Battery cell performance prediction method, terminal device, and computer-readable storage medium

Abstract

The present application relates to the field of batteries, and in particular to a method for predicting battery cell performance, a terminal device, and a computer-readable storage medium. The method comprises: obtaining input data, wherein the input data comprises a first particle size distribution of battery electrode particles; inputting the input data into a first model, and outputting a first result; wherein the first result is used to characterize the performance of the battery cell; and the first model is a multidimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution. Through the above method, the particle characteristics of the electrode active material particles can be more realistically reflected, thereby helping to improve the accuracy of the prediction results; and by inputting multiple different particle size distributions, the distribution mean and tolerance of the battery cell performance can be obtained, which helps to further improve the accuracy of the prediction results. In addition, predicting battery cell performance through the first model does not rely on human resources and can greatly improve prediction efficiency.

Description

Technical Field

[0001] The present application relates to the field of batteries, and in particular to a battery cell performance prediction method, a terminal device, and a computer-readable storage medium. Background Art

[0002] With the increasing popularity of electric vehicles, performance requirements for power batteries are gradually increasing. The quality of power batteries is closely related to the precise control during the battery cell production process. Among them, the particle size of the electrode active material particles has a significant impact on the overall performance of the battery cell.

[0003] In practical applications, due to the diversity of production processes and specifications, the particle properties of electrode active material particles often vary. Currently, the impact of different particle properties on battery cell performance is typically evaluated through experimental methods. This method consumes a large amount of human resources and is inefficient. In addition, the existing method relies on the professional level of human resources and cannot guarantee the accuracy of the evaluation results. Summary of the Invention

[0004] The present application provides a battery cell performance prediction method, terminal device, and computer-readable storage medium, which can accurately predict the impact of particle size distribution on battery cell performance.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a method for predicting battery cell performance is provided, comprising:

[0007] Obtaining input data, the input data including a first particle size distribution of battery electrode particles; wherein the particle size distribution is used to characterize the distribution of particles of different particle sizes in the battery electrode particles;

[0008] The input data is input into a first model, and a first result is output; wherein the first result is used to characterize the performance of the battery cell; and the first model is a multidimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution.

[0009] In the examples of this application, a first model is used to simulate the dynamic processes within a battery under a certain particle size distribution, thereby predicting the performance of the battery cell. The particle size distribution represents the radius of each particle in the electrode particles, which can more realistically reflect the particle characteristics of the electrode active material particles, thereby helping to improve the accuracy of the prediction results. Furthermore, predicting battery cell performance using the first model does not rely on human resources, significantly improving prediction efficiency.

[0010] In an implementation of the first aspect, the method further includes:

[0011] Get battery parameters;

[0012] The first model is constructed according to the battery parameters.

[0013] In the embodiment of the present application, the terminal device can automatically construct the first model according to the battery parameters, which has a high degree of intelligence and helps to save human resources.

[0014] In an implementation of the first aspect, the battery parameters include geometric parameters and material parameters of the battery cell;

[0015] The constructing the first model according to the battery parameters includes:

[0016] determining a geometric structure of the battery according to the geometric parameters;

[0017] The first model is constructed according to the material parameters and the geometric structure.

[0018] In an implementation of the first aspect, the material parameter includes a solid-phase diffusion rate;

[0019] The first model includes a solid-phase diffusion model, which is used to describe the diffusion process of lithium ions within the electrode material of the battery within and between particles;

[0020] The constructing the first model according to the material parameters and the geometric structure includes:

[0021] constructing a solid-phase diffusion matrix according to the solid-phase diffusion rate, wherein the solid-phase diffusion matrix is ​​used to describe the diffusion state of the electrode particles in three dimensions during the solid-phase diffusion process;

[0022] The solid-phase diffusion model is constructed according to the solid-phase diffusion matrix.

