Design method, device and equipment for homogenate feeding of lithium ion battery and storage medium

Abstract

The invention relates to the technical field of lithium ion battery manufacturing, and discloses a homogenate feeding design method and device of a lithium ion battery and a computer storage medium, the method comprises the following steps: based on basic characteristic data of different types of raw materials of the lithium ion battery, constructing a non-uniformity characterization model of a mixture and obtaining a non-uniformity characterization result; and inputting the non-uniformity characterization result into a finite element model of the battery pole piece, and solving the finite element model in combination with an applied load and a boundary condition to obtain an internal cracking behavior simulation result of the mixture under different working conditions. And constructing an optimization model which takes the homogenate feeding scheme as a design variable, takes a design target as a target function and meets design constraint conditions, and solving to obtain a target homogenate feeding design scheme. The problems of neglecting of non-uniformity and cracking of the mixture in a traditional mode can be solved, the risks of stress concentration and capacity fading in the battery are reduced, homogenate feeding is more scientific and accurate, and the performance and reliability of the battery are improved.

Description

Technical Field

[0001] This application relates to the field of lithium-ion battery manufacturing technology, specifically to a method, apparatus, equipment, and computer-readable storage medium for designing a slurry feeding process for lithium-ion batteries. Background Technology

[0002] Lithium-ion batteries are widely used in modern electronic devices and electric vehicles. In the manufacturing process of lithium-ion batteries, slurry mixing is a crucial step that directly affects battery performance and quality. However, traditional slurry mixing design and research methods mainly focus on theoretical analysis and experimental trial and error, often neglecting the non-uniformity of the mixture and the resulting cracking behavior. This can lead to problems such as stress concentration, capacity decay, and even safety hazards within the battery. Summary of the Invention

[0003] In view of the above problems, this application provides a method, apparatus and computer-readable storage medium for designing a slurry feeding method for lithium-ion batteries, which solves the problem that the non-uniformity of the mixture and the resulting cracking behavior are not considered in the slurry feeding design of lithium-ion batteries in the prior art.

[0004] According to one aspect of the embodiments of this application, a method for designing the slurry feeding of a lithium-ion battery is provided, the method comprising: Based on the basic characteristic data of different types of raw materials for lithium-ion batteries, a non-uniformity characterization model of the mixture composed of the different types of raw materials is constructed, and the non-uniformity characterization results are obtained. The non-uniformity characterization results are input into the finite element model of the battery electrode, which is a component of the lithium-ion battery, and the finite element model is solved by combining the applied load and boundary conditions to obtain the simulation results of the internal cracking behavior of the mixture under different working conditions. Based on the simulation results of internal cracking behavior under different working conditions, an optimization model is constructed with the slurry feeding scheme as the design variable, the design objective reflecting the battery cycle life and crack resistance as the objective function, and the design constraints are satisfied. The optimization model is then solved to obtain the target slurry feeding design scheme.

[0005] In an alternative approach, the method further includes: Battery electrode samples, which are components of the lithium-ion battery, were prepared according to the target homogenization and feeding design scheme, and the performance of the battery electrode samples was tested to obtain experimental test results. The experimental test results are compared with the simulation results corresponding to the target homogenization and feeding design scheme. If the error is greater than the target threshold, the parameters of the optimization model are dynamically adjusted until the error is less than the target threshold, and then the final homogenization and feeding design scheme is output.

[0006] In one alternative approach, the different types of raw materials include: positive electrode active materials, negative electrode active materials, conductive agents, and binders; the basic characteristic data include: particle size distribution, chemical composition, and surface morphology.

[0007] In one alternative approach, the non-uniformity characterization results include: the spatial distribution probability density function of different components and the interparticle interaction coefficient matrix.

[0008] In one alternative approach, the applied load includes: charge / discharge volume expansion load and mechanical vibration load; the boundary conditions include: electrode edge fixing constraint.

[0009] In one alternative approach, the simulation results of the internal cracking behavior include: stress distribution, strain variation, crack location, and quantitative values ​​of cracking degree.

[0010] In one alternative approach, the design variables are: mixture composition, proportion, and stirring process parameters; the objective function is: the battery cycle life reaches a preset number of cycles and the cracking degree quantification value is less than a preset threshold; the design constraints are: battery electrode size, raw material cost ceiling, and production process constraints.

