Battery charging parameter determination method and device and storage medium

Abstract

The invention discloses a battery charging parameter determination method and device and a storage medium. The method for determining the charging parameters of the battery comprises the following steps of: constructing a simulation equation for simulating the growth of lithium dendrites on the basis of a phase field method; adjusting an initial value and / or a boundary condition of the simulation equation, and solving the simulation equation aiming at a plurality of groups of charging parameters to obtain a plurality of groups of solving results for indicating and predicting the generation condition of the lithium dendrites; and determining a target charging parameter of the battery based on the plurality of groups of solving results. According to the method for determining the charging parameters of the battery disclosed by the invention, the growth of the lithium dendrites is accurately simulated based on the phase field method, and the determined charging parameters can inhibit the generation of the lithium dendrites in the charging process, so that the charging efficiency and safety of the battery are improved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, and storage medium for determining battery charging parameters by simulating a lithium dendrite formation process using a phase field. Background Art

[0002] With the rapid development of energy technology, various types of batteries (including lithium batteries, solid-state batteries, etc.) with advantages such as high energy density, good safety, and long cycle life are becoming more and more widely used in electric vehicles, energy storage systems, consumer electronics and other fields, and have broad prospects. However, these batteries are prone to the problem of lithium dendrite growth during the charging process, especially at high current density. The formation of lithium dendrites not only reduces the cycle life of the battery, but may also penetrate the solid electrolyte, causing battery short circuit, thereby causing serious safety problems. Therefore, how to effectively inhibit the growth of lithium dendrites during the charging process becomes the key. Summary of the Invention

[0003] To address the above issues, this application discloses a method, device, and storage medium for determining battery charging parameters. The method uses a phase-field method to accurately simulate the growth of lithium dendrites in a battery under different charging parameters, thereby efficiently determining the charging parameters that inhibit lithium dendrite growth and thereby improving the battery's charging efficiency and safety.

[0004] In a first aspect, the present application provides a method for determining battery charging parameters. The method may include: constructing a simulation equation for simulating lithium dendrite growth based on a phase field method; adjusting initial values and / or boundary conditions of the simulation equation, and solving the simulation equation for multiple sets of charging parameters to obtain multiple sets of solution results indicating predicted lithium dendrite formation conditions; and determining target charging parameters for the battery based on the multiple sets of solution results.

[0005] According to some embodiments of the present application, the simulation equations may include: a nonlinear phase field control equation; a lithium ion diffusion equation; and a potential control equation.

[0006] According to some embodiments of the present application, constructing the nonlinear phase field control equation may include: determining the elastic free energy density function based on the elastic modulus, elastic strain tensor and Poisson's ratio of the components of the battery; and using the elastic free energy density function to determine the nonlinear phase field control equation with the phase field parameters as dependent variables.

[0007] According to some embodiments of the present application, the expression of the nonlinear phase field control equation may be:

[0008]

[0009] Where ξ represents the phase field parameter, which is the dependent variable of the above nonlinear phase field control equation. a represents the electric potential, represents the lithium ion concentration, represents time, f mech represents the elastic free energy density function. α represents the symmetry factor, which can be 0.5 as an example. n represents the number of electrons participating in the reaction, which can be 1 as an example. F represents the Faraday constant, which is 96500 C / mol. R represents the ideal gas constant, which is 8.314 J / (mol·K). T represents the temperature, which is 298.15 K. L σ and L η E represents the interface migration rate and reaction rate constant, with values of 5.6×10-4m3 / (J×s) and 1.39×10-4m3 / (J×s) respectively. m and E e represents the elastic modulus of lithium metal and electrolyte, with values of 4.9 GPa and 99.7 GPa respectively. ε represents the elastic strain tensor. ν represents the Poisson's ratio, with a value of 0.2. W represents the barrier height, with a value of 5×105 J / m3; κ represents the anisotropy function of the interfacial energy. represents the interface energy coefficient, which is 1.5×10-8 J / m, δ represents the anisotropic strength of the dendrite, which is 0.05; ω represents the anisotropic modulus of the dendrite, which is 4; θ represents the angle between the surface discovery and the crystal orientation, which is represented by ξ.

[0010] According to some embodiments of the present application, the expression of the elastic free energy density function is: where E is the elastic modulus, ε is the elastic strain tensor, and ν is the Poisson's ratio.

[0011] According to some embodiments of the present application, constructing a lithium ion diffusion equation may include: determining effective lithium ion diffusion coefficients associated with components of the battery; and using the effective lithium ion diffusion coefficients to determine a lithium ion diffusion equation with lithium ion concentration as a dependent variable.

