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

Abstract

The invention relates to the field of batteries, in particular to a cell performance prediction method, terminal equipment and a computer readable storage medium. The method comprises the following steps: acquiring input data, wherein the input data comprises 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 for representing the performance of the battery cell; the first model is a multi-dimensional equivalent model used for describing the dynamic process in the battery under the first particle size distribution. According to the method, the particle characteristics of the electrode active material particles can be truly reflected, so that the accuracy of a prediction result can be improved; and the distribution mean value and tolerance of the battery cell performance can be obtained by inputting different particle size distribution for many times, so that the accuracy of a prediction result can be further improved. In addition, the performance of the battery cell is predicted through the first model without depending on human resources, so that the prediction efficiency can be greatly improved.

Description

Technical Field

[0001] This application relates to the field of batteries, and in particular, to a method for predicting the performance of battery cells, a terminal device, and a computer-readable storage medium. Background Art

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

[0003] In practical applications, due to the diversity of production processes and specification standards, the particle characteristics of electrode active material particles often vary. Currently, it is usually through experimental methods to evaluate the impact of different particle characteristics on the performance of battery cells. This method will consume a large amount of human resources and has a low evaluation efficiency. In addition, the existing method depends on the professional level of human resources and cannot guarantee the accuracy of the evaluation results. Summary of the Invention

[0004] This application provides a method for predicting the performance of battery cells, a terminal device, and a computer-readable storage medium, which can accurately predict the impact of the particle size distribution of particles on the performance of battery cells.

[0005] To achieve the above object, this application adopts the following technical solutions: In a first aspect, a method for predicting the performance of battery cells is provided, including: Obtain input data, where the input data includes the first particle size distribution of battery electrode particles; wherein, the particle size distribution is used to characterize the distribution of particles with different particle sizes in the battery electrode particles; 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 multi-dimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution.

[0006] In the embodiments of this application, the dynamic process inside the battery under a certain particle size distribution is simulated through the first model, so as to predict the performance of the battery cell. Among them, the particle size distribution can represent the particle 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. In addition, predicting the performance of the battery cell through the first model does not depend on human resources and can greatly improve the prediction efficiency.

[0007] In one implementation manner of the first aspect, the method further includes: Obtain battery parameters; Construct the first model according to the battery parameters.

[0008] In the embodiments of the present application, the terminal device can automatically construct a first model based on battery parameters, with a relatively high degree of intelligence, which helps to save human resources.

[0009] In one implementation manner of the first aspect, the battery parameters include the geometric parameters and material parameters of the battery cell; The constructing the first model according to the battery parameters includes: Determining the geometric structure of the battery according to the geometric parameters; Constructing the first model according to the material parameters and the geometric structure. In one implementation manner of the first aspect, the material parameters include the solid-phase diffusion rate; The first model includes a solid-phase diffusion model, and the solid-phase diffusion model is used to describe the diffusion process of lithium ions inside the electrode material of the battery within and between particles; The constructing the first model according to the material parameters and the geometric structure includes: Constructing a solid-phase diffusion matrix according to the solid-phase diffusion rate, and the solid-phase diffusion matrix is used to describe the diffusion states in three dimensions during the solid-phase diffusion process of the electrode particles; Constructing the solid-phase diffusion model according to the solid-phase diffusion matrix. The first model in the embodiments of the present application adds the particle size distribution dimension, which is equivalent to considering the diffusion dimension between electrode particles. Therefore, the first model in the embodiments of the present application can more accurately reflect the diffusion situation of particles inside the battery cell, which helps to improve the accuracy of subsequent predictions.

[0010] In one implementation manner 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 inside the electrode material; the third parameter is used to represent the particle diffusion rate in the radial direction of the particles inside the electrode material; The constructing the solid-phase diffusion matrix according to the solid-phase diffusion rate includes: Setting the 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 of the solid-phase diffusion matrix to a preset value.

[0011] Optionally, the first parameter being 0 means not considering the particle diffusion rate in the thickness direction of the battery; And / or, the second parameter being 0 means not considering the diffusion rate between particles inside the electrode material.

[0012] In an implementation manner of the first aspect, constructing the first model according to the material parameters and the geometric structure includes: Setting the particles with the same electrode thickness in the first model to the same liquid phase concentration.

