Lithium ion battery cycle expansion force prediction method and device, equipment, medium and program product

Abstract

The invention relates to a lithium ion battery cycle expansion force prediction method and device, equipment, a medium and a program product. The method comprises the following steps: establishing a simulation model and an expansion force model of a lithium ion battery; the expansive force model represents expansive force generated by the lithium ion battery under any solid-phase lithium concentration, any battery temperature and any loss lithium concentration; the simulation model is used for simulating the cycle process from cycle charging and discharging to the end of life of the lithium ion battery under the specified charging and discharging working condition, cycle simulation data are obtained, and the cycle simulation data comprise battery temperature data, solid-phase lithium concentration data and loss lithium concentration data in the cycle process; and determining a cyclic expansion force prediction result of the lithium ion battery by using an expansion force model according to the cyclic simulation data. Therefore, the expansive force condition of the lithium ion battery in the process from cyclic charging and discharging to the end of life can be quickly and accurately predicted.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of lithium ion batteries, and in particular to a lithium ion battery cycle swelling force prediction method and device, equipment, medium and program product. BACKGROUND

[0002] Lithium ion batteries are widely used in the field of new energy vehicles due to their excellent electrochemical performance, long cycle life, no memory effect, and good high and low temperature adaptability. During the cycle charging and discharging process of the battery, lithium ions repeatedly deintercalate between the positive and negative electrodes, and at the same time, a solid electrolyte interface (SEI) film is continuously generated on the surface of the negative electrode, and even lithium precipitation occurs in the negative electrode. These side reactions will cause the electrode material (especially the negative electrode) to swell significantly. Under the mechanical structure constraint of the battery module, the volume expansion of the electrode material will be converted into swelling force, and will continuously accumulate with the increase of the cycle charging and discharging times, thereby increasing the risk of damage to the structure of the battery module. Therefore, accurately predicting the maximum swelling force generated by the lithium battery during the cycle charging and discharging to the end of life (EOL, End Of Live) is of great significance to the selection and structure design of the battery module material, wherein the end of life may be, for example, the capacity retention rate of the battery decays to 80%, or the capacity of the battery decays to less than 80% of the initial capacity.

[0003] In other words, the swelling force generated in the lithium battery is one of the key parameters that need to be focused on during the design and use of the battery. At present, the swelling force is mainly obtained by experimental test method, that is, by measuring the swelling force of the lithium ion battery through cycle charging and discharging test to the end of life. This way of measuring the swelling force during the cycle charging and discharging process based on experimental means needs to consume a lot of time, the process is complicated, the cycle is long, and the research and development cost is high, which seriously restricts the research and development efficiency. SUMMARY

[0004] Therefore, the present disclosure proposes a lithium ion battery cycle swelling force prediction method and device, equipment, medium and program product, which can quickly and accurately predict the swelling force change of the lithium ion battery during the cycle charging and discharging to the end of life, and provide effective theoretical tools and data support for battery structure design, module integration and life evaluation.

[0005] According to an aspect of the present disclosure, a lithium-ion battery cycle swelling force prediction method is provided, comprising: establishing a simulation model of a lithium-ion battery and a swelling force model; wherein the swelling force model represents the swelling force generated by the lithium-ion battery under any solid-phase lithium concentration, any battery temperature and any loss lithium concentration; simulating, by using the simulation model, a cycle process of the lithium-ion battery under a specified charge-discharge condition, to obtain cycle simulation data, the cycle simulation data comprising: battery temperature data caused by heat generation in the cycle process, solid-phase lithium concentration data caused by electrochemical reaction and loss lithium concentration data caused by side reaction; and determining, by using the swelling force model, a cycle swelling force prediction result of the lithium-ion battery according to the cycle simulation data, wherein the cycle swelling force prediction result comprises a maximum swelling force generated by the lithium-ion battery in the cycle process.

[0006] According to another aspect of the present disclosure, a lithium-ion battery cycle swelling force prediction device is provided, comprising: a modeling module configured to establish a simulation model of a lithium-ion battery and a swelling force model; wherein the swelling force model represents the swelling force generated by the lithium-ion battery under any solid-phase lithium concentration, any battery temperature and any loss lithium concentration; a simulation module configured to simulate, by using the simulation model, a cycle process of the lithium-ion battery under a specified charge-discharge condition, to obtain cycle simulation data, the cycle simulation data comprising: battery temperature data caused by heat generation in the cycle process, solid-phase lithium concentration data caused by electrochemical reaction and loss lithium concentration data caused by side reaction; and a prediction module configured to determine, by using the swelling force model, a cycle swelling force prediction result of the lithium-ion battery according to the cycle simulation data, wherein the cycle swelling force prediction result comprises a maximum swelling force generated by the lithium-ion battery in the cycle process.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising a memory, a processor and a computer program stored in the memory, the processor executes the computer program to implement the steps of the above method.

[0008] According to another aspect of the present disclosure, a non-volatile computer readable storage medium is provided, having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the above method.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, or a non-volatile computer readable storage medium carrying the computer program, the computer program being executed by a processor to implement the steps of the above method.

[0010] According to aspects of the present disclosure, by establishing a simulation model and an expansion force model of a lithium ion battery, simulating the cycle process of the lithium ion battery under a specified charge-discharge condition to the end of life by using the simulation model, accurate cycle simulation data can be quickly obtained, and based on the precise cycle simulation data, the cycle expansion force prediction result of the lithium ion battery can be quickly obtained by using the expansion force model, so that the maximum expansion force of the lithium ion battery in the entire cycle life (i.e. cycle charging and discharging to the end of life) can be quickly and accurately predicted without actually performing complete charging and discharging test on the lithium ion battery, and the cost is low and the efficiency is high, which is beneficial to providing effective reference for subsequent battery structure and battery material design.

[0011] Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the present disclosure and serve to explain the principles of the present disclosure.

[0013] Figure 1 A flowchart of a lithium ion battery cycle expansion force prediction method according to an embodiment of the present disclosure is shown.

[0014] Figure 2 A schematic diagram of a lithium ion battery cycle expansion force prediction process according to an embodiment of the present disclosure is shown.

[0015] Figure 3 A capacity retention rate curve comparison schematic diagram according to an embodiment of the present disclosure is shown.

[0016] Figure 4 A maximum expansion force curve comparison schematic diagram according to an embodiment of the present disclosure is shown.

[0017] Figure 5 A block diagram of a lithium ion battery cycle expansion force prediction device according to an embodiment of the present disclosure is shown.

[0018] Figure 6 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0019] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference signs in the drawings represent functionally identical or similar elements. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0020] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having," "contains," "containing," or any other variation thereof, are open-ended, and include one or more stated features, integers, elements, steps, components, or functions but do not exclude the presence or addition of one or more other features, integers, elements, steps, components, functions, or groups thereof.

[0021] When an element is referred to as being "connected," "coupled," "responsive," or "in communication with" another element, it can be directly connected, coupled, responsive, or in communication with the other element, or intervening elements can be present.

[0022] Although the terms first, second, third, etc. can be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another element / operation. Thus, a first element / operation in some embodiments could be termed a second element / operation in other embodiments without departing from the teachings of the present inventive concept.

[0023] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0024] In addition, for the purpose of convenience and brevity, detailed descriptions of well-known devices, methods, procedures, components, and circuits are not described in detail herein. It is appreciated that those skilled in the art will readily connect the aspects of the present disclosure with the state of the art.

[0025] As described above, the expansion force in the cycle process is currently measured based on experimental means, which requires a large amount of time and significantly increases the research and development cost. Therefore, it is of important engineering application value to establish a computer model capable of quickly predicting the maximum expansion force of a lithium ion battery in an EOL state, so as to improve the battery research and development efficiency. In view of this, the embodiment of the present disclosure proposes an expansion force prediction method based on a computer model, which can quickly and accurately predict the change of the expansion force of a lithium ion battery in the cycle process, and provide effective theoretical tools and data support for battery structure design, module integration and life evaluation.

[0026] In actual applications, the lithium ion battery cycle swelling force prediction method of the embodiments of the present disclosure can be deployed on various terminal devices through software or hardware modification. The terminal device involved in the embodiments of the present disclosure can refer to a device with a wireless connection function and / or a wired connection function. The wireless connection function refers to the connection with other devices through wireless connection modes such as Wi-Fi and Bluetooth. The terminal device involved in the embodiments of the present disclosure can also communicate with other devices through the wired connection function. The terminal device involved in the embodiments of the present disclosure can be touch screen or non-touch screen, or even without a screen. The touch screen can be controlled by clicking, sliding, and the like on the display screen through fingers, styluses, and the like. The non-touch screen device can be connected to a mouse, a keyboard, a touch panel, and the like. The device without a screen can be a Bluetooth speaker without a screen, and the like. For example, the terminal device of the present application can include but is not limited to a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a handheld device, a tablet computer, a notebook computer, a palm computer, a computing device, and the like.

[0027] The lithium ion battery cycle swelling force prediction method of the embodiments of the present disclosure can also be deployed on a server. The server can be located in the cloud or locally, and can be a physical device or a virtual device such as a virtual machine, a container, and the like. The server has a wireless communication function, wherein the wireless communication function can be provided in a chip (system) or other components or assemblies of the server. It can refer to a device with a wireless connection function. The wireless connection function refers to the connection with other servers or terminal devices through wireless connection modes such as Wi-Fi and Bluetooth. The server involved in the embodiments of the present disclosure can also have a wired connection function for communication. For example, the server can receive the cycle measured data of the lithium ion battery sent by the terminal device. The lithium ion battery cycle swelling force prediction method of the embodiments of the present disclosure is executed by the server to obtain the cycle swelling force prediction result of the lithium ion battery, and the cycle swelling force prediction result is returned to the terminal device to show the user the determined cycle swelling force prediction result in the terminal device.

[0028] It should be noted that the electrodes mentioned in the embodiments of the present disclosure can be positive electrodes and / or negative electrodes, and the specific meaning depends on the specific application scenario. In addition, the swelling force mentioned in the embodiments of the present disclosure can be described as the swelling force generated due to the swelling of the electrode, or as the swelling force generated due to the swelling of the battery cell. The embodiments of the present disclosure do not limit this. The lithium ion battery mentioned in the embodiments of the present disclosure can be a battery containing basic structures such as electrodes, electrolytes, and separators. It can also be a battery cell in a module structure or a battery pack. The embodiments of the present disclosure do not limit this.

[0029] Figure 1A flow chart of a lithium-ion battery cycle swelling force prediction method according to an embodiment of the present disclosure is shown. As shown in Figure 1 the method comprises steps S11 to S13.

[0030] In step S11, a simulation model of a lithium-ion battery and a swelling force model are established; wherein the swelling force model represents the swelling force generated by the lithium-ion battery at any solid-phase lithium concentration, any battery temperature and any loss lithium concentration.

[0031] In actual applications, a battery simulation modeling software open to the art, such as pyBaMM software, can be used by those skilled in the art to realize the establishment of a simulation model of a lithium-ion battery (i.e. an electrochemical-thermal-side reaction coupled simulation model), and the present disclosure does not limit the specific steps for constructing a simulation model using pyBaMM software. For example, the simulation model of the lithium-ion battery can be established by obtaining and according to the battery design parameters and the battery physical parameters of the lithium-ion battery; wherein the battery design parameters may, for example, at least include: positive electrode thickness, negative electrode thickness, positive electrode area, negative electrode area, battery width, battery length, separator thickness, separator porosity, particle radius of positive electrode active material, particle radius of negative electrode active material, initial solid-phase volume fraction of positive electrode, initial solid-phase volume fraction of negative electrode, initial liquid-phase volume fraction, etc.; and the battery physical parameters may, for example, at least include: reaction rate constant, solid-phase diffusion coefficient, diffusion activation energy, Bruggeman coefficient, solid-phase effective conductivity, liquid-phase effective conductivity, charge transfer coefficient, partial molar volume, Poisson ratio, Young's modulus, maximum lithium intercalation concentration, thermal conductivity, convective heat transfer coefficient and radiative heat transfer coefficient, etc. In actual applications, the battery physical parameters used in each of the above-mentioned electrochemical model, side reaction model and heat generation model can obtain the specific values of each parameter through pre-calibration or experimental measurement, or can also estimate the initial value according to historical experience and then calibrate, and the present disclosure does not limit this.