[0012] According to some embodiments of the present application, the lithium ion diffusion equation may be expressed as:

[0013]

[0014] Where c represents the lithium ion concentration, which is the dependent variable of the lithium ion diffusion equation and also serves as the bridge for coupling this equation with the aforementioned nonlinear phase field control equation. φ represents the electric potential, D eff represents the effective diffusion coefficient of lithium ions, D e represents the diffusion coefficient of lithium ions in the electrode (that is, lithium metal), D sThe diffusion coefficient of lithium ions in the electrolyte can be 7.5×10-15m 2 / s and 7.5×10-12m 2 / s.c s It represents the solid phase lithium atom concentration, which is 76900 mol / m 3 , c0 represents the standard volume concentration of lithium ions. ξ represents the phase field parameter. n represents the number of electrons participating in the reaction, which can be 1 for example. F represents the Faraday constant, which is 96500 C / mol. R represents the ideal gas constant, which is 8.314 J / (mol·K). T represents the temperature, which is 298.15 K.

[0015] According to some embodiments of the present application, the expression of the effective diffusion coefficient of lithium ions is D eff =D e h(ξ)+D s (1-h(ξ)); where, D e is the lithium metal diffusion coefficient of lithium ions, D s is the electrolyte diffusion coefficient of lithium ions, and h(ξ) is the interpolation function.

[0016] According to some embodiments of the present application, constructing the potential control equation may include: determining the effective conductivity of components associated with the battery; and using the effective conductivity to determine the potential control equation with potential as a dependent variable.

[0017] According to some embodiments of the present application, the potential control equation may be expressed as:

[0018]

[0019] Among them, φ represents the electric potential, which is the dependent variable of the above-mentioned electric potential control equation and also serves as the bridge for coupling this equation with the above-mentioned nonlinear phase field control equation and the above-mentioned lithium ion diffusion equation. eff represents the effective conductivity, σ s Represents the electrode conductivity of lithium ions, with a value of 1×107S / m, σ e Indicates the electrolyte conductivity of lithium ions, with a value of 0.01S / m. s It represents the solid phase lithium atom concentration, which is 76900 mol / m 3 ξ represents the phase field parameter, n represents the number of electrons participating in the reaction, and can be 1 for example. F represents the Faraday constant, which is 96500 C / mol. R represents the ideal gas constant, which is 8.314 J / (mol·K). T represents the temperature, which is 298.15 K.

[0020] According to some embodiments of the present application, the expression of the effective conductivity is σ eff=σ s h(ξ)+σ e (1-h(ξ)); where σ s is the electrode conductivity of lithium ions, σ e is the electrolyte conductivity of lithium ions, and h(ξ) is the interpolation function.

[0021] According to some embodiments of the present application, the adjusting of the initial value and / or boundary conditions of the simulation equation may at least include: setting randomly distributed lithium dendrite nucleation sites on the interface of the battery using preset rules.

[0022] According to some embodiments of the present application, the pre-rules include generating uniformly distributed random numbers using a random function, and introducing the lithium dendrite nucleation sites based on the random numbers.

[0023] According to some embodiments of the present application, the solution result may at least indicate the lithium dendrite growth area; determining the target charging parameter of the battery based on the multiple sets of solution results may include: specifying the charging parameter corresponding to the minimum value of the lithium dendrite growth area as the target charging parameter of the battery.

[0024] According to some embodiments of the present application, the battery may include a solid-state battery, and the charging parameters include pulse charging parameters, which may include at least charging time, duty cycle, and / or charging voltage.

[0025] In a second aspect, the present application provides a device for determining battery charging parameters. The device may include: a construction module configured to construct a simulation equation for simulating lithium dendrite growth based on a phase field method; a calculation module configured to adjust the initial value and / or boundary conditions of the simulation equation and solve the simulation equation for multiple sets of charging parameters to obtain multiple sets of solution results indicating predicted lithium dendrite formation conditions; and a determination module configured to determine target charging parameters for the battery based on the multiple sets of solution results.

[0026] A third aspect of the present application provides a computing system, which may include: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method for determining battery charging parameters as described above may be implemented.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining battery charging parameters as described above can be implemented.

[0028] In a fifth aspect, the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the method for determining battery charging parameters as described above can be implemented.

[0029] In a sixth aspect, the present application provides a device for establishing a battery equivalent simulation model. The device for establishing a battery equivalent simulation model may include the method device or computing system for determining battery charging parameters as described above.

