A method and system for predicting the evolution of lithium-based metal anode interfaces in all-solid-state batteries

Abstract

This invention belongs to the field of secondary battery technology and discloses a prediction method and system for the interface evolution of lithium-based metal anode in all-solid-state batteries. The steps are as follows: (1) Couple the phase-field model, lithium diffusion model, electrochemical model, and explicit nucleation model of the lithium-based metal anode to obtain an interface evolution model. The explicit nucleation model controls the nucleation process of voids by changing the phase-field order parameter values ​​at the grid nodes in the phase-field model; (2) Construct a two-dimensional half-cell computational domain composed of lithium-based metal electrode and solid electrolyte, and perform grid division on the computational domain, setting the initial conditions and boundary conditions of the interface evolution model; (3) Perform transient calculations on the interface evolution model based on the physical property parameters and current density of the lithium-based metal anode to obtain the phase-field order parameter and stripping capacity, and then obtain the evolution morphology changes of interface voids with the stripping process. This invention achieves a balance between interface stability and lithium diffusion capability.

Description

Technical Field

[0001] This invention belongs to the technical field of secondary batteries, and more specifically, relates to a method and system for predicting the evolution of the lithium-based metal anode interface in all-solid-state batteries. Background Technology

[0002] With the continuous growth of energy demand and the increasing severity of environmental problems, energy storage technologies with high energy density, high safety, and long cycle life have become a research hotspot. Lithium-based metal anodes are considered ideal anode materials for next-generation high-energy-density batteries due to their ultra-high theoretical specific capacity and extremely low electrochemical potential. Solid-state electrolytes, compared to traditional liquid electrolytes, possess intrinsic safety characteristics and hold promise for solving the problem of lithium dendrite growth in lithium metal electrodes.

[0003] However, the application of all-solid-state lithium metal batteries still faces many challenges, the most significant of which is the interface problem between the lithium metal electrode and the electrolyte. Unlike the solid-liquid interface of traditional lithium-ion batteries that use liquid electrolytes, in all-solid-state lithium metal batteries, because both the electrode and electrolyte are solid, it is difficult to ensure sufficient contact between the two surfaces, tending to form an interface in a point-to-point manner. This loss of interfacial contact leads to a reduction in the electrochemical active area, and current flows only through the active sites on the interface, which may result in very high interfacial impedance. The formation and evolution of interfacial voids is an important interfacial problem. Even if a good interface is initially achieved through surface treatment, repeated lithium deposition and stripping cycles can still gradually deteriorate the interfacial contact.

[0004] To improve the interfacial stability of lithium-based metal anodes, researchers have proposed various strategies, including interfacial modification, alloying, and composite electrode design. Among these, lithium metal alloying is considered one of the most effective solutions. Lithium can be mixed with indium, aluminum, magnesium, zinc, silicon, silver, bismuth, etc., to form binary lithium alloys, which offer advantages such as high potential, high lithium diffusion coefficient, and improved interfacial wettability, thus reducing interfacial resistance, increasing critical current density, and improving interfacial stability. However, the lithium diffusion rate in the electrode plays a crucial role in interfacial evolution; a lower lithium diffusion rate is more likely to lead to interfacial void formation and interfacial failure. Experiments have confirmed that the lithium diffusion rate in lithium alloys is lower than that in lithium metal. The increase in interfacial stability caused by alloying and the decrease in bulk diffusion rate are contradictory. Therefore, how to balance the interfacial stability and lithium diffusion capacity of lithium-magnesium alloy anodes remains a pressing problem to be solved. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method and system for predicting the interface evolution of lithium-based metal anodes in all-solid-state batteries, which aims to solve the problem that existing methods are difficult to balance the interface stability and lithium diffusion capability of lithium-based metal anodes.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for predicting the evolution of the interface of an all-solid-state lithium-based metal anode is provided, the method comprising the following steps:

[0007] (1) Construct a phase-field model that distinguishes between the voids and the metal phase of the lithium-based metal anode to be predicted, as well as a lithium diffusion model, an electrochemical model, and an explicit nucleation model;

[0008] (2) The phase field model, lithium diffusion model, electrochemical model and explicit nucleation model of lithium-based metal anode are coupled to obtain the interface evolution model. The explicit nucleation model controls the nucleation process of voids by changing the phase field order parameter value at the grid node in the phase field model.

