes according to the final material ratio scheme to obtain the process parameter combination data of the anode material. This method effectively alleviates the volume change problem of silicon-carbon anode materials during charge and discharge processes, improves the structural stability of the material, and thus significantly enhances the cycle stability of the battery. It provides reliable process guidance for large-scale industrial production, improves production efficiency and product quality, and promotes the widespread application and industrial development of silicon-carbon anode materials for new energy batteries. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart of a method for improving the cycle stability of silicon-carbon anode materials in new energy batteries, provided as an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the crystal structure of silicon-carbon material obtained by analyzing it using an X-ray diffractometer, as provided in an embodiment of the present invention.

[0054] Figure 3 A schematic diagram of silicon-carbon precursor particle size measured by scanning electron microscopy, provided for an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the silicon element distribution in the silicon-carbon material provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] In one embodiment, such as Figure 1 As shown, this application provides a method for improving the cycle stability of silicon-carbon anode materials for new energy batteries, which may include the following steps:

[0058] Step S101: Obtain initial microstructure data of silicon-carbon material; initial microstructure data includes particle size, porosity and crystal structure parameters.

[0059] Specifically, the particle size distribution (particle size range 50-200 nm) was determined using a scanning electron microscope (SEM, resolution ≤10 nm), the porosity (range 10%-30%) was measured using a mercury porosimeter (accuracy 0.01 m³ / kg), and the crystal structure parameters (such as the (111) interplanar spacing of silicon, 0.313 nm) were obtained using an X-ray diffractometer (XRD, CuKα radiation). Among these, particle size reflects the size of the basic unit of silicon-carbon material and has an important influence on the specific surface area of ​​the material and the contact area with the electrolyte; porosity determines the proportion of pore space in the total volume of the material and is related to the transport path and storage space of lithium ions inside the material; crystal structure parameters clarify the arrangement of atoms in silicon-carbon material and play a decisive role in the electrical and mechanical properties of the material.

[0060] Step S102: Based on the initial microstructure data, a finite element mesh model is established to simulate the stress distribution during the lithiation process, and the critical stress value of the structurally weak region is obtained.

[0061] Specifically, based on the acquired initial microstructure data, advanced finite element analysis software, such as ANSYS, is used to construct a finite element mesh model that accurately reflects the microstructure of silicon-carbon materials. During model construction, grain boundary distribution parameters (including grain size and orientation information) and phase transformation characteristic parameters are fully considered. Anisotropic elastic tensors are calculated based on the phase transformation characteristic parameters to generate a model containing the grain boundary topology. Subsequently, by extracting lithium-ion diffusion path data from the model and combining it with a preset concentration gradient field, relevant algorithms are used to calculate the local strain increment. The local strain increment and the anisotropic elastic tensor are then iteratively solved to obtain a stress gradient distribution map. Regions exceeding the phase transformation threshold are identified from this map, and the interfacial bonding strength parameters of these regions are extracted and matched with a preset fracture toughness database to successfully obtain the critical stress value of the structurally weak regions.

[0062] Step S103: Based on the critical stress value, a porous composite buffer structure is designed and a three-dimensional conductive network is constructed using atomic layer deposition and carbon nanotube doping processes to obtain the resistivity distribution matrix and coverage topology map of the conductive network.

[0063] This study designs a porous composite buffer structure based on the critical stress values ​​of structurally weak regions. By designing parameters such as the geometry, pore distribution, and size of the buffer structure, it effectively alleviates stress concentration during the lithiation process of silicon-carbon materials. Simultaneously, atomic layer deposition (ALD) technology is used to precisely control the thickness and uniformity of the deposited atomic layers on the material surface. Combined with carbon nanotube doping, carbon nanotubes with excellent electrical properties are uniformly dispersed in the material system to construct a three-dimensional conductive network. During this process, the stress distribution gradient data of the porous composite buffer structure is calculated based on the critical stress values, thereby determining the path optimization parameters for ALD, such as deposition angle and coverage density. Then, combined with the carbon nanotube doping concentration gradient, a specific formula is used to calculate the connectivity threshold of the three-dimensional conductive network (including node contact probability and interlayer conductivity). This connectivity threshold is input into a deposition cycle control model to obtain the number of ALD cycles and residence time. The deposition process is executed according to these parameters, ultimately obtaining the resistivity distribution matrix and coverage topology map of the conductive network.

[0064] Step S104: Adjust the ratio of silicon-carbon material to conductive additives according to the resistivity distribution matrix and coverage topology map to obtain the final material ratio scheme.