[0013] In an implementation manner of the first aspect, the method further includes: 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 the preset conditions, determining the target threshold of the particle size according to the second particle size distribution; If the second result does not meet the preset conditions, continuously adjusting the second particle size distribution until the result output by the first model meets the preset conditions, and obtaining the latest adjusted third particle size distribution; Determining the target threshold of the particle size according to the third particle size distribution.

[0014] In the embodiments of the present application, the dynamic process inside the battery under a certain particle size distribution is simulated through the first model, so as to predict the performance of the battery cell. Among them, the particle size distribution can represent the particle radius of each particle in the electrode particles, and can more realistically reflect the particle characteristics of the electrode active material particles, thus helping to improve the accuracy of the prediction results. In addition, predicting the performance of the battery cell through the first model does not rely on human resources and can greatly improve the prediction efficiency.

[0015] In a second aspect, an embodiment of the present application provides a device for predicting the performance of a battery cell, including: An acquisition unit for acquiring input data, where the input data includes the first particle size distribution of the battery electrode particles and the working condition data; wherein, the particle size distribution is used to characterize the distribution of particles with different particle sizes in the battery electrode particles.

[0016] A prediction unit for inputting the input data into the 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 multi-dimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution.

[0017] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for predicting the performance of a battery cell as described in any item of the first aspect above is implemented.

[0018] Fourthly, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the cell performance prediction method as described in any one of the above first aspects.

[0019] Fifthly, an embodiment of the present application provides a computer program product, which when running on a terminal device, enables the terminal device to execute the cell performance prediction method as described in any one of the above first aspects.

[0020] It can be understood that the beneficial effects of the above second to fifth aspects can be referred to the relevant descriptions in the above first aspect, and will not be elaborated here.

[0021] The above description is only an overview of the technical solution of the present application. In order to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered as limiting the present application. And in all the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 is a schematic diagram of the construction process of the first model provided by an embodiment of the present application; Figure 2 is a schematic diagram of the cell model provided by an embodiment of the present application; Figure 3 is a schematic diagram of the process of the cell performance prediction method provided by an embodiment of the present application; Figure 4 is a schematic diagram of the particle size distribution provided by an embodiment of the present application; Figure 5 is a structural block diagram of the cell performance prediction device provided by an embodiment of the present application; Figure 6 is a schematic diagram of the structure of the terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will describe in detail the embodiments of the technical solution of the present application with reference to the drawings. The following embodiments are only used to illustrate the technical solution of the present application more clearly, so they are only examples and cannot be used to limit the protection scope of the present application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used herein are for the purpose of describing specific embodiments only 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 description of the drawings are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity, specific order, or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "a plurality of" is more than two, unless otherwise specifically defined.

[0026] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase does not necessarily refer to the same embodiment at every occurrence in the specification, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments of this application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0028] In the description of the embodiments of this application, the term "a plurality of" means more than two (including two). Similarly, "a plurality of groups" means more than two groups (including two groups), and "a plurality of pieces" means more than two pieces (including two pieces).

[0029] In the description of the embodiments of this application, the orientation or positional relationship indicated by technical terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the embodiments of this application and simplifying the description, and does 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 this application.

[0030] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific situations.

[0031] With the popularization of electric vehicles, the performance requirements for power batteries are gradually increasing. The quality of power batteries is closely related to the precise control in the production process of battery cells. Among them, the particle size of the electrode active material particles has a significant impact on the overall performance of the battery cell.

[0032] In practical applications, due to the diversity of production processes and specification standards, the particle characteristics of the electrode active material particles often vary. Currently, it is usually through experimental methods to evaluate the impact of different particle characteristics on the performance of the battery cell. This method will consume a large amount of human resources and the evaluation efficiency is low; in addition, the existing method depends on the professional level of manpower and cannot guarantee the accuracy of the evaluation results.

[0033] Based on this, the embodiments of the present application provide a method for predicting the performance of a battery cell. In the embodiments of the present application, the dynamic process inside the battery under a certain particle size distribution is simulated through a first model, so as to predict the performance of the battery cell. Among them, the particle size distribution can characterize the distribution of particles with different particle sizes in the battery electrode particles, and can more truly reflect the particle characteristics of the electrode active material particles, thereby helping to improve the accuracy of the prediction results. In addition, predicting the performance of the battery cell through the first model does not depend on human resources and can greatly improve the prediction efficiency.