[0023] The first model in the embodiment of the present application adds the dimension of particle size distribution, which is equivalent to considering the diffusion dimension between electrode particles. Therefore, the first model in the embodiment of the present application can more accurately reflect the diffusion of particles within the battery cell, which helps to improve the accuracy of subsequent predictions.

[0024] In an implementation of the first aspect, the solid-phase diffusion rate includes a first parameter, a second parameter, and a third parameter; wherein the first parameter is used to represent the particle diffusion rate in the thickness direction of the battery; the second parameter is used to represent the diffusion rate between particles within the electrode material; and the third parameter is used to represent the particle diffusion rate in the radial direction of the particles within the electrode material.

[0025] The constructing a solid-phase diffusion matrix according to the solid-phase diffusion rate includes:

[0026] setting data on the diagonal of the solid-phase diffusion matrix according to the first parameter, the second parameter, and the third parameter;

[0027] The data on the non-diagonal lines of the solid-phase diffusion matrix are set to preset values.

[0028] Optionally, the first parameter being 0 indicates that the particle diffusion rate in the thickness direction of the battery is not considered;

[0029] And / or, the second parameter being 0 indicates that the diffusion rate between particles inside the electrode material is not considered.

[0030] In an implementation of the first aspect, constructing the first model according to the material parameters and the geometric structure includes:

[0031] The particles with the same electrode thickness in the first model are set to have the same liquid phase concentration.

[0032] In an implementation of the first aspect, the method further includes:

[0033] adjusting the first particle size distribution to obtain a second particle size distribution;

[0034] inputting the second particle size distribution into the first model and outputting a second result;

[0035] If the second result meets a preset condition, determining a target threshold value of the particle size according to the second particle size distribution;

[0036] If the second result does not meet the preset condition, continue adjusting the second particle size distribution until the result output by the first model meets the preset condition, thereby obtaining the latest adjusted third particle size distribution;

[0037] A target threshold value of particle size is determined based on the third particle size distribution.

[0038] In the examples of this application, a first model is used to simulate the dynamic processes within a battery under a certain particle size distribution, thereby predicting the performance of the battery cell. The particle size distribution represents the radius of each particle in the electrode particles, which can more realistically reflect the particle characteristics of the electrode active material particles, thereby helping to improve the accuracy of the prediction results. Furthermore, predicting battery cell performance using the first model does not rely on human resources, significantly improving prediction efficiency.

[0039] In a second aspect, an embodiment of the present application provides a battery cell performance prediction device, comprising:

[0040] The acquisition unit is used to acquire input data, wherein the input data includes a first particle size distribution of battery electrode particles and operating condition data; wherein the particle size distribution is used to characterize the distribution of particles of different particle sizes in the battery electrode particles.

[0041] A prediction unit is used to input the input data into a first model and output a first result; wherein the first result is used to characterize the performance of the battery cell; the first model is a multidimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution.

[0042] In a third aspect, an embodiment of the present application provides a terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting battery cell performance as described in any one of the first aspects above is implemented.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the battery cell performance prediction method as described in any one of the first aspects above is implemented.

[0044] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the battery cell performance prediction method described in any one of the first aspects above.

[0045] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0046] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0048] Figure 1 is a schematic diagram of the construction process of the first model provided in an embodiment of the present application;

[0049] Figure 2 is a schematic diagram of a battery cell model provided in an embodiment of the present application;

[0050] Figure 3 Schematic diagram of the process of predicting battery cell performance according to an embodiment of the present application;

[0051] Figure 4 is a schematic diagram of the particle size distribution provided in the examples of the present application;

[0052] Figure 5 This is a structural block diagram of a battery cell performance prediction device provided in an embodiment of the present application;

[0053] Figure 6 It is a structural diagram of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0056] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0057] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0059] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0060] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0061] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0062] With the increasing popularity of electric vehicles, performance requirements for power batteries are gradually increasing. The quality of power batteries is closely related to the precise control during the battery cell production process. Among them, the particle size of the electrode active material particles has a significant impact on the overall performance of the battery cell.