[0009] (3) Construct a two-dimensional half-cell computational domain consisting of lithium-based metal electrodes and solid electrolytes, and mesh the computational domain, setting the initial conditions and boundary conditions of the interface evolution model.

[0010] (4) Transient calculations are performed on the interface evolution model based on the physical properties and current density of the lithium-based metal anode to predict the interface evolution.

[0011] Furthermore, the lithium atom migration process described by the lithium diffusion model only occurs in the metallic phase specified by the phase field model; the interfacial current flux calculated by the electrochemical model is added to the boundary conditions of the phase field as a source of vacancies, and the interfacial current density is added to the lithium diffusion model as a boundary condition; the lithium atom concentration near the reaction interface calculated by the lithium diffusion model affects the electrochemical model by causing an increase in concentration overpotential.

[0012] Furthermore, the explicit nucleation model first calculates the nucleation probability p. nuc Then, generate a set of random numbers p from 0 to 1 on the boundary of the nucleus. r When a certain position satisfies p r <p nuc At that time, the phase field order parameter c at this point v It becomes 1; when the numerical mutation of the phase field sequence parameter near the boundary accumulates to a predetermined level, the phase field model naturally undergoes phase separation, realizing void nucleation.

[0013] Furthermore, the phase field order parameter c inside the electrode in the initial phase field model... v Set all to 0.

[0014] Furthermore, a triangular mesh is used to discretize the computational domain, and the mesh is refined in the electrode region and the electrode-electrolyte interface region.

[0015] Furthermore, the maximum characteristic length of the grid in the electrode region and the electrode-electrolyte interface region is one-tenth of the interface thickness.

[0016] Furthermore, when the interface changes from a void-free state to a void-containing state, forming a spherical void with radius r, the change in free energy during this transformation process is expressed as:

[0017] ΔG=-r 3 F α ΔG v +r 2 F β γ s +r 2 F γ (γ SSE -γ int )

[0018] Wherein, ΔG v Represents volumetric energy density; γ s γ represents the surface energy of a metal. SSE γ represents the surface energy of a solid electrolyte. int F represents the interfacial energy between a metal and a solid electrolyte. α F β and F γ These are the geometric parameters related to the interface contact angle α:

[0019]

[0020] F β = 2π(1-cosα)

[0021] F γ =πsin 2 α

[0022] Furthermore, the energy barrier that needs to be overcome for the void nucleation process is:

[0023]

[0024] The nucleation process can be represented by the Boltzmann distribution, with a nucleation probability p. nuc for:

[0025]

[0026] Where, k B It is the Boltzmann constant.

[0027] By setting the physical property parameters corresponding to different alloy compositions, the nucleation probability can be affected, thereby controlling the evolution process of interface voids.

[0028] Furthermore, the phase-field model is expressed as:

[0029]

[0030] Where ψ is the free energy functional F with respect to c v Chemical potential obtained by variation; M = D * Ω / RT represents the phase field diffusion migration parameter; D* represents the vacancy diffusion rate in lithium-based metals, which is related to magnesium content; Ω is the molar volume of the metal, R is the universal gas constant, and T is the temperature;

[0031] The electrode free energy functional F is:

[0032]

[0033] Among them, W c g(c) represents the barrier height; v ) is a double-well function, g(c v )=(c v -c eq ) 2 (1-c v ) 2 For a double-well function, in c eq Takes the minimum value between and 1, where c eq c represents the equilibrium vacancy concentration. eq =exp(-h v / RT), h v It is the molar enthalpy of vacancy formation; k c It is the gradient energy coefficient; h(c) v f is the interpolation function; m V represents the chemical energy of the metallic phase; V is the electrode volume.