[0034] In the embodiments of the present application, the first model can be constructed in advance, and then the first model is used to predict the performance of the battery cell. First, the construction process of the first model will be introduced below.

[0035] See Figure 1 , which is a schematic diagram of the construction process of the first model provided by the embodiments of the present application. By way of example and not limitation, as Figure 1 shown, the construction process of the first model can include the following steps: S101, obtain battery parameters.

[0036] Among them, the battery parameters include the geometric parameters and material parameters of the battery cell.

[0037] For example, the geometric parameters of a battery cell can include the cell size, the geometric size of the positive electrode, the geometric size of the negative electrode, the geometric size of the separator, the geometric size of the current collector, and coating parameters, etc. Among them, the cell size can include the length and width of the cell, which are crucial for the overall layout and packaging of the cell. In addition, the cell size can also include the internal structure of the cell, such as the thickness of the electrode sheet, which determines the energy density of the cell. The geometric size of the separator can include the separator thickness, which affects the internal resistance of the cell and the overall performance of the battery. The coating parameters can include the coating weight, which affects the balance between the weight and energy density of the cell.

[0038] For example, the material parameters of a battery cell can include the particle size distribution of the electrode particles, the specific surface area distribution, the main reaction interface rate, the solid-phase diffusion rate, the type and concentration of the electrolyte, the conductive performance of the electrolyte, and the electrochemical performance of the electrode material, etc. Among them, the particle size distribution of the electrode particles affects the contact area between the electrode and the electrolyte, thus 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 charge and discharge rate and capacity of the battery.

[0039] Optionally, a cell design table can be obtained, which can include the battery parameters as described above. In one implementation, the user inputs the cell design table into the terminal device; correspondingly, after the terminal device receives the user input to the cell design table, it parses out the required battery parameters from the cell design table.

[0040] Optionally, the battery parameters can also include the operating condition data of the battery. For example, the operating condition data can include the charge and discharge process and the ambient temperature, etc.

[0041] Optionally, the operating condition data can be different for different application scenarios.

[0042] For example, in the test scenario of direct current resistance (DCR), the operating condition data can include: the ambient temperature is -10, 10, 20, 25 degrees; and the charge and discharge process is to discharge from the fully charged state at 0.33C (or charge from the fully discharged state) to 50% SOC, stand still for at least one hour, then charge at 1C for 5 minutes in the 50% SOC state and stand still for at least 30 minutes, then discharge at 1C for 5 minutes back to 50% SOC and stand still for at least 30 minutes. In this way, the direct current resistance of the charge and discharge at 50% SOC can be obtained. The same method can be used to adjust the initial power to other SOCs to obtain the DCR at other SOCs.

[0043] For another example, the rate performance refers to the performance of electrochemical energy storage devices such as batteries under different charge and discharge currents (or powers). In the test scenario of rate performance, the operating condition data may include the charge and discharge process of a certain rate from 0 to 100% charge or from 100% to 0 discharge.

[0044] S102. Construct a first model according to the battery parameters.

[0045] In the embodiments of the present application, the terminal device can automatically construct a first model according to the battery parameters, with a relatively high degree of intelligence, which helps to save human resources.

[0046] In the embodiments of the present application, the first model is a multi-dimensional equivalent model used to describe 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 interfacial electrochemical polarization. Optionally, the first model in the embodiments of the present application may include a solid-phase diffusion model, a liquid-phase diffusion model, an ohmic polarization model, and an interfacial 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 inside the electrode material of the battery within and between particles. 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 interfacial reaction model is used to characterize the interfacial electrochemical polarization process, that is, the diffusion and reaction of lithium ions at the interface between the electrode and the electrolyte.

[0047] In one implementation, the process of constructing the first model according to the battery parameters may include: Determine the geometric structure of the battery according to the geometric parameters; construct the first model according to the material parameters and the geometric structure. Optionally, a simulation software can be used to construct the first model. Specifically, input the battery parameters into the simulation software; correspondingly, the simulation software constructs the geometric structure of the battery according to the geometric parameters, and then generates each sub-model in the first model, such as the solid-phase diffusion model, the liquid-phase diffusion model, the ohmic polarization model, and the interfacial reaction model, according to the material parameters and the geometric structure. Optionally, the simulation software can display the constructed geometric structure of the battery to the user.