[0063] In practical applications, due to the diversity of production processes and specifications, the particle properties of electrode active material particles often vary. Currently, the impact of different particle properties on battery cell performance is typically evaluated through experimental methods. This method consumes a large amount of human resources and is inefficient. In addition, the existing method relies on the professional level of human resources and cannot guarantee the accuracy of the evaluation results.

[0064] Based on this, an embodiment of the present application provides a method for predicting battery cell performance. In this embodiment of the present application, a first model is used to simulate the dynamic process within the battery under a certain particle size distribution, thereby predicting the performance of the battery cell. Among them, the particle size distribution can characterize the distribution of particles of different particle sizes in the battery electrode particles, and can more realistically reflect the particle characteristics of the electrode active material particles, thereby helping to improve the accuracy of the prediction results. In addition, predicting battery cell performance through the first model does not rely on human resources, which can greatly improve the prediction efficiency.

[0065] In the embodiment of the present application, a first model can be pre-built and then used to predict the performance of the battery cell. The following first introduces the construction process of the first model.

[0066] See also Figure 1 , is a schematic diagram of the construction process of the first model provided in the embodiment of the present application. As an example and not a limitation, Figure 1 As shown, the construction process of the first model may include the following steps:

[0067] S101, obtaining battery parameters.

[0068] The battery parameters include geometric parameters and material parameters of the battery cell.

[0069] For example, the geometric parameters of a battery cell may include the cell size, the geometric dimensions of the positive electrode, the geometric dimensions of the negative electrode, the geometric dimensions of the separator, the geometric dimensions of the current collector, and coating parameters, etc. Among them, the cell size may include the length and width of the cell, which is crucial for the overall layout and packaging of the cell. In addition, the cell size may also include the internal structure of the cell, such as the thickness of the pole piece, which determines the energy density of the cell. The geometric dimensions of the separator may include the thickness of the separator, which affects the internal resistance of the cell and the overall performance of the battery. The coating parameters may include the coating weight, which affects the balance between the weight and energy density of the cell.

[0070] For example, the material parameters of a battery cell can include the electrode particle size distribution, specific surface area distribution, primary reaction interface rate, solid-phase diffusion rate, electrolyte type and concentration, electrolyte conductivity, and the electrochemical properties of the electrode material. The electrode particle size distribution affects the contact area between the electrode and the electrolyte, thereby affecting the efficiency of the electrochemical reaction. The specific surface area distribution of the electrode particles reflects the active surface area of ​​the electrode material and has a significant impact on the battery's charge and discharge rate and capacity.

[0071] Optionally, a cell design table may be obtained, which may include the battery parameters described above. In one implementation, a user inputs the cell design table into a terminal device; upon receiving the user input into the cell design table, the terminal device parses the cell design table to extract the required battery parameters.

[0072] Optionally, the battery parameters may also include battery operating condition data, such as the charging and discharging process and the ambient temperature.

[0073] Optionally, the operating condition data may be different for different application scenarios.

[0074] For example, in a direct current resistance (DCR) test scenario, operating condition data may include: ambient temperatures of -10, 10, 20, and 25 degrees Celsius; and a charge and discharge process consisting of discharging from a fully charged state at 0.33C (or charging from a fully discharged state) to 50% SOC, allowing it to rest for at least an hour, then charging at 1C for 5 minutes at 50% SOC and allowing it to rest for at least 30 minutes, then discharging at 1C for 5 minutes back to 50% SOC and allowing it to rest for at least 30 minutes. This allows the DC resistance of the charge and discharge process to be measured at 50% SOC. The same method can be used to obtain the DCR at other SOCs after the initial adjustment.

[0075] For example, rate performance refers to the performance of electrochemical energy storage devices such as batteries under different charge and discharge current (or power) conditions. In the case of rate performance testing, operating data may include the charge and discharge process from 0 to 100% or from 100% to 0 at a certain rate.

[0076] S102: Construct a first model according to battery parameters.