[0065] Specifically, after obtaining the resistivity distribution matrix and coverage topology map of the conductive network, the initial parameters contained therein, namely the original ratio of silicon-carbon material to conductive additives, are obtained. Based on this original ratio, the particle dispersion parameters of the conductive network distribution density in the material are determined through a corresponding calculation model. If the calculated distribution density is lower than a preset dispersion threshold, it indicates that the current material ratio is not conducive to the effective construction and performance of the conductive network. At this time, a ratio correction coefficient containing silicon-carbon material and conductive additives is generated. The material composition parameters are updated using this ratio correction coefficient, and capacity decay rate data is generated through experiments or simulations. The capacity decay rate data is coupled with the interface impedance spectrum for analysis, specifically including feature extraction of both to obtain the time-series characteristics of decay rate (such as the trend of capacity decay rate with the number of cycles) and the frequency domain characteristics of impedance spectrum, and then multi-source data fusion is performed to generate a dynamic correlation matrix. Based on the dynamic correlation matrix, the topological parameters of the conductive network model (such as the quantitative indicators of node connectivity and path conduction efficiency) are determined, and combined with the preset optimization weight coefficients obtained by iterative calculation by the gradient descent algorithm, a structural optimization strategy is generated. Based on this strategy, the node connections of the conductive network model are updated to obtain optimized topology data. This optimized data is then input into the conductive network model to generate coupling analysis results. Based on these results, particle dispersion parameters are corrected and optimal component parameters are extracted. A corresponding mapping relationship is constructed using the capacity decay rate curve, and the final material formulation scheme is determined based on this mapping relationship. This scheme includes parameter combinations that meet both dispersion thresholds and conductive network stability requirements, providing precise material formulation guidance for the preparation of high-performance silicon-carbon anode materials.

[0066] Step S105: Based on the final material ratio scheme, perform cycle performance testing using roll-to-roll deposition and continuous heat treatment processes to obtain the process parameter combination data of the negative electrode material.

[0067] After determining the final material formulation, the actual process testing phase begins. First, the deposition rate parameters for the roll-to-roll deposition process are obtained, as these parameters directly impact the deposition efficiency and quality. Based on these parameters, the vacuum level of the deposition chamber is precisely adjusted to ensure uniform and stable material deposition at different rates, thus obtaining real-time deposition thickness data. This real-time thickness data is then input into a pre-established thickness deviation prediction model. This model uses machine learning algorithms or mathematical fitting methods to predict the deposition thickness deviation, yielding a predicted value. Based on this prediction, a deposition rate compensation command is generated to adjust the deposition rate in real-time, reducing thickness deviation. Simultaneously, the heat treatment temperature parameters for the continuous heat treatment process are adjusted according to the deposition rate compensation command, resulting in adjusted heat treatment temperature data. This adjusted data is then input into a composition deviation analysis model. This model analyzes the relationship between material composition and temperature to generate a composition deviation correction coefficient, which updates the heat treatment temperature parameters to ensure the stability of the material composition during heat treatment. Finally, the heat treatment temperature parameters and deposition rate parameters are combined to generate process parameter combination data. This data is used to precisely control the coordinated operation of roll-to-roll deposition and continuous heat treatment equipment, ensuring the stable production of silicon-carbon anode materials with good cycle performance during large-scale production.

[0068] The aforementioned method, computer equipment, and storage medium for improving the cycle stability of silicon-carbon anode materials in new energy batteries firstly acquire initial microstructure data of the silicon-carbon material. Then, based on this initial microstructure data, a finite element mesh model is constructed to simulate the stress distribution during lithiation, thereby determining the critical stress values ​​in structurally weak regions. Next, based on the obtained critical stress values, a porous composite buffer structure is designed, and a three-dimensional conductive network is constructed using atomic layer deposition and carbon nanotube doping processes. The resistivity distribution matrix and coverage topology of the conductive network are then obtained. Subsequently, the ratio of silicon-carbon material to conductive additives is adjusted according to the resistivity distribution matrix and coverage topology to obtain the final material ratio scheme. Finally, cycle performance tests are conducted using roll-to-roll deposition and continuous heat treatment processes according to the final material ratio scheme to obtain the process parameter combination data of the anode material. This method effectively alleviates the volume change problem of silicon-carbon anode materials during charge and discharge processes, improves the structural stability of the material, and thus significantly enhances the cycle stability of the battery. It provides reliable process guidance for large-scale industrial production, improves production efficiency and product quality, and promotes the widespread application and industrial development of silicon-carbon anode materials for new energy batteries.

[0069] In one embodiment, establishing a finite element mesh model based on initial microstructure data to simulate the stress distribution during the lithiation process and obtaining the critical stress value of the structurally weak region may include the following steps:

[0070] Step S201: Obtain grain boundary distribution parameters and phase transformation characteristic parameters from the initial microstructure data; the grain boundary distribution parameters include grain size and orientation information.

[0071] Step S202: Calculate the anisotropic elastic tensor based on the phase transition characteristic parameters to generate a finite element mesh model including grain boundary topology.

[0072] Step S203: Extract the lithium-ion diffusion path data from the finite element mesh model and calculate the local strain increment by combining it with the preset concentration gradient field.

[0073] Step S204: Iteratively solve the stress gradient distribution map based on the local strain increment and the anisotropic elastic tensor.