[0048] It should be noted that the simulation software is not specifically limited in the embodiments of the present application.

[0049] Optionally, the first model can be obtained by improving the traditional point-to-point (Pseudo-2-Dimension, P2D) model.

[0050] In the traditional P2D model, the particle sizes of all particles in the electrode particles are usually regarded as the same, that is, the P2D model is used to describe the dynamic process inside the battery under the same particle size. Different from the traditional P2D model, in the first model of the embodiments of the present application, the particle sizes of the particles in the electrode particles are regarded as different, taking into account the particle size of each particle and the distribution of particles with different particle sizes in the electrode particles. By comparison, the first model of the embodiments of the present application can more realistically reflect the particle characteristics of the electrode active material particles inside the battery, thus helping to improve the prediction accuracy.

[0051] It can be understood that in the embodiments of the present application, the first model is a multi-dimensional equivalent model, which refers to a model that considers the characteristics and interactions of the system in multiple dimensions and describes and analyzes the actual physical system more comprehensively and accurately. Since it takes into account the complete geometric shape of the object in three-dimensional space, the distribution of physical parameters, and the interactions between dimensions, the multi-dimensional equivalent model can more realistically reflect the complex characteristics of the actual system.

[0052] Exemplarily, refer to Figure 2 , which is a schematic diagram of the battery cell model provided by the embodiments of the present application. As an example rather than a limitation, as Figure 2 shown, the battery cell model may include a positive electrode, a negative electrode, and a separator. Among them, the x-axis dimension represents the thickness direction of the battery cell, that is, the distance between the positive electrode and the negative electrode. The y-axis dimension represents the diffusion direction between the electrode particles. The z-axis dimension represents the radial direction of the particles inside the electrode material.

[0053] It should be noted that Figure 2 the x, y, and z axes shown do not refer to geometric directions, but to the dimensions of the model. Figure 2 shows the x, y, and z axes, indicating that the battery cell model considers the dynamic processes in these three dimensions.

[0054] It can be understood that for the traditional P2D model, it is equivalent to only considering the dynamic processes in the two dimensions of x and z. Therefore, the traditional P2D model can be called a two-dimensional model. In the traditional P2D model, the solid-phase concentration cs represents a numerical value of a particle size existing on a certain electrode thickness, that is, the cs values and their changes of all particles are the same. In the first model of the embodiments of the present application, as Figure 2 shown, it considers the dynamic processes in the three dimensions of x, y, and z, adding the y particle size distribution dimension. In other words, in the first model, the solid-phase concentration cs is no longer a numerical value of a particle size existing on a certain electrode thickness, but a lithium concentration on a particle radius corresponding to a particle size, that is, the cs values and their changes of particles with different particle sizes are different.

[0055] 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 according to the material parameters and the geometric structure may include: Construct a solid-phase diffusion matrix according to the solid-phase diffusion rate, where the solid-phase diffusion matrix is used to describe the diffusion states in three dimensions during the solid-phase diffusion process of the electrode particles; Construct the solid-phase diffusion model according to the solid-phase diffusion matrix. It can be understood that the traditional P2D model only considers the diffusion states in two dimensions, namely the two dimensions of the thickness direction of the battery cell and the direction of lithium-ion diffusion inside the electrode particles. The first model in the embodiments of the present application adds the particle size distribution dimension, which is equivalent to considering the diffusion dimension between the electrode particles. Therefore, the first model in the embodiments of the present application can more accurately reflect the diffusion situation of the particles inside the battery cell, which helps to improve the accuracy of subsequent predictions.

[0056] 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 the particles inside the electrode material; the third parameter is used to represent the particle diffusion rate in the radial direction of the particles inside the electrode material. Correspondingly, the step of constructing a solid-phase diffusion matrix according to the solid-phase diffusion rate may include: Set the data on the diagonal of the solid-phase diffusion matrix according to the first parameter, the second parameter, and the third parameter; Set the data on the non-diagonal of the solid-phase diffusion matrix to a preset value.

[0057] Optionally, the preset value can be 0.

[0058] Exemplarily, the solid-phase diffusion matrix is shown as follows: ; 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.

[0059] Optionally, the first parameter being 0 means not considering the particle diffusion rate in the thickness direction of the battery.

[0060] Optionally, the second parameter being 0 means not considering the diffusion rate between the particles inside the electrode material.

[0061] It can be understood that since the first model describes the dynamic process inside the battery under a certain particle size distribution, in actual applications, r in the above solid-phase diffusion matrix is not a numerical value, but a matrix that includes the particle sizes of each particle.

[0062] In the above solid-phase diffusion matrix, when different parameters take different numerical values, different dynamic processes inside the battery can be described. For example, when the first parameter is 0, the second parameter is 0, and the third parameter is not 0, it is the traditional P2D model. When the first parameter is 0 and the second parameter is not 0, it can describe the dynamic process when the particle diffusion rate in the battery thickness direction is not considered, 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 are considered. When both the first parameter and the second parameter are not 0, it can describe the dynamic process when the diffusion rates in three dimensions are considered. It can be seen that the solid-phase diffusion matrix provided by the embodiments of the present application covers more cases, has a wider application range and higher flexibility.

[0063] Optionally, the solid-phase diffusion model can be constructed based on Fick's law or the diffusion principle of phase-field chemical potential.

[0064] Exemplarily, the solid-phase diffusion model constructed based on Fick's law is as follows: ; The solid-phase diffusion model constructed based on the diffusion principle of phase-field chemical potential is as follows: ; In the formula, cs represents the solid-phase concentration, t and represent time, represents divergence, and are Boltzmann constants. Among them, ; ; ; .

[0065] In one implementation manner, the steps of constructing the first model according to material parameters and geometric structures may include: Setting the particles with the same electrode thickness in the first model to have the same liquid-phase concentration.

[0066] Among them, the interface reaction model in the first model involves the liquid-phase concentration cl. Based on the above setting, it is equivalent to making the liquid-phase concentration cl of all particle radii the same on the same electrode thickness in the interface reaction model.

[0067] Optionally, the interface reaction model can adopt the Butler-Volmar equation, or an equation based on the Coupled Ion - Electron Transfer (CIET) theory. The specific form of the interface reaction model in the embodiments of the present application is not limited.

[0068] Based on the first model constructed above, the method for predicting the performance of the battery cell will be introduced below.

[0069] See Figure 3 , which is a schematic flowchart of the method for predicting the performance of the battery cell provided by the embodiments of the present application. As an example rather than a limitation, as Figure 3 shown, the method for predicting the performance of the battery cell may include the following steps: S301, obtain input data.

[0070] In the embodiments of the present application, the input data includes the first particle size distribution of the battery electrode particles; wherein, the particle size distribution is used to characterize the distribution of particles with different particle sizes in the battery electrode particles.

[0071] S302, input the input data into the first model and output the first result.

[0072] Among them, the first result is used to characterize the performance of the battery cell.

[0073] It can be understood that various sub-models in the first model simulate the dynamic process inside the battery according to the input data, so as to calculate the performance of the battery cell.

[0074] For example, in the application scenario of the rate test, the first result can represent the interval of performance at different rates. In the application scenario of the temperature performance test, the first result can represent the interval of performance at different temperatures. In the application scenario of the lithium plating potential rate test, the first result can represent the interval of the lithium plating potential rate. In the application scenario of the DCR test, the first result can represent the interval of the DCR.

[0075] In the embodiments of the present application, the dynamic process inside the battery under a certain particle size distribution is simulated through the first model, so as to predict the performance of the battery cell. Among them, the particle size distribution can characterize the distribution of particles with 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 result. In addition, predicting the performance of the battery cell through the first model does not depend on human resources and can greatly improve the prediction efficiency.

[0076] See Figure 4 , which is a schematic diagram of the particle size distribution provided by the embodiments of the present application. As an example rather than a limitation, as Figure 4As shown, the abscissa represents the particle size and the ordinate represents the particle quantity. The particle size distribution of the actual production line shows significant diversity, and this variation is subject to various factors, including but not limited to different production batches and raw materials from different suppliers. The production process of each batch may introduce unique process fluctuations, and the particle characteristics of the raw materials provided by different suppliers may also vary, which will directly affect the particle size distribution of the final product. Therefore, accurately measuring and understanding this variation in particle size distribution is crucial for ensuring the stability of product quality.