[0011] According to another aspect of the embodiments of this application, a slurry feeding design device for lithium-ion batteries is provided, comprising: The module is used to construct a non-uniformity characterization model of a mixture composed of different types of raw materials based on the basic characteristic data of different types of raw materials for lithium-ion batteries, and to obtain the non-uniformity characterization results. The simulation module is used to input the non-uniformity characterization results into the finite element model of the battery electrode, which is a component of the lithium-ion battery, and solve the finite element model by combining the applied load and boundary conditions to obtain the simulation results of the internal cracking behavior of the mixture under different working conditions. The design module is used to construct an optimization model based on the simulation results of internal cracking behavior under different working conditions. The optimization model is based on the slurry feeding scheme as the design variable and the design objective reflecting the battery cycle life and crack resistance as the objective function, and satisfies the design constraints. The optimization model is then solved to obtain the target slurry feeding design scheme.

[0012] According to another aspect of the embodiments of this application, a method for generating a slurry feeding design scheme for a lithium-ion battery is provided, which adopts the slurry feeding design method for lithium-ion batteries provided in the embodiments of this application, including: Obtain the performance requirements and production constraints of the target lithium-ion battery, and define the performance requirements and production constraints as the objective function and design constraints, respectively. The objective function and the design constraints are input into the optimization model, and the optimization model is solved to output a target homogenization feeding design scheme that matches the objective function and the design constraints.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein the storage medium stores at least one executable instruction, which, when executed on a lithium-ion battery slurry feeding design apparatus, causes the lithium-ion battery slurry feeding design apparatus to perform operations such as the lithium-ion battery slurry feeding design method of this application.

[0014] This application embodiment constructs a non-uniformity characterization model of the mixture based on the basic characteristic data of different types of raw materials for lithium-ion batteries, and obtains the non-uniformity characterization results. The non-uniformity characterization results are input into the finite element model of the battery electrode, and the finite element model is solved by combining the applied load and boundary conditions to obtain the simulation results of the internal cracking behavior of the mixture under different working conditions. Based on the simulation results of the internal cracking behavior under different working conditions, an optimization model is constructed with the slurry feeding scheme as the design variable, the design objective as the objective function, and the design constraints are satisfied. The optimization model is solved to obtain the target slurry feeding design scheme, which can effectively solve the problem of ignoring the non-uniformity and cracking of the mixture in the traditional method, reduce the risk of internal stress concentration and capacity decay in the battery, make the slurry feeding more scientific and precise, and improve the battery performance and reliability.

[0015] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] Figure 1 A schematic flowchart illustrating an embodiment of the slurry feeding design method for lithium-ion batteries provided in this application is shown.

[0018] Figure 2 A schematic diagram of an embodiment of the slurry feeding design device for lithium-ion batteries provided in this application is shown.

[0019] Figure 3 A flowchart illustrating an embodiment of the method for generating a slurry feeding design scheme for lithium-ion batteries provided in this application is shown. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0022] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0023] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0024] Lithium-ion batteries are widely used in modern electronic devices and electric vehicles. In the manufacturing process of lithium-ion batteries, slurry mixing is a crucial step that directly affects battery performance and quality. However, traditional slurry mixing design and research methods mainly focus on theoretical analysis and experimental trial and error, often neglecting the non-uniformity of the mixture and the resulting cracking behavior. This can lead to problems such as stress concentration, capacity decay, and even safety hazards within the battery. Therefore: Figure 1 A flowchart illustrating an embodiment of the slurry feeding design method for lithium-ion batteries provided in this application is shown. This method is performed by a slurry feeding design device for lithium-ion batteries. Please refer to... Figure 1 As shown, the method includes the following steps: S110: Based on the basic characteristic data of different types of raw materials for lithium-ion batteries, construct a non-uniformity characterization model of the mixture composed of the different types of raw materials, and obtain the non-uniformity characterization results.

[0025] Lithium-ion batteries are secondary batteries that operate on the principle of lithium-ion intercalation / deintercalation reactions. They consist of a positive electrode, a negative electrode, an electrolyte, and a separator, and are used to provide electrical energy for electronic devices or electric vehicles. Different types of raw materials refer to the four basic materials that constitute the electrode mixture of lithium-ion batteries, including but not limited to: positive electrode active materials, negative electrode active materials, conductive agents, and binders. Basic characteristic data refer to quantitative parameters describing the physicochemical properties of raw materials, including but not limited to: particle size distribution, chemical composition, and surface morphology data.

