Electrochemical energy conversion device simulation method, device, equipment, storage medium and program product

Abstract

The present application relates to a method, device, equipment, storage medium and program product for simulating an electrochemical energy conversion device. The electrochemical energy conversion device includes a positive electrode, a negative electrode and a diaphragm region, the positive electrode is a polycrystalline particle, and the diaphragm region is filled with an electrolyte; the method includes: obtaining a diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, liquid-phase electrochemical performance data and a first electrochemical model of the electrolyte, and exchange current density data on the surface of the polycrystalline particles; based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data, constructing a second electrochemical model at the interface between the polycrystalline particles and the electrolyte; based on the diffusion model, the first electrochemical model and the second electrochemical model, simulating the electrochemical energy conversion device. The use of this method can improve the convenience of crystal material design.

Description

Technical Field

[0001] The present application relates to the field of electrochemical technology, and in particular to a simulation method, device, computer equipment, computer-readable storage medium and computer program product for an electrochemical energy conversion device. Background Art

[0002] With the continuous development of materials science, nanotechnology and new energy technology, crystalline materials play an increasingly important role in electrochemical energy conversion devices.

[0003] In traditional technology, it is usually necessary to first produce a physical crystal material and apply the crystal material to an electrochemical energy conversion device. Then, the performance of the electrochemical energy conversion device is tested through physical and chemical testing or simulation methods. The technicians then analyze the performance of the electrochemical energy conversion device determined by the test based on their experience, and reversely determine the performance of the polycrystalline particles based on the analysis results, thereby guiding the design of the polycrystalline particles.

[0004] However, traditional methods usually require repeated and tedious processing, analysis, parameter adjustment and reprocessing to obtain crystal materials that meet the needs of electrochemical energy conversion devices. Summary of the invention

[0005] Based on this, it is necessary to provide a simulation method, device, computer equipment, computer-readable storage medium and computer program product for an electrochemical energy conversion device that can improve the convenience of crystal material design in order to solve the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for simulating an electrochemical energy conversion device, wherein the electrochemical energy conversion device comprises a positive electrode, a negative electrode and a diaphragm region, wherein the positive electrode is polycrystalline particles, and the diaphragm region is filled with an electrolyte; the method for simulating an electrochemical energy conversion device comprises:

[0007] Obtaining the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte, and the exchange current density data on the surface of the polycrystalline particles;

[0008] Based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data, a second electrochemical model at the interface between the polycrystalline particles and the electrolyte is constructed;

[0009] Based on the diffusion model, the first electrochemical model and the second electrochemical model, an electrochemical energy conversion device simulation is performed.

[0010] In one embodiment, obtaining a diffusion model of a polycrystalline particle includes:

[0011] Acquisition step: acquiring microstructure characterization data of polycrystalline particles, and constructing a polycrystalline structure model based on the microstructure characterization data;

[0012] The microscopic characterization diffusion performance data corresponding to the polycrystalline structure model is obtained, and the diffusion model of the polycrystalline particles is constructed based on the microscopic characterization diffusion performance data and the polycrystalline structure model.

[0013] In one embodiment, the polycrystalline structure model includes at least one anisotropic grain region and at least one isotropic grain region; the microscopic characterization diffusion performance data includes a first diffusion coefficient corresponding to each anisotropic grain, a second diffusion coefficient corresponding to each isotropic grain, and a third diffusion coefficient at each grain boundary;

[0014] Based on the microscopic characterization diffusion performance data and the polycrystalline structure model, a diffusion model of polycrystalline particles is constructed, including:

[0015] A diffusion model of the polycrystalline particles is constructed based on the polycrystalline structure model, the first diffusion coefficients, the second diffusion coefficients and the third diffusion coefficients.

[0016] In one embodiment, the microstructure characterization data includes at least one of grain size data, grain crystallinity data, grain geometry data, and grain orientation data.

[0017] In one embodiment, after simulating the electrochemical energy conversion device based on the diffusion model, the first electrochemical model, and the second electrochemical model, the method further includes:

[0018] Obtain device simulation performance data and performance requirement information of an electrochemical energy conversion device;

[0019] Based on the device simulation performance data and performance requirement information, the microstructure characterization data of the polycrystalline structure model is adjusted, a new polycrystalline structure model is generated, and the process jumps to the acquisition step.

[0020] In one embodiment, the exchange current density data includes a point exchange current density value of at least one surface point on the surface of a polycrystalline particle; the polycrystalline particle includes a plurality of surface layer grains located on the outer surface of the polycrystalline particle; obtaining the exchange current density data on the surface of the polycrystalline particle includes:

[0021] Obtaining intrinsic exchange current density values ​​of polycrystalline particles, external normal vectors of each surface point, and surface layer grain orientation data of surface layer grains at each surface point;

[0022] Based on the external normal vector corresponding to each surface point, the surface layer grain orientation data and the intrinsic exchange current density value, the point exchange current density value of each surface point is detected.

[0023] In a second aspect, the present application further provides an electrochemical energy conversion device simulation device, the electrochemical energy conversion device comprising a positive electrode, a negative electrode and a diaphragm region, the positive electrode is polycrystalline particles, and the diaphragm region is filled with an electrolyte; the electrochemical energy conversion device simulation device comprises:

[0024] An acquisition module, used to acquire the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte, and the exchange current density data on the surface of the polycrystalline particles;

[0025] A construction module for constructing a second electrochemical model at the interface between the polycrystalline particles and the electrolyte based on the solid phase electrochemical performance data, the liquid phase electrochemical performance data and the exchange current density data;

[0026] The simulation module is used to simulate the electrochemical energy conversion device based on the diffusion model, the first electrochemical model and the second electrochemical model.

[0027] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, the computer program is used for simulating an electrochemical energy conversion device, the electrochemical energy conversion device includes a positive electrode, a negative electrode and a diaphragm region, the positive electrode is a polycrystalline particle, and the diaphragm region is filled with an electrolyte; when the processor executes the computer program, the following steps are implemented:

[0028] Obtaining the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte, and the exchange current density data on the surface of the polycrystalline particles;

[0029] Based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data, a second electrochemical model at the interface between the polycrystalline particles and the electrolyte is constructed;

[0030] Based on the diffusion model, the first electrochemical model and the second electrochemical model, an electrochemical energy conversion device simulation is performed.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, the computer program being used for simulating an electrochemical energy conversