Method and system for predicting lithium-based metal negative electrode interface evolution of all-solid-state battery

Abstract

The invention belongs to the related technical field of secondary batteries, and discloses an all-solid-state battery lithium-based metal negative electrode interface evolution prediction method and system, and the method comprises the steps: (1) coupling a phase field model, a lithium diffusion model, an electrochemical model and an explicit nucleation model of a lithium-based metal negative electrode to obtain an interface evolution model, the explicit nucleation model controls the nucleation process of the cavity by changing the phase field sequence parameter value at the grid node in the phase field model; (2) constructing a two-dimensional half-cell computational domain composed of the lithium-based metal electrode and the solid electrolyte, performing grid division on the computational domain, and setting initial conditions and boundary conditions of an interface evolution model; and (3) carrying out transient calculation on the interface evolution model based on the physical property parameters and the current density of the lithium-based metal negative electrode so as to obtain a phase field sequence parameter and a stripping capacity, and further obtaining the evolution morphology change of the interface cavity along with the stripping process. According to the invention, the balance between the interface stability and the lithium diffusivity is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field related to secondary batteries, and more specifically, relates to a method and system for predicting the interface evolution of lithium-based metal negative electrodes in all-solid-state batteries. Background Art

[0002] With the continuous growth of energy demand and increasingly severe 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 offer inherent safety compared to traditional liquid electrolytes and are expected to address the problem of lithium dendrite growth on lithium metal electrodes.

[0003] However, the application of all-solid-state lithium metal batteries still faces many challenges, the most important of which is the interface problem between the lithium metal electrode and the electrolyte. Unlike the solid-liquid interface of traditional lithium-ion batteries using liquid electrolytes, in all-solid-state lithium metal batteries, since both the electrode and the electrolyte are solid, sufficient contact between the two surfaces is difficult to ensure, and the interface tends to form in a point-to-point manner. This loss of interfacial contact causes a reduction in the electrochemical active area, and the current will only flow through the active points on the interface, which may lead to very high interfacial impedance. The formation and evolution of interfacial voids is an important interfacial problem. Even if a good interface is initially obtained through surface treatment, repeated lithium deposition and stripping cycles may still gradually deteriorate the interfacial contact.

[0004] To improve the interfacial stability of lithium-based metal anodes, researchers have proposed a variety of strategies, including interface modification, alloying, and composite electrode design. Among them, 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 have advantages such as high potential, high lithium diffusion coefficient, and improved interfacial wettability, which are beneficial for reducing interfacial resistance, increasing critical current density, and improving interfacial stability. However, the lithium diffusion rate in the electrode plays an important role in the interface evolution. A lower lithium diffusion rate is more likely to lead to the formation of interfacial voids, causing interface failure. Experiments have confirmed that the lithium diffusion rate in lithium alloys is lower than that of lithium metal. The increase in interfacial stability caused by alloying and the decrease in bulk diffusion rate are a contradiction. Therefore, how to balance the interfacial stability and lithium diffusion capacity of lithium-magnesium alloy anodes remains an urgent problem to be solved. Summary of the Invention

[0005] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a method and system for predicting the interface evolution of lithium-based metal negative electrodes of all-solid-state batteries, which aims to solve the problem that the existing methods are difficult to take into account both the interface stability and lithium diffusion capacity of lithium-based metal negative electrodes.

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

[0007] (1) Construct a phase field model to distinguish between the void and 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) coupling a phase field model, a lithium diffusion model, an electrochemical model, and an explicit nucleation model of a lithium-based metal negative electrode to obtain an interface evolution model, wherein the explicit nucleation model controls the nucleation process of the void by changing the values of the phase field order parameters at the grid nodes in the phase field model;

[0009] (3) Construct a two-dimensional half-cell computational domain consisting of a lithium-based metal electrode and a solid electrolyte, mesh the computational domain, and set the initial and boundary conditions of the interface evolution model;

[0010] (4) Based on the physical properties and current density of the lithium-based metal negative electrode, the interface evolution model is transiently calculated to achieve the prediction of interface evolution.

[0011] Furthermore, the lithium atom migration process described by the lithium diffusion model only occurs in the metal 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 as a boundary condition to the lithium diffusion model; 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 , and then generate a set of random numbers p from 0 to 1 on the boundary of the nucleus r ; When a position satisfies p r <p nuc When the phase field order parameter c at this point v becomes 1; when the numerical mutation of the phase field order parameter near the boundary accumulates to a predetermined degree, the phase field model naturally undergoes phase separation and realizes void nucleation.