[0074] Step S205: Based on the stress gradient distribution map, identify regions that exceed the phase transition threshold and extract the interface bonding strength parameters of the regions.

[0075] Furthermore, the stress gradient distribution map is a graphical representation of the rate of change of internal stress in a material at various points in space. This map can accurately identify regions where stress changes drastically during lithiation, i.e., regions exceeding the phase transition threshold.

[0076] Step S206: Match the interface strength parameters with the preset fracture toughness database to obtain the critical stress value of the weak area of ​​the structure.

[0077] A fracture toughness database is a collection of information in the field of materials science used to store and manage data related to the fracture toughness of materials. The database contains fracture toughness data for various materials under different conditions, covering a wide range of materials such as metals, ceramics, polymers, and composites. For each material, detailed information such as its composition, microstructure, and heat treatment state is recorded, along with fracture toughness values ​​under different testing environments, such as temperature, humidity, and loading rate.

[0078] First, grain boundary distribution parameters and phase transition characteristic parameters are obtained from the initial microstructure data, with the grain boundary distribution parameters encompassing grain size and orientation information. Next, based on the obtained phase transition characteristic parameters, an anisotropic elastic tensor is derived using a specific calculation method, generating a finite element mesh model containing the grain boundary topology. Then, for the constructed finite element mesh model, lithium-ion diffusion path data is extracted, and combined with a pre-defined concentration gradient field, local strain increments are calculated using relevant algorithms. The local strain increments are then iteratively calculated with the anisotropic elastic tensor to obtain a stress gradient distribution map. Based on this map, regions exceeding the phase transition threshold are identified, and the interfacial bonding strength parameters for these regions are extracted. Finally, the extracted interfacial bonding strength parameters are compared and matched with a pre-defined fracture toughness database to obtain the critical stress values ​​for structurally weak regions.

[0079] At the level of materials performance research, by accurately obtaining key parameters such as grain boundary distribution and phase transition characteristics, a finite element mesh model that can truly reflect the microscopic properties of materials can be constructed. This allows for in-depth analysis of stress distribution during lithiation, accurate identification of structurally weak regions, and determination of their critical stress values. This will contribute to the development of silicon-carbon anode materials with good cycle stability, promote the improvement of new energy battery performance, meet the urgent needs of electric vehicles, energy storage systems, and other fields for high-performance batteries, and further promote the development of the new energy industry.

[0080] In one embodiment, a porous composite buffer structure is designed based on a critical stress value, and a three-dimensional conductive network is constructed using atomic layer deposition and carbon nanotube doping processes to obtain the resistivity distribution matrix and coverage topology map of the conductive network. This may include the following steps:

[0081] Step S301: Calculate the stress distribution gradient data of the porous composite buffer structure based on the critical stress value.

[0082] Step S302: Determine the path optimization parameters for atomic layer deposition based on stress distribution gradient data; the path optimization parameters include deposition angle and coverage density.

[0083] Preferably, the atomic layer deposition process has a deposition temperature of 200-300℃, a gas flow rate of 50-100 sccm, and a cycle count of 50-200.

[0084] Step S303: Based on the path optimization parameters and the carbon nanotube doping concentration gradient, the connectivity threshold of the three-dimensional conductive network is calculated; the connectivity threshold includes the node contact probability and the cross-layer conductivity.

[0085] Preferably, the node contact probability refers to the probability of effective contact between conductive nodes in a three-dimensional conductive network, and the cross-layer conductivity is used to measure the conductivity efficiency between different layers in a three-dimensional conductive network; the carbon nanotube doping concentration gradient is 0.5-2 mg / cm³, and it exhibits an exponential decay distribution along the material thickness direction to optimize the cross-layer conductivity.

[0086] Step S304: Input the connectivity threshold into the deposition cycle control model to obtain the number of cycles and residence time for atomic layer deposition.

[0087] Furthermore, the deposition cycle control model is a mathematical model constructed based on an understanding of various physicochemical factors in the atomic layer deposition process. This model comprehensively considers multiple key factors affecting the deposition process, such as the characteristics of the deposited material, the properties of the substrate material, the flow rate and concentration of reactant gases, and the deposition temperature.

[0088] Step S305: Perform the deposition process according to the number of cycles and residence time to obtain the resistivity distribution matrix and coverage topology map of the conductive network.

[0089] First, based on the obtained critical stress values ​​of the structurally weak areas, a professional mechanical analysis model and algorithm are used to accurately calculate the stress distribution gradient data of the porous composite buffer structure. Next, based on the stress distribution gradient data and the characteristics of the atomic layer deposition (ALD) process, the path optimization parameters for ALD are determined through analysis and derivation. These parameters include the deposition angle and coverage density. Subsequently, the determined path optimization parameters are combined with the carbon nanotube doping concentration gradient, and using specific calculation formulas and related theories, the connectivity threshold of the three-dimensional conductive network is calculated. This connectivity threshold specifically encompasses the node contact probability and interlayer conductivity. The calculated connectivity threshold is then input into a pre-constructed deposition cycle control model. After model calculation, the required number of cycles and residence time for ALD are obtained. Finally, based on the determined number of cycles and residence time, the ALD process is strictly executed to obtain the resistivity distribution matrix and coverage topology map of the conductive network.