[0077] In the embodiment of the present application, the terminal device can automatically construct the first model according to the battery parameters, which has a high degree of intelligence and helps to save human resources.

[0078] In an embodiment of the present application, the first model is a multidimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution. Among them, the dynamic process inside the battery includes four polarization effects of the battery, namely solid-phase polarization, liquid-phase polarization, ohmic polarization and interface electrochemical polarization. Optionally, the first model in the embodiment of the present application may include a solid-phase diffusion model, a liquid-phase diffusion model, an ohmic polarization model and an interface reaction model. The solid-phase diffusion model is used to characterize the solid-phase polarization process, that is, to describe the diffusion process of lithium ions within and between particles in the electrode material of the battery. The liquid-phase diffusion model is used to characterize the liquid-phase polarization process, that is, to describe the movement process of ions in the electrolyte of the battery. The ohmic polarization model is used to characterize the ohmic polarization process, that is, the polarization effect caused by resistance. The interface reaction model is used to characterize the interface electrochemical polarization process, that is, the diffusion and reaction of lithium ions at the interface between the electrode and the electrolyte.

[0079] In one implementation, the process of constructing the first model according to the battery parameters may include:

[0080] The geometric structure of the battery is determined according to the geometric parameters; and a first model is constructed according to the material parameters and the geometric structure.

[0081] Optionally, the first model can be constructed using simulation software. Specifically, the battery parameters are input into the simulation software; in response, the simulation software constructs the battery geometry based on the geometric parameters. The simulation software then generates various sub-models within the first model based on the material parameters and the geometry, such as the solid-phase diffusion model, the liquid-phase diffusion model, the ohmic polarization model, and the interface reaction model. Optionally, the simulation software can display the constructed battery geometry to the user.

[0082] It should be noted that the simulation software is not specifically limited in the embodiments of the present application.

[0083] Optionally, the first model may be obtained by improving a traditional Pseudo-2-Dimension (P2D) model.

[0084] In traditional P2D models, the particle sizes of all particles in electrode particles are usually considered to be the same, that is, the P2D model is used to describe the dynamic process inside the battery under the same particle size. Unlike the traditional P2D model, the first model in the embodiment of the present application regards the particle sizes of particles in the electrode particles as different, taking into account the particle size of each particle and the distribution of particles of different particle sizes in the electrode particles. By comparison, it can be seen that the first model in the embodiment of the present application can more realistically reflect the particle characteristics of the electrode active material particles inside the battery, thereby helping to improve the prediction accuracy.

[0085] It is understood that in the embodiments of this application, the first model is a multidimensional equivalent model, which refers to a model that considers the characteristics and interactions of the system in multiple dimensions to provide a more comprehensive and accurate description and analysis of the actual physical system. Because it considers the complete geometric shape of the object in three-dimensional space, the distribution of physical parameters, and the interactions between various dimensions, the multidimensional equivalent model can more realistically reflect the complex characteristics of the actual system.

[0086] For example, see Figure 2 , is a schematic diagram of a cell model provided in an embodiment of the present application. As an example and not a limitation, Figure 2 As shown in the figure, the battery cell model can include a positive electrode, a negative electrode, and a separator. The x-axis dimension represents the thickness of the battery cell and the distance between the positive and negative electrodes. The y-axis dimension represents the diffusion direction between electrode particles. The z-axis dimension represents the radial direction of particles within the electrode material.

[0087] It should be noted that Figure 2 The x, y, and z axes shown are not used to refer to geometric directions, but rather to the dimensions of the model. Figure 2 The three axes x, y, and z are shown, indicating that the battery cell model takes into account the dynamic processes in these three dimensions.