[0030] The method for determining battery charging parameters disclosed in this application accurately simulates the growth of lithium dendrites based on the phase field method. The determined charging parameters can inhibit the formation of lithium dendrites during the charging process, thereby improving the charging efficiency and safety of the battery.

[0031] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present application will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0033] Figure 1 is an exemplary flow chart of a method for determining battery charging parameters according to some embodiments of the present application;

[0034] Figure 2 is an exemplary schematic diagram of changes in relevant parameters during the lithium dendrite formation process according to some embodiments of the present application;

[0035] Figure 3 is an exemplary schematic diagram of initial values and boundary conditions related to a battery to be tested according to some embodiments of the present application;

[0036] Figure 4 is an exemplary schematic diagram of another change in relevant parameters during the lithium dendrite formation process according to some embodiments of the present application;

[0037] Figure 5 is an exemplary schematic diagram of pulse voltage charging according to some embodiments of the present application;

[0038] Figure 6 is an exemplary schematic diagram of the growth morphology of lithium dendrites under different pulse voltage charging according to some embodiments of the present application;

[0039] Figure 7 is an exemplary schematic diagram of the deposition area of lithium dendrites under different pulse voltage charging according to some embodiments of the present application;

[0040] Figure 8 is an exemplary module diagram of a battery cell equivalent simulation model establishment device according to some embodiments of the present application;

[0041] Figure 9 is an exemplary block diagram of a computing device according to some embodiments of the present application. DETAILED DESCRIPTION

[0042] To make the above-mentioned objects, features, and advantages of the present application more clearly understood, the specific embodiments of the present application are described in detail below. The following description sets forth many specific details to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar modifications without violating the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0043] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used in this application and in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. Words such as "include" or "comprise" used in this application mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The terms "and / or" or "and / or" used in this application include any and all combinations of one or more related listed items.

[0044] The terms "including", "having" and their cognates used in this application are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the aforementioned items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the aforementioned items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the aforementioned items.

[0045] It should be noted that the terms "first", "second", "third", etc. used in this application are only used to distinguish descriptions and should not be understood as indicating or implying relative importance. When a component is referred to as being "fixed to", "mounted on" or "set on" another component, it can be directly on the other component or there can be other components in the middle. When a component is considered to be "connected" to another component, it can be directly connected to the other component or there can be other components in the middle at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0046] Some preferred embodiments of the present application are described below. It should be noted that the following description is for illustrative purposes and is not intended to limit the scope of protection of the present application. The steps involved in the present application can be performed precisely in order, or various steps can be processed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or more operations can be removed from these processes.

[0047] In response to the shortcomings of the existing technology, this application proposes a method for accurately simulating the growth of lithium dendrites and determining charging parameters based on the phase field method. The determined charging parameters can inhibit the formation of lithium dendrites during the charging process, thereby improving the charging efficiency and safety of the battery.

[0048] Figure 1 This is an exemplary flow chart of a method for determining battery charging parameters according to some embodiments of the present application. The battery may include, but is not limited to, lithium-ion batteries (such as lithium cobalt oxide, ternary lithium, lithium iron phosphate, etc.), lead-acid batteries, nickel-metal hydride batteries, sodium-ion batteries, solid-state batteries, etc. Figure 1 The process 100 shown in the figure can be implemented in a computing device, such as an industrial computer, a server, a computer, a tablet, a smart mobile device, etc. In some embodiments, the process 100 can be stored in a storage device (such as a storage unit of the computing device or an external storage device) in the form of a program or instruction. When the program or instruction is executed, the process 100 can be implemented. Figure 1 As shown, process 100 may include the following operations.

[0049] Step 110 : constructing a simulation equation for simulating lithium dendrite growth based on a phase field method.

[0050] It can be understood that the construction of simulation equations using the phase field method can be based on the divergence and deduction of knowledge based on natural laws such as thermodynamics and reaction kinetics, thereby obtaining simulation equations related to the formation of lithium dendrites. In the present application, the simulation equations may include nonlinear phase field control equations, lithium ion diffusion equations, and potential control equations. Among them, the nonlinear phase field control equation can be used to describe the complex morphology during the evolution of lithium dendrites, the lithium ion diffusion equation can be used to describe the concentration change during the lithium ion diffusion process, and the potential control equation can be used to describe the chemical reaction potential of the electrode-electrolyte system.