[0090] This embodiment utilizes critical stress values ​​to guide the calculation of stress distribution gradients in porous composite buffer structures, precisely planning atomic layer deposition paths and coordinating with carbon nanotube doping concentration gradients to determine the connectivity threshold of the conductive network. This effectively constructs a more structurally sound and higher-performing three-dimensional conductive network. By precisely controlling the number of atomic layer deposition cycles and residence time, the deposition process can be highly tailored to material requirements. The resulting resistivity distribution matrix and coverage topology map provide detailed data for a deeper understanding of the conductive network's performance, helping to further optimize the internal conductive pathways of the material and improve its overall conductivity.

[0091] In one embodiment, the connectivity threshold can be calculated using the following formula:

[0092] ;

[0093] in, Indicates the connectivity threshold. This represents the structure factor, whose value is related to the material porosity. When the porosity is 10%-20%, =0.8-1.2; Indicates the number of conductive channels. Indicates the volume of a single channel. Indicates the total volume. The characteristic length is obtained through SEM image analysis and represents the average length of the conductive channel (range 0.1-1 μm). The correlation length represents the uniformity of carbon nanotube distribution and is calculated statistically using transmission electron microscopy (TEM) (typical value 0.5-2 μm).

[0094] This embodiment utilizes the aforementioned formula to calculate the connectivity threshold, organically combining path optimization parameters with the carbon nanotube doping concentration gradient. This enables the scientific and precise determination of the connectivity characteristics of the three-dimensional conductive network, laying a solid foundation for constructing a more rational and superior three-dimensional conductive network. By precisely controlling the number of atomic layer deposition cycles and residence time, the deposition process is highly adapted to material requirements. The obtained resistivity distribution matrix and coverage topology map provide detailed data for in-depth understanding of the conductive network performance, helping to further optimize the internal conductive pathways of the material and comprehensively improve the overall conductivity of the material.

[0095] In one embodiment, adjusting the ratio of silicon-carbon material to conductive additives based on the resistivity distribution matrix and coverage topology map to obtain the final material ratio scheme may include the following steps:

[0096] Step S401: Obtain the initial parameters of the resistivity distribution matrix and coverage topology map; the initial parameters include the original ratio of silicon-carbon material to conductive additive.

[0097] Step S402: Calculate and determine the particle dispersion parameters of the conductive network distribution density in the material based on the original ratio values.

[0098] If the distribution density is lower than a preset dispersion threshold, a ratio correction coefficient including silicon-carbon materials and conductive additives is generated.

[0099] Step S403: Update the material composition parameters using the proportional correction coefficient to generate capacity decay rate data.

[0100] Step S404: Couple the capacity decay rate data with the interface impedance spectrum to obtain the coupling analysis results for optimizing the topology of the conductive network model.

[0101] Step S405: Based on the coupling analysis results, correct the particle dispersion parameters and extract the optimal component parameters, and construct the corresponding mapping relationship by combining the capacity decay rate curve.

[0102] Preferably, the coupling analysis employs a combination of principal component analysis (PCA) and grey relational analysis (GRA). Principal component analysis can effectively extract the main features of capacity decay rate data and interfacial impedance spectrum, reducing data dimensionality, while grey relational analysis can measure the degree of correlation between these features, thereby generating a dynamic correlation matrix.

[0103] Step S406: Determine the final material ratio scheme based on the mapping relationship; the final material ratio scheme includes a combination of parameters that meet the requirements of dispersion threshold and conductive network stability.

[0104] Preferably, the mapping relationship is constructed using a support vector regression (SVR) model, which can accurately capture the complex relationship between the capacity decay rate curve and the material ratio under small sample and nonlinear conditions.

[0105] Specifically, the initial parameters in the resistivity distribution matrix and coverage topology map are first obtained. These initial parameters cover the original ratio values ​​of silicon-carbon materials and conductive additives. Based on these original ratio values, a computational model and method are used to determine the particle dispersion parameters of the conductive network distribution density in the material. After obtaining the particle dispersion parameters, their distribution density is compared with a preset dispersion threshold. If the distribution density is lower than the preset threshold, it indicates that the dispersion of the conductive network under the current material ratio is poor. At this time, a specific algorithm is used to generate a ratio correction coefficient containing silicon-carbon materials and conductive additives. Using this ratio correction coefficient, the material composition parameters are updated, and capacity decay rate data is generated through experimental testing or simulation calculation. Subsequently, the capacity decay rate data is coupled with the interfacial impedance spectrum for analysis. Data fusion and analysis techniques are used to obtain the coupling analysis results aimed at optimizing the topology of the conductive network model. Based on the coupling analysis results, the previously calculated particle dispersion parameters are corrected, the optimal composition parameters are extracted, and a corresponding mapping relationship between the two is constructed by combining them with the capacity decay rate curve. Finally, the final material ratio scheme is determined based on the constructed mapping relationship.