[0088] It is understandable that the traditional P2D model is equivalent to considering only the dynamic processes in the two dimensions of x and z, so the traditional P2D model can be called a two-dimensional model. In the traditional P2D model, the solid phase concentration cs represents the value of the particle size at a certain electrode thickness, that is, the solid phase concentration cs value and its change of all particles are the same. In the first model of the embodiment of the present application, as shown in FIG. Figure 2 As shown in the figure, the dynamic processes in the three dimensions x, y, and z are considered, with the addition of the y-dimension of particle size distribution. In other words, the solid phase concentration cs in the first model is no longer the value of a particle size at a certain electrode thickness, but rather the lithium concentration at a particle radius corresponding to a particle size. In other words, the solid phase concentration cs value and its variation are different for particles of different particle sizes.

[0089] As described above, the material parameters include the solid-phase diffusion rate. The first model includes a solid-phase diffusion model, which is used to describe the recombination process of electrons and ions inside the electrode material of the battery. In one implementation, the step of constructing the first model based on the material parameters and the geometric structure may include:

[0090] constructing a solid-phase diffusion matrix according to the solid-phase diffusion rate, wherein the solid-phase diffusion matrix is ​​used to describe the diffusion state of the electrode particles in three dimensions during the solid-phase diffusion process;

[0091] The solid-phase diffusion model is constructed according to the solid-phase diffusion matrix.

[0092] It is understandable that the traditional P2D model is equivalent to considering only two dimensions of diffusion state, namely the direction of the battery cell thickness and the direction of lithium ion diffusion within the electrode particles. The first model in the embodiment of the present application adds the dimension of particle size distribution, which is equivalent to considering the dimension of diffusion between electrode particles. Therefore, the first model in the embodiment of the present application can more accurately reflect the diffusion of particles within the battery cell, which helps to improve the accuracy of subsequent predictions.

[0093] In one implementation, the solid-phase diffusion rate includes a first parameter, a second parameter, and a third parameter; wherein the first parameter is used to represent the particle diffusion rate in the thickness direction of the battery; the second parameter is used to represent the diffusion rate between particles within the electrode material; and the third parameter is used to represent the particle diffusion rate in the radial direction of the particles within the electrode material. Accordingly, the step of constructing a solid-phase diffusion matrix based on the solid-phase diffusion rate may include:

[0094] Setting data on the diagonal of the solid-phase diffusion matrix according to the first parameter, the second parameter, and the third parameter;

[0095] Set the data on the off-diagonal lines of the solid-phase diffusion matrix to preset values.

[0096] Optionally, the default value can be 0.

[0097] For example, the solid-phase diffusion matrix is ​​as follows:

[0098] ;

[0099] Where Ds is the solid phase diffusion matrix, is the first parameter, is the second parameter, is the third parameter, and r is the particle radius.

[0100] Optionally, the first parameter being 0 indicates that the particle diffusion rate in the thickness direction of the battery is not considered.

[0101] Optionally, the second parameter being 0 indicates that the diffusion rate between particles inside the electrode material is not considered.

[0102] It is understandable that since the first model describes the dynamic process inside the battery under a certain particle size distribution, in practical applications, r in the above solid-phase diffusion matrix is ​​not a numerical value, but a matrix including the particle size of each particle.

[0103] In the above-mentioned solid-phase diffusion matrix, when different parameters take different values, different dynamic processes inside the battery can be described. For example, the first parameter is 0, the second parameter is 0, and the third parameter is not 0, which is a traditional P2D model. The first parameter is 0, and the second parameter is not 0. It can describe the dynamic process without considering the particle diffusion rate in the thickness direction of the battery, the diffusion rate between particles inside the electrode material, and the particle diffusion rate in the radial direction of the particles inside the electrode material. When both the first parameter and the second parameter are not 0, it can describe the dynamic process when the diffusion rate in three dimensions is considered. It can be seen that the solid-phase diffusion matrix provided in the embodiment of the present application covers a wider range of situations, has a wider range of applications, and is more flexible.

[0104] Alternatively, solid-phase diffusion models can be constructed based on Fick's laws or the diffusion principle of phase-field chemical potential.