[0026] The mixture refers to a slurry formed by mixing positive or negative electrode active materials with conductive agents and binders in a certain proportion, used to coat current collectors to prepare battery electrodes. The non-uniformity characterization model is a mathematical model constructed based on statistical methods to describe the spatial distribution differences of different components in the mixture and the interparticle interactions. The non-uniformity characterization results refer to the output data of the non-uniformity characterization model, including the spatial probability density functions of different components and the interparticle interaction coefficient matrix.

[0027] S120: Input the non-uniformity characterization results into the finite element model of the battery electrode, which is a component of the lithium-ion battery, and solve the finite element model by combining the applied load and boundary conditions to obtain the simulation results of the internal cracking behavior of the mixture under different working conditions.

[0028] Among them, battery electrodes refer to electrode assemblies made by coating a mixture and then drying and rolling, including a metal current collector and an active material layer attached thereto. The finite element model is a computational model that discretizes the battery electrode structure into mesh elements and describes its mechanical behavior through mathematical equations.

[0029] The applied load refers to the external mechanical excitation simulated in the finite element analysis, including the charge / discharge volume expansion load (maximum expansion rate 10%) and the mechanical vibration load (frequency 10Hz, amplitude 0.5mm). The boundary condition equation constrains the physical limitations on the degrees of freedom of the finite element model, specifically the fixed constraint on the electrode edge.

[0030] Different operating conditions refer to combinations of conditions that simulate the actual working state of the battery, including the charge-discharge cycle stage (0%-100% SOC) and the mechanical vibration environment.

[0031] S130: Based on the simulation results of internal cracking behavior under different working conditions, an optimization model is constructed with the homogenization feeding scheme as the design variable, the design objective reflecting the battery cycle life and crack resistance as the objective function, and the design constraint conditions are satisfied. The optimization model is then solved to obtain the target homogenization feeding design scheme.

[0032] Among them, the simulation results of internal cracking behavior refer to the quantitative data output by the finite element model solution, including stress distribution, strain change, crack location and crack degree, specifically manifested as: stress distribution cloud map, strain change curve, crack location coordinates and crack degree quantitative value (range of 0-1).

[0033] The slurry feeding scheme refers to the combination of technical parameters for preparing lithium-ion battery electrode mixtures. Design variables include the mixture composition (mass fractions of positive electrode, negative electrode, conductive agent, and binder), the ratio (solid-liquid ratio), and the stirring process parameters (speed and time). The design objective refers to the specific technical indicators set during the slurry feeding design optimization process, based on the performance and usage requirements of lithium-ion batteries, and achievable through the optimization model. Specifically, it aims to improve battery cycle life to a preset number of cycles while ensuring no significant internal cracking occurs. The objective function is a function that transforms the design objective into a mathematical expression, used to evaluate the quality of the design variables. Specifically, the objective function is: achieving a preset number of battery cycle lives (e.g., 1000 cycles) and a cracking degree quantification value less than a preset threshold (e.g., 0.8). Design constraints refer to the non-adjustable limitations that must be met during the optimization process, including battery electrode size, raw material cost limits, and production process constraints.

[0034] The optimization model refers to a mathematical programming model with design variables as independent variables, an objective function as dependent variable, and design constraints as the constraint domain. The target homogenization feeding design scheme refers to the combination of technical parameters obtained from the optimization model, including the mass fraction of each raw material, the solid-liquid ratio, the stirring speed, and the time.

[0035] For example, by using a genetic algorithm to solve the optimization model, after 50 iterations, the target slurry feeding design scheme is obtained: the mass fraction of positive electrode material is 85%, the mass fraction of negative electrode material is 10%, the mass fraction of conductive agent is 3%, the mass fraction of binder is 2%, the solid-liquid ratio is 90:10, the stirring speed is 1000 rpm, and the stirring time is 30 min.

[0036] The technical solution of this embodiment can effectively solve the problems of ignoring the non-uniformity and cracking of the mixture in the traditional method, reduce the risk of internal stress concentration and capacity decay in the battery, make the slurry feeding more scientific and precise, and improve battery performance and reliability.