[0106] This embodiment, through precise analysis of the initial parameters of the resistivity distribution matrix and coverage topology map, provides a deep understanding of the original state of the conductive network within the material. Calculating particle dispersion parameters based on the original proportions and adjusting them according to the dispersion threshold effectively improves the uniformity of the conductive network distribution within the material. Coupled analysis of capacity decay rate data with interfacial impedance spectroscopy helps construct more efficient conductive pathways, enhancing the overall electrical performance of the material. The determined final material proportioning scheme significantly improves the comprehensive performance of silicon-carbon anode materials, enhancing the cycle stability, charge / discharge efficiency, and lifespan of new energy batteries.

[0107] In one embodiment, coupling analysis of capacity decay rate data with interface impedance spectrum to obtain coupling analysis results for optimizing the topology of the conductive network model may include the following steps:

[0108] Step S501: Extract features from the capacity decay rate data and interface impedance spectrum to obtain the decay rate time-series features and impedance spectrum frequency domain features; the decay rate time-series features include the trend of capacity decay rate with the number of cycles.

[0109] Step S502: Multi-source data fusion is performed on the attenuation rate time-series characteristics and impedance spectrum frequency domain characteristics to generate a dynamic correlation matrix.

[0110] Step S503: Determine the topology parameters of the conductive network model based on the dynamic correlation matrix; the topology parameters include quantitative indicators of node connectivity and path conduction efficiency.

[0111] Step S504: Combine the topology parameters with the preset optimization weight coefficients to generate a structure optimization strategy; the optimization weight coefficients are obtained by iterative calculation using the gradient descent algorithm.

[0112] Step S505: Update the node connection relationship of the conductive network model based on the structural optimization strategy to obtain the optimized topology data.

[0113] Step S506: Input the optimized topology data into the conductive network model to generate coupling analysis results with improved conduction paths.

[0114] First, for the capacity decay rate data and interface impedance spectrum, a data feature extraction algorithm is used to obtain the time-series features of the decay rate from the capacity decay rate data. This feature mainly reflects the trend of capacity decay rate change with the number of cycles. Simultaneously, the frequency domain features of the impedance spectrum are extracted from the interface impedance spectrum. Then, the obtained time-series features of the decay rate and the frequency domain features of the impedance spectrum are subjected to multi-source data fusion processing. Using data fusion models and techniques, a dynamic correlation matrix reflecting the relationship between the two is generated. Based on this dynamic correlation matrix, the topological parameters of the conductive network model are determined through in-depth analysis and calculation. These parameters include quantitative indicators such as node connectivity and path conduction efficiency, used to accurately describe the topological characteristics of the conductive network. Next, the determined topological parameters are combined with preset optimization weight coefficients obtained through iterative calculation using the gradient descent algorithm. Taking into account various factors, a targeted structural optimization strategy is generated. According to this optimization strategy, the node connectivity relationships of the conductive network model are updated and adjusted to obtain optimized topological data. Finally, the optimized topological data is input into the conductive network model. After model calculation and simulation, coupling analysis results with improved conduction paths are generated.

[0115] By deeply extracting and fusing features from capacity decay rate data and interfacial impedance spectra, we can accurately understand the performance variation patterns of conductive networks under different operating conditions. Determining topology parameters and combining them with an optimized weighting coefficient generation strategy can effectively optimize node connections and path conduction in the conductive network, significantly improving its overall performance and thus enhancing charge transport efficiency and electrical properties. The optimized conductive network topology can strengthen the stability and reliability of silicon-carbon anode materials in batteries.

[0116] In one embodiment, the process of performing cycle performance testing using roll-to-roll deposition and continuous heat treatment processes based on the final material formulation to obtain process parameter combination data for the anode material may include the following steps:

[0117] Step S601: Obtain the deposition rate parameters of the roll-to-roll deposition process, adjust the vacuum level of the deposition chamber according to the deposition rate parameters, and obtain real-time deposition thickness data.

[0118] Preferably, the deposition rate of the roll-to-roll deposition process is 0.1-1 m / min, and the vacuum degree is [missing information]. .

[0119] Step S602: Input the real-time deposition thickness data into the thickness deviation prediction model to obtain the thickness deviation prediction value.

[0120] Step S603: Generate deposition rate compensation command based on the thickness deviation prediction value.

[0121] Step S604: Adjust the heat treatment temperature parameters of the continuous heat treatment process according to the deposition rate compensation command to obtain heat treatment temperature adjustment data.

[0122] Step S605: Input the heat treatment temperature adjustment data into the composition deviation analysis model to generate composition deviation correction coefficients and update the heat treatment temperature parameters.

[0123] Step S606: Generate process parameter combination data based on heat treatment temperature parameters and deposition rate parameters; the process parameter combination data is used to control the coordinated operation of roll-to-roll deposition and continuous heat treatment equipment.