[0032] The simulation model includes an electrochemical model, a heat generation model and a side reaction model which are coupled with each other. The electrochemical model is used to describe the electrochemical reaction in the lithium ion battery to determine the solid-phase lithium concentration data caused by the electrochemical reaction; the heat generation model is used to describe the heat generation process in the lithium ion battery to determine the battery temperature data caused by the heat generation; and the side reaction model is used to describe the side reaction in the lithium ion battery to determine the loss lithium concentration data caused by the side reaction. The mutual coupling between the electrochemical model, the heat generation model and the side reaction model can be understood as that the output of the electrochemical model can be used as the input of the parameters in the heat generation model and the side reaction model, the output of the heat generation model can be used as the input of the parameters in the electrochemical model and the side reaction model, and the output of the side reaction model can be used as the input of the parameters in the electrochemical model and the heat generation model, so as to realize the mutual coupling between the electrochemical model, the heat generation model and the side reaction model.

[0033] In some embodiments, an electrochemical-thermal coupling simulation model can be established based on the mechanism of the physical and chemical processes inside the lithium ion battery. The electrochemical model includes five important reactions inside the battery: 1) the deintercalation reaction of lithium at the interface between the electrode active particles and the electrolyte; 2) the solid-phase charge conservation; 3) the liquid-phase charge conservation; 4) the solid-phase diffusion of lithium ions; and 5) the liquid-phase diffusion of lithium ions. Thus, the electrochemical model in the simulation model can include the deintercalation reaction equation, the solid-phase charge conservation equation, the liquid-phase charge conservation equation, the lithium ion solid-phase diffusion equation and the lithium ion liquid-phase diffusion equation.

[0034] The deintercalation reaction equation can be constructed using the Bulter-Volmer kinetic equation, and can describe the deintercalation reaction of lithium at the interface between the electrode active particles and the electrolyte, and can be specifically used to determine the deintercalation reaction current density on the surface of the positive and negative electrodes when lithium ions are deintercalated or intercalated in the positive and negative electrodes. Thus, the deintercalation reaction current density can refer to the current size of the electrode reaction per unit area when lithium ions are deintercalated (delithiated) or intercalated (lithiated) on the electrode surface during the charging and discharging process of the lithium ion battery. Considering that the stress generated by the SEI film and the expansion of the electrode active particles during the charging and discharging process will affect the deintercalation reaction of lithium, the deintercalation reaction equation can be an equation constructed by introducing the hydrostatic stress generated by the electrode active particles, so that the mutual coupling between the electrochemical model and the side reaction equation can be realized, and the deintercalation reaction of lithium in the electrochemical reaction can be more accurately described. Exemplarily, the deintercalation reaction equation can be represented by formula (1-1) to formula (1-4):

[0035] (1-1)

[0036] (1-2)

[0037] (1-3)

[0038] (1-4)

[0039] in, The current density representing the lithium insertion / extraction reaction of the electrode. The exchange current density of the lithium insertion / extraction reaction at the representative electrode (which can be calculated using the reaction rate constant), specifically, , The reaction rate constant of the electrode. and F and R represent the charge transfer coefficients of the anodic and cathodic reactions, respectively (e.g., both 0.5), where F represents the Faraday constant and R represents the gas constant. The overpotential represents the insertion / extraction reaction, and T represents the battery temperature (which can be obtained using a heat generation model). It represents the solid-state potential (also known as the electrode potential). It represents the liquid phase potential (i.e., the potential in the electrolyte). Represents the electrode equilibrium potential. Represents hydrostatic stress. The particle radius represents the active particles of the electrode (i.e., the material particles in the active electrode material). This represents the solid-phase lithium concentration (i.e., the lithium ion concentration in the electrode active particles). This represents the liquid phase lithium concentration (i.e., the lithium ion concentration in the electrolyte). Represents the maximum solid-phase lithium intercalation concentration (e.g., in the cathode). It can be 23,000 moles per cubic meter (mol / m³) ), negative electrode It can be 30555 mol / ), This represents the initial solid-phase lithium concentration (the solid-phase lithium concentration at the initial moment of charging and discharging). The partial molar volume representing the electrode active material. This represents the Young's modulus of the electrode active particles. Poisson's ratio represents the active particles of the electrode. Represents the total current density. The current density representing the dissolution side reaction of the electrode active material (which can be calculated using the equation for the dissolution side reaction of the active material). The current density for the side reaction generated by the SEI film (which can be calculated using the SEI film side reaction equation) is given. This represents the current density of the SEI membrane regeneration side reaction (which can be calculated using the SEI membrane regeneration side reaction equation). This is the current density for the lithium plating side reaction (which can be calculated using the lithium plating side reaction equation at the negative electrode). This represents the SEI film resistance (i.e., the resistance of the SEI film caused by all side reactions).

[0040] in, , F, R, K , , , , For a constant value, and It can be determined separately by the lithium-ion solid-phase diffusion equation and the lithium-ion liquid-phase diffusion equation; and For example, the simulation capabilities of the pyBaMM software can be used to perform simulation calculations on the simulation model to obtain the potential. This embodiment of the disclosure does not limit the calculation process for the solid-phase potential and the liquid-phase potential. It is known that the electrode equilibrium potential... The electrode equilibrium potential changes with the state of charge (SOC). Therefore, the relationship curve between the electrode equilibrium potential and SOC can be obtained through button charge experiments. Furthermore, based on the relationship curve between the equilibrium potential and the state of charge (SOC), the electrode equilibrium potential at any SOC can be obtained. .

[0041] Among them, SEI film resistance It is based on the change in film thickness of the SEI film. As determined by the SEI film conductivity, specifically, the SEI film resistance can be expressed as: That is, the SEI film resistance is the ratio between the change in SEI film thickness and the SEI film conductivity, where... This represents the conductivity of the SEI film (i.e., the conductivity of the SEI film, which is a constant). The change in film thickness is also included. This can be expressed as formula (1-5):

[0042] (1-5)

[0043] in, This represents the lithium concentration loss due to side reactions in the formation of the SEI film. This represents the lithium concentration lost due to side reactions during SEI membrane regeneration. This represents the lithium concentration loss due to lithium plating side reactions. Represents the molar mass of the SEI membrane (a constant). This represents the density of the SEI film (a constant). Represents the molar mass of lithium (a constant). Represents the lithium concentration (a constant); This represents the specific surface area of ​​the electrode active particles, i.e. , represents the solid phase volume fraction (which can be calculated by the active material dissolution side reaction equation).

[0044] wherein the loss lithium concentration caused by the SEI film generation side reaction is determined based on the SEI film generation side reaction current density, the loss lithium concentration caused by the SEI film regeneration side reaction is determined based on the SEI film regeneration side reaction current density, and the loss lithium concentration caused by the lithium precipitation side reaction is determined based on the lithium precipitation side reaction current density; illustratively, the loss lithium concentration caused by the SEI film generation side reaction is is represented as: It should be understood that the loss lithium concentration at any time can be obtained by integrating ; the loss lithium concentration caused by the SEI film regeneration side reaction is represented as: It should be understood that the loss lithium concentration at any time can be obtained by integrating ; the loss lithium concentration caused by the lithium precipitation side reaction is represented as: It should be understood that the loss lithium concentration at any time can be obtained by integrating . It should be understood that the loss lithium concentration at any time can be obtained by integrating . .

[0045] wherein the solid phase charge conservation equation can describe the charge conservation process in the electrode, and is specifically used to determine the solid phase current density (i.e., the current density in the active particles of the electrode); illustratively, the solid phase charge conservation equation can be represented as formula (2):

[0046] (2)

[0047] wherein, represents the solid phase current density, represents the solid phase effective conductivity (i.e., the effective conductivity of the active particles of the electrode), represents the gradient of the solid phase potential. Wherein, is a constant value, wherein, can be obtained by using the pyBaMM software to simulate the battery simulation model.

[0048] wherein the liquid phase charge conservation equation can describe the charge conservation process in the electrolyte, and is specifically used to determine the liquid phase current density (i.e., the current density in the electrolyte); illustratively, the liquid phase charge conservation equation can be represented as formula (3):

[0049] (3)

[0050] wherein, represents the liquid phase current density, represents the effective conductivity of the liquid phase (i.e., the effective conductivity of the electrolyte), is the gradient of the liquid phase potential (i.e., the gradient of the potential in the electrolyte), represents the lithium concentration in the liquid phase, is the activity coefficient related to the lithium concentration in the liquid phase, is the lithium ion transference number, represents the gradient of , wherein, , , , is a constant value, and the lithium ion transference number refers to the ratio of the flow rate of lithium ions per unit time per unit cross section to the total charge flow rate in the electrolyte or the electrode active material, can be obtained by experimental testing; can be obtained by simulating the battery simulation model using the pyBaMM software; the activity coefficient can be obtained by experimental testing, is the activity, which can also be referred to as a thermodynamic factor, in practical applications, a relationship curve between the lithium concentration in the liquid phase and the activity coefficient can be obtained by experimental testing, and based on the relationship curve between the lithium concentration in the liquid phase and the activity coefficient, the activity coefficient under different lithium ion concentrations in the liquid phase can be obtained.

[0051] wherein, the lithium ion solid phase diffusion equation can describe the diffusion process of lithium ions in the electrode active particle due to the lithium concentration gradient, and is specifically used to determine the solid phase lithium concentration (i.e., the lithium ion concentration in the electrode active particle); wherein, the lithium ion solid phase diffusion equation can be an equation constructed by introducing the influence of the active particle stress on the diffusion of lithium ions in the particle, considering that the active particle stress will affect the diffusion of lithium ions in the active particle, in order to make the lithium ion solid phase diffusion equation more accurately describe the diffusion process of lithium ions in the active particle, the lithium ion solid phase diffusion equation can be described by Fick's second diffusion law due to the lithium concentration gradient in the electrode active particle. Exemplarily, the lithium ion solid phase diffusion equation can be represented by formula (4):

[0052] (4)

[0053] wherein, represents the solid phase diffusion coefficient (i.e., the effective diffusion coefficient of lithium ions in the electrode active particle, which is a constant value), represents the lithium concentration in the electrode active particle at any time, represents the radius of any radius in the electrode active particle, represents the partial molar volume of the electrode active material,​ Young's modulus representing the active particles of the electrode. Poisson's ratio represents the active particles of the electrode; where, It considers the effect of active particle stress on lithium-ion solid-phase diffusion and is used to correct the solid-phase diffusion coefficient. This allows for the determination of the solid-phase lithium concentration using the lithium-ion solid-phase diffusion equation. More accurate; Representing partial derivatives, for example, represent right The partial derivatives, and so on, will not be elaborated further. It should be understood that the solid-phase lithium concentration at any given time can be obtained by integrating equation (4). .

[0054] The lithium-ion liquid-phase diffusion equation describes the diffusion process of lithium ions in the electrolyte and is specifically used to determine the liquid-phase lithium concentration (i.e., the lithium ion concentration in the electrolyte). Fick's second diffusion law can be used to describe the diffusion of lithium ions in the electrolyte to obtain the lithium-ion liquid-phase diffusion equation. For example, the lithium-ion liquid-phase diffusion equation can be expressed as formula (5):

[0055] (5)

[0056] in, Represents the liquid volume fraction. Represents the concentration of lithium in the liquid phase. Represents any location within a lithium-ion battery. Represents the specific surface area of ​​the electrode active particles. , Represents the volume fraction of the solid phase. This represents the liquid phase diffusion coefficient (i.e., the effective diffusion coefficient of lithium ions in the electrolyte). The liquid phase diffusion coefficient can be calculated using the Brügmann coefficient and the diffusion activation energy. Specifically, , Brugmann coefficient, Let be the initial liquid phase diffusion coefficient. For diffusion activation energy, For reference temperature (e.g., 298.15K (i.e., 25℃)). This refers to the battery temperature. Among them, , , , , This is a constant. It should be understood that the liquid lithium concentration at any given time can be obtained by integrating formula (5-1). In this embodiment of the disclosure, the above formula is used. The calculated specific surface area is used to determine the liquid-phase lithium concentration. The change in the specific surface area of the particles due to the loss of electrode active material dissolution is considered, so the calculated liquid-phase lithium concentration is more accurate, thereby facilitating the determination of more accurate de-intercalation reaction current density, liquid-phase current density, and further facilitating the determination of more accurate battery voltage, solid-phase potential, liquid-phase potential, and other data, thereby achieving more accurate electrochemical simulation.