[0051] The growth process of lithium dendrites is mainly determined by the system free energy and the chemical reaction rate. For the system free energy, in addition to the Helmholtz free energy, gradient energy, electrostatic potential energy, etc., this application takes elastic free energy into consideration. The chemical reaction rate can be calculated by the Butler-Volmer equation. Based on this, using the Allen-Kahn equation and the Butler-Volmer equation, combined with various free energy related expressions, the nonlinear phase field control equation shown in the following formula (1) can be obtained:

[0052]

[0053] Where ξ represents the phase field parameter, which is the dependent variable of the above nonlinear phase field control equation. a represents the electric potential, represents the lithium ion concentration, represents time, f mech Represents the elastic free energy density function. The elastic free energy density function can be determined based on parameters such as the elastic modulus, elastic strain tensor, and Poisson's ratio of the components of the battery, including but not limited to electrodes, electrolytes, etc. For example, the elastic free energy density function can be shown as the following formula (2):

[0054]

[0055] Among them, C ijkl It can be expressed as the following formula (3):

[0056]

[0057] In the above, E represents the elastic modulus, which can be composed of the elastic moduli of the electrode (e.g., lithium electrode) and the electrolyte of the battery. ε represents the elastic strain tensor, and ν represents the Poisson's ratio. The elastic modulus E can be expressed as follows (4):

[0058] E=h(ξ)E m +[1-h(ξ)]E e (4)

[0059] Among them, E m represents the elastic modulus of the electrode (e.g., lithium electrode), E e represents the elastic model of the electrolyte, and h(ξ) represents the interpolation function, which can be expressed as follows:

[0060] h(ξ)=ξ 3 (10-15ξ+6ξ 2 ) (5)

[0061] In addition, in the above formula (1), g(ξ) represents a double-well function, which can be expressed as the following formula (6):

[0062] g(ξ)=Wξ 2 (1-ξ) 2 (6)

[0063] Where W represents the barrier height.

[0064] In the above formula (1), κ represents the anisotropy function of the interfacial energy, which can be expressed as the following formula (7):

[0065]

[0066] in, represents the interface energy coefficient, δ represents the anisotropic strength of the dendrite, ω represents the anisotropic modulus of the dendrite, and θ represents the angle between the surface discovery and the crystal orientation, which can be represented by ξ. For example, θ can be represented by the following formula (8):

[0067]

[0068] In the above expressions, α represents the symmetry factor, which can be exemplarily taken as 0.5. n represents the number of electrons participating in the reaction, which can be exemplarily taken as 1. F represents the Faraday constant, which is 96500 C / mol. R represents the ideal gas constant, which is 8.314 J / (mol·K). T represents the temperature, which is 298.15 K. L σ and L η E represents the interface migration rate and reaction rate constant, with values of 5.6×10-4m3 / (J×s) and 1.39×10-4m3 / (J×s) respectively. m and E e represents the elastic modulus of lithium metal and electrolyte, with values of 4.9 GPa and 99.7 GPa respectively. ε represents the elastic strain tensor. ν represents the Poisson's ratio, with a value of 0.2. W represents the barrier height, with a value of 5×105 J / m3; k represents the anisotropy function of the interfacial energy. represents the interface energy coefficient, which is 1.5×10-8 J / m, δ represents the anisotropic strength of the dendrite, which is 0.05; ω represents the anisotropic modulus of the dendrite, which is 4; θ represents the angle between the surface discovery and the crystal orientation, which is represented by ξ.

[0069] In the present application, the lithium ion diffusion equation can be determined based on the effective lithium ion diffusion coefficients of the components associated with the battery. The components may include but are not limited to electrodes, electrolytes, etc. The effective lithium ion diffusion coefficient can be determined based on the diffusion coefficient of lithium ions in the electrode (i.e., lithium metal) and the diffusion coefficient of lithium ions in the electrolyte. For example, the effective lithium ion diffusion coefficient D eff It can be determined based on the following formula (9):

[0070] D eff =D e h(ξ)+D s (1-h(ξ)) (9)

[0071] Among them, D e represents the diffusion coefficient of lithium ions in the electrode (that is, lithium metal), D s represents the diffusion coefficient of lithium ions in the electrolyte. Using the Nernst-Planck formula and combining the above formula (9), the lithium ion diffusion equation can be expressed as the following formula (10):

[0072]

[0073] Where c represents the lithium ion concentration, which is the dependent variable of the lithium ion diffusion equation and also serves as the bridge for coupling this equation with the aforementioned nonlinear phase field control equation. φ represents the electric potential, D eff represents the effective diffusion coefficient of lithium ions, D e represents the diffusion coefficient of lithium ions in the electrode (that is, lithium metal), D s The diffusion coefficient of lithium ions in the electrolyte can be 7.5×10-15m 2 / s and 7.5×10-12m 2 / s.c s It represents the solid phase lithium atom concentration, which is 76900 mol / m 3 , c0 represents the standard volume concentration of lithium ions. ξ represents the phase field parameter. n represents the number of electrons participating in the reaction, which can be 1 for example. F represents the Faraday constant, which is 96500 C / mol. R represents the ideal gas constant, which is 8.314 J / (mol·K). T represents the temperature, which is 298.15 K.