[0037] In an alternative approach, the method further includes: S140: Prepare battery electrode samples as components of the lithium-ion battery according to the target homogenization and feeding design scheme, and conduct performance tests on the battery electrode samples to obtain experimental test results.

[0038] Among them, battery electrode samples refer to electrode specimens prepared according to the target slurry feeding design scheme, used for experimental verification. Experimental test results refer to the quantitative data obtained by testing the battery electrode samples, including the measured value of charge-discharge cycle life (cycles) and the actual crack area (μm2) observed by scanning electron microscopy.

[0039] Specifically, in S140: S141: Weigh the positive electrode active material, negative electrode active material, conductive agent and binder according to the raw material mass fraction ratio determined in the target homogenization feeding design scheme, and add solvent according to the solid-liquid ratio specified in the scheme, and mix and stir at the specified stirring speed and time to prepare electrode mixture.

[0040] S142: The electrode mixture is uniformly coated on the surface of the metal current collector, and then dried and rolled to produce battery electrode samples with standard dimensions.

[0041] S143: Assemble the battery electrode sample with the separator, electrolyte and counter electrode into a coin cell or pouch cell; perform charge-discharge cycle test on the assembled battery, record the battery capacity decay curve in a constant current charge-discharge mode, and stop the test when the capacity retention rate drops to the target capacity (e.g., 80%), and obtain the measured value of charge-discharge cycle life.

[0042] S144: After the battery electrode sample has undergone cyclic testing, it is disassembled, and a scanning electron microscope is used to capture microscopic morphology images of the selected area. The actual crack area is then measured using image processing software.

[0043] S145: The measured values ​​of charge-discharge cycle life and the actual crack area are summarized into experimental test results.

[0044] S150: Compare the experimental test results with the simulation results corresponding to the target homogenization feeding design scheme. If the error is greater than the target threshold, dynamically adjust the parameters of the optimization model until the error is less than the target threshold, and output the final homogenization feeding design scheme.

[0045] The simulation results corresponding to the target homogenization feeding design scheme refer to the calculation of the stress distribution, strain change, crack location and crack degree of the homogenization feeding design scheme output by the finite element model, to obtain the predicted cycle life value and crack area.

[0046] The target threshold refers to the maximum allowable deviation between experimental test results and simulation results, defined as a cycle life error > 5% or a crack area error > 10%. It can also be adjusted according to the actual situation, and no limit is set here.

[0047] The final homogenization and feeding design scheme refers to the combination of technical parameters that meets the target threshold requirements after experimental verification and iterative adjustments. For example, the stirring speed is adjusted to 950 rpm and the stirring time is adjusted to 35 min.

[0048] In the above-mentioned optional methods, by preparing battery electrode samples and conducting performance tests, comparing the experimental test results with the simulation results, and dynamically adjusting and optimizing the model parameters until the error is less than the target threshold, a more accurate and reliable slurry feeding design scheme is output, which further ensures the improvement of battery performance.

[0049] In one alternative approach, different types of raw materials include: positive electrode active materials, negative electrode active materials, conductive agents, and binders; basic characteristic data include: particle size distribution, chemical composition, and surface morphology.

[0050] Among them, positive electrode active materials refer to compounds that can reversibly insert / deintercalate lithium ions, including but not limited to: lithium cobalt oxide, ternary materials, and lithium iron phosphate. Negative electrode active materials refer to materials that can reversibly store lithium, including but not limited to: graphite, silicon-carbon composite materials, and lithium titanate. Conductive agents refer to additives that improve the conductivity of the electrode, including but not limited to: carbon black, carbon nanotubes, and graphene. Binders refer to polymeric materials that fix the active materials and current collectors, including polyvinylidene fluoride or styrene-butadiene rubber.

[0051] Particle size distribution refers to the statistical distribution characteristics of raw material particle size, obtained by laser particle size analyzer and characterized by D50 and D90 values. Chemical composition refers to the elemental composition and content of raw materials, obtained by X-ray fluorescence spectroscopy or inductively coupled plasma analysis. Surface morphology refers to the surface geometric characteristics of raw material particles, obtained by scanning electron microscopy, including roughness and porosity parameters.