[0034] The expression for the lithium diffusion model is:

[0035]

[0036] Where c represents the concentration of lithium atoms, and J represents the lithium diffusion flux. The interpolation function h(c) v D is used to emphasize that there is no diffusion in the cavity. * This indicates the diffusion rate of lithium atoms in the metallic phase;

[0037] The expression for the electrochemical model is:

[0038]

[0039] Where, σ SSE ,φ SSE σ represents the conductivity and potential of the electrolyte, respectively. an ,φ anThese represent the conductivity and potential of the negative electrode, respectively. Since the void is non-conductive, interpolation is needed to obtain the conductivity of the transition region:

[0040] σ an =f(c v )σ m

[0041] Where, σ m Electrical conductivity in the electrode metal phase.

[0042] Furthermore, the boundary conditions for the phase-field model and the lithium diffusion model are as follows:

[0043] The phase-field model is calculated only in the electrode region, and boundary conditions are set for the electrode / electrolyte interface:

[0044]

[0045] Where n represents the direction of the boundary normal, i loc Electrode / electrolyte interface current density; other boundaries in the phase-field model are zero-flux boundaries.

[0046] The lithium diffusion model is calculated only in the electrode region, and boundary conditions are set for the electrode / electrolyte interface:

[0047]

[0048] In the lithium diffusion model, the remaining boundaries are zero flux boundaries.

[0049] Furthermore, the lithium-based metal includes lithium metal and binary lithium-based alloys.

[0050] The present invention also provides a prediction system for the evolution of the all-solid-state lithium-based metal anode interface. The system includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it performs the prediction method for the evolution of the all-solid-state lithium-based metal anode interface as described above.

[0051] The present invention also provides a computer-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method for predicting the evolution of the all-solid-state lithium-based metal anode interface as described above.

[0052] In summary, compared with the prior art, the prediction method and system for the evolution of the lithium-based metal anode interface in all-solid-state batteries provided by this invention have the following advantages:

[0053] 1. A coupled phase-field model, lithium diffusion model, electrochemical model, and explicit nucleation model are used to derive an interface evolution model. This model reflects the complete process, taking into account the influence of electrode composition on void nucleation, and simulates the entire process from void nucleation and growth to interface failure. Simultaneously, lithium metal alloying can enhance interfacial contact (a beneficial effect), but as the proportion of lithium in the alloy decreases, the diffusion coefficient decreases (a negative effect). Therefore, there exists an optimal alloy composition to balance these two effects and achieve higher exfoliation capability. Based on this, this method can screen for the optimal alloy composition under different exfoliation scenarios, thereby achieving a balance between interface stability and lithium diffusion capability.

[0054] 2. By setting different physical property parameters, the interface evolution model can simulate the interface evolution of lithium metal and various lithium-based alloy electrodes, and has strong applicability.

[0055] 3. In the phase-field model, the internal c of the electrode v All values ​​are set to 0, meaning no micro-voids or impurities are pre-set at the interface. This setting ensures that the research can focus on the interface evolution induced by void nucleation growth, exclude pre-existing micro-voids or impurities from acting as nucleation sites and interfering with the tracking of interface evolution, and ensure that void formation originates from the spontaneous phase separation process of the phase field model.

[0056] 4. By setting the physical property parameters corresponding to different alloy compositions, the nucleation probability can be affected from three aspects: volumetric energy density, metal surface energy, and interface contact angle, thereby controlling the interface void evolution process.

[0057] 5. The interface evolution model can guide the selection of electrode composition and the corresponding optimal operating current conditions: by constructing a relationship diagram of current density, alloy composition and peelable capacity, the design of electrode composition and the selection of optimal operating conditions can be guided. Attached Figure Description

[0058] Figure 1 This is a flowchart of a method for predicting the evolution of the interface of an all-solid-state lithium-based metal anode provided by the present invention;

[0059] Figure 2 Schematic diagram of the model construction principle in the method for predicting the evolution of the lithium-based metal anode interface in solid-state batteries provided in this embodiment of the invention;

[0060] Figure 3 This is a schematic diagram of the geometric structure, boundary conditions, and initial value settings in the method for predicting the evolution of the lithium-based metal anode interface in solid-state batteries provided in this embodiment of the invention.