[0105] For example, the solid-phase diffusion model based on Fick's law is as follows:

[0106] ;

[0107] The solid-phase diffusion model constructed based on the diffusion principle of phase-field chemical potential is as follows:

[0108] ;

[0109] Where cs represents the solid phase concentration, t and Indicates time, represents the divergence, and is the Boltzmann constant. ; ; ; .

[0110] In one implementation, the step of constructing the first model according to the material parameters and the geometric structure may include:

[0111] The particles with the same electrode thickness in the first model are set to have the same liquid phase concentration.

[0112] Among them, the interface reaction model in the first model is designed to the liquid phase concentration cl. Based on the above settings, it is equivalent to making the liquid phase concentration cl of all particle radii on the same electrode thickness the same in the interface reaction model.

[0113] Optionally, the interface reaction model may adopt the Butler-Volmar equation, or an equation based on the coupled ion-electron transfer theory (CIET). The specific form of the interface reaction model is not limited in the present embodiment.

[0114] Based on the first model constructed above, the battery cell performance prediction method is introduced below.

[0115] See also Figure 3 , is a flow chart of the method for predicting cell performance provided by the embodiment of the present application. As an example and not a limitation, Figure 3 As shown, the cell performance prediction method may include the following steps:

[0116] S301, obtaining input data.

[0117] In an embodiment of the present application, the input data includes a first particle size distribution of battery electrode particles; wherein the particle size distribution is used to characterize the distribution of particles of different particle sizes in the battery electrode particles.

[0118] S302: Input the input data into the first model and output a first result.

[0119] The first result is used to characterize the performance of the battery cell.

[0120] It can be understood that the various sub-models in the first model simulate the dynamic process inside the battery according to the input data, thereby calculating the performance of the battery cell.

[0121] For example, in the application scenario of rate testing, the first result can represent the performance range at different rates. In the application scenario of temperature performance testing, the first result can represent the performance range at different temperatures. In the application scenario of lithium deposition potential impact rate testing, the first result can represent the lithium deposition potential impact rate range. In the application scenario of DCR testing, the first result can represent the DCR range.

[0122] In the examples of this application, a first model is used to simulate the dynamic processes within a battery under a certain particle size distribution, thereby predicting the performance of the battery cell. The particle size distribution can characterize the distribution of particles of different sizes within the battery electrode particles, more realistically reflecting the particle characteristics of the electrode active material particles, thereby helping to improve the accuracy of the prediction results. Furthermore, predicting battery cell performance using the first model does not rely on human resources, significantly improving prediction efficiency.

[0123] See also Figure 4 , is a schematic diagram of the particle size distribution provided in the embodiment of the present application. As an example and not a limitation, Figure 4 As shown in the figure, the horizontal axis represents particle size, and the vertical axis represents particle count. Particle size distribution across actual production lines exhibits significant variability, influenced by a variety of factors, including but not limited to different production batches and raw materials from different suppliers. Each batch's production process may introduce unique process fluctuations, and raw materials from different suppliers may also have different particle characteristics, which directly affect the final product's particle size distribution. Therefore, accurately measuring and understanding this variation in particle size distribution is crucial to ensuring consistent product quality.

[0124] Based on this consideration, in one embodiment, the method further includes:

[0125] adjusting the first particle size distribution to obtain a second particle size distribution;

[0126] inputting the second particle size distribution into the first model and outputting a second result;

[0127] If the second result meets a preset condition, determining a target threshold value of the particle size according to the second particle size distribution;

[0128] If the second result does not meet the preset condition, continue adjusting the second particle size distribution until the result output by the first model meets the preset condition, thereby obtaining the latest adjusted third particle size distribution;

[0129] A target threshold value of particle size is determined based on the third particle size distribution.

[0130] It is understood that the preset conditions may vary in different application scenarios. For example, in a rate test scenario, the preset condition may be that the rate meets a preset threshold. In a DCR test scenario, the preset condition may be that the DCR meets a preset threshold. In a lithium deposition potential impact rate test scenario, the preset condition may be that the lithium deposition window meets a preset threshold. And so on.