[0057] wherein, considering that the side reaction during the charging and discharging process will cause the change of the liquid-phase volume fraction (such as decrease), the liquid-phase volume fraction is expressed as the initial liquid-phase volume fraction of the electrolyte and the change amount of the liquid-phase volume fraction caused by the side reaction , the difference between them, i.e. , the change amount of the liquid-phase volume fraction is determined based on the change amount of the film thickness of the SEI film caused by the side reaction , which can be calculated by using the above formula (1-5) , and then the change amount of the liquid-phase volume fraction can be calculated by using the formula , it should be understood that by integrating , the change amount of the liquid-phase volume fraction at any time can be obtained .

[0058] It is considered that one or more side reactions will occur during the cycle of the lithium ion battery, which will cause the capacity attenuation (i.e. lithium concentration loss) of the lithium ion battery, for example: 1) SEI film generation reaction at the negative electrode, which is due to the generation of SEI film which needs to extract lithium ions from the electrolyte, this part of lithium is fixed in the chemical composition of the SEI film, and cannot return to the cycle even in the subsequent charging and discharging process; 2) active material dissolution and SEI film regeneration reaction, that is, in the use process of the lithium battery, water reacts with the electrolyte to generate hydrogen fluoride (HF) with strong corrosion, which will gradually corrode the active material of the electrode, causing the loss of active material and the cracking and damage of the SEI film, which will expose new contact interfaces, which will trigger a new round of SEI film growth, which will continuously consume lithium ions; 3) lithium precipitation reaction at the negative electrode, which is due to the fact that under certain conditions (such as low temperature, large current charging, negative electrode overcharging, etc.), when the lithium ion reaches the surface of the negative electrode graphite, it cannot be embedded in time, and when the lithium ion concentration on the surface of the negative electrode is too high and the potential is lower than the deposition potential of metallic lithium (0V), the lithium ion will directly obtain electrons and be reduced into metallic lithium, which is deposited on the surface or gap of the graphite negative electrode, which also causes the loss of lithium concentration.

[0059] The side reaction model includes at least one of an SEI film generation side reaction equation, an active material dissolution side reaction equation, an SEI film regeneration side reaction equation, and a lithium precipitation side reaction equation; the SEI film generation side reaction equation is used to determine the current density in the SEI film generation process to determine the loss lithium concentration caused by SEI film generation; the active material dissolution side reaction equation is used to determine the solid phase volume fraction in the dissolution process of the electrode active material to determine the specific surface area of the active material particles; the SEI film regeneration side reaction equation is used to determine the current density in the SEI film generation process triggered again due to the dissolution loss of the electrode active material to determine the loss lithium concentration caused by SEI film regeneration, and the lithium precipitation side reaction equation is used to determine the current density in the lithium precipitation process of the negative electrode to determine the loss lithium concentration caused by the lithium precipitation of the negative electrode.

[0060] wherein, considering that the stress generated by the expansion of the active particles in the charging and discharging process will affect the SEI film generation side reaction, the SEI film generation side reaction equation can be an equation constructed by introducing the hydrostatic stress generated by the electrode active particles, thereby facilitating the calculation of more accurate SEI film generation side reaction current density, and further obtaining more accurate loss lithium concentration, and exemplarily, the SEI film generation side reaction equation can be represented by formulas (6-1) and (6-2):

[0061] (6-1)

[0062] (6-2)

[0063] wherein, represents the SEI film generation side reaction current density, represents the SEI film generation side reaction exchange current density (which can be calibrated by cycle measurement data, and is an empirical value), represents the charge transfer coefficient (which is a constant value, for example, it can be 0.5), represents the overpotential of the SEI film generation side reaction, represents the equilibrium potential of the SEI film generation side reaction (which is an empirical value, for example, it can be 0.4 V), represents the hydrostatic stress (calculated by the above formula (1-3)), represents the partial molar volume of the electrode active material (at this time, it can be the negative electrode active material), represents the solid phase potential, represents the liquid phase potential, represents the total current density; RSEI represents the film resistance of the SEI film caused by the side reaction; F represents the Faraday constant, R represents the gas constant, and T represents the battery temperature. The SEI film generation side reaction current density can refer to the current per unit area generated by the SEI film during the charging and discharging process of the lithium ion battery.

[0064] wherein the active material dissolution side reaction equation describes the loss of active material due to hydrogen fluoride, and also takes into account the stress generated by the expansion of active particles during the charging and discharging process, which will affect the dissolution of active material, thus the active material dissolution side reaction equation can be an equation constructed by introducing the hydrostatic stress generated by the electrode active particles, thereby facilitating the calculation of a more accurate solid phase volume fraction. For example, the active material dissolution side reaction equation can be represented by formulas (7-1) to (7-3):

[0065] (7-1)

[0066] (7-2)

[0067] (7-3)

[0068] wherein, represents the active material dissolution side reaction current density, represents the active material loss side reaction exchange current density (which can be calibrated by cycle measurement data, and is an empirical value), represents the overpotential of the active material dissolution side reaction, represents the equilibrium potential of the active material dissolution side reaction (which is an empirical value, for example, it can be 4V), represents the initial solid phase volume fraction (i.e., the initial volume fraction of the electrode, which is a constant value), represents the solid phase volume fraction (i.e., the actual volume fraction of the electrode during charging and discharging), represents the maximum solid phase lithium intercalation concentration (i.e., the maximum lithium intercalation concentration of the electrode), represents the electrode thickness. The active material dissolution side reaction current density can refer to the current per unit area generated by the dissolution of active material during the charging and discharging process of the lithium ion battery. It should be understood that the electrode solid phase volume fraction at any time during the cycle charging and discharging process can be obtained by formulas (7-1) to (7-3), and thus the electrode solid phase volume fraction changed due to the dissolution reaction of the electrode active material can be obtained.

[0069] As described above, during the use of the lithium battery, moisture reacts with the electrolyte to generate hydrogen fluoride (HF) with strong corrosion. Hydrogen fluoride will gradually corrode the active material of the electrode, causing the loss of active material and exposing new interfaces, which will trigger the growth of a new SEI film. Therefore, the regeneration process of the SEI film is proportional to the loss of active material, and the influence of the hydrostatic stress of the active particles on the regeneration process of the SEI film is considered. Exemplarily, the SEI film regeneration side reaction equation can be represented by formulas (8-1) to (8-2):

[0070] (8-1)

[0071] (8-2)

[0072] wherein, represents the SEI film regeneration side reaction current density, represents the SEI film regeneration side reaction exchange current density (which can be calibrated by cycle test data, and is an empirical value), represents the charge transfer coefficient (which is an empirical constant value, for example, 0.5), represents the overpotential of the SEI film regeneration side reaction, represents the equilibrium potential of the SEI film regeneration side reaction (an empirical value, for example, 0.4 V), and is a fitting parameter (a constant value); it should be understood that based on the above formula (7-3) = represents the volume fraction of the electrode active material dissolved. The SEI film regeneration side reaction current density can refer to the current size per unit area generated by the SEI film regeneration during the charging and discharging process of the lithium ion battery.

[0073] wherein, the negative electrode lithium precipitation side reaction equation can determine the current density during the lithium precipitation process of the negative electrode. Similarly, the stress generated by the expansion of the active particles during the charging and discharging process will affect the lithium precipitation process of the negative electrode. Therefore, the negative electrode lithium precipitation side reaction equation can be an equation constructed by introducing the hydrostatic stress generated by the electrode active particles, thereby facilitating the calculation of a more accurate negative electrode lithium precipitation side reaction current density, and further obtaining a more accurate lithium loss concentration. Exemplarily, the lithium precipitation side reaction equation can be represented by formulas (9-1) to (9-2):

[0074] (9-1)

[0075] (9-2)

[0076] wherein, representative lithium precipitation side reaction current density, representative lithium precipitation side reaction exchange current density (which can be calibrated by cyclic experimental data, and is an empirical value), and representative charge transfer coefficient (which is an empirical value, for example, both can be taken as 0.5), overpotential of lithium precipitation side reaction, representative equilibrium potential of negative electrode lithium precipitation side reaction (which is an empirical value, for example, can be 0 V); when <0, lithium precipitation reaction occurs, that is, when only the loss of lithium concentration caused by the negative electrode lithium precipitation side reaction is considered. The negative electrode lithium precipitation side reaction current density can refer to the current per unit area generated by the negative electrode lithium precipitation during the charging and discharging process of the lithium ion battery. The lithium precipitation side reaction model shown in formulas (8-1) to (8-2) can determine a more accurate negative electrode lithium precipitation side reaction current density by considering the hydrostatic stress of the active particles and the change of the solid phase volume fraction when the negative electrode precipitates lithium, and then facilitate more accurate determination of the loss of lithium concentration caused by the negative electrode lithium precipitation side reaction.

[0077] It should be noted that one or more of the above-mentioned side reactions can occur during the charging and discharging process of the lithium ion battery, for example, the SEI film generation reaction and the active material dissolution reaction can occur simultaneously at the beginning of charging, and the SEI film generation reaction, the active material dissolution reaction and the SEI film regeneration reaction can also occur simultaneously. The negative electrode lithium precipitation reaction can not occur at the beginning of the charging and discharging process, or can occur from the beginning of a certain charging and discharging process. Therefore, in the simulation of the charging and discharging process by using the simulation model, if a certain side reaction does not occur, the current density of the side reaction in the simulation model can be set to 0, for example, if the negative electrode does not precipitate lithium (i.e. .

[0078] As described above, the simulation model can further include a heat generation model; wherein the heat generation model can be constructed according to the energy conservation equation to calculate the battery temperature by calculating the heat generation in the heat generation model; specifically, the heat generation model can be determined based on the heat dissipation power, the liquid phase ohmic heat generation power, the solid phase ohmic heat generation power, the polarization heat generation power and the reversible heat power; wherein the heat dissipation power represents the power of the heat dissipated by the lithium battery; the liquid phase ohmic heat generation power represents the heat generation power when the current flows through the electrolyte; the solid phase ohmic heat generation power represents the heat generation power when the current flows through the active material of the positive and negative electrodes; the polarization heat generation power represents the power of the heat generated due to the polarization phenomenon of the electrode; and the reversible heat power represents the power of the heat generated due to the entropy change of the electrode in the electrochemical reaction. Exemplarily, the heat generation model can be represented by formula (10):

[0079] ​ (10)

[0080] wherein, represents the density of the lithium battery material, represents the specific heat capacity of the lithium battery material, subscript , represents the negative or positive electrode, T is the battery temperature, is the thermal conductivity of the lithium battery material, represents the heat dissipation power, represents the liquid phase current density (obtained from the above liquid phase charge conservation equation), represents the gradient of the liquid phase potential, represents the liquid phase ohmic heat generation power, represents the solid phase current density (obtained from the above solid phase charge conservation equation), represents the gradient of the solid phase potential, represents the solid phase ohmic heat generation power, represents the specific surface area of the positive or negative active material particles, represents the individual reaction current density of the positive or negative electrode, represents the overpotential of the positive or negative electrode, represents the polarization heat generation power, represents the open circuit potential of the positive or negative electrode, represents the entropy heat coefficient of the positive or negative electrode, represents the reversible heat generation power. It should be understood that the battery temperature can be obtained by integrating equation (10).

[0081] wherein, may include the polarization heat generation power generated by the deintercalation reaction , the polarization heat generation power generated by the active material dissolution reaction , the polarization heat generation power generated by the negative SEI film growth reaction , the polarization heat generation power generated by the negative SEI film regrowth reaction , and the polarization heat generation power generated by the negative lithium precipitation reaction ; may include the reversible heat generation power generated by the deintercalation reaction , the reversible heat generation power generated by the active material dissolution reaction , the reversible heat generation power generated by the negative SEI film growth reaction , the reversible heat generation power generated by the negative SEI film regrowth reaction , and the reversible heat generation power generated by the negative lithium precipitation reaction .