[0061] Figure 4 This is a diagram showing the effect of the interface evolution calculated in the method for predicting the interface evolution of lithium-based metal anodes in solid-state batteries provided in this embodiment of the invention.

[0062] Figure 5 This is a graph showing the relationship between stripping current density, magnesium content, and stripping capacity constructed in the method for predicting the evolution of the lithium-based metal anode interface in solid-state batteries provided in this embodiment of the invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0064] Please see Figure 1 This invention provides a method for predicting the evolution of the interface of an all-solid-state lithium-based metal anode, the prediction method mainly including the following steps:

[0065] S1. Construct a phase-field model that distinguishes between the voids and the metallic phase of the lithium-based metal anode to be predicted, as well as a lithium diffusion model, an electrochemical model, and an explicit nucleation model.

[0066] Specifically, a phase-field model of the lithium-based metal anode alloy system is constructed using the Cahn-Hilliard phase-field model in a conserved form, and the phase-field order parameter c is used. v c represents the relative concentration of vacancies within the electrode. v =1 indicates a hollow region, c v =0 represents the metallic region. The free energy of the lithium-based metal anode alloy system can be written as:

[0067]

[0068] Among them, W c g(c v W is a double-well function describing the equilibrium state of metals and voids. c The potential barrier height is related to the metal / void interface energy γ. s (i.e., the surface energy of the metal) is related to the interface thickness δ, W c =12γ s / δ;g(c v )=(c v -c eq ) 2 (1-c v ) 2 For a double-well function, in c eq Takes the minimum value between and 1, where c eq c represents the equilibrium vacancy concentration. eq =exp(-h v / RT), h vIt is the molar enthalpy of vacancy formation, where R is the universal gas constant and T is the temperature; κ c It is the gradient energy coefficient, which can also be derived from the metal / void interface energy γ. s The thickness δ of the interface determines κ. c =1.5γ s δ;h(c v ) is an interpolation function that represents a smooth and continuous energy transition at the interface. Specifically, in this embodiment... f m The chemical energy of the metallic phase is expressed by the classical formula for the chemical energy of a binary system:

[0069] f m =h v c v +RT(c v ln c v +(1-c v )ln(1-c v (2)

[0070] The phase-field model is represented as:

[0071]

[0072] Where ψ is the free energy functional F with respect to c v Chemical potential obtained by variation; M = D * Ω / RT represents the phase field diffusion migration parameter; g′(c v ) represents the double-well function g(c v The derivative of f'; D* represents the vacancy diffusion rate in lithium-based metals, which is related to magnesium content; Ω is the molar volume of the metal; f' m f m The derivative of .

[0073] The expression for the lithium diffusion model is:

[0074]

[0075] Where c represents the concentration of lithium atoms, and J represents the lithium diffusion flux. The interpolation function h(c) v D is used to emphasize that there is no diffusion in the cavity. * This represents the diffusion rate of lithium atoms in the metallic phase, assumed to be equal to the lithium diffusion rate.

[0076] The expression for the electrochemical model is:

[0077]

[0078] Where, σ SSE ,φ SSE σ represents the conductivity and potential of the electrolyte, respectively.an ,φ an These represent the conductivity and potential of the negative electrode, respectively. Since the void is non-conductive, interpolation is needed to obtain the conductivity of the transition region:

[0079] σ an =f(c v )σ Li (9)

[0080] Because electrode potential is sensitive to conductivity, a steeper, higher-order interpolation function is needed to reflect changes in interface conductivity. Specifically, the interpolation function f(c v The preferred option is f(c) v )=16(1-c v ) 15 -15(1-c v ) 16 .

[0081] For the stripping process, the electrode exchange current density reflects the rate at which lithium ions enter the electrolyte and vacancies are generated on the electrode surface, and can be expressed by the Butler-Volmer equation:

[0082]

[0083] Among them, i 0,ref The reference exchange current density is represented by c0, which represents the initial lithium atom concentration, and α is the α value. a α represents the anode charge transfer coefficient. c =1-α a η represents the cathode charge transfer coefficient, and η represents the overpotential, reflecting the degree to which the electrode potential deviates from the equilibrium potential.