[0013] Furthermore, the internal phase field order parameter c of the electrode in the phase field model at the initial moment is v Set them all to 0.

[0014] Furthermore, a triangular mesh is used to discretize the calculation area, and the mesh is encrypted in the electrode area and the electrode-electrolyte interface area.

[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, a spherical void with a radius of r is formed. The change in free energy of this transition 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] Among them, ΔG v represents the volume energy density; γ s represents the metal surface energy, γ SSE represents the surface energy of the solid electrolyte, γ int represents the interfacial energy between metal and solid electrolyte, F α 、F β and F γ is the geometric parameter 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 in the void nucleation process is:

[0023]

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

[0025]

[0026] Among them, k B 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 variational calculation; 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 the 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 is the barrier height; g(c v ) is a double-well function, g(c v )=(c v -c eq ) 2 (1-c v ) 2 is a double-well function, in c eq and 1, where c eq represents the equilibrium vacancy concentration, c eq =exp(-h v / RT), h v is the molar enthalpy of vacancy formation; k c is the gradient energy coefficient; h(c v ) is the interpolation function; f m represents the chemical energy of the metal phase; V is the electrode volume.

[0034] The expression of 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 ) is used to emphasize that there is no diffusion in the cavity, D * represents the diffusion rate of lithium atoms in the metal phase;

[0037] The expression of the electrochemical model is:

[0038]

[0039] Among them, σ SSE ,φ SSE denote the conductivity and potential of the electrolyte, σ an ,φ anDenote the conductivity and potential of the negative electrode respectively. Since the void has no conductivity, interpolation is required to obtain the conductivity of the transition region:

[0040] σ an =f(c v )σ m

[0041] Among them, σ m The electrical conductivity in the metal phase of the electrode.

[0042] Furthermore, the boundary conditions of the phase field model and the lithium diffusion model are:

[0043] Calculate the phase field model only in the electrode region and set boundary conditions on the electrode / electrolyte interface:

[0044]

[0045] Among them, n represents the direction of the boundary normal, i loc Current density at the electrode / electrolyte interface; the remaining 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] The remaining boundaries in the lithium diffusion model are zero flux boundaries.

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

[0050] The present invention also provides a prediction system for the interface evolution of an all-solid-state lithium-based metal negative electrode, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing the above-described method for predicting the interface evolution of an all-solid-state lithium-based metal negative electrode when executing the computer program.

[0051] The present invention also provides a computer-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the above-mentioned prediction method for the interface evolution of the all-solid-state lithium-based metal negative electrode.

[0052] In general, compared with the prior art, the above technical solutions conceived by the present invention provide a method and system for predicting the interface evolution of lithium-based metal anodes in all-solid-state batteries, which have the following beneficial effects:

[0053] 1. The interface evolution model is derived by coupling the phase-field model, lithium diffusion model, electrochemical model, and explicit nucleation model. This interface evolution model reflects the entire process, taking into account the influence of electrode composition on the void nucleation process, and simulating the entire process of void nucleation, growth, and interface failure. Meanwhile, alloying lithium metal 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 stripping ability. Based on this, this method can screen the optimal alloy composition for different stripping scenarios, thereby achieving a balance between interface stability and lithium diffusion ability.

[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. c inside the electrode in the phase field model v are all set to 0, and no tiny 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 and growth, eliminates the pre-existing tiny voids or impurities acting as nucleation sites to interfere with the tracking of interface evolution, and ensures that the void formation comes from the spontaneous phase separation process of the phase field model.

[0056] 4. By setting the physical properties corresponding to different alloy compositions, the nucleation probability can be influenced from three aspects: volume 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 current density-alloy composition-strippable capacity relationship diagram, it can guide the design of electrode composition and the selection of optimal operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a method for predicting the interface evolution 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 interface evolution of lithium-based metal negative electrodes in solid-state batteries provided by an embodiment of the present 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 interface evolution of lithium-based metal negative electrodes of solid-state batteries provided by an embodiment of the present invention;

[0061] Figure 4 This is a rendering of the interface evolution calculated in the method for predicting the interface evolution of a lithium-based metal negative electrode of a solid-state battery provided by an embodiment of the present invention;

[0062] Figure 5 This is a relationship diagram of stripping current density-magnesium content-stripping capacity constructed in the method for predicting the interface evolution of lithium-based metal negative electrodes of solid-state batteries provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0064] See also Figure 1 The present invention provides a method for predicting the interface evolution of an all-solid-state lithium-based metal anode, the method mainly comprising the following steps:

[0065] S1. Construct a phase field model to distinguish the void and metal phases 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, the Cahn-Hilliard phase field model of the conservation form is used to construct the phase field model of the lithium-based metal negative electrode alloy system, and the phase field order parameter c is used v represents the relative concentration of vacancies in the electrode, c v =1 indicates a hollow area, c v =0 indicates the metal region, and the free energy of the lithium-based metal negative electrode alloy system can be written as:

[0067]

[0068] Among them, W c g(c v ) is the double-well function describing the equilibrium state of metal and void, W c is the barrier height, and 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 is a double-well function, in c eq and 1, where c eq represents the equilibrium vacancy concentration, c eq =exp(-h v / RT), h vis the molar enthalpy of vacancy formation, R is the universal gas constant, and T is the temperature; κ c is the gradient energy coefficient, which can also be expressed by the metal / void interface energy γ s and the interface thickness δ, κ c =1.5γ s δ;h(c v ) is an interpolation function, indicating a smooth and continuous transition of energy at the interface. Specifically, in this embodiment, f m The chemical energy of the metal phase is expressed by the classic binary system chemical energy formula:

[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 expressed as:

[0071]

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

[0073] The expression of 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 ) is used to emphasize that there is no diffusion in the cavity, D * represents the diffusion rate of lithium atoms in the metal phase and is assumed to be equal to the lithium diffusion rate.

[0076] The expression of the electrochemical model is:

[0077]

[0078] Among them, σ SSE ,φ SSE denote the conductivity and potential of the electrolyte, σan ,φ an Denote the conductivity and potential of the negative electrode respectively. Since the void has no conductivity, interpolation is required to obtain the conductivity of the transition region:

[0079] σ an =f(c v )σ Li (9)

[0080] Since the electrode potential is sensitive to the conductivity, a steeper high-order interpolation function is needed to reflect the conductivity change of the interface. Specifically, the interpolation function f(c v ) is preferably 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, which can be expressed by the Butler-Volmer equation:

[0082]

[0083] Among them, i 0,ref represents the reference exchange current density, c0 represents the lithium atomic concentration at the initial moment, α a represents the anode charge transfer coefficient, α c =1-α a represents the cathode charge transfer coefficient, and η represents the overpotential, which reflects the degree to which the electrode potential deviates from the equilibrium potential:

[0084] η=φ s -φ l -E Θ +η c (11)

[0085] Among them, φ s represents the electrode potential, φ l represents the electrolyte potential, η c represents the concentration overpotential, E Θ Indicates the electrode reference equilibrium potential; for lithium battery electrodes, E Θ =0; concentration overpotential η c It is caused by the local concentration change on the electrode surface and describes the potential change caused by the difference between the reactant concentration at the electrode interface and the bulk concentration. It 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 is 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, a spherical void with a radius of r is formed. 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] Among them, ΔG v represents the volume energy density; γ s represents the metal surface energy (i.e., the interface energy between metal and voids), γ SSE represents the surface energy of the solid electrolyte, γ int represents the interfacial energy between metal and solid electrolyte, F α 、F β and F γ is the geometric parameter 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 interface energy between the metal and the solid electrolyte, and the interface energy between the metal and the solid electrolyte satisfy the mechanical equilibrium:

[0095] γ s cosα+γ SSE =γ int (17)

[0096] Let equation (13) be differentiated with respect to r and set to 0, and we can get the critical radius r c satisfy:

[0097]

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

[0099]

[0100] The nucleation of a void can be seen as a transition of the system from an initial state to a more stable state, but it needs to overcome a certain energy barrier (ΔG c ), this process can be expressed by Boltzmann distribution, and the probability of nucleation can be expressed as:

[0101]

[0102] Among them, k B is the Boltzmann constant.

[0103] Volume energy density ΔG v is related to the overpotential caused by the interface impedance and is expressed as:

[0104]

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

[0106] The steps of the explicit nucleation model are:

[0107] (I) Calculation of 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 position satisfies p r <p nuc When the phase field order parameter c at this point v becomes 1;

[0110] (IV) When the numerical mutations of the phase field order parameter near the boundary accumulate to a predetermined level, the phase field model naturally undergoes phase separation, achieving void nucleation.

[0111] S2, the phase field model, lithium diffusion model, electrochemical model, and explicit nucleation model of the lithium-based metal negative electrode are coupled to obtain an interface evolution model. The explicit nucleation model controls the nucleation process of the void by changing the value of the phase field order parameter at the grid node in the phase field model.