[0124] Specifically, the deposition rate parameters for the roll-to-roll deposition process are first obtained. Based on these parameters, and according to the deposition process principles and relevant empirical formulas, the vacuum level of the deposition chamber is precisely adjusted. During this process, real-time deposition thickness data is acquired through real-time monitoring equipment. Next, the obtained real-time deposition thickness data is input into a pre-constructed and fully validated thickness deviation prediction model. This model uses advanced data processing algorithms and machine learning techniques to analyze and calculate the input data, outputting a predicted thickness deviation value. Subsequently, based on the predicted thickness deviation value, a deposition rate compensation command is generated using automated control logic. This command aims to reasonably adjust the deposition rate to reduce deposition thickness deviation. Based on the deposition rate compensation command, the heat treatment temperature parameters for the continuous heat treatment process are adjusted accordingly, and the heat treatment temperature adjustment data is recorded and acquired. The heat treatment temperature adjustment data is input into a composition deviation analysis model. This model, based on the complex relationship between material composition and temperature, uses a professional analysis algorithm to generate a composition deviation correction coefficient, which is then used to update the heat treatment temperature parameters. Finally, the updated heat treatment temperature parameters and deposition rate parameters are combined and calculated by the system to generate a combined process parameter data.

[0125] From the perspective of production process optimization, this embodiment effectively improves the stability and controllability of the production process through precise control and coordinated management of key parameters such as deposition rate, vacuum level, and heat treatment temperature. The application of thickness deviation prediction models and composition deviation analysis models allows for the early prediction and timely correction of deviations during production, significantly improving product consistency and yield. From the perspective of product performance assurance, precise control of process parameters ensures the structural integrity and compositional stability of silicon-carbon anode materials during deposition and heat treatment, which is beneficial for improving the electrical performance and cycle stability of the materials, thereby enhancing the overall performance of new energy batteries. Furthermore, the combined process parameter data enables efficient coordinated operation of roll-to-roll deposition and continuous heat treatment equipment, greatly improving production efficiency and reducing production costs. This provides a solid technical guarantee for the large-scale industrial production of silicon-carbon anode materials for new energy batteries, powerfully promoting the development of the new energy industry towards high quality and large-scale production.

[0126] To make the technical solutions and advantages of this application clearer, the present invention will be described in detail below with reference to the accompanying drawings, embodiments, and comparative examples. Those skilled in the art can understand the technical principles based on the content of this application and achieve the technical effects by reasonably selecting process parameters. It should be noted that the following embodiments are for illustrative purposes only and do not constitute a limitation on the scope of protection of this application.

[0127] In one embodiment, Example 1 provides a method for preparing silicon-carbon anode materials based on the method of the present invention, which may include the following steps:

[0128] 1. Acquisition of initial microstructure data, such as Figure 2 As shown, a schematic diagram of the crystal structure of silicon-carbon material obtained by X-ray diffraction is provided.

[0129] The crystal structure was analyzed using X-ray diffraction (XRD, Cu Kα radiation), which determined that the silicon phase was mainly composed of a diamond cubic structure with low crystallinity, while the carbon phase was an amorphous structure.

[0130] like Figure 3 As shown, a particle size diagram of silicon-carbon precursors measured by scanning electron microscopy is provided. The particle size of the silicon-carbon precursors was measured using a scanning electron microscope (SEM, model SU8010, resolution 5nm), and the average particle size was statistically determined to be [value missing]. The particle size distribution range is .

[0131] like Figure 4 As shown, a distribution diagram of silicon element in silicon-carbon materials is provided: Figures a and b show that silicon element is uniformly distributed.

[0132] The porosity was measured to be 18% using a mercury porosimeter (accuracy 0.01 m³ / kg).

[0133] 2. Finite element mesh model construction and stress simulation:

[0134] Extracted grain boundary distribution parameters: grain size 20-50 nm, orientation distribution uniformity index 0.85; phase transformation characteristic parameters include lithiation expansion coefficient. .

[0135] Anisotropic elastic tensors were calculated based on phase transition characteristic parameters, and a three-dimensional finite element mesh model (element size 2nm) containing grain boundary topology was generated using ANSYS software.

[0136] Extract lithium-ion diffusion path data and combine it with a preset concentration gradient field. The local strain increment was calculated, and the stress gradient distribution map was obtained through iterative solution. The structural weak region with a critical stress value of 80 MPa (mainly distributed at the grain boundary) was identified.

[0137] 3. Design of porous composite buffer structure and construction of three-dimensional conductive network:

[0138] Based on the critical stress value, the stress distribution gradient data was calculated, and the optimal parameters for the atomic layer deposition path were determined: deposition angle 45°, coverage density 1.2nm / cycle.

[0139] The carbon nanotube doping concentration gradient was designed to decrease exponentially from 2 mg / cm³ to 0.5 mg / cm³ along the thickness direction, using the formula... Calculate the connectivity threshold, where =1.0 (porosity 18%) , The results showed that the node contact probability was ≥90% and the cross-layer conductivity was ≥85%.