[0052] Among the above-mentioned optional methods, by clarifying the specific types of different raw materials and the types of basic characteristic data, the main components of lithium-ion battery electrode mixtures are covered, making the construction of non-uniformity characterization models more comprehensive and accurate. This helps to simulate the actual state of the mixture more precisely, improve the credibility of simulation results, and thus optimize the slurry feeding scheme and improve battery performance.

[0053] In one alternative approach, the non-homogeneity characterization results include: the spatial distribution probability density function of different components and the interparticle interaction coefficient matrix.

[0054] The spatial distribution probability density function of different components refers to a continuous function established through statistical methods, describing the positional distribution of the positive electrode active material, negative electrode active material, conductive agent, and binder within the mixture. The interparticle interaction coefficient matrix is ​​a mathematical matrix characterizing the intensity of mechanical interactions between particles of different components, including contact stiffness coefficient and friction coefficient parameters.

[0055] In the above-mentioned optional methods, by clarifying the specific content of the non-uniformity characterization results, including the probability density function and the inter-particle interaction coefficient matrix, key data support is provided for further finite element model solving and optimization model construction. This helps to more accurately simulate the internal structure and interaction of the mixture, thereby accurately designing the slurry feeding scheme and improving battery performance.

[0056] In one alternative approach, the applied loads include: charge / discharge volume expansion loads and mechanical vibration loads; the boundary conditions include: electrode edge fixing constraints.

[0057] The charge / discharge volume expansion load refers to the volume change of the active material caused by lithium-ion insertion / extraction in the finite element simulation. In this embodiment, a periodic strain load with a maximum expansion rate of 10% is specifically set. The mechanical vibration load refers to the mechanical impact in the battery's operating environment in the finite element simulation, specifically by applying a sinusoidal dynamic excitation with a frequency of 10Hz and an amplitude of 0.5mm.

[0058] Among them, the electrode edge fixing constraint refers to the boundary condition that restricts all degrees of freedom of the battery electrode boundary region in the finite element model, simulating the fixed state of the electrode when it is assembled into the battery casing.

[0059] Among the above-mentioned optional methods, by specifying the specific types of applied loads and boundary conditions, the typical load conditions that the battery may face in actual operation are covered, making the solution of the finite element model more in line with the actual working conditions. This allows the model to more realistically reflect the internal cracking behavior of the mixture, thereby optimizing the slurry feeding scheme and improving the reliability and performance of the battery in actual use.

[0060] In one alternative approach, the simulation results of internal cracking behavior include: stress distribution, strain variation, crack location, and quantified values ​​of cracking degree.

[0061] Among them, stress distribution refers to the spatial distribution cloud map of stress inside the mixture output by the finite element model, reflecting the stress state of the material. Strain change refers to the curve of equivalent plastic strain changing with time output by the finite element model, characterizing the material deformation accumulation process. Crack location refers to the set of three-dimensional coordinates of crack points predicted by the finite element model, represented by mesh node coordinates. Crack degree quantification value refers to the local damage scalar calculated based on the damage mechanics model, with a value ranging from 0 to 1. Values ​​greater than 0.8 are considered high-risk areas for cracking.

[0062] Among the above-mentioned optional methods, by clarifying the specific quantitative form of the simulation results of internal cracking behavior, detailed and accurate data basis is provided for the subsequent optimization model construction, which helps to set the objective function and constraints more scientifically, thereby designing a better slurry feeding scheme and effectively reducing the risk of internal stress concentration and capacity decay in the battery.

[0063] In one alternative approach, the design variables are: mixture composition, proportion, and stirring process parameters; the objective function is: the battery cycle life reaches a preset number of cycles and the cracking degree quantification value is less than a preset threshold; the design constraints are: battery electrode size, raw material cost ceiling, and production process constraints.

[0064] The composition of the mixture refers to the mass percentage ratio of the positive electrode active material, negative electrode active material, conductive agent, and binder in the slurry feeding scheme. The ratio refers to the mass proportion of the solid component to the solvent in the mixture, expressed as a solid-liquid ratio. The stirring process parameters refer to the combination of mechanical stirring speed and time during the mixture preparation process; the speed is measured in rpm, and the time in minutes.

[0065] The preset number of cycles is the target threshold for battery cycle life set in the optimization model, with a value range of 800-1200 cycles. In this embodiment, it is set to 1000 charge-discharge cycles by default. The preset threshold is the maximum cracking risk value allowed in the optimization model, with a default value of 0.8.