[0084] η=φ s -φ l -E Θ +η c (11)

[0085] Where, φ s φ represents the electrode potential. l η represents the electrolyte potential. c E represents the concentration overpotential. Θ This represents the electrode reference equilibrium potential; for lithium battery electrodes, E Θ =0; Concentration overpotential η c Caused by localized concentration changes at the electrode surface, this describes the potential change resulting from the difference between the reactant concentration at the electrode interface and the bulk concentration, and can be expressed as:

[0086]

[0087] The explicit nucleation model is:

[0088] For electrode systems, voids are more likely to nucleate at the electrode / electrolyte interface. This is a non-uniform nucleation process, where the energy barrier required for nucleation at the boundary is lower and related to the contact angle α between the void and the surface. When the interface transitions from a void-free state to a void-containing state, forming a spherical void with radius r, the change in free energy during this transition can be expressed as:

[0089] ΔG=-r 3 F α ΔG v +r 2 F β γ s +r 2 F γ (γ SSE -γ int (13)

[0090] Wherein, ΔG v Represents volumetric energy density; γ s γ represents the surface energy of a metal (i.e., the interfacial energy between the metal and the void). SSE γ represents the surface energy of a solid electrolyte. int F represents the interfacial energy between a metal and a solid electrolyte. α F β and F γ These are the geometric parameters related to the interface contact angle α:

[0091]

[0092] F β =2π(1-cosα) (15)

[0093] F γ =πsin 2 α (16)

[0094] Based on Young's equation, the surface energy of the solid electrolyte, the interfacial energy between the metal and the solid electrolyte, and the interfacial energy between the metal and the solid electrolyte satisfy a mechanical equilibrium:

[0095] γ s cosα+γ SSE =γ int (17)

[0096] Differentiating equation (13) with respect to r and setting it to 0, we can obtain the critical radius r. c satisfy:

[0097]

[0098] The critical radius r cSubstituting into equation (13), we obtain the nucleation energy barrier for heterogeneous nucleation:

[0099]

[0100] The nucleation of cavities can be viewed as a transition of the system from an initial state to a more stable state, but it requires overcoming a certain energy barrier (ΔG). c This process can be represented by the Boltzmann distribution, and the probability of nucleation can be expressed as:

[0101]

[0102] Where, k B It is the Boltzmann constant.

[0103] Volumetric energy density ΔG v Related to overpotential caused by interface impedance, it is expressed as:

[0104]

[0105] Among them, R int This represents the interface impedance.

[0106] The steps for using an explicit nucleation model are as follows:

[0107] (I) Calculate the nucleation probability p nuc ;

[0108] (II) Generate a set of random numbers p from 0 to 1 on the boundary of the nucleus. r ;

[0109] (III) When a certain position satisfies p r <p nuc At that time, the phase field order parameter c at this point v Change to 1;

[0110] (IV) When the numerical mutation of the phase field sequence parameter near the boundary accumulates to a predetermined level, the phase field model naturally undergoes phase separation, realizing void nucleation.

[0111] S2, the phase-field model, lithium diffusion model, electrochemical model and explicit nucleation model of lithium-based metal anode are coupled to obtain interface evolution model. The explicit nucleation model controls the nucleation process of voids by changing the phase-field order parameter values ​​at the grid nodes in the phase-field model.

[0112] Please see Figure 2The phase field distinguishes between the metallic phase and the void phase, and the explicit nucleation model influences the nucleation process of the phase field. The lithium diffusion model describes the lithium atom migration process, which only occurs in the metallic phase specified by the phase field model; therefore, the lithium diffusion model is unidirectionally influenced by the phase field. Since voids are non-conductive, discontinuous contact at the interface affects the interfacial electrochemical reaction in the electrochemical model. The interfacial current flux calculated by the electrochemical model is added to the boundary conditions of the phase field as a source of vacancies; therefore, the phase field and the electrochemical model achieve bidirectional coupling. The interfacial current density also reflects the consumption of lithium atoms in the interfacial electrochemical reaction and is added to the lithium diffusion model as a boundary condition. The lithium atom concentration near the reaction interface calculated by the lithium diffusion model causes an increase in concentration overpotential, thus affecting the electrochemical model; therefore, the lithium diffusion model and the electrochemical model can also be bidirectionally coupled. The explicit nucleation model controls the nucleation process of voids by changing the phase field order parameter values ​​at the grid nodes in the phase field model.