[0082] wherein, when neg, the specific surface area of the negative active material particles, the deintercalation reaction current density of the negative electrode (obtained from the deintercalation reaction model), the overpotential of the negative electrode in the deintercalation reaction, the entropy heat coefficient of the negative electrode (obtained from experimental measurement, the open circuit potential of the negative electrode with respect to temperature), the current density of the negative active material dissolution reaction, the overpotential of the negative active material dissolution reaction; when the pos is, the specific surface area of the positive active material particles (i.e. , the positive solid phase volume fraction determined by the active material loss model, the radius of the positive active material particles, the deintercalation reaction current density of the positive electrode (obtained from the deintercalation reaction model), the overpotential of the positive electrode, the entropy heat coefficient of the positive electrode (obtained from experimental measurement, the open circuit potential of the positive electrode with respect to temperature), the current density of the positive active material dissolution reaction, the overpotential of the positive active material dissolution reaction, and the like, which will not be repeated.

[0083] It should be noted that the various electrochemical models, side reaction models and heat generation models provided by the embodiments of the present disclosure are some possible implementation manners provided by the embodiments of the present disclosure. Actually, under the inspiration of the embodiments of the present disclosure, a person skilled in the art can self-design the electrochemical model, the side reaction model and the heat generation model, as long as the lithium battery simulation model can be constructed, and the embodiments of the present disclosure do not limit this.

[0084] In the embodiments of the present disclosure, the cyclic expansion force model is established on the basis of the electrochemical model, and the hydrostatic stress is introduced into the above deintercalation reaction equation and each side reaction equation, which considers the influence of stress on the electrochemical reaction and the side reaction, so that the solid phase lithium concentration and the loss lithium concentration of the side reaction can be more accurately obtained, and then more accurate expansion force prediction can be realized.

[0085] As described above, the swelling force model characterizes the swelling force generated by the lithium-ion battery at any solid-phase lithium concentration, any battery temperature, and any loss lithium concentration, that is, the swelling force model can describe the changes in the swelling force caused by the changes in the solid-phase lithium concentration due to the deintercalation of lithium from the active particles during the charging and discharging process, the changes in the battery temperature due to heat generation, and the changes in the loss lithium concentration due to side reactions; for example, assuming that the electrode material is isotropic, that is, the strains in the x, y, and z directions are the same, and not considering the tangential strain, the control mechanism equation of the swelling force model can be represented by formulas (11-1) to (11-4):

[0086] (11-1)

[0087] (11-2)

[0088] (11-3)

[0089] (11-4)

[0090] wherein, , and represent the volume strain of the battery in three directions; represents the Young's modulus of the battery; represents the Poisson's ratio of the battery; , and represent the normal stress of the battery in three directions, represents the normal stress in the direction perpendicular to the large surface of the battery, and the swelling force of the lithium-ion battery includes the normal stress in the direction perpendicular to the large surface of the battery (i.e., the thickness of the cell, the large surface of the cell) , or in other words, the normal stress in the direction perpendicular to the large surface of the battery can be regarded as the swelling force generated in the battery;

[0091] wherein the Young's modulus of the battery (i.e., the Young's modulus of the battery) can be determined by the Young's modulus of the electrode active particles, and the Poisson's ratio of the battery (i.e., the Poisson's ratio of the battery) can be determined by the Poisson's ratio of the electrode active particles, specifically, the Young's modulus of the battery can be represented by: , and the Poisson's ratio of the battery can be represented by: , represents the Young's modulus of the electrode active particles, represents the Poisson's ratio of the electrode active particles, , , , and are empirical parameters for converting the particle Poisson's ratio into the battery Poisson's ratio and the particle Young's modulus into the battery Young's modulus.

[0092] wherein, represents the first mapping relationship, represents the state of lithium intercalation (i.e., the state of charge), , represents the solid-phase lithium concentration, represents the maximum solid-phase lithium intercalation concentration; represents the thermal expansion coefficient of the lithium battery, represents the difference between the battery temperature at any time during the charging and discharging process and the battery temperature at the initial time of the charging and discharging process, represents the thermal expansion strain of the battery due to temperature change; the first mapping relationship represents the intercalation strain of the electrode at any solid-phase lithium concentration (i.e., represents the strain caused by the deintercalation of active particles, or describes the mapping relationship between the solid-phase lithium concentration and the electrode strain);

[0093] wherein, represents the second mapping relationship, , represents the loss lithium concentration caused by the SEI film generation side reaction (calculated by the formula ), represents the loss lithium concentration caused by the SEI film regeneration side reaction (calculated by the formula ), represents the loss lithium concentration caused by the lithium precipitation side reaction (calculated by the formula ), it should be understood that if no lithium precipitation occurs in the charging and discharging process, ; the second mapping relationship represents the non-eliminable strain of the electrode at any loss lithium concentration (i.e., represents the strain of the electrode caused by the side reaction, or describes the mapping relationship between the loss lithium concentration and the strain of the electrode).

[0094] In actual applications, the first mapping relationship can be determined by experimental testing, and the second mapping relationship can be obtained by calibrating the cycle measurement data, and the embodiments of the present disclosure do not limit this. In addition, the first mapping relationship and the second mapping relationship can be expressed in the form of a function or a graph, and then the corresponding strain can be obtained by function calculation or table lookup for the first mapping relationship and the second mapping relationship.

[0095] In practical applications, the above-mentioned electrochemical model, heat generation model, and side reaction model can be used to calculate the solid-phase lithium concentration change of the lithium ion battery during the cycle process, the lithium ion concentration loss caused by the SEI generation side reaction, the lithium ion concentration loss caused by the SEI regeneration side reaction, the lithium ion concentration loss caused by the lithium precipitation side reaction, and the temperature change of the lithium ion battery during the cycle process. Then, the above change amounts are input into the expansion force model, and the expansion force model can be set with the upper and lower large surfaces of the battery (i.e., the cell) as fixed boundary conditions, and other surfaces as free boundary conditions, etc. Thus, the expansion force change at the large surface of the cell can be calculated, and the maximum expansion force generated by the battery during the cycle charging and discharging to the end of life can be obtained, thereby providing an effective reference for the material and structure design of the battery.

[0096] In step S12, the cycle process of the lithium ion battery under the specified charging and discharging condition is simulated by using the simulation model to cycle charge and discharge to the end of life, and cycle simulation data is obtained, which includes: battery temperature data caused by heat generation during the cycle process, solid-phase lithium concentration data caused by electrochemical reaction, and loss lithium concentration data caused by side reaction.

[0097] The end of life of the lithium ion battery can be when the capacity retention rate decays to a specified capacity retention rate (such as 80%), and the number of charge and discharge cycles of the lithium ion battery when it cycles to the end of life is the cycle life of the lithium ion battery. It should be understood that a person skilled in the art can customize the specified charging and discharging condition according to actual needs, for example, the charging and discharging condition used when actually measuring the cycle life of the lithium ion battery can be used as the specified charging and discharging condition, which can be understood as a single charging and discharging process in the cycle charging and discharging. Exemplarily, the specified charging and discharging condition can include: constant current (or constant power, such as 1C current) charging to the upper limit cutoff voltage (such as 4.2V), standing for a specified time (such as 1800 seconds), discharging to the lower limit cutoff voltage (such as 2.8V) with constant current (or constant power, such as 1C current), and standing again for a specified time (such as 1800 seconds) to complete one complete charging and discharging process.

[0098] As described above, the electrochemical-thermal-side reaction coupling simulation model of the lithium ion battery can be established in the battery simulation modeling software such as pyBaMM software, and thus the specified charging and discharging condition and the constraint simulation boundary (such as the ambient temperature of 25°C, the negative electrode lithium precipitation judgment condition of The cycle termination condition is set to ≤80% capacity retention, etc., so that the electrochemical-thermal-side reaction coupled simulation model can simulate the cycle process of a lithium-ion battery to the end of its lifespan under these constrained simulation boundaries and specified charge-discharge conditions. It can also monitor in real time the solid-phase lithium concentration, battery temperature, and lithium concentration loss due to various side reactions at any point during the simulated cycle charge-discharge process. This allows for the acquisition of battery temperature data (characterizing the battery temperature at any point during the cycle charge-discharge process), solid-phase lithium concentration data (characterizing the electrode lithium insertion / extraction concentration at any point during the cycle charge-discharge process), and lithium concentration loss data (characterizing the lithium concentration loss at any point during the cycle charge-discharge process). It should be understood that the electrochemical model and the thermal generation model can obtain the solid-phase lithium concentration data and battery temperature data during the cycle, while the side reaction model can obtain the lithium concentration loss data caused by all side reactions during the cycle.

[0099] In the process of simulating cyclic charging and discharging using battery simulation modeling software such as pyBaMM, information such as the solid-state potential output by the simulation model can be detected in real time. This allows for the acquisition of information such as battery voltage, charging and discharging duration (including the charging and discharging duration for one cycle), and capacity retention rate during the cyclic charging and discharging process. The battery voltage and capacity retention rate can then be used to control the simulation of the cyclic charging and discharging process. For example, pyBaMM software can be configured to acquire the solid-state potential of the positive electrode near the current collector and the negative electrode near the current collector at various moments during the simulated cyclic charging and discharging process. This allows for the calculation of the solid-state potential of the positive electrode near the current collector at any given time. Solid-state potential near the current collector end of the negative electrode The difference between the values ​​yields the battery voltage, which is the battery voltage at any given time. Furthermore, the charging and discharging process can be controlled by determining whether the battery voltage reaches the upper and lower cutoff voltages at any given moment during the simulation. The charging time from the lower cutoff voltage to the upper cutoff voltage, and the discharging time from the upper cutoff voltage to the lower cutoff voltage, can also be recorded during a single charge / discharge cycle. Then, by integrating the discharge current over the discharge time, the battery discharge capacity after any number of charge / discharge cycles can be obtained. The battery discharge capacity can be expressed as: That is, the battery discharge capacity is the integral of the discharge current over the discharge time; and then the capacity retention rate of the lithium battery during cyclic charging and discharging can be calculated based on the battery discharge capacity and the battery rated capacity using formula (12):

[0100] (12)

[0101] in, is the capacity retention rate of the lithium ion battery, and the capacity retention rate is the ratio of the discharge capacity of the lithium ion battery at the nth cycle to the rated capacity of the lithium ion battery. It should be understood that the capacity retention rate can be recorded after each cycle simulation is completed, and the cycle simulation is stopped when the capacity retention rate reaches the cycle stop condition, so as to realize the simulation control of the cycle simulation process. is the rated capacity of the battery. It should be understood that the capacity retention rate after each cycle simulation process is completed can be recorded during the cycle charging and discharging process, and the cycle simulation is stopped after the capacity retention rate reaches the cycle stop condition, so as to realize the simulation control of the cycle charging and discharging process.

[0102] In practical applications, the simulation data of the battery voltage, the battery temperature, the charging and discharging time length, etc. obtained by simulating the cycle charging and discharging process by using the simulation model can be compared with the measured data of the lithium ion battery subjected to the cycle charging and discharging, and the changes of the voltage, the temperature, the capacity (which can be obtained by integrating the charging and discharging time length and the charging and discharging current) of the battery at different cycle times are analyzed to verify the accuracy of the simulation model, so as to obtain a simulation model with higher accuracy, thereby facilitating more accurate charging and discharging simulation of the lithium ion battery.

[0103] In step S13, the cycle swelling force prediction result of the lithium ion battery is determined according to the cycle simulation data by using the swelling force model, wherein the cycle swelling force prediction result includes the maximum swelling force generated by the lithium ion battery in the cycle process.

[0104] After obtaining the cycle simulation data, the battery temperature, the solid-phase lithium concentration and the loss lithium concentration at any time in the battery temperature data, the solid-phase lithium concentration data and the loss lithium concentration data can be input into the swelling force model for calculation to obtain the swelling force at any time in the cycle process, and further the maximum swelling force generated in the entire cycle process can be obtained.