[0066] Among these, the battery electrode size refers to the design specifications of the battery electrode's length, width, and active coating thickness, serving as a non-adjustable constraint in the optimization model. The raw material cost ceiling refers to the maximum allowable raw material procurement cost per unit mass of the mixture, measured in monetary units. Production process constraints refer to production condition limitations such as the stirring equipment's speed range and drying temperature limits.

[0067] Among the above-mentioned optional methods, by clearly defining the specific contents of design variables, objective functions and design constraints, the key factors and actual limitations in the homogenization and feeding process are fully considered, making the optimization model more in line with actual production. Under multiple constraints, the homogenization and feeding scheme can be accurately optimized, improving battery cycle life and controlling the degree of cracking, thereby enhancing battery performance and reliability.

[0068] Figure 2 A schematic diagram of an embodiment of the slurry feeding design device for lithium-ion batteries provided in this application is shown. Please refer to... Figure 2 As shown, the device 300 includes: a construction module 310, a simulation module 320, and a design module 330.

[0069] The construction module 310 is used to construct a non-uniformity characterization model of the mixture composed of the different types of raw materials based on the basic characteristic data of different types of raw materials for lithium-ion batteries, and to obtain the non-uniformity characterization results. The simulation module 320 is used to input the non-uniformity characterization results into the finite element model of the battery electrode, which is a component of the lithium-ion battery, and solve the finite element model by combining the applied load and boundary conditions to obtain the simulation results of the internal cracking behavior of the mixture under different working conditions. Design module 330 is used to construct an optimization model based on the simulation results of internal cracking behavior under different working conditions. The optimization model is based on the slurry feeding scheme as the design variable and the design objective reflecting the battery cycle life and crack resistance as the objective function, and satisfies the design constraints. The optimization model is then solved to obtain the target slurry feeding design scheme.

[0070] In an alternative embodiment, the device 300 further includes: The testing module is used to prepare battery electrode samples, which are components of the lithium-ion battery, according to the target homogenization and feeding design scheme, and to perform performance tests on the battery electrode samples to obtain experimental test results. The adjustment module is used to compare the experimental test results with the simulation results corresponding to the target homogenization and feeding design scheme. If the error is greater than the target threshold, the parameters of the optimization model are dynamically adjusted until the error is less than the target threshold, and then the final homogenization and feeding design scheme is output.

[0071] In one alternative approach, the different types of raw materials include: positive electrode active materials, negative electrode active materials, conductive agents, and binders; the basic characteristic data include: particle size distribution, chemical composition, and surface morphology.

[0072] In one alternative approach, the non-uniformity characterization results include: the spatial distribution probability density function of different components and the interparticle interaction coefficient matrix.

[0073] In one alternative approach, the applied load includes: charge / discharge volume expansion load and mechanical vibration load; the boundary conditions include: electrode edge fixing constraint.

[0074] In one alternative approach, the simulation results of the internal cracking behavior include: stress distribution, strain variation, crack location, and quantitative values ​​of cracking degree.

[0075] In one alternative approach, the design variables are: mixture composition, proportion, and stirring process parameters; the objective function is: the battery cycle life reaches a preset number of cycles and the cracking degree quantification value is less than a preset threshold; the design constraints are: battery electrode size, raw material cost ceiling, and production process constraints.

[0076] The technical solution of this embodiment can effectively solve the problems of ignoring the non-uniformity and cracking of the mixture in the traditional method, reduce the risk of internal stress concentration and capacity decay in the battery, make the slurry feeding more scientific and precise, and improve battery performance and reliability.

[0077] It should be noted that the lithium-ion battery slurry feeding design device provided in the above embodiments and the lithium-ion battery slurry feeding design method provided in the foregoing embodiments belong to the same concept. The specific way in which each module and unit performs operations has been described in detail in the method embodiments, and will not be repeated here.

[0078] Figure 3 A flowchart illustrating an embodiment of the method for generating a slurry feeding design scheme for a lithium-ion battery provided in this application is shown, employing the slurry feeding design method for lithium-ion batteries as provided in this application. Please refer to... Figure 3 As shown, the method includes the following steps: S210: Obtain the performance requirements and production constraints of the target lithium-ion battery, and define the performance requirements and production constraints as the objective function and design constraints, respectively.