[0074] In the present application, the potential control equation can be determined based on the effective conductivity of the components associated with the battery. The components may include, but are not limited to, electrodes, electrolytes, etc. The effective conductivity can be determined based on the electrode conductivity of lithium ions and the electrolyte conductivity of lithium ions. For example, the effective conductivity σ eff It can be determined based on the following formula (11):

[0075] σ eff =σ s h(ξ)+σ e (1-h(ξ)) (11)

[0076] Among them, σ s Represents the electrode conductivity of lithium ions and is used to describe the conductivity characteristics of electrodes (e.g., lithium metal).e Represents the electrolyte conductivity of lithium ions and is used to describe the conductivity characteristics of the electrolyte. Using the Poisson equation and combining the above equation (11), the potential control equation can be expressed as the following equation (12):

[0077]

[0078] Among them, φ represents the electric potential, which is the dependent variable of the above-mentioned electric potential control equation and also serves as the bridge for coupling this equation with the above-mentioned nonlinear phase field control equation and the above-mentioned lithium ion diffusion equation. eff represents the effective conductivity, σ e Represents the electrode conductivity of lithium ions, with a value of 1×107S / m, σ e Indicates the electrolyte conductivity of lithium ions, with a value of 0.01S / m. s It represents the solid phase lithium atom concentration, which is 76900 mol / m 3 ξ represents the phase field parameter, n represents the number of electrons participating in the reaction, and can be 1 for example. F represents the Faraday constant, which is 96500 C / mol. R represents the ideal gas constant, which is 8.314 J / (mol·K). T represents the temperature, which is 298.15 K.

[0079] The three equations above respectively describe the complex morphology of lithium dendrite evolution, the concentration change of lithium ion diffusion process, and the chemical reaction potential of the electrode-electrolyte system. Figure 2 The changes in morphology, lithium ion concentration, and potential during the formation of lithium dendrites over time can be shown as Figure 2 shown.

[0080] The above three equations (including equation 1, equation 10 and equation 12) are combined to form the simulation equation.

[0081] Step 120 , adjusting the initial value and / or boundary conditions of the simulation equation, and solving the simulation equation for multiple sets of charging parameters to obtain multiple sets of solution results indicating predicted lithium dendrite formation conditions.

[0082] It can be known that the solution of the equation (for example, the above partial differential equation) requires the given initial values and / or boundary conditions in order to solve the definite solution. In the present application, the given initial values and / or set boundary conditions may include but are not limited to the following: the negative electrode is a lithium metal negative electrode, and the electrolyte is Li3OCl; the lithium metal phase field parameter ξ=1, the electrolyte ξ=0, and the interface is represented by a gradient; the lithium metal negative electrode sets the lithium dendrite nucleation site as the initial morphology of the interface; the boundary flux of ξ is 0; c0 is the boundary condition of lithium ion concentration, the initial concentration is 1000 mol / m3, and it is uniformly distributed; φ0 is the boundary condition of potential (voltage), the initial potential is uniformly distributed, etc. The battery boundary size is 6μm×6μm, for details, please refer to Figure 3 As shown in .

[0083] In order to better simulate the randomness in the process of lithium dendrite formation, the above-mentioned initial values and / or boundary conditions can be adjusted to impart randomness. Exemplarily, the above-mentioned adjustment may include setting randomly distributed lithium dendrite nucleation sites on the interface of the battery. That is to say, the initial form of the above-mentioned interface may be to set lithium dendrite nucleation sites at random positions near the lithium metal. A feasible method is to call the basic random number generation tool random() function to generate uniformly distributed random numbers on the basis of the nucleation sites on the preset electrode-electrolyte interface, introduce random nucleation points, and simulate the randomness of lithium dendrite growth on the actual electrode surface.

[0084] Combine Figure 4 The changes in morphology, lithium ion concentration, and potential during the random generation of lithium dendrites shown can be closer to the randomness of lithium dendrite generation.