[0131] Optionally, if the second result does not meet the preset conditions, statistical characteristics between the second result and the preset conditions, such as standard deviation, can be calculated; and then the second particle size distribution can be adjusted according to the statistical characteristics, such as narrowing or widening the particle size distribution.

[0132] Through the above method, using the simulation of the first model and inputting multiple different particle size distributions, the statistical characteristics of the battery cell performance (such as the distribution mean and tolerance) can be obtained, which helps to further improve the accuracy of the prediction results; and the target threshold value of the particle size that meets the preset conditions can be accurately determined, so that while meeting product quality requirements, production costs and efficiency can also be effectively controlled.

[0133] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0134] Corresponding to the battery cell performance prediction method described in the above embodiment, Figure 5 This is a structural block diagram of the battery cell performance prediction device provided in an embodiment of the present application. For the sake of convenience, only the parts related to the embodiment of the present application are shown.

[0135] Reference Figure 5 , the battery cell performance prediction device 5 includes:

[0136] The acquisition unit 51 is configured to acquire input data, wherein the input data includes a first particle size distribution of battery electrode particles; wherein the particle size distribution is used to characterize the distribution of particles of different particle sizes in the battery electrode particles.

[0137] The prediction unit 52 is used to input the input data into a first model and output a first result; wherein the first result is used to characterize the performance of the battery cell; the first model is a multidimensional equivalent model used to describe the dynamic process inside the battery under the first particle size distribution.

[0138] Optionally, the acquiring unit 51 is further configured to:

[0139] Get battery parameters;

[0140] The first model is constructed according to the battery parameters.

[0141] Optionally, the battery parameters include geometric parameters and material parameters of the battery cell; accordingly, the acquisition unit 51 is further configured to:

[0142] determining a geometric structure of the battery according to the geometric parameters;

[0143] The first model is constructed according to the material parameters and the geometric structure.

[0144] Optionally, the material parameters include solid-phase diffusion rate;

[0145] The first model includes a solid-phase diffusion model, which is used to describe the diffusion process of lithium ions within particles and between particles in the electrode material of the battery.

[0146] Accordingly, the acquiring unit 51 is further configured to:

[0147] constructing a solid-phase diffusion matrix according to the solid-phase diffusion rate, wherein the solid-phase diffusion matrix is ​​used to describe the diffusion state of the electrode particles in three dimensions during the solid-phase diffusion process;

[0148] The solid-phase diffusion model is constructed according to the solid-phase diffusion matrix.

[0149] Optionally, the solid-phase diffusion rate includes a first parameter, a second parameter, and a third parameter; wherein the first parameter is used to represent the particle diffusion rate in the thickness direction of the battery; the second parameter is used to represent the diffusion rate between particles inside the electrode material; and the third parameter is used to represent the particle diffusion rate in the radial direction of the particles inside the electrode material.

[0150] Accordingly, the acquiring unit 51 is further configured to:

[0151] setting data on the diagonal of the solid-phase diffusion matrix according to the first parameter, the second parameter, and the third parameter;

[0152] The data on the non-diagonal lines of the solid-phase diffusion matrix are set to preset values.

[0153] Optionally, the first parameter being 0 indicates that the particle diffusion rate in the thickness direction of the battery is not considered;

[0154] And / or, the second parameter being 0 indicates that the diffusion rate between particles inside the electrode material is not considered.

[0155] Optionally, the acquiring unit 51 is further configured to:

[0156] The particles with the same electrode thickness in the first model are set to have the same liquid phase concentration.

[0157] Optionally, the prediction unit 52 is further configured to:

[0158] adjusting the first particle size distribution to obtain a second particle size distribution;

[0159] inputting the second particle size distribution into the first model and outputting a second result;

[0160] If the second result meets a preset condition, determining a target threshold value of the particle size according to the second particle size distribution;

[0161] If the second result does not meet the preset condition, continue adjusting the second particle size distribution until the result output by the first model meets the preset condition, thereby obtaining the latest adjusted third particle size distribution;

[0162] A target threshold value of particle size is determined based on the third particle size distribution.