[0140] Input the deposition cycle control model, determine the number of cycles to be 100, the residence time to be 30s / cycle, execute the atomic layer deposition process, and obtain the resistivity distribution matrix. And coverage topology diagram (conductive network coverage ≥ 95%).

[0141] 4. Material ratio optimization and process parameter determination:

[0142] The initial ratio was silicon-carbon material: conductive additive = 9:1 (mass ratio). Calculation of particle dispersion parameters revealed that the distribution density (0.05 particles / μm²) was lower than the preset threshold (0.1 particles / μm²). A ratio correction coefficient of 1.2 was generated, and the ratio was updated to 8:2.

[0143] By coupling the capacity decay rate data (0.1% / time) with the interface impedance spectrum (initial impedance 50mΩ), the topology of the conductive network is optimized, the optimal component parameters are extracted, and the mapping relationship between the capacity decay rate curve and the ratio is constructed.

[0144] Roll-to-roll deposition process parameters: deposition rate 0.5 m / min, vacuum degree The real-time deposition thickness deviation is ≤±2%; the continuous heat treatment temperature parameter is set to 500℃, and after composition deviation correction, it is stabilized within ±5℃, generating process parameter combination data control equipment to operate in coordination.

[0145] 5. Cyclic performance test results:

[0146] After 100 cycles of testing at a current density of 0.1C, the capacity retention rate was 93%, the internal resistance increased to 60mΩ, and the crack density in the stress concentration area was 0.1 cracks / μm².

[0147] In one embodiment, Comparative Example 1 is provided, which includes a method for preparing conventional silicon-carbon anode materials (without a three-dimensional conductive network), and may include the following steps:

[0148] 1. Material preparation process:

[0149] A single porous carbon buffer structure is used, with silicon-carbon material: conductive additive = 9:1 (mass ratio), without atomic layer deposition and carbon nanotube doping.

[0150] Roll-to-roll deposition process parameters: deposition rate 0.5 m / min, vacuum degree Stress simulation and mix design optimization were not performed.

[0151] 2. Cyclic performance test results:

[0152] Under the same test conditions, the capacity retention rate was 76% after 100 cycles, the internal resistance increased to 120 mΩ, and the crack density in the stress concentration area was 0.4 cracks / μm².

[0153] In one embodiment, as shown in Table 1, a performance comparison table is provided between Example 1 and Comparative Example 1 obtained from simulation experiments:

[0154] Table 1

[0155]

[0156] As can be seen from the comparison between Example 1 and Comparative Example 1, the present invention significantly improves the cycle stability of silicon-carbon anode materials through the whole-process control of finite element stress simulation, porous composite buffer structure design, three-dimensional conductive network construction and ratio optimization.

[0157] Improved stress dispersion efficiency: The porous composite buffer structure reduces crack density in stress concentration areas by 75%;

[0158] Conductivity optimization: The construction of a three-dimensional conductive network reduces resistivity by 80% and increases conductive network coverage by 35%;

[0159] Extended cycle life: Capacity retention is improved by 22% compared to traditional processes, and the increase in internal resistance is reduced by 72%.

[0160] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0161] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method for improving the cycle stability of silicon-carbon anode materials for new energy batteries as described above.

[0162] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0163] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0164] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for improving the cycle stability of silicon-carbon anode materials in new energy batteries, characterized in that, The method includes: Obtain initial microstructure data of silicon-carbon materials; the initial microstructure data includes particle size, porosity, and crystal structure parameters; A finite element mesh model was established based on the initial microstructure data to simulate the stress distribution during the lithiation process, and the critical stress value of the structurally weak region was obtained. Based on the critical stress value, a porous composite buffer structure was designed and a three-dimensional conductive network was constructed using atomic layer deposition and carbon nanotube doping processes to obtain the resistivity distribution matrix and coverage topology map of the conductive network. The ratio of silicon-carbon material to conductive additives is adjusted according to the resistivity distribution matrix and coverage topology diagram to obtain the final material ratio scheme. Based on the final material formulation scheme, the cycle performance test was carried out using roll-to-roll deposition and continuous heat treatment processes to obtain the process parameter combination data of the anode material. The step of designing a porous composite buffer structure based on the critical stress value and constructing a three-dimensional conductive network using atomic layer deposition and carbon nanotube doping processes to obtain the resistivity distribution matrix and coverage topology map of the conductive network includes: The stress distribution gradient data of the porous composite buffer structure is calculated based on the critical stress value; the path optimization parameters for atomic layer deposition are determined based on the stress distribution gradient data; the path optimization parameters include deposition angle and coverage density. Based on the path optimization parameters and the carbon nanotube doping concentration gradient, the connectivity threshold of the three-dimensional conductive network is calculated; the connectivity threshold includes the node contact probability and the cross-layer conductivity. The connectivity threshold is calculated using the following formula: ； in, Indicates the connectivity threshold. This represents the structure factor, whose value is related to the material porosity. When the porosity is 10%-20%, =0.8-1.2; Indicates the number of conductive channels. Indicates the volume of a single channel. Indicates the total volume. The characteristic length, obtained through SEM image analysis, represents the average length of the conductive channel, ranging from 0.1 to 1. ; The correlation length, which reflects the uniformity of carbon nanotube distribution, is calculated statistically using transmission electron microscopy. The connectivity threshold is input into the deposition cycle control model to obtain the number of cycles and residence time for atomic layer deposition; the deposition process is executed according to the number of cycles and residence time to obtain the resistivity distribution matrix and coverage topology map of the conductive network.