[0085] After the initial value and / or boundary conditions are given, the simulation equation can be solved in combination with some data provided by the charging parameters, such as charging time t, charging voltage V, etc., to obtain a solution describing the formation of lithium dendrites. For example, the solved phase field parameters. For multiple sets of charging parameters, multiple solutions can be performed to obtain corresponding multiple sets of solution results. Regarding the solution of the simulation equation, existing commercial software can be used, such as COMSOL Multiphysics, ANSYS Parametric Design Language, MATLAB Partial Differential Equation Toolbox, etc. Or use open source tools such as Python-based FEniCS, FiPy, etc. This application is not specifically limited.

[0086] Step 130: Determine target charging parameters of the battery based on the multiple sets of solution results.

[0087] In the present application, the solution result can at least indicate the lithium dendrite generation area. For example, the solution result obtained by the above means can be output in the form of an image, so as to display the generation morphology of lithium dendrites on the image. The lithium dendrite generation area is obtained by calculating the area of the generated morphology. In some embodiments, the charging parameter corresponding to the minimum value of the lithium dendrite growth area in the multiple groups of solution results can be designated as the target charging parameter of the battery. Charging the battery with this charging parameter can suppress the generation of lithium dendrites to the greatest extent, thereby improving the charging efficiency and the safety of the battery.

[0088] In the present application, the battery may include a solid-state battery. As a new type of battery, solid-state batteries have broader prospects. However, they are more prone to the problem of lithium dendrite growth during the charging process at high current density. The charging method provided in this application can better optimize the charging of solid-state batteries and ensure battery safety. The charging parameters may include pulse charging parameters, including but not limited to charging time, duty cycle, charging voltage, etc. or any combination thereof. Combined Figure 5 The exemplary schematic diagram of pulse voltage charging is shown, which illustrates the parameters of pulse charging voltage, duty cycle, etc. Thus, corresponding to the above initial values and / or boundary conditions, φ0 is the selected pulse voltage, which is 0.1V.

[0089] The following is an illustrative example of the above process using a specific implementation process. It should be noted that the following content is only for illustration and does not limit the present application.

[0090] Assume that the battery parameters of the battery are: the negative electrode is lithium metal, the electrolyte is Li3Ocl, the Young's modulus is 99.7GPa, the Poisson's ratio is 0.2, and the density is 2.01g / cm 3 , lithium metal phase field parameter ξ = 1, electrolyte ξ = 0, the interface is represented by a gradient; the lithium metal negative electrode sets the lithium dendrite nucleation site as the initial morphology of the interface; the boundary flux of ξ is 0; c0 is the boundary condition of lithium ion concentration, the initial concentration is 1000 mol / m3, which is uniformly distributed; φ0 is the boundary condition of electric potential, the initial potential is uniformly distributed, etc.

[0091] The charging amplitude of the pulse charging is set to 0.1V, the pulse period is set to 4s, 20s, 40s, the duty cycle is set to 50%, 60%, 70%, and the following nine groups of embodiments are set in combination:

[0092] Example 1: The charging amplitude is 0.1V, the cycle is 4s, and the duty cycle is 50%;

[0093] Example 2: The charging amplitude is 0.1V, the cycle is 4s, and the duty cycle is 60%;

[0094] Example 3: The charging amplitude is 0.1V, the cycle is 4s, and the duty cycle is 70%;

[0095] Example 4: The charging amplitude is 0.1V, the cycle is 20s, and the duty cycle is 50%;

[0096] Example 5: The charging amplitude is 0.1V, the cycle is 20s, and the duty cycle is 60%;

[0097] Example 6: The charging amplitude is 0.1V, the cycle is 20s, and the duty cycle is 70%;

[0098] Example 7: The charging amplitude is 0.1V, the cycle is 40s, and the duty cycle is 50%;

[0099] Example 8: The charging amplitude is 0.1V, the cycle is 40s, and the duty cycle is 60%;

[0100] Example 9: The charging amplitude is 0.1V, the cycle is 40s, and the duty cycle is 70%.

[0101] Based on the above equations, the lithium dendrite formation morphologies corresponding to Examples 1 to 9 are obtained, as shown in FIG. Figure 6 As shown. Figure 6 The lithium growth area in is calculated and the result is as follows Figure 7 Schematic diagram of lithium deposition area as shown. Figure 7 As shown, the charging parameters corresponding to the first embodiment, that is, the lithium deposition area under a cycle of 4s and a duty cycle of 50%, is the smallest and can be designated as the target charging parameters.

[0102] The method for determining battery charging parameters disclosed in this application accurately simulates the growth of lithium dendrites based on the phase field method. The determined charging parameters can inhibit the formation of lithium dendrites during the charging process, thereby improving the charging efficiency and safety of the battery.

[0103] It should be noted that the above Figure 1 The description of each step in the description is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, under the guidance of this specification, Figure 1 Various modifications and changes may be made to the various steps in the present invention. However, these modifications and changes are still within the scope of this specification.