[0105] As described above, the control mechanism equation of the swelling force model can be expressed as the above formula (11-1) to (11-4), and thus in some embodiments, step S13 determines the cycle swelling force prediction result of the lithium ion battery according to the cycle simulation data by using the swelling force model, including:

[0106] In step S131, for each charging and discharging process in the cycle process, the thermal expansion strain of the battery due to temperature change is determined according to the battery temperature and the thermal expansion coefficient of the battery cell in the charging and discharging process.

[0107] In step S132, the intercalation strain of the battery due to lithium concentration change is determined according to the solid-phase lithium concentration and the maximum solid-phase lithium intercalation concentration in the charging and discharging process by using the first mapping relationship.

[0108] In step S133, the non-eliminable strain of the battery due to lithium concentration loss is determined according to the loss lithium concentration and the maximum solid-phase lithium intercalation concentration in the charging and discharging process by using the second mapping relationship.

[0109] Step S134, according to the thermal expansion strain, intercalation strain and non-eliminable strain, the maximum expansion force in the charging and discharging process is determined;

[0110] Step S135, based on the maximum expansion force in each charging and discharging process in the cycle process, the cycle expansion force prediction result is determined.

[0111] In step S131, as described above, the thermal expansion strain of the battery due to temperature change can be expressed as: That is, the difference between the battery temperature at any time during the charging and discharging process and the battery temperature at the initial time of the charging and discharging process is multiplied by the thermal expansion coefficient to obtain the thermal expansion strain generated at any time.

[0112] In step S132, first, the ratio between the solid-phase lithium concentration and the maximum solid-phase lithium intercalation concentration, i.e. , is calculated to obtain the lithium intercalation state SOC, and then the intercalation strain generated at the lithium intercalation state SOC is determined based on the first mapping relationship. As described above, the first mapping relationship can be expressed in a function form or a graph form. Therefore, the lithium intercalation state can be brought into the first mapping relationship in the function form, or the first mapping relationship in the graph form can be looked up based on the lithium intercalation state to obtain the intercalation strain generated at any lithium intercalation state at any time. The present embodiment does not limit this.

[0113] In step S133, first, the ratio between the sum of the loss lithium concentrations caused by various side reactions and the maximum solid-phase lithium intercalation concentration, i.e. , is calculated, and then the non-eliminable strain generated at is determined based on the second mapping relationship. As described above, the second mapping relationship can be expressed in a function form or a graph form. Therefore, the can be brought into the first mapping relationship in the function form, or the first mapping relationship in the graph form can be looked up based on the to obtain the non-eliminable strain generated at any at any time. The present embodiment does not limit this.

[0114] In step S134, the thermal expansion strain, the intercalation strain and the non-eliminable strain at any time in any charging and discharging process are known, and the volume strain of the battery in three directions can be calculated by using the above formula (11-1), i.e. That is, the volume strain of the battery in three directions at any time should be the sum of the thermal expansion strain, the intercalation strain and the non-eliminable strain at the time, and then the normal stress of the battery in three directions at any time in the current charging and discharging process can be calculated by using the above formulas (11-2) to (11-4), and the maximum normal stress in the direction perpendicular to the large surface of the battery in the current charging and discharging process can be taken as the maximum expansion force in the current charging and discharging process.

[0115] It should be understood that the maximum expansion force in each charging and discharging process in the cycle process can be obtained through the above steps S131 to S134, and in step S135, the cycle expansion force prediction result can include the maximum value of the maximum expansion force in all charging and discharging processes in the cycle process, and can also directly include the maximum expansion force in all charging and discharging processes in the cycle process, and the embodiments of the present disclosure do not limit this.

[0116] According to the cycle expansion force prediction method of the embodiments of the present disclosure, by establishing the simulation model and the expansion force model of the lithium ion battery, the simulation model is used to simulate the cycle process of the lithium ion battery in the specified charging and discharging condition to the end of life cycle, the accurate cycle simulation data can be quickly obtained, and then based on the accurate cycle simulation data, the cycle expansion force prediction result of the lithium ion battery can be quickly obtained by using the expansion force model, so that without actually testing the lithium ion battery in the complete charging and discharging process, the maximum expansion force of the lithium ion battery in the entire cycle life (that is, the cycle charging and discharging to the end of life) can be quickly and accurately predicted, which is low in cost and high in efficiency, and is beneficial to providing effective reference for subsequent battery structure and battery material design.

[0117] It can be understood that in the process of establishing the above simulation model and expansion force model, some parameters may not be accurate, which may be empirical values, or may not be able to obtain accurate values through experiments, and thus, for example, Figure 2As shown, the parameters in the simulation model (i.e., the electrochemical reaction model, the thermal reaction model, and the side reaction model) and the swelling force model can also be calibrated using part of the measured data in the lithium-ion battery cycle charging and discharging process, and the calibrated simulation model and the calibrated swelling force model are used to perform the above steps S12 to S13. Specifically, the battery parameters (such as the above battery design parameters and battery physical parameters) can be input into the simulation modeling software to establish the simulation model composed of the electrochemical reaction model (i.e., the electrochemical model), the thermal reaction model (i.e., the heat generation model), and the side reaction model, to determine the simulation data such as the deintercalation lithium concentration (i.e., the solid-phase lithium concentration), the temperature, and the loss lithium concentration using the electrochemical model, and then calibrate the cycle life model (i.e., the above simulation model) using part of the measured data and the simulation data, and input the lithium concentration and temperature data output by the calibrated cycle life model into the swelling force model to obtain the simulation swelling force data, and calibrate the swelling force model using the simulation swelling force data and part of the measured data, so that the calibrated swelling force model can output the accurate swelling force change in the cycle process. Specifically, in some embodiments, the method can further include:

[0118] Step S21, obtaining cycle measured data of the lithium-ion battery, the cycle measured data including: measured capacity retention rate and measured swelling force of the lithium-ion battery in a specified charging and discharging condition in a cycle N times charging and discharging process, wherein the lithium-ion battery after the cycle N times charging and discharging process does not reach the end of life;

[0119] Step S22, simulating the cycle charging and discharging process of the lithium-ion battery in the specified charging and discharging condition using the simulation model to obtain the simulation capacity retention rate, the battery temperature, the solid-phase lithium concentration, and the loss lithium concentration in the cycle N times charging and discharging process;

[0120] Step S23, determining the simulation swelling force in the cycle N times charging and discharging process according to the battery temperature, the solid-phase lithium concentration, and the loss lithium concentration in the cycle N times charging and discharging process using the swelling force model;

[0121] Step S24, calibrating the simulation model and the swelling force model according to the fitting degree between the simulation capacity retention rate and the measured capacity retention rate in the cycle N times charging and discharging process, and the fitting degree between the measured cell swelling force and the simulation swelling force in the cycle N times charging and discharging process, to obtain the calibrated simulation model and the calibrated swelling force model.

[0122] In step S21, the skilled in the art can test the measured capacity retention rate and the measured swelling force of the lithium ion battery in the N-time cycle charging and discharging process under the specified charging and discharging condition (for example, the charging and discharging condition proposed in the above embodiment of the present disclosure) by using any known experimental means in the art, and the present disclosure does not limit the way of obtaining the measured capacity retention rate and the measured swelling force. It should be understood that the measured capacity retention rate and the measured swelling force in the N-time cycle charging and discharging process can be understood as the capacity retention rate and the swelling force actually measured after each charging and discharging process in the N-time cycle charging and discharging process.

[0123] As described above, the end of life of the lithium ion battery can be the state when the capacity retention rate decays to a specified capacity retention rate (such as 80%), and the number of charging and discharging cycles when the lithium ion battery is cycled to the end of life is the cycle life of the lithium ion battery. Therefore, the number of cycle charging and discharging in the process of collecting cycle measurement data can be much smaller than the cycle life, for example, if the cycle life of the battery is 2000 times, N in the cycle measurement data can be 600 times, and the present disclosure does not limit this.

[0124] In step S22, the specific implementation of step S12 described above can be referred to to realize the simulation of the cycle charging and discharging process of the lithium ion battery under the specified charging and discharging condition by using the simulation model, and obtain the simulation capacity retention rate, the battery temperature, the solid-phase lithium concentration and the loss lithium concentration in the N-time cycle charging and discharging process, which will not be repeated here.

[0125] In step S23, the specific implementation of step S13 described above can be referred to to realize the determination of the simulation swelling force in the N-time cycle charging and discharging process by using the swelling force model according to the battery temperature, the solid-phase lithium concentration and the loss lithium concentration in the N-time cycle charging and discharging process, which will not be repeated here.

[0126] In step S24, the present disclosure does not limit the calculation method of the fitting degree between the measured data and the simulation data, for example, the similarity, the curve trend, the curve slope, the curve coincidence degree, etc. between the measured data and the simulation data can be calculated, and the present disclosure does not limit this. Exemplarily, based on the simulation capacity retention rate in the N-time cycle charging and discharging process and the measured capacity retention rate in the N-time cycle charging and discharging process, two capacity retention rate change curves varying with the number of charging and discharging can be generated, so as to determine the fitting degree between the two capacity retention rate change curves; similarly, based on the measured cell swelling force in the N-time cycle charging and discharging process and the simulation swelling force in the N-time cycle charging and discharging process, two swelling force change curves varying with the number of charging and discharging can be generated, so as to determine the fitting degree between the two swelling force change curves.

[0127] It should be understood that the higher the fitting degree between the simulation capacity retention rate and the measured capacity retention rate in the Nth cycle of charging and discharging process, the more accurate the simulation model can represent. The higher the fitting degree between the measured cell swelling force and the simulation swelling force in the Nth cycle of charging and discharging process, the more accurate the swelling force model can represent. Thus, the simulation model can be calibrated based on the fitting degree between the simulation capacity retention rate and the measured capacity retention rate in the Nth cycle of charging and discharging process, and the swelling force model can be calibrated based on the fitting degree between the measured cell swelling force and the simulation swelling force in the Nth cycle of charging and discharging process.

[0128] In some embodiments, calibrating the simulation model and the swelling force model can include: calibrating at least one of the SEI film generation side reaction current density , the active material dissolution side reaction current density , the SEI film regeneration side reaction current density , the lithium precipitation side reaction current density, and the SEI film conductivity in the simulation model; and calibrating at least one of the battery Young's modulus , the battery Poisson's ratio , and the second mapping relationship in the swelling force model. It should be understood that these calibrated parameters can not be directly obtained by experimental means, and can all be empirical values, so they can not accurately represent the actual state. Therefore, these parameters can be calibrated based on the above cycle measurement data, so as to improve the accuracy of the calibrated simulation model and the swelling force model, and thus improve the swelling force prediction accuracy.

[0129] The calibration target of the simulation model and the swelling force model can be that the simulation data (i.e., the simulation capacity retention rate in the Nth cycle of charging and discharging process) determined by the simulation model and the measured data (i.e., the measured capacity retention rate in the Nth cycle of charging and discharging process) reach a high fitting degree (e.g., greater than 95%), and the simulation data (i.e., the simulation swelling force in the Nth cycle of charging and discharging process) determined by the swelling force model and the measured data (i.e., the measured swelling force in the Nth cycle of charging and discharging process) reach a high fitting degree (e.g., greater than 95%). Thus, the simulation model and the swelling force model can be iteratively calibrated for multiple rounds by iteratively adjusting the above calibratable parameters in the simulation model and the swelling force model, that is, the above steps S21 to S24 can be iteratively performed for multiple rounds until the simulation model and the swelling force model after adjusting the parameters reach the above calibration target, obtaining the calibrated simulation model and the calibrated swelling force model. The simulation model used in the above step S12 can be the calibrated simulation model, and the swelling force model used in the above step S13 can be the calibrated swelling force model.

[0130] According to the embodiments of the present disclosure, by acquiring the cycle measured data of the lithium ion battery, and calibrating the simulation model and the swelling force model by using the cycle measured data, the calibrated swelling force model and the calibrated simulation model can be more accurate, and the swelling force prediction process can be performed by using the calibrated simulation model and the calibrated swelling force model, that is, the cycle charging and discharging process of the lithium ion battery under the specified charging and discharging condition can be simulated more accurately by using the calibrated simulation model, so that more accurate cycle simulation data can be obtained, and more accurate cycle swelling force prediction results of the lithium ion battery can be obtained by using the calibrated swelling force model based on the more accurate cycle simulation data, so that the change of the maximum swelling force of the lithium ion battery during the entire cycle life (i.e. cycle charging and discharging to the end of life) can be quickly and accurately predicted only by performing part of the cycle charging and discharging test on the lithium ion battery (i.e. without performing the entire cycle life charging and discharging test on the lithium battery), thereby realizing the accurate prediction of the cycle swelling force of the lithium battery.

[0131] In some embodiments, based on the cycle swelling force prediction method provided by the above embodiments of the present disclosure, the present disclosure provides a cycle swelling force prediction process, which specifically comprises:

[0132] Step 1: In the pyBaMM software, first input the design parameters of the battery (such as the positive electrode thickness, the negative electrode thickness, the separator thickness, the positive electrode active material particle size, the negative electrode active material particle size, the separator porosity, the positive electrode solid phase and liquid phase volume fraction, and the negative electrode solid phase and liquid phase volume fraction) to establish the geometric model of the battery cell, and then establish the above electrochemical-thermal coupling simulation model without considering the side reaction.

[0133] Step 2: Obtain part of the cycle life data and swelling force data of the lithium battery by measurement, that is, the capacity retention rate curve and the maximum swelling force curve of the lithium battery during the cycle process, and the capacity retention rate curve and the swelling force curve can be processed into text format and input into the pyBaMM software.

[0134] Step 3: On the basis of the completion of the above steps 1 and 2, add the SEI film generation side reaction equation, the active material dissolution side reaction equation, the SEI film regeneration side reaction equation, and the lithium precipitation side reaction equation in the model, and input the parameters required by the side reaction, including the density and molar mass of the SEI film, the exchange current density of the SEI film generation side reaction, the SEI film conductivity, the equilibrium potential of the SEI film generation and regeneration side reaction, the density and molar mass of Li, the exchange current density of the lithium precipitation side reaction, the equilibrium potential of the lithium precipitation side reaction, the exchange current density of the active material dissolution side reaction, the equilibrium potential of the active material capacity side reaction, the initial film thickness, etc.

[0135] Step 5: Input the Young's modulus of the battery in the swelling force model Battery Poisson ratio coefficient of thermal expansion Lithium insertion / extraction strain function of electrode material Side reactions lead to an unavoidable strain function in the electrode material. The expansion force model is established using temperature T. The electrochemical-thermal coupling simulation model in step 1 can be used to obtain the concentration change data of lithium insertion / extraction and temperature change data during the cycling process. The side reaction model in step 3 can be used to obtain the concentration change data of lithium loss caused by all side reactions during the cycling process. The obtained lithium insertion / extraction concentration data, temperature data and the concentration data of lithium loss caused by all side reactions during the cycling process are input into the expansion force model for calculation.

[0136] Step 6: Correct the exchange current density, SEI film conductivity, and Young's modulus of the electrode material for the above-mentioned side reactions. Poisson's ratio of electrode material and non-eliminable strain function The measured capacity retention rate curves and expansion force curves of the cyclic process were fitted to calibrate the aforementioned simulation model and expansion force model. Specifically, a well-fitted simulated capacity retention rate curve can be obtained by correcting the side reaction current density and SEI membrane conductivity parameters (by changing their values); as mentioned above, In other words, the strain caused by lithium insertion / extraction from particles can be determined experimentally. The strain caused by side reactions cannot currently be determined through actual measurements. It is necessary to input the values ​​into a simulation model for calculation and calibration. The battery's Young's modulus and Poisson's ratio also need to be input into the model and calibrated using simulation results. For the aforementioned expansion force model, different boundary conditions can be applied to calculate the expansion force variation curve and the measured expansion force curve. Then, the two curves can be fitted together... Correction of Young's modulus and Poisson's ratio of the battery.

[0137] Step 7: Utilize the calibrated expansion force model to output the expansion force data of the lithium-ion battery during cycling, thereby obtaining the predicted results of the cycle expansion force of the lithium-ion battery.

[0138] An example is a lithium iron manganese phosphate battery cell constant current charge and discharge cycle. The above-mentioned prediction method is used to predict the cycle expansion force of the lithium iron manganese phosphate battery cell. The positive electrode uses lithium iron manganese phosphate material, the negative electrode uses graphite material, and the electrolyte uses LiPF6 solvent material. A simulation model and an expansion force model are established. The parameters used in the model can include: negative electrode thickness 44 um, separator thickness 13 um, positive electrode thickness 72 um, negative electrode particle diameter 11 um, positive electrode particle diameter 1.1 um, negative electrode solid phase volume fraction 0.67, positive electrode solid phase volume fraction 0.63, negative electrode liquid phase volume fraction 0.29, positive electrode liquid phase volume fraction 0.32, separator porosity 0.42, negative electrode maximum lithium intercalation concentration 30555 mol / m 3 , positive electrode maximum lithium intercalation concentration 22900 mol / m 3 , negative electrode reaction rate constant 4e-11 m / s, positive electrode reaction rate constant 2e-11 m / s, negative electrode diffusion coefficient 4e-14 m 2 / s, positive electrode diffusion coefficient 6e-16 m 2 / s, negative electrode diffusion activation energy 50000 J / mol, positive electrode diffusion activation energy 49000 J / mol, negative electrode conductivity 460 S / m, positive electrode conductivity 11 S / m, initial electrolyte concentration 1050 mol / m 3 , negative electrode density 2240 kg / m 3 , positive electrode density 3600 kg / m 3 , separator density 1210 kg / m 3 , negative electrode thermal conductivity 2.6 W / (m·K), positive electrode thermal conductivity 2.4 W / (m·K), separator thermal conductivity 2 W / (m·K), negative electrode specific heat capacity 881 J / (kg·K), positive electrode specific heat capacity 1001 J / (kg·K), separator specific heat capacity 1978 J / (kg·K), SEI film density 2690 kg / m 3 , SEI film molar mass 0.05 kg / mol, exchange current density of SEI generation and regeneration side reaction 3E-7 A / m 2 , SEI generation and regeneration side reaction equilibrium potential 0.4 V, fitting parameters and h are 1.2 and 2.1 respectively, exchange current density of active material dissolution side reaction 1E-5 A / m 2 , equilibrium potential of active material dissolution side reaction 4 V, exchange current density of lithium precipitation side reaction 2E-5 A / m 2, the equilibrium potential of the side reaction of lithium extraction is 0 V, the initial film thickness is 5 nm, the SEI film conductivity is 5e-6 S / m, the thermal expansion coefficient of the positive electrode current collector is 23.6e-6 (1 / K), the thermal expansion coefficient of the positive electrode is 8.62e-6 (1 / K), the thermal expansion coefficient of the separator is 1.33e-4 (1 / K), the thermal expansion coefficient of the negative electrode is 4.06e-6 (1 / K), the thermal expansion coefficient of the negative electrode current collector is 1.7-5 (1 / K), the Young's modulus of the positive electrode current collector is 70 GPa, the Poisson's ratio of the positive electrode current collector is 0.34, the Young's modulus of the positive electrode is 0.04 GPa, the Poisson's ratio of the positive electrode is 0.32, the Young's modulus of the separator is 0.5 GPa, the Poisson's ratio of the separator is 0.35, the Young's modulus of the negative electrode is 0.02 GPa, the Poisson's ratio of the negative electrode is 0.3, the Young's modulus of the negative electrode current collector is 117 GPa, and the Poisson's ratio of the negative electrode current collector is 0.35, and the empirical parameters 、 、m、 and are 0.14, 0.5, 1.22, 0.652 and 2.2 respectively, the molar volume of the positive electrode material is 3.5E-6 m 3 / mol, and the molar volume of the negative electrode material is 5.19E-6 m 3 / mol.

[0139] And the specified charge and discharge conditions in the cycle process of the simulation model are as follows:

[0140] 1) constant current constant voltage charging to 4.2V at 1C current;

[0141] 2) stand for 1800s;

[0142] 3) constant current discharging to 2.8V at 1C current;

[0143] 4) stand for 1800s;

[0144] 5) repeat steps 1) to 4) for cycling.

[0145] Wherein, the temperature is set to 25℃, and the cycle stopping condition is set to the capacity retention rate reaching the EOL state.

[0146] Then, based on the above parameters and the specified charge and discharge conditions, the capacity retention rate curve comparison diagram shown in Figure 3 and the maximum expansion force curve comparison diagram shown in Figure 4 can be obtained by using the prediction method of the embodiment of the present disclosure, as Figure 3 and 4As shown, by fitting the measured capacity retention rate of 600 cycles and the swelling force data during the cycle process to calibrate the simulation model and the swelling force model, it can be effectively and accurately predicted that the corresponding cycle number (i.e. the number of charge and discharge) of the lithium iron manganese phosphate battery cell reaching the EOL state (80% capacity retention rate) during the cycle process is 2100, and the maximum swelling force of the battery cell at this time is 7285N.

[0147] The embodiment of the present disclosure provides a prediction method for the maximum swelling force of a lithium ion battery during a cycle process. In the above embodiment, only the measured swelling force data of the first 600 cycles is used to predict the swelling force of the battery cell when reaching the EOL state, and a high prediction accuracy is ensured, that is, the change of the swelling force of the lithium ion battery during the cycle process can be quickly determined, thereby providing a reference for the design of the battery cell module in a short time, greatly reducing the time required for measuring the swelling force, and accelerating the research and development process of the lithium battery.

[0148] Figure 5 A block diagram of a lithium ion battery cycle swelling force prediction device according to an embodiment of the present disclosure is shown as follows. Figure 5 As shown, the device comprises:

[0149] A modeling module 501 is configured to establish a simulation model of a lithium ion battery and a swelling force model. The swelling force model represents the swelling force generated by the lithium ion battery under any solid-phase lithium concentration, any battery temperature and any loss lithium concentration.

[0150] A simulation module 502 is configured to simulate the cycle process of the lithium ion battery under a specified charge and discharge condition by using the simulation model, to obtain cycle simulation data. The cycle simulation data includes battery temperature data caused by heat generation, solid-phase lithium concentration data caused by electrochemical reaction and loss lithium concentration data caused by side reaction during the cycle process.

[0151] A prediction module 503 is configured to determine the cycle swelling force prediction result of the lithium ion battery by using the swelling force model according to the cycle simulation data. The cycle swelling force prediction result includes the maximum swelling force generated by the lithium ion battery during the cycle process.

[0152] In a possible implementation, the device further includes a calibration module configured to: obtain measured cycle data of the lithium ion battery, the measured cycle data including measured capacity retention and measured swelling force of the lithium ion battery in a cycle N times of charging and discharging process under the specified charging and discharging condition, wherein the lithium ion battery after the cycle N times of charging and discharging process does not reach the end of life; simulate the cycle charging and discharging process of the lithium ion battery under the specified charging and discharging condition by using the simulation model to obtain simulation capacity retention, battery temperature, solid-phase lithium concentration and loss lithium concentration in the cycle N times of charging and discharging process; determine simulation swelling force in the cycle N times of charging and discharging process according to the battery temperature, the solid-phase lithium concentration and the loss lithium concentration in the cycle N times of charging and discharging process by using the swelling force model; and calibrate the simulation model and the swelling force model according to a fitting degree between the simulation capacity retention and the measured capacity retention in the cycle N times of charging and discharging process and a fitting degree between the measured cell swelling force and the simulation swelling force in the cycle N times of charging and discharging process, to obtain a calibrated simulation model and a calibrated swelling force model.

[0153] In a possible implementation, the determination of the cycle swelling force prediction result of the lithium ion battery by using the swelling force model according to the cycle simulation data includes: determining a thermal expansion strain of the battery caused by temperature change according to the battery temperature and a preset thermal expansion coefficient in each charging and discharging process in the cycle process; determining an intercalation strain of the battery caused by lithium concentration change according to the solid-phase lithium concentration and the maximum solid-phase lithium intercalation concentration in the charging and discharging process by using the first mapping relationship, the first mapping relationship representing an intercalation strain of the electrode at any solid-phase lithium concentration; determining an irreducible strain of the battery caused by lithium concentration loss according to the loss lithium concentration in the charging and discharging process and the maximum solid-phase lithium intercalation concentration by using the second mapping relationship, the second mapping relationship representing an irreducible strain of the electrode at any loss lithium concentration; and determining the maximum swelling force in the charging and discharging process according to the thermal expansion strain, the intercalation strain and the irreducible strain, and determining the cycle swelling force prediction result based on the maximum swelling force in each charging and discharging process in the cycle process.