[0079] The target lithium-ion battery refers to the specific lithium-ion battery model or specification that is the subject of this slurry feeding design. Performance requirements refer to the electrochemical performance requirements for the target lithium-ion battery, including at least target cycle life and crack resistance. Production constraints refer to the objective limitations that must be met when producing the target lithium-ion battery, including the upper limit of raw material costs and the feasibility boundary of the established production process.

[0080] S220: Input the objective function and the design constraints into the optimization model, solve the optimization model, and output the target homogenization feeding design scheme that matches the objective function and the design constraints.

[0081] The objective function refers to the mathematical expression used in the optimization model to evaluate the performance requirements and determine the quality of the design variables. Design constraints refer to the set of mathematical restrictions imposed on the design variables in the optimization model, which must satisfy production constraints.

[0082] The technical solution of this embodiment solves the efficiency bottleneck problem of repeatedly performing complex simulations and optimization iterations for different target batteries by directly calling the pre-built optimization model and inputting new performance requirements and production constraints. It realizes the rapid and accurate generation of the optimal slurry feeding design scheme, improves battery R&D efficiency and reduces development costs.

[0083] Another aspect of this application provides a computer-readable storage medium storing at least one executable instruction that, when executed on a lithium-ion battery slurry feeding design apparatus, causes the lithium-ion battery slurry feeding design apparatus to perform the operation of the lithium-ion battery slurry feeding design method as described above. This computer-readable storage medium may be included in the lithium-ion battery slurry feeding design equipment described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0084] Another aspect of this application provides a computer program product or computer program that includes at least one executable instruction that, when executed on a lithium-ion battery slurry feeding design apparatus, causes the lithium-ion battery slurry feeding design apparatus to perform the lithium-ion battery slurry feeding design method as described above.

[0085] Specifically, the executable instructions can be used to cause the lithium-ion battery slurry feeding design device to perform the following operations: Based on the basic characteristic data of different types of raw materials for lithium-ion batteries, a non-uniformity characterization model of the mixture composed of the different types of raw materials is constructed, and the non-uniformity characterization results are obtained. The non-uniformity characterization results are input into the finite element model of the battery electrode, which is a component of the lithium-ion battery, and the finite element model is solved by combining the applied load and boundary conditions to obtain the simulation results of the internal cracking behavior of the mixture under different working conditions. Based on the simulation results of internal cracking behavior under different working conditions, an optimization model is constructed with the slurry feeding scheme as the design variable, the design objective reflecting the battery cycle life and crack resistance as the objective function, and the design constraints are satisfied. The optimization model is then solved to obtain the target slurry feeding design scheme.

[0086] In an alternative approach, the method further includes: Battery electrode samples, which are components of the lithium-ion battery, were prepared according to the target homogenization and feeding design scheme, and the performance of the battery electrode samples was tested to obtain experimental test results. The experimental test results are compared with the simulation results corresponding to the target homogenization and feeding design scheme. If the error is greater than the target threshold, the parameters of the optimization model are dynamically adjusted until the error is less than the target threshold, and then the final homogenization and feeding design scheme is output.

[0087] In one alternative approach, the different types of raw materials include: positive electrode active materials, negative electrode active materials, conductive agents, and binders; the basic characteristic data include: particle size distribution, chemical composition, and surface morphology.

[0088] In one alternative approach, the non-uniformity characterization results include: the spatial distribution probability density function of different components and the interparticle interaction coefficient matrix.

[0089] In one alternative approach, the applied load includes: charge / discharge volume expansion load and mechanical vibration load; the boundary conditions include: electrode edge fixing constraint.

[0090] In one alternative approach, the simulation results of the internal cracking behavior include: stress distribution, strain variation, crack location, and quantitative values ​​of cracking degree.

[0091] In one alternative approach, the design variables are: mixture composition, proportion, and stirring process parameters; the objective function is: the battery cycle life reaches a preset number of cycles and the cracking degree quantification value is less than a preset threshold; the design constraints are: battery electrode size, raw material cost ceiling, and production process constraints.

[0092] The technical solution of this embodiment can effectively solve the problems of ignoring the non-uniformity and cracking of the mixture in the traditional method, reduce the risk of internal stress concentration and capacity decay in the battery, make the slurry feeding more scientific and precise, and improve battery performance and reliability.