[0113] S3 constructs a two-dimensional half-cell computational domain consisting of lithium-based metal electrodes and solid electrolytes, and performs mesh generation on the computational domain, setting the initial conditions and boundary conditions for the interface evolution model.

[0114] In this embodiment, the width and height of both the electrode and the electrolyte are W, with a scale on the micrometer scale. (See...) Figure 3 .

[0115] Initially, the electrode and electrolyte interface are in complete contact, i.e., the electrode interior c in the phase-field model. v All values ​​are set to 0, meaning no micro-voids or impurities are pre-set at the interface. This setting ensures that the research can focus on the interface evolution induced by void nucleation growth, exclude pre-existing micro-voids or impurities from acting as nucleation sites and interfering with the tracking of interface evolution, and ensure that void formation originates from the spontaneous phase separation process of the phase field model.

[0116] The phase-field model is calculated only in the electrode region, and boundary conditions are set for the electrode / electrolyte interface:

[0117]

[0118] In the phase-field model, the remaining boundaries are zero-flux boundaries.

[0119] The lithium diffusion model is calculated only in the electrode region, and boundary conditions are set for the electrode / electrolyte interface:

[0120]

[0121] In the lithium diffusion model, the remaining boundaries are zero flux boundaries.

[0122] In the electrochemical model, a grounded boundary condition is applied to the electrode current collector, with continuous boundaries on both sides, and a stripping current density i is applied to the electrolyte side; an interfacial exchange current density is set at the electrode / electrolyte interface.

[0123] The computational region is discretized using a triangular mesh, and the mesh is further refined in the electrode region and the electrode-electrolyte interface region to improve computational accuracy. Specifically, the maximum feature length of the mesh in the electrode region and the electrode-electrolyte interface region is preferably one-tenth of the interface thickness.

[0124] S4 uses the physical properties and current density of the lithium-based metal anode to perform transient calculations on the interface evolution model in order to predict the interface evolution.

[0125] In one implementation, the MUMPS solver is used to perform transient calculations on the interface evolution model. Using the constructed electrochemical-concentration-nucleation-phase-field coupled interface evolution model, the evolutionary morphology of interface voids during the exfoliation process is obtained, represented by phase-field order parameters, such as... Figure 4 As shown.

[0126] In this embodiment, a lithium-magnesium alloy is preferred, and the relationship between the stripping capacity and the stripping current density and magnesium content was obtained, such as... Figure 5 As shown in the diagram, at lower current densities, the peelable capacity initially increases and then decreases with increasing magnesium content in the electrode, indicating an optimal magnesium content for best performance. At higher current densities, the peelable capacity continuously decreases with increasing magnesium content, thus lithium metal electrodes are superior. This phase diagram can guide electrode composition design and selection of optimal operating conditions in industrial applications.

[0127] The present invention also provides a prediction system for the evolution of the all-solid-state lithium-based metal anode interface. The system includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it performs the prediction method for the evolution of the all-solid-state lithium-based metal anode interface as described above.

[0128] The present invention also provides a computer-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method for predicting the evolution of the all-solid-state lithium-based metal anode interface as described above.