[0104] The present application also discloses a battery life prediction device. The battery life prediction model establishment device can be used to perform the following Figure 1 For details of the steps shown in , please refer to the corresponding drawings. Figure 8 is an exemplary module diagram of a battery life prediction device according to some embodiments of the present application, such as Figure 8As shown, the battery life prediction device 800 may include a construction module 810 , a calculation module 820 and a determination module 830 .

[0105] The construction module 810 can be configured to construct a simulation equation for simulating the growth of lithium dendrites based on the phase field method. The simulation equations may include a nonlinear phase field control equation, a lithium ion diffusion equation, and a potential control equation. Among them, the nonlinear phase field control equation can be used to describe the complex morphology during the evolution of lithium dendrites, the lithium ion diffusion equation can be used to describe the concentration change during the lithium ion diffusion process, and the potential control equation can be used to describe the chemical reaction potential of the electrode-electrolyte system. The construction module 810 can determine the elastic free energy density function based on the elastic modulus, elastic strain tensor and Poisson's ratio of the components of the battery, and use the elastic free energy density function to determine the nonlinear phase field control equation with the phase field parameters as the dependent variable. For the lithium ion diffusion equation, the construction module 810 can determine the effective diffusion coefficient based on the diffusion coefficient of lithium ions in the electrode and the diffusion coefficient of lithium ions in the electrolyte, and then use it to construct a lithium ion diffusion equation with the lithium ion concentration as the dependent variable. For the potential control equation, the construction module 810 can determine the effective conductivity based on the electrode conductivity of lithium ions and the electrolyte conductivity of lithium ions, and then use it to construct the potential control equation with potential as the dependent variable.

[0106] The calculation module 820 can be configured to adjust the initial value and / or boundary conditions of the simulation equation, and solve the simulation equation for multiple sets of charging parameters to obtain multiple sets of solution results indicating the predicted lithium dendrite formation situation. The calculation module 820 can randomly adjust the above data to give randomness to the lithium dendrite generation simulation. For example, the calculation module 820 can set randomly distributed lithium dendrite nucleation sites on the interface of the battery. Furthermore, the calculation module 820 can also solve the simulation equation to determine the solution result corresponding to each set of charging parameters.

[0107] The determination module 830 can be configured to determine the target charging parameters of the battery based on the multiple sets of solution results. The solution results can be output in the form of an image, thereby displaying the growth morphology of lithium dendrites on the image. The determination module 830 can calculate the area of the lithium dendrite growth by calculating the area of the generated morphology, and the charging parameter corresponding to the minimum value of the lithium dendrite growth area can be designated as the target charging parameter of the battery.

[0108] For other descriptions of the above components, please refer to this application Figure 1-Figure 7 part.

[0109] It should be understood that Figure 8The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will understand that the above-mentioned method and system can be implemented using computer-executable instructions and / or contained in a processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of the present application. Not only can the hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, it can also be implemented using software executed by various types of processors, and it can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0110] It should be noted that the above description of modules is for ease of description only and does not limit this application to the scope of the embodiments illustrated. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form subsystems connected to other modules without departing from these principles. For example, the modules may share a single storage module, or each module may have its own storage module. Such variations are within the scope of protection of this application.

[0111] The present application also provides a computing device. Figure 9 The example block diagram of the computing device shown in FIG. 900 according to some embodiments of the present application. The computing device 900 may include a computer program product for implementing the processes described in the embodiments of the present application (for example, Figure 1-Figure 7 ) or systems (e.g., Figure 8 ). For example, the computing device 900 can be implemented by hardware, software program, firmware or a combination thereof. For convenience, Figure 9 Only one computing device is drawn in the figure, but the computing functions related to the process and / or system / apparatus described in the embodiments of the present application can be implemented in a distributed manner by a group of similar platforms to disperse the processing load of the system.

[0112] In some embodiments, the computing device 900 may include a processor 910, a memory 920, an input / output component 930, and a communication port 940. In some embodiments, the processor (e.g., CPU) 910 may execute program instructions in the form of one or more processors. In some embodiments, the memory 920 includes different forms of program memory and data memory, such as a hard disk, a read-only memory (ROM), a random access memory (RAM), etc., for storing various data files processed and / or transmitted by the computer. In some embodiments, the input / output component 930 may be used to support input / output between the computing device 900 and other components. In some embodiments, the communication port 940 may be connected to a network for data communication. An exemplary computing device may include program instructions executed by the processor 910 stored in a read-only memory (ROM), a random access memory (RAM), and / or other types of non-transitory storage media. The methods and / or processes of the embodiments of the present application may be implemented in the form of program instructions. The computing device 900 may also receive the programs and data disclosed in this application via network communication.