[0154] In a possible implementation, the swelling force model is represented as:

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] wherein, , and represent the volumetric strain of the battery in three directions; represents the Young's modulus of the battery; represents the Poisson's ratio of the battery; , and represent the normal stress of the battery in three directions, represents the normal stress in the direction perpendicular to the large surface of the battery, and the swelling force of the lithium ion battery includes the normal stress in the direction perpendicular to the large surface of the battery;

[0160] wherein, represents the first mapping relationship, represents the lithium intercalation state, , represents the solid-phase lithium concentration, represents the maximum solid-phase lithium intercalation concentration; represents the thermal expansion coefficient, represents the difference between the battery temperature at any moment during the charging and discharging process and the battery temperature at the initial moment of the charging and discharging process;

[0161] wherein, represents the second mapping relationship, , represents the loss lithium concentration caused by the SEI film generation side reaction, represents the loss lithium concentration caused by the SEI film regeneration side reaction, represents the loss lithium concentration caused by the lithium precipitation side reaction.

[0162] In a possible implementation, the electrochemical model is used to describe the electrochemical reaction in the lithium ion battery to determine the solid-phase lithium concentration data caused by the electrochemical reaction; the heat production model is used to describe the heat production process in the lithium ion battery to determine the battery temperature data caused by the heat production; the side reaction model is used to describe the side reaction in the lithium ion battery to determine the loss lithium concentration data caused by the side reaction; and the electrochemical model includes a deintercalation reaction equation, a solid-phase charge conservation equation, a liquid-phase charge conservation equation, a lithium ion solid-phase diffusion equation and a lithium ion liquid-phase diffusion equation.

[0163] wherein, the deintercalation reaction equation is an equation constructed by introducing the hydrostatic stress generated by the electrode active particles, and the deintercalation reaction equation is expressed as:

[0164] ;

[0165] ;

[0166] ;

[0167] ;

[0168] wherein, represents the deintercalation lithium reaction current density of the electrode, represents the exchange current density of the deintercalation lithium reaction of the electrode, and respectively represent the charge transfer coefficients of the anode reaction and the cathode reaction, F represents the Faraday constant, and R represents the gas constant, represents the overpotential of the deintercalation reaction, and T represents the battery temperature, represents the solid phase potential, represents the liquid phase potential, represents the electrode equilibrium potential, represents the hydrostatic stress, represents the particle radius of the electrode active particle, represents the solid phase lithium concentration, represents the initial solid phase lithium concentration, represents the partial molar volume of the electrode active material, represents the Young's modulus of the electrode active particle, represents the Poisson ratio of the electrode active particle, represents the total current density, represents the electrode active material dissolution side reaction current density, is the SEI film generation side reaction current density, is the SEI film regeneration side reaction current density, is the lithium precipitation side reaction current density; represents the SEI film resistance;

[0169] wherein the solid phase charge conservation equation is represented as: ; wherein, represents the solid phase current density, represents the solid phase effective conductivity, represents the gradient of the solid phase potential;

[0170] wherein the liquid phase charge conservation equation is represented as: ; wherein, represents the liquid phase current density, represents the liquid phase effective conductivity, is the gradient of the liquid phase potential, represents the liquid phase lithium concentration, is the activity coefficient related to the liquid phase lithium concentration ; is the lithium ion transfer number;

[0171] wherein the lithium ion solid phase diffusion equation is represented as: ; wherein, represents a solid phase diffusion coefficient, represents an arbitrary time, represents an arbitrary radius within the electrode active particle;

[0172] wherein the lithium ion liquid phase diffusion equation is represented as: ; wherein, represents a liquid phase volume fraction, represents a liquid phase lithium concentration, represents an arbitrary position in the lithium ion battery, represents a specific surface area of the electrode active particle, , represents a solid phase volume fraction, represents a liquid phase diffusion coefficient.

[0173] In a possible implementation, the liquid phase volume fraction is represented as a difference between an initial liquid phase volume fraction of the electrolyte and a liquid phase volume fraction variation amount caused by a side reaction, the liquid phase volume fraction variation amount being determined based on a film thickness variation amount of an SEI film caused by the side reaction, the SEI film resistance being determined based on the film thickness variation amount, and the film thickness variation amount being determined based on a loss lithium concentration caused by an SEI film generation side reaction, a loss lithium concentration caused by an SEI film regeneration side reaction, and a loss lithium concentration caused by a lithium precipitation side reaction; wherein the loss lithium concentration caused by the SEI film generation side reaction is determined based on a current density of the SEI film generation side reaction, the loss lithium concentration caused by the SEI film regeneration side reaction is determined based on a current density of the SEI film regeneration side reaction, and the loss lithium concentration caused by the lithium precipitation side reaction is determined based on a current density of the lithium precipitation side reaction.

[0174] In a possible implementation, the liquid phase volume fraction is represented as , represents an initial liquid phase volume fraction, represents the liquid phase volume fraction variation amount, wherein the liquid phase volume fraction variation amount is represented as: , represents the film thickness variation amount, , represents a loss lithium concentration caused by an SEI film generation side reaction, represents a loss lithium concentration caused by an SEI film regeneration side reaction, represents a loss lithium concentration caused by a lithium precipitation side reaction, represents a molar mass of the SEI film, represents a density of the SEI film, represents a molar mass of lithium, wherein, the SEI film resistance is represented as: wherein, wherein, the SEI film conductivity is represented as: the SEI film regeneration side reaction induced loss of lithium concentration is represented as: the lithium precipitation side reaction induced loss of lithium concentration is represented as: .

[0175] In one possible implementation, the side reaction model comprises at least one of: an SEI film generation side reaction equation, an active material dissolution side reaction equation, an SEI film regeneration side reaction equation, and a lithium precipitation side reaction equation; the SEI film generation side reaction equation is used to determine the current density in the SEI film generation process, the active material dissolution side reaction equation is used to determine the solid volume fraction in the active material dissolution process, the SEI film regeneration side reaction equation is used to determine the current density in the SEI film generation process triggered again due to the dissolution loss of the electrode active material, and the lithium precipitation side reaction equation is used to determine the current density in the lithium precipitation process of the negative electrode;

[0176] wherein, the SEI film generation side reaction equation is represented as:

[0177] ;

[0178] ;

[0179] wherein, represents the SEI film generation side reaction current density, represents the SEI film generation side reaction exchange current density, represents the charge transfer coefficient, represents the overpotential of the SEI film generation side reaction, represents the equilibrium potential of the SEI film generation side reaction, represents the hydrostatic stress, represents the partial molar volume of the active material, represents the solid phase potential, represents the liquid phase potential, represents the total current density; represents the SEI film resistance caused by the side reaction; F represents the Faraday constant, R represents the gas constant, and T represents the battery temperature;

[0180] wherein, the active material dissolution side reaction equation is represented as:

[0181] ;

[0182] ;

[0183] ;

[0184] wherein, represents the active material dissolution side reaction current density, represents the active material loss side reaction exchange current density, represents the overpotential of the active material dissolution side reaction, represents the equilibrium potential of the active material dissolution side reaction, represents the initial solid phase volume fraction, represents the solid phase volume fraction, represents the maximum solid phase lithium intercalation concentration, represents the electrode thickness;

[0185] wherein, the SEI film regeneration side reaction equation is represented as:

[0186] ;

[0187] ;

[0188] wherein, represents the SEI film regeneration side reaction current density, represents the SEI film regeneration side reaction exchange current density, represents the charge transfer coefficient, represents the overpotential of the SEI film regeneration side reaction, represents the equilibrium potential of the SEI film regeneration side reaction, and is a fitting parameter;

[0189] wherein, the lithium precipitation side reaction equation is represented as:

[0190] ;

[0191] ;

[0192] wherein, represents the lithium precipitation side reaction current density, represents the lithium precipitation side reaction exchange current density, and represents the charge transfer coefficient, is the overpotential of the lithium precipitation side reaction, represents the equilibrium potential of the negative electrode lithium precipitation side reaction; when <0, the lithium precipitation reaction occurs.

[0193] In a possible implementation, the calibration of the simulation model and the swelling force model comprises: calibrating at least one of a SEI film generation side reaction current density, an active material dissolution side reaction current density, a SEI film regeneration side reaction current density, a lithium precipitation side reaction current density, and a SEI film conductivity in the simulation model; and calibrating at least one of a battery Young's modulus, a battery Poisson's ratio, and a second mapping relationship in the swelling force model.

[0194] According to the cycle swelling force prediction device provided by the embodiment of the present disclosure, by establishing the simulation model and the swelling force model of the lithium ion battery, the simulation model is used to simulate the cycle process of the lithium ion battery from the specified charge-discharge working condition to the end of life, accurate cycle simulation data can be quickly obtained, and the swelling force model is used to quickly obtain the cycle swelling force prediction result of the lithium ion battery based on the accurate cycle simulation data. In this way, without actually performing complete charge-discharge test on the lithium ion battery, the maximum swelling force of the lithium ion battery in the whole cycle life (i.e. from the cycle charge-discharge to the end of life) can be quickly and accurately predicted, and the cost is low and the efficiency is high, which is beneficial to providing effective reference for subsequent battery structure and battery material design.

[0195] In some embodiments, the device provided by the embodiment of the present disclosure has functions or contains modules which can be used to execute the method described in the above method embodiment, and the specific implementation can refer to the description of the above method embodiment. For brevity, details are not repeated here.

[0196] The embodiment of the present disclosure also provides an electronic device, including a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to realize the steps of the above method.

[0197] The embodiment of the present disclosure also provides a non-volatile computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the above method.

[0198] The embodiment of the present disclosure also provides a computer program product, including a computer program or a non-volatile computer readable storage medium carrying the computer program, and the computer program is executed by a processor to realize the steps of the above method.

[0199] Figure 6 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 can be provided as a server or a terminal device. Referring to Figure 6The electronic device 1900 includes a processing component 1922, which is further composed of one or more processors, and a memory resource represented by the memory 1932, for storing instructions executable by the processing component 1922, such as an application program. The application program stored in the memory 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above method.

[0200] The electronic device 1900 can further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows Server TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM or the like.

[0201] In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932 including computer program instructions, is also provided, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.

[0202] The computer readable storage medium can be a tangible device that can retain and store programs for use by an instruction execution device. The computer readable storage medium, for example, can be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanism that can store and read instructions such as one or more of the following: a punch card, an eddy current card, a magnetic strip on a card, as well as any suitable combination of the foregoing. The computer readable storage medium used here is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or an electrical signal passing through a wire.

[0203] The computer program (or computer readable program instructions) described herein can be downloaded from a computer readable storage medium to various computing / processing devices by way of a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0204] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing / processing device, partly on the user's computing / processing device, as a stand-alone software package, partly on the user's computing / processing device and partly on a remote computing / processing device or entirely on the remote computing / processing device or server. In the latter scenario, the remote computing / processing device can be connected to the user's computing / processing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing / processing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0205] The computer readable program instructions can also be loaded onto a computing / processing device, other programmable data processing apparatus, or other device to cause a series of operations to be performed on the computing / processing device, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computing / processing device, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0206] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data storage cycles that change state. The instructions can be executed by one or more processors of a computer, to cause a series of operational elements or steps to be performed on the computer to produce a computer implemented process. Such instructions can also be stored and / or executed by other computer-readable media. Computer-readable media storing the computer readable instructions can include computers, processors, or other programmable data processing apparatuses capable of receiving, storing, and / or executing instructions.