2. The method according to claim 1, characterized in that, The step of establishing a finite element mesh model based on the initial microstructure data to simulate the stress distribution during the lithiation process and obtaining the critical stress values ​​of the structurally weak regions includes: Acquire grain boundary distribution parameters and phase transition characteristic parameters from the initial microstructure data; the grain boundary distribution parameters include grain size and orientation information. The anisotropic elastic tensor is calculated based on the phase transition characteristic parameters to generate a finite element mesh model including grain boundary topology. The lithium-ion diffusion path data in the finite element mesh model is extracted, and the local strain increment is calculated by combining it with the preset concentration gradient field. The stress gradient distribution map is obtained by iteratively solving the local strain increment and each of the anisotropic elastic tensors. Based on the stress gradient distribution map, regions exceeding the phase transition threshold are identified, and the interfacial bonding strength parameters of the regions are extracted. The interface is combined with strength parameters and matched with a preset fracture toughness database to obtain the critical stress value of the weak area of ​​the structure.

3. The method according to claim 1, characterized in that, The step of adjusting the ratio of silicon-carbon material to conductive additives based on the resistivity distribution matrix and coverage topology map to obtain the final material ratio scheme includes: Obtain the initial parameters for the resistivity distribution matrix and coverage topology map; the initial parameters include the original ratio values ​​of silicon-carbon material and conductive additives; The particle dispersion parameters for determining the distribution density of the conductive network in the material are calculated based on the original ratio values. If the distribution density is lower than a preset dispersion threshold, a ratio correction coefficient including silicon carbon material and conductive additives is generated. The material composition parameters are updated using the aforementioned proportional correction coefficient to generate capacity decay rate data; The capacity decay rate data is coupled with the interface impedance spectrum for analysis to obtain the coupling analysis results for optimizing the topology of the conductive network model. Based on the coupling analysis results, the particle dispersion parameters are corrected and the optimal component parameters are extracted. A corresponding mapping relationship is then constructed by combining the capacity decay rate curve. The final material ratio scheme is determined based on the mapping relationship; the final material ratio scheme includes a combination of parameters that meet the requirements of dispersion threshold and conductive network stability.

4. The method according to claim 3, characterized in that, The coupling analysis of the capacity decay rate data with the interface impedance spectrum to obtain coupling analysis results for optimizing the topology of the conductive network model includes: Feature extraction is performed on the capacity decay rate data and the interface impedance spectrum to obtain the decay rate time-series feature and the impedance spectrum frequency domain feature; the decay rate time-series feature includes the trend of capacity decay rate with the number of cycles. Multi-source data fusion is performed on the attenuation rate time-series characteristics and the impedance spectrum frequency domain characteristics to generate a dynamic correlation matrix; The topological parameters of the conductive network model are determined based on the dynamic correlation matrix; the topological parameters include quantitative indicators of node connectivity and path conduction efficiency. The topology parameters are combined with preset optimization weight coefficients to generate a structure optimization strategy; the optimization weight coefficients are obtained by iterative calculation using the gradient descent algorithm. The node connection relationships of the conductive network model are updated based on the aforementioned structural optimization strategy to obtain optimized topology data. The optimized topology data is input into the conductive network model to generate coupling analysis results with improved conduction paths.

5. The method according to claim 1, characterized in that, The process involves performing cycle performance tests using roll-to-roll deposition and continuous heat treatment based on the final material formulation scheme to obtain process parameter combination data for the anode material, including: Obtain the deposition rate parameters of the roll-to-roll deposition process, adjust the vacuum level of the deposition chamber according to the deposition rate parameters, and obtain real-time deposition thickness data; The real-time deposition thickness data is input into the thickness deviation prediction model to obtain the thickness deviation prediction value; A deposition rate compensation command is generated based on the predicted thickness deviation value; The heat treatment temperature parameters of the continuous heat treatment process are adjusted according to the deposition rate compensation command to obtain heat treatment temperature adjustment data. The heat treatment temperature adjustment data is input into the composition deviation analysis model to generate a composition deviation correction coefficient and update the heat treatment temperature parameters. Process parameter combination data is generated based on the heat treatment temperature parameters and deposition rate parameters; the process parameter combination data is used to control the coordinated operation of roll-to-roll deposition and continuous heat treatment equipment.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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