[0077] Based on this consideration, in one embodiment, the method further includes: Adjust the first particle size distribution to obtain a second particle size distribution; Input the second particle size distribution into the first model and output a second result; If the second result meets the preset conditions, determine the target threshold of the particle size according to the second particle size distribution; If the second result does not meet the preset conditions, continue to adjust the second particle size distribution until the result output by the first model meets the preset conditions, and obtain the latest adjusted third particle size distribution; Determine the target threshold of the particle size according to the third particle size distribution.

[0078] It can be understood that the preset conditions are different in different application scenarios. For example, in the application scenario of rate testing, the preset condition can be that the rate meets the preset threshold. In the application scenario of DCR testing, the preset condition can be that the DCR meets the preset threshold. In the application scenario of lithium plating potential strike rate testing, the preset condition can be that the lithium plating window meets the preset threshold. And so on.

[0079] Optionally, if the second result does not meet the preset conditions, the statistical characteristics between the second result and the preset conditions can be calculated, such as the standard deviation, etc.; then the second particle size distribution can be adjusted according to this statistical characteristic, such as narrowing or widening the particle size distribution, etc.

[0080] Through the above method, by using the simulation of the first model and inputting different particle size distributions multiple times, the statistical characteristics of the cell performance (such as distribution mean and tolerance) can be obtained, which helps to further improve the accuracy of the prediction result; and the target threshold of the particle size that meets the preset conditions can be accurately determined, so that while meeting the product quality requirements, the production cost and efficiency can also be effectively controlled.

[0081] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0082] Corresponding to the battery cell performance prediction method described in the above embodiments, Figure 5 It is a structural block diagram of a battery cell performance prediction device provided by an embodiment of the present application. For the sake of convenience of description, only parts related to the embodiments of the present application are shown.

[0083] Referring to Figure 5 , the battery cell performance prediction device 5 includes: An acquisition unit 51, configured to acquire input data, where 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 with different particle sizes in the battery electrode particles.

[0084] A prediction unit 52, configured 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 multi-dimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution.

[0085] Optionally, the acquisition unit 51 is further configured to: Acquire battery parameters; Construct the first model according to the battery parameters.

[0086] Optionally, the battery parameters include geometric parameters and material parameters of the battery cell; correspondingly, the acquisition unit 51 is further configured to: Determine the geometric structure of the battery according to the geometric parameters; Construct the first model according to the material parameters and the geometric structure. Optionally, the material parameters include the solid-phase diffusion rate; The first model includes a solid-phase diffusion model, and the solid-phase diffusion model is used to describe the diffusion process of lithium ions inside the electrode material of the battery between particles and within particles.

[0087] Correspondingly, the acquisition unit 51 is further configured to: Construct a solid-phase diffusion matrix according to the solid-phase diffusion rate, and the solid-phase diffusion matrix is used to describe the diffusion state in three dimensions during the solid-phase diffusion process of the electrode particles; Construct the solid-phase diffusion model according to the solid-phase diffusion matrix.

[0088] 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 battery thickness direction; the second parameter is used to represent the diffusion rate between particles inside the electrode material; the third parameter is used to represent the particle diffusion rate in the radial direction of the particles inside the electrode material; Correspondingly, the acquisition unit 51 is further configured to: Set the data on the diagonal of the solid-phase diffusion matrix according to the first parameter, the second parameter, and the third parameter; Set the data on the off-diagonal of the solid-phase diffusion matrix to a preset value.

[0089] Optionally, when the first parameter is 0, it means that the particle diffusion rate in the battery thickness direction is not considered; And / or, when the second parameter is 0, it means that the diffusion rate between particles inside the electrode material is not considered.

[0090] Optionally, the obtaining unit 51 is further configured to: Set the particles with the same electrode sheet thickness in the first model to the same liquid phase concentration.

[0091] Optionally, the prediction unit 52 is further configured to: Adjust the first particle size distribution to obtain a second particle size distribution; Input the second particle size distribution into the first model and output a second result; If the second result meets the preset condition, determine the target threshold of the particle size according to the second particle size distribution; If the second result does not meet the preset condition, continue to adjust the second particle size distribution until the result output by the first model meets the preset condition, and obtain the latest adjusted third particle size distribution; Determine the target threshold of the particle size according to the third particle size distribution.

[0092] It should be noted that for the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part specifically, and details are not described here again.