[0207] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational elements or steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable elements, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0208] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational elements or steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable elements, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0209] Embodiments of the present disclosure have been described above, and the description is intended to be illustrative of the embodiments and not exhaustive, and is not limited to the embodiments disclosed. Numerous modifications and adaptations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of words in this document is intended to best explain the principles of the embodiments, practical application, or improvement over the technology in the market, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting the cycling expansion force of a lithium-ion battery, characterized in that, include: A simulation model and an expansion force model for a lithium-ion battery are established; wherein, the expansion force model characterizes the expansion force generated by the lithium-ion battery under arbitrary solid phase lithium concentration, arbitrary battery temperature, and arbitrary loss lithium concentration. The simulation model is used to simulate the cyclic charging and discharging process of the lithium-ion battery under specified charging and discharging conditions until the end of its lifespan, and cyclic simulation data is obtained. The cyclic simulation data includes: battery temperature data due to heat generation during the cyclic process, solid lithium concentration data due to electrochemical reactions, and lithium concentration loss data due to side reactions. Using the expansion force model and the cycle simulation data, the predicted cycle expansion force of the lithium-ion battery is determined, wherein the predicted cycle expansion force includes the maximum expansion force generated by the lithium-ion battery during the cycle.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the cycle test data of the lithium-ion battery, which includes the measured capacity retention rate and measured expansion force of the lithium-ion battery during N charge-discharge cycles under the specified charge-discharge conditions, wherein the lithium-ion battery has not reached the life end point after N charge-discharge cycles. The simulation model is used to simulate the cyclic charge-discharge process of the lithium-ion battery under the specified charge-discharge conditions, and the simulated capacity retention rate, battery temperature, solid phase lithium concentration and loss lithium concentration are obtained during N cycles of charge-discharge. The expansion force model is used to determine the simulated expansion force during N cycles of charge and discharge based on the battery temperature, solid lithium concentration, and lost lithium concentration. Based on the degree of fit between the simulated capacity retention rate and the measured capacity retention rate during N cycles of charge and discharge, and the degree of fit between the measured cell expansion force and the simulated expansion force during N cycles of charge and discharge, the simulation model and the expansion force model are calibrated to obtain the calibrated simulation model and the calibrated expansion force model.

3. The method according to claim 1 or 2, characterized in that, The step of determining the predicted cycle expansion force of the lithium-ion battery based on the cycle simulation data using the expansion force model includes: For any charge-discharge process in the cycle, the thermal expansion strain of the battery due to temperature change is determined based on the battery temperature and the preset thermal expansion coefficient during the charge-discharge process. The first mapping relationship is used to determine the intercalation strain of the battery caused by the change in lithium concentration based on the solid-phase lithium concentration and the maximum solid-phase lithium intercalation concentration during the charging and discharging process; the first mapping relationship characterizes the intercalation strain of the electrode under any solid-phase lithium concentration. Using the second mapping relationship, the non-eliminable strain of the battery due to lithium concentration loss is determined based on the lithium concentration loss during the charging and discharging process and the maximum solid-phase lithium intercalation concentration; the second mapping relationship characterizes the non-eliminable strain of the electrode under any lithium concentration loss. The maximum expansion force during the charging and discharging process is determined based on the thermal expansion strain, the intercalation strain, and the non-eliminable strain. The predicted cyclic expansion force is determined based on the maximum expansion force during each charge and discharge cycle.

4. The method according to claim 3, characterized in that, The expansion force model is expressed as follows: ； ； ； ； in, , and The volumetric strain represents the three directions of the battery; Represents the Young's modulus of the battery; Represents the Poisson's ratio of a battery; , and The normal stresses represent the three directions of the battery. This represents the normal stress in the direction perpendicular to the large surface area of ​​the battery. The expansion force of a lithium-ion battery includes the normal stress in this direction perpendicular to the large surface area of ​​the battery. in, This represents the first mapping relationship. This represents the lithium intercalation state. , Represents the solid-phase lithium concentration. Represents the maximum solid-phase lithium intercalation concentration; Represents the coefficient of thermal expansion. It represents the difference between the battery temperature at any moment during the charging and discharging process and the battery temperature at the initial moment of the charging and discharging process. in, This represents the second mapping relationship. , This represents the lithium concentration loss due to side reactions in the formation of the SEI film. This represents the lithium concentration lost due to side reactions during SEI membrane regeneration. This represents the lithium concentration lost due to lithium plating side reactions.

5. The method according to any one of claims 1 to 4, characterized in that, The simulation model includes a coupled electrochemical model, a thermal generation model, and a side reaction model; the electrochemical model is used to describe the electrochemical reactions in lithium-ion batteries to determine the solid-phase lithium concentration data due to the electrochemical reactions. The heat generation model is used to describe the heat generation process in the lithium-ion battery in order to determine the battery temperature data due to heat generation. The side reaction model is used to describe the side reactions in the lithium-ion battery in order to determine the lithium concentration loss due to the side reactions; The electrochemical model includes: intercalation / deintercalation reaction equation, solid phase charge conservation equation, liquid phase charge conservation equation, lithium ion solid phase diffusion equation, and lithium ion liquid phase diffusion equation; The deintercalation reaction equation is constructed by introducing hydrostatic stress generated by the active electrode particles, and is expressed as follows: ； ； ； ； in, The current density representing the lithium insertion / extraction reaction of the electrode. The exchange current density representing the lithium insertion / extraction reaction of the electrode. and F and R represent the charge transfer coefficients of the anodic and cathodic reactions, respectively, where F represents the Faraday constant and R represents the gas constant. The overpotential represents the insertion / extraction reaction, and T represents the cell temperature. Represents solid-state potential. Represents the liquid phase potential. Represents the electrode equilibrium potential. Represents hydrostatic stress. Represents the particle radius of the electrode active particles. Represents the solid-phase lithium concentration. Represents the initial solid-phase lithium concentration. The partial molar volume representing the electrode active material. Young's modulus representing the active particles of the electrode. Poisson's ratio represents the active particles of the electrode. Represents the total current density. The current density of the side reaction representing the dissolution of the electrode active material. To generate the side reaction current density for the SEI film, The current density for the side reaction during SEI membrane regeneration. This represents the current density of the lithium plating side reaction. Represents the SEI film resistance; The solid-phase charge conservation equation is expressed as follows: ;in, Represents solid-state current density. Represents the effective conductivity of the solid phase. The gradient representing the solid-state potential; The liquid phase charge conservation equation is expressed as follows: ;in, Represents liquid phase current density, Represents the effective conductivity of the liquid phase. The gradient of the liquid phase potential. Represents the concentration of lithium in the liquid phase. To match the concentration of liquid lithium The relevant activity coefficient, This refers to the lithium-ion transfer number; The lithium-ion solid-phase diffusion equation is expressed as follows: ;in, Represents the solid-phase diffusion coefficient. Represents any time. Represents any radius within the active electrode particle; The lithium-ion liquid-phase diffusion equation is expressed as follows: ;in, Represents the liquid volume fraction. Represents the concentration of lithium in the liquid phase. Represents any location within a lithium-ion battery. This represents the specific surface area of ​​the electrode active particles. , Represents the volume fraction of the solid phase. This represents the liquid phase diffusion coefficient.

6. The method according to claim 5, characterized in that, The liquid phase volume fraction is expressed as the difference between the initial liquid phase volume fraction of the electrolyte and the change in liquid phase volume fraction caused by the side reaction. The change in liquid phase volume fraction is determined based on the change in SEI membrane thickness caused by the side reaction. The SEI membrane resistance is determined based on the change in membrane thickness, which is based on the lithium concentration loss caused by the SEI membrane formation side reaction, the lithium concentration loss caused by the SEI membrane regeneration side reaction, and the lithium concentration loss caused by the lithium plating side reaction. The lithium concentration lost due to the SEI film formation side reaction is determined based on the current density of the SEI film formation side reaction, the lithium concentration lost due to the SEI film regeneration side reaction is determined based on the current density of the SEI film regeneration side reaction, and the lithium concentration lost due to the lithium plating side reaction is determined based on the current density of the lithium plating side reaction.

7. The method according to claim 6, characterized in that, The liquid phase volume fraction is expressed as: , Represents the initial liquid volume fraction. This represents the change in the liquid phase volume fraction, wherein the change in the liquid phase volume fraction is expressed as: , This represents the change in film thickness. , This represents the lithium concentration loss due to side reactions in the formation of the SEI film. This represents the lithium concentration lost due to side reactions during SEI membrane regeneration. This represents the lithium concentration loss due to lithium plating side reactions. Represents the molar mass of the SEI membrane. The density of the SEI film is represented. Represents the molar mass of lithium. Represents the density of lithium; The SEI film resistance is expressed as: ,in, Represents the conductivity of the SEI film; The lithium concentration loss due to the side reactions in SEI film formation is expressed as: The lithium concentration loss due to side reactions during SEI membrane regeneration is expressed as: The lithium concentration loss due to lithium plating side reactions is expressed as: .

8. The method according to claim 5, characterized in that, The side reaction model includes at least one of the following: SEI film formation side reaction equation, active material dissolution side reaction equation, SEI film regeneration side reaction equation, and lithium plating side reaction equation; the SEI film formation side reaction equation is used to determine the current density during the SEI film formation process, the active material dissolution side reaction equation is used to determine the solid phase volume fraction during the dissolution process of the electrode active material, the SEI film regeneration side reaction equation is used to determine the current density that triggers the SEI film formation process again due to the dissolution loss of the electrode active material, and the lithium plating side reaction equation is used to determine the current density during the lithium plating process of the negative electrode. The side reaction equation for SEI film formation is expressed as follows: ； ； in, This represents the current density of the side reaction during SEI film formation. This represents the exchange current density of the side reaction during SEI film formation. Represents the charge transfer coefficient. The overpotential representing the side reaction in SEI film formation. The equilibrium potential representing the side reaction leading to SEI film formation. Represents hydrostatic stress. Represents the partial molar volume of the active material. Represents solid-state potential. Represents the liquid phase potential. Represents the total current density; The membrane resistance of the SEI membrane is represented by the side reaction; F represents the Faraday constant, R represents the gas constant, and T represents the cell temperature. The equation for the dissolution side reaction of the active material is expressed as follows: ； ； ； in, The current density represents the side reaction current density of the dissolution of active materials. The exchange current density of the side reaction representing the loss of active materials. The overpotential representing the side reaction of dissolution of active materials. The equilibrium potential representing the side reaction of the dissolution of active materials. Represents the initial solid volume fraction. Represents the volume fraction of the solid phase. Represents the maximum solid-phase lithium intercalation concentration. Represents electrode thickness; The SEI membrane regeneration side reaction equation is expressed as follows: ； ； in, This represents the current density of the side reaction during SEI film regeneration. This represents the exchange current density of the SEI film regeneration side reaction. Represents the charge transfer coefficient. The overpotential representing the side reaction of SEI film regeneration. The equilibrium potential represents the side reaction of SEI film regeneration. and These are the fitting parameters; The lithium plating side reaction equation is expressed as follows: ； ； in, Represents the current density of the lithium plating side reaction. Represents the exchange current density of the lithium plating side reaction. and Represents the charge transfer coefficient. This is the overpotential for the lithium plating side reaction. This represents the equilibrium potential of the lithium plating side reaction at the negative electrode; when When the value is less than 0, lithium plating occurs.

9. The method according to claim 2, characterized in that, The calibration of the simulation model and the expansion force model includes: Calibrate at least one of the following in the simulation model: SEI film formation side reaction current density, active material dissolution side reaction current density, SEI film regeneration side reaction current density, lithium plating side reaction current density, and SEI film conductivity. Calibrate at least one of the following in the expansion force model: battery Young's modulus, battery Poisson's ratio, and second mapping relationship.

10. A lithium-ion battery cycle expansion force prediction device, characterized in that, include: The modeling module is used to establish a simulation model and an expansion force model for lithium-ion batteries; wherein, the expansion force model characterizes the expansion force generated by lithium-ion batteries under arbitrary solid-phase lithium concentration, arbitrary battery temperature, and arbitrary loss lithium concentration. The simulation module is used to simulate the cycle process of the lithium-ion battery under specified charge and discharge conditions to the end of its life using the simulation model, and to obtain cycle simulation data. The cycle simulation data includes: battery temperature data due to heat generation during the cycle, solid lithium concentration data due to electrochemical reactions, and lithium concentration loss data due to side reactions. The prediction module is used to determine the predicted cycle expansion force of the lithium-ion battery based on the cycle simulation data using the expansion force model, wherein the predicted cycle expansion force includes the maximum expansion force generated by the lithium-ion battery during the cycle.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.

12. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

13. A computer program product comprising a computer program, or a non-volatile computer-readable storage medium carrying a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

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