[0093] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0095] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0096] According to one aspect of the embodiments of this application, a computer system is also provided, including a Central Processing Unit (CPU), which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from storage into random access memory (RAM), such as performing the methods described above. Various programs and data required for system operation are also stored in the RAM. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0097] The following components are connected to the I / O interface: input components including keyboards, mice, etc.; output components including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage components including hard drives; and communication components including network interface cards such as LAN (Local Area Network) cards and modems. The communication components perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage components as required.

[0098] The above description is merely a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.

Claims

1. A method for slurry feeding design of a lithium-ion battery, characterized in that, The method comprises: constructing a non-uniformity characterization model of a mixed material composed of different types of raw materials of a lithium ion battery based on basic characteristic data of the different types of raw materials, and obtaining a non-uniformity characterization result; inputting the non-uniformity characterization result into a finite element model of a battery pole piece which is a component of the lithium ion battery, and solving the finite element model in combination with a load and a boundary condition to obtain internal cracking behavior simulation results of the mixed material under different working conditions; constructing an optimization model with a homogenate feeding scheme as a design variable, a design objective reflecting a battery cycle life and a cracking resistance performance as an objective function, and satisfying a design constraint condition based on the internal cracking behavior simulation results under different working conditions, and solving the optimization model to obtain a target homogenate feeding design scheme.

2. The method of claim 1, wherein, The method further comprises: preparing a battery pole piece sample which is a component of the lithium ion battery according to the target homogenate feeding design scheme, and performing performance testing on the battery pole piece sample to obtain experimental testing results; comparing the experimental testing results with simulation results corresponding to the target homogenate feeding design scheme, and if an error is greater than a target threshold, dynamically adjusting parameters of the optimization model until the error is less than the target threshold, and outputting a final homogenate feeding design scheme.

3. The method of claim 1, wherein, The different types of raw materials include: positive active material, negative active material, conductive agent and binder; and the basic characteristic data includes: particle size distribution, chemical composition and surface morphology.

4. The method of claim 1, wherein, The non-uniformity characterization result includes: a distribution probability density function of different components in space and an inter-particle interaction coefficient matrix.

5. The method of claim 1, wherein, The load includes: a charge-discharge volume expansion load and a mechanical vibration load; and the boundary condition includes: an edge fixed constraint of the pole piece.

6. The method of claim 1, wherein, The internal cracking behavior simulation result includes: stress distribution, strain change, cracking position and cracking degree quantitative value.

7. The method of claim 6, wherein, The design variable is: a mixed material composition, a ratio and a stirring process parameter; the objective function is: a battery cycle life reaching a preset number of times and a cracking degree quantitative value being less than a preset threshold; and the design constraint condition is: a battery pole piece size, an upper limit of raw material cost and a production process constraint.

8. A homogenate feeding design device for a lithium ion battery, characterized by, The device comprises: a construction module configured to construct a non-uniformity characterization model of a mixed material composed of different types of raw materials of a lithium ion battery based on basic characteristic data of the different types of raw materials, and obtain a non-uniformity characterization result; a simulation module configured to input the non-uniformity characterization result into a finite element model of a battery pole piece which is a component of the lithium ion battery, and solve the finite element model in combination with a load and a boundary condition to obtain internal cracking behavior simulation results of the mixed material under different working conditions; a design module configured to construct an optimization model with a homogenate feeding scheme as a design variable, a design objective reflecting a battery cycle life and a cracking resistance performance as an objective function, and satisfying a design constraint condition based on the internal cracking behavior simulation results under different working conditions, and solve the optimization model to obtain a target homogenate feeding design scheme.

9. A method for generating a homogenate recipe design for a lithium-ion battery, the method comprising: receiving a plurality of input parameters; determining a plurality of homogenate recipes based on the plurality of input parameters; and outputting the plurality of homogenate recipes. The method comprises the following steps: obtaining performance requirements and production constraints of a target lithium ion battery, and defining the performance requirements and the production constraints as an objective function and design constraints; inputting the objective function and the design constraints into the optimization model, and solving the optimization model to output a target homogenate feeding design scheme matching the objective function and the design constraints.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the homogenate feeding design device of the lithium ion battery, causes the homogenate feeding design device of the lithium ion battery to perform the operations of the homogenate feeding design method of the lithium ion battery according to any one of claims 1-7.

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