[0093] In addition, Figure 5 The shown battery cell performance prediction device can be a software unit, a hardware unit, or a unit combining software and hardware built into an existing terminal device, can also be integrated into the terminal device as an independent attachment, or exist as an independent terminal device.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0095] Figure 6 is a schematic structural diagram of the terminal device provided by the embodiment of the present application. As Figure 6 shown, the terminal device 6 of this embodiment includes: at least one processor 60 ( Figure 6 only 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. When the processor 60 executes the computer program 62, the steps in any of the foregoing method embodiments for predicting the performance of each battery cell are implemented.

[0096] The terminal device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 6 merely an example of the terminal device 6, which does not constitute a limitation on the terminal device 6, and may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0097] The processor 60 may be a Central Processing Unit (CPU), and the processor 60 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0098] In some embodiments, the memory 61 may be an internal storage unit of the terminal device 6, such as the hard disk or memory of the terminal device 6. In some other embodiments, the memory 61 may also be an external storage device of the terminal device 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the terminal device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the terminal device 6. 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 to be output.

[0099] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in the above various method embodiments can be implemented.

[0100] An embodiment of the present application provides a computer program product, and when the computer program product runs on a terminal device, the terminal device can implement the steps in the above various method embodiments when executed.

[0101] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0102] In the above embodiments, the descriptions of each embodiment have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0104] In the embodiments provided in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0105] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A method for predicting the performance of an electric cell, characterized in that, Including: Obtain input data, where the input data includes the first particle size distribution of battery electrode particles; wherein, the particle size distribution is used to characterize the distribution of particles with different particle sizes in the battery electrode particles; Input the input data into a first model and output a first result; wherein, the first result is used to characterize the performance of a battery cell; the first model is a multi-dimensional equivalent model for describing the dynamic process inside the battery under the first particle size distribution.

2. The method for predicting the performance of an electric cell according to claim 1, wherein The method further includes: Obtain battery parameters; Construct the first model according to the battery parameters.

3. The method for predicting the performance of an electric cell according to claim 2, wherein, The battery parameters include the geometric parameters and material parameters of the battery cell; The constructing the first model according to the battery parameters includes: Determine the geometric structure of the battery according to the geometric parameters; Construct the first model according to the material parameters and the geometric structure.

4. The method for predicting the performance of an electric cell according to claim 3, wherein The material parameters include the solid-phase diffusion rate; The first model includes a solid-phase diffusion model, and the solid-phase diffusion model is used to describe the diffusion process of lithium ions inside the electrode material of the battery within and between particles; The constructing the first model according to the material parameters and the geometric structure includes: Construct a solid-phase diffusion matrix according to the solid-phase diffusion rate, and the solid-phase diffusion matrix is used to describe the diffusion states in three dimensions during the solid-phase diffusion process of the electrode particles; Construct the solid-phase diffusion model according to the solid-phase diffusion matrix.

5. The method for predicting the performance of an electric cell according to claim 4, wherein 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 battery thickness direction; the second parameter is used to represent the diffusion rate between particles inside the electrode material; the third parameter is used to represent the particle diffusion rate in the radial direction of the particles inside the electrode material; The constructing the solid-phase diffusion matrix according to the solid-phase diffusion rate includes: Set the data on the diagonal of the solid-phase diffusion matrix according to the first parameter, the second parameter, and the third parameter; Set the data on the non-diagonal of the solid-phase diffusion matrix to a preset value.

6. The method for predicting the performance of an electric cell according to claim 5, wherein, The first parameter being 0 means not considering the particle diffusion rate in the battery thickness direction; And / or, the second parameter being 0 means not considering the diffusion rate between particles inside the electrode material.

7. The method for predicting the performance of an electric cell according to claim 3, wherein The constructing the first model according to the material parameters and the geometric structure includes: Set the particles with the same electrode sheet thickness in the first model to the same liquid-phase concentration.

8. The method for predicting the performance of an electric cell according to any one of claims 1 to 7, characterized in that, The method further includes: Adjust the first particle size distribution to obtain a second particle size distribution; Input the second particle size distribution into the first model and output a second result; If the second result meets a preset condition, determine a target threshold for the particle size according to the second particle size distribution; If the second result does not meet the preset condition, continue to adjust the second particle size distribution until the result output by the first model meets the preset condition, and obtain the latest adjusted third particle size distribution; Determine a target threshold for the particle size according to the third particle size distribution.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the battery cell performance prediction method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method for predicting the performance of an electrode cell according to any one of claims 1 to 8.

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