[0163] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0164] in addition, Figure 5 The cell performance prediction device shown can be a software unit, a hardware unit, or a combination of software and hardware units built into an existing terminal device, or can be integrated into the terminal device as an independent pendant, or can exist as an independent terminal device.

[0165] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0166] Figure 6 This is a schematic diagram of the structure of the terminal device provided in the embodiment of the present application. Figure 6 As shown, the terminal device 6 of this embodiment includes: at least one processor 60 ( Figure 6Only one is shown in the figure) a processor, a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, wherein the processor 60 implements the steps of any of the above-mentioned battery cell performance prediction method embodiments when executing the computer program 62.

[0167] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 6 It is only an example of the terminal device 6 and does not constitute a limitation on the terminal device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0168] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0169] In some embodiments, the memory 61 may be an internal storage unit of the terminal device 6, such as a hard drive or memory of the terminal device 6. In other embodiments, the memory 61 may also be an external storage device of the terminal device 6, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the terminal device 6. Furthermore, the memory 61 may include both an internal storage unit of the terminal device 6 and an external storage device. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is about to be output.

[0170] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0171] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0172] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0173] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0174] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0175] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0176] The units described as separate components 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0177] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for predicting battery cell performance, characterized in that: include: Obtaining input data, the input data including a first particle size distribution of battery electrode particles; wherein the particle size distribution is used to characterize the distribution of particles of different particle sizes in the battery electrode particles; Inputting the input data into a first model and outputting a first result; wherein the first result is used to characterize the performance of the battery cell; the first model is a multidimensional equivalent model used to describe the dynamic process within the battery under the first particle size distribution; the first model includes a solid-phase diffusion model, which is used to describe the diffusion process of lithium ions within and between particles in the battery electrode material; The method further comprises: Obtaining battery parameters; wherein the battery parameters include a first parameter, a second parameter, and a third parameter; wherein the first parameter is used to represent the particle diffusion rate in the thickness direction of the battery; the second parameter is used to represent the diffusion rate between particles within the electrode material; and the third parameter is used to represent the particle diffusion rate in the radial direction of the particles within the electrode material; setting data on the diagonal of the solid-phase diffusion matrix according to the first parameter, the second parameter, and the third parameter; Setting the data on the non-diagonal line of the solid-phase diffusion matrix to a preset value; The solid-phase diffusion model is constructed according to the solid-phase diffusion matrix.

2. The method for predicting battery cell performance according to claim 1, wherein: The battery parameters include geometric parameters and material parameters of the battery cell; The constructing the first model according to the battery parameters includes: determining a geometric structure of the battery according to the geometric parameters; The first model is constructed according to the material parameters and the geometric structure.

3. The method for predicting battery cell performance according to claim 1, wherein: The first parameter being 0 indicates that the particle diffusion rate in the thickness direction of the battery is not considered; And / or, the second parameter being 0 indicates that the diffusion rate between particles inside the electrode material is not considered.

4. The method for predicting battery cell performance according to claim 2, wherein: The constructing the first model according to the material parameters and the geometric structure includes: The particles with the same electrode thickness in the first model are set to have the same liquid phase concentration.

5. The method for predicting battery cell performance according to any one of claims 1 to 4, characterized in that: The method further comprises: adjusting the first particle size distribution to obtain a second particle size distribution; inputting the second particle size distribution into the first model and outputting a second result; If the second result meets a preset condition, determining a target threshold value of the particle size according to the second particle size distribution; If the second result does not meet the preset condition, continue adjusting the second particle size distribution until the result output by the first model meets the preset condition, thereby obtaining the latest adjusted third particle size distribution; A target threshold value of particle size is determined based on the third particle size distribution.

6. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the battery cell performance prediction method according to any one of claims 1 to 5 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the battery cell performance prediction method according to any one of claims 1 to 5 is implemented.

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