[0112] See also Figure 2, the phase field distinguishes between the metal phase and the void phase, and the explicit nucleation model affects the nucleation process of the phase field; the lithium atom migration process described by the lithium diffusion model only occurs in the metal phase specified by the phase field model, so the lithium diffusion model is affected unidirectionally by the phase field; since the voids are non-conductive, the discontinuous contact at the interface will affect the interfacial electrochemical reaction in the electrochemical model, and the interfacial current flux calculated by the electrochemical model will be added to the boundary conditions of the phase field as a source of vacancies, so the phase field and electrochemical models are bidirectionally coupled; 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 will cause an increase in the concentration overpotential, thereby affecting the electrochemical model, so the lithium diffusion model and the electrochemical model can also be bidirectionally coupled; the explicit nucleation model controls the void nucleation process by changing the value of the phase field order parameter at the grid node in the phase field model.

[0113] S3, construct a two-dimensional half-cell calculation domain consisting of a lithium-based metal electrode and a solid electrolyte, mesh the calculation domain, and set the initial conditions and boundary conditions of the interface evolution model.

[0114] In this embodiment, the width and height of the electrode and electrolyte are both W, and the scale is in the micron level. Figure 3 .

[0115] Initially, the electrode and electrolyte interface are in complete contact, that is, the internal c of the electrode in the phase field model v are all set to 0, and no tiny 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 and growth, eliminates the pre-existing tiny voids or impurities acting as nucleation sites to interfere with the tracking of interface evolution, and ensures that the void formation comes from the spontaneous phase separation process of the phase field model.

[0116] Calculate the phase field model only in the electrode region and set boundary conditions on the electrode / electrolyte interface:

[0117]

[0118] The remaining boundaries in the phase field model 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] The remaining boundaries in the lithium diffusion model are zero flux boundaries.

[0122] In the electrochemical model, a grounded boundary condition is applied to the electrode current collector, the left and right sides are continuity boundaries, and the stripping current density i is applied to the electrolyte side; the interface exchange current density is set at the electrode / electrolyte interface.

[0123] The calculation area is discretized using a triangular grid, and the grid is encrypted in the electrode area and the electrode-electrolyte interface area to improve the calculation accuracy; specifically, the maximum characteristic length of the grid in the electrode area and the electrode-electrolyte interface area is preferably one tenth of the interface thickness.

[0124] S4, based on the physical properties and current density of the lithium-based metal negative electrode, the interface evolution model is transiently calculated to predict the interface evolution.

[0125] In one embodiment, 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 the interface voids during the stripping process is obtained, which is represented by the phase field order parameter, as shown in FIG. Figure 4 shown.

[0126] In this embodiment, lithium-magnesium alloy is preferred, and the relationship between stripping capacity, stripping current density and magnesium content is obtained, as shown in FIG. Figure 5 As shown in Figure 2, at lower current densities, the strippable capacity first increases and then decreases with increasing magnesium content in the electrode, indicating an optimal magnesium content for optimal performance. At higher current densities, the strippable capacity decreases steadily with increasing magnesium content, indicating that lithium metal electrodes are superior. This phase diagram can guide electrode composition design and the selection of optimal operating conditions in industrial applications.

[0127] The present invention also provides a prediction system for the interface evolution of an all-solid-state lithium-based metal negative electrode, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing the above-described method for predicting the interface evolution of an all-solid-state lithium-based metal negative electrode when executing the computer program.

[0128] The present invention also provides a computer-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the above-mentioned prediction method for the interface evolution of the all-solid-state lithium-based metal negative electrode.

[0129] It will be easily understood by those skilled in the art 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 in the scope of protection of the present invention.

Claims

1. A method for predicting the interface evolution of an all-solid-state lithium-based metal anode, characterized in that: The prediction method includes the following steps: (1) coupling a phase field model, a lithium diffusion model, an electrochemical model, and an explicit nucleation model of a lithium-based metal negative electrode to obtain an interface evolution model, wherein the explicit nucleation model controls the nucleation process of the void by changing the values of the phase field order parameters at the grid nodes in the phase field model; (2) Construct a two-dimensional half-cell computational domain consisting of a lithium-based metal electrode and a solid electrolyte, mesh the computational domain, and set the initial and boundary conditions of the interface evolution model; (3) Based on the physical properties and current density of the lithium-based metal negative electrode, the interface evolution model is transiently calculated to predict the interface evolution.

2. The method for predicting the interface evolution of an all-solid-state lithium-based metal anode according to claim 1, wherein: The lithium atom migration process described by the lithium diffusion model occurs only in the metal phase specified by the phase field model. The interfacial current flux calculated by the electrochemical model is added to the phase field boundary conditions as a source of vacancies, and the interfacial current density is added as a boundary condition to the lithium diffusion model. The lithium atomic concentration near the reaction interface calculated by the lithium diffusion model affects the electrochemical model by causing an increase in concentration overpotential.