[0129] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the evolution of an all-solid-state lithium-based metal anode interface, characterized in that, The prediction method comprises the following steps: (1) coupling a phase field model of a lithium-based metal negative electrode, a lithium diffusion model, an electrochemical model, and an explicit nucleation model to obtain an interface evolution model, wherein the explicit nucleation model controls a nucleation process of a cavity by changing a phase field order parameter value at a grid node in the phase field model; (2) constructing a two-dimensional half-cell calculation domain composed of a lithium-based metal electrode and a solid-state electrolyte, and performing grid division on the calculation domain, and setting initial conditions and boundary conditions of the interface evolution model; (3) performing transient calculation on the interface evolution model based on physical property parameters and current density of the lithium-based metal negative electrode to realize prediction of interface evolution; The explicit nucleation model first calculates the nucleation probability. Then generate a set of random numbers from 0 to 1 on the boundary of the nucleus. When a certain position satisfies At that time, the phase field sequence parameter at this point It becomes 1; when the numerical mutation of the phase field order parameter near the boundary accumulates to a predetermined level, the phase field model naturally undergoes phase separation, realizing void nucleation; Phase field order parameter c in electrode interior at initial moment in phase field model v are all set to 0; when the interface is transformed from a state without a cavity to a state with a cavity, a spherical cavity with a radius of r is formed, and the change in free energy of this transformation process is represented as: wherein represents the volumetric energy density; represents the metal surface energy, represents the surface energy of the solid-state electrolyte, represents the interfacial energy between the metal and the solid-state electrolyte, , and is the interfacial contact angle geometric shape parameters: 。 2.The method of claim 1, wherein: The lithium atom migration process described by the lithium diffusion model only occurs in the metal phase specified by the phase field model; the interface current flux calculated by the electrochemical model is added to the boundary conditions of the phase field as a source of vacancies, and the interface current density is added to the lithium diffusion model as a boundary condition; The lithium atom concentration near the reaction interface calculated by the lithium diffusion model affects the electrochemical model by causing an increase in the concentration overpotential. 3.The method of claim 1, wherein: The energy barrier that needs to be overcome in the cavity nucleation process is: The nucleation process is expressed by the Boltzmann distribution, the probability of nucleation is P = exp(-ΔG / kT) wherein is the Boltzmann constant; By setting different alloy composition corresponding physical property parameters to affect the nucleation probability, the interface cavity evolution process is controlled.

4. The method for predicting the evolution of the all-solid-state lithium-based metal anode interface as described in claim 1, characterized in that: The phase field model is represented as: wherein is the electrode free energy functional with respect to the chemical potential obtained from the variation; denotes the phase field diffusion mobility parameter; is the molar volume of the metal, R is the universal gas constant, T is the temperature; The electrode free energy functional is: wherein, is the barrier height; is the double-well function, is the double-well function, wherein represents the equilibrium vacancy concentration, , is the vacancy formation molar enthalpy; is the gradient energy coefficient; is the interpolation function; represents the metal phase chemical energy; is the electrode volume; The expression of the lithium diffusion model is: wherein, c denotes the concentration of lithium atoms, J denotes the lithium diffusion flux; interpolation function for emphasizing that no diffusion exists in the voids, denotes the diffusion rate of lithium atoms in the metallic phase; The expression of the electrochemical model is: wherein, respectively represent the conductivity and the potential of the electrolyte, respectively represent the conductivity and the potential of the negative electrode; since the void has no electrical conductivity, interpolation is required to obtain the conductivity of the transition region: wherein, Electrical conductivity in the electrode metal phase. 5.The method of claim 4, wherein: The boundary conditions of the phase field model and the lithium diffusion model are: The phase field model is calculated only in the electrode region, and the boundary conditions are set for the electrode / electrolyte interface: wherein denotes the normal direction of the boundary, electrode / electrolyte interface current density, the remaining boundaries in the phase field model are zero flux boundaries; The lithium diffusion model is calculated only in the electrode region, and the boundary conditions are set for the electrode / electrolyte interface: The remaining boundary in the lithium diffusion model is a zero flux boundary.

6. A system for predicting all-solid-state lithium-based metal anode interface evolution, characterized by: The system comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to execute the prediction method of the interface evolution of the all-solid-state lithium-based metal negative electrode according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that: The computer readable storage medium stores machine executable instructions, and when the machine executable instructions are called and executed by the processor, the machine executable instructions cause the processor to implement the prediction method of the interface evolution of the all-solid-state lithium-based metal negative electrode according to any one of claims 1-5.

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