[0113] For ease of understanding, Figure 9 Only one processor is drawn as an example. However, it should be noted that the computing device 900 in the embodiment of the present application may include multiple processors, so the operations and / or methods implemented by one processor described in the embodiment of the present application may also be implemented jointly or independently by multiple processors. For example, if in the present application, the processor of the computing device 900 performs operations A and B, it should be understood that operations A and B may also be performed jointly or independently by two different processors of the computing device 900 (for example, the first processor performs operation A, the second processor performs operation B, or the first and second processors perform operations A and B jointly).

[0114] While the basic concepts have been described herein, it will be apparent to those skilled in the art that the detailed disclosure herein is merely illustrative and does not constitute a limitation of the present application. Although not expressly provided herein, those skilled in the art may make various modifications, improvements, and amendments to the present application. Such modifications, improvements, and amendments are suggested herein and remain within the spirit and scope of the exemplary embodiments of the present application.

[0115] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this application does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0116] Similarly, it should be noted that, in order to simplify the description of this application and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment or its description. However, this disclosure method does not mean that the subject matter of this application requires more features than those recited in the claims. In fact, the features of an embodiment may be fewer than all the features of the individual embodiments disclosed above.

[0117] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other variations may also fall within the scope of this application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this application may be considered consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly introduced and described in this application.

Claims

1. A method for determining battery charging parameters, characterized in that: The determination method includes: A simulation equation for simulating lithium dendrite growth was constructed based on the phase field method; Adjusting the initial value and / or boundary conditions of the simulation equation, and solving the simulation equation for multiple sets of charging parameters to obtain multiple sets of solution results indicating predicted lithium dendrite formation conditions; Based on the multiple sets of solution results, target charging parameters of the battery are determined.

2. The method for determining battery charging parameters according to claim 1, wherein: The elastic free energy density function is determined based on the elastic modulus, elastic strain tensor and Poisson's ratio of the components of the battery, and is expressed as: Where E is the elastic modulus, ε is the elastic strain tensor, and v is the Poisson's ratio.

3. The method for determining battery charging parameters according to claim 1, wherein: The effective lithium ion diffusion coefficient is determined based on the lithium metal diffusion coefficient of the lithium ion and the electrolyte diffusion coefficient, and the expression is: D eff =D e h(ξ)+D s (1-h(ξ)); where D e is the lithium metal diffusion coefficient of lithium ions, D s is the electrolyte diffusion coefficient of lithium ions, and h(ξ) is the interpolation function.

4. The method for determining battery charging parameters according to claim 1, wherein: The effective conductivity is determined based on the electrode conductivity of lithium ions and the electrolyte conductivity, and the expression is: σ eff =σ s h(ξ)+σ e (1-h(ξ)); where σ s is the electrode conductivity of lithium ions, σ e is the electrolyte conductivity of lithium ions, and h(ξ) is the interpolation function.

5. The method for determining battery charging parameters according to claim 1, wherein: The adjusting of the initial value and / or boundary conditions of the simulation equation at least includes: Randomly distributed lithium dendrite nucleation sites are set on the interface of the battery using preset rules.

6. The method for determining battery charging parameters according to claim 5, wherein: The pre-rule includes generating uniformly distributed random numbers using a random function, and introducing the lithium dendrite nucleation sites based on the random numbers.

7. The method for determining battery charging parameters according to claim 1, wherein: The solution result at least indicates the lithium dendrite growth area; and determining the target charging parameter of the battery based on the multiple sets of solution results includes: The charging parameter corresponding to the minimum value of the lithium dendrite growth area is designated as the target charging parameter of the battery.

8. The method for determining battery charging parameters according to any one of claims 1 to 7, characterized in that: The battery comprises a solid-state battery, and the charging parameters comprise pulse charging parameters, including at least charging time, duty cycle, and / or charging voltage.

9. A device for determining battery charging parameters, characterized in that: The determining device comprises: a building module configured to build a simulation equation for simulating lithium dendrite growth based on a phase field method; a calculation module configured to adjust initial values and / or boundary conditions of the simulation equation and solve the simulation equation for multiple sets of charging parameters to obtain multiple sets of solution results indicating predicted lithium dendrite formation conditions; The determination module is configured to determine the target charging parameter of the battery based on the multiple sets of solution results.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for determining battery charging parameters according to any one of claims 1 to 8.

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