3. The method for predicting the interface evolution of an all-solid-state lithium-based metal anode according to claim 1, wherein: The explicit nucleation model first calculates the nucleation probability p nuc , and then generate a set of random numbers p from 0 to 1 on the boundary of the nucleus r ; When a position satisfies p r <p nuc When the phase field order parameter c at this point v becomes 1; when the numerical mutation of the phase field order parameter near the boundary accumulates to a predetermined degree, the phase field model naturally undergoes phase separation and realizes void nucleation.

4. The method for predicting the interface evolution of an all-solid-state lithium-based metal anode according to claim 3, wherein: The internal phase field order parameter c of the electrode in the phase field model at the initial moment v Set them all to 0.

5. The method for predicting the interface evolution of an all-solid-state lithium-based metal anode according to claim 1, wherein: When the interface changes from a void-free state to a void-containing state, a spherical void with a radius of r is formed. The change in free energy of this transition process is expressed as: ΔG=-r 3 F α ΔG v +r 2 F β γ s +r 2 F γ (γ SSE -γ int ) Among them, ΔG v represents the volume energy density; γ s represents the metal surface energy, γ SSE represents the surface energy of the solid electrolyte, γ int represents the interfacial energy between metal and solid electrolyte, F α 、F β and F γ is the geometric parameter related to the interface contact angle α: F β =2π(1-cosα) F γ =πsin 2 a.

6. The method for predicting the interface evolution of an all-solid-state lithium-based metal anode according to claim 3, wherein: The energy barrier that needs to be overcome in the void nucleation process is: The nucleation process is represented by the Boltzmann distribution, and the probability of nucleation p nuc for: Among them, k B is the Boltzmann constant; By setting the physical property parameters corresponding to different alloy compositions, the nucleation probability is affected, thereby controlling the evolution process of interface voids.

7. The method for predicting the interface evolution of an all-solid-state lithium-based metal anode according to claim 1, wherein: The phase field model is expressed as: where ψ is the electrode free energy functional F with respect to c v Chemical potential obtained by variational calculation; M = D * Ω / RT represents the phase field diffusion migration parameter; D* represents the vacancy diffusion rate in the lithium-based metal, which is related to the magnesium content; Ω is the molar volume of the metal, R is the universal gas constant, and T is the temperature; The electrode free energy functional F is: Among them, W c is the barrier height; g(c v ) is a double-well function, g(c v )=(c v -c eq ) 2 (1-c v ) 2 is a double-well function, where c eq represents the equilibrium vacancy concentration, c eq =exp(-h v / RT), h v is the molar enthalpy of vacancy formation; k c is the gradient energy coefficient; h(c v ) is the interpolation function; f m represents the chemical energy of the metal phase; V is the electrode volume; The expression of the lithium diffusion model is: Where c represents the concentration of lithium atoms, J represents the lithium diffusion flux; the interpolation function h(c v ) is used to emphasize that there is no diffusion in the cavity, D * represents the diffusion rate of lithium atoms in the metal phase; The expression of the electrochemical model is: Among them, σ SSE ,φ SSE denote the conductivity and potential of the electrolyte, σ an ,φ an represent the conductivity and potential of the negative electrode respectively; since the void has no conductivity, interpolation is required to obtain the conductivity of the transition region: s an =f(c v )s m Among them, σ m The electrical conductivity in the metal phase of the electrode.

8. The method for predicting the interface evolution of an all-solid-state lithium-based metal anode according to claim 1 or 2, wherein: The boundary conditions of the phase field model and lithium diffusion model are: Calculate the phase field model only in the electrode region and set boundary conditions on the electrode / electrolyte interface: Among them, n represents the direction of the boundary normal, i loc The current density at the electrode / electrolyte interface, and the remaining boundaries in the phase field model are zero flux boundaries; The lithium diffusion model is calculated only in the electrode region, and boundary conditions are set for the electrode / electrolyte interface: The remaining boundaries in the lithium diffusion model are zero flux boundaries.

9. A prediction system for the interface evolution of an all-solid-state lithium-based metal anode, characterized by: The system includes a memory and a processor, the memory stores a computer program, and the processor executes the prediction method for the interface evolution of the all-solid-state lithium-based metal negative electrode according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by the processor, the machine-executable instructions prompt the processor to implement the prediction method for the interface evolution of the all-solid-state lithium-based metal negative electrode according to any one of claims 1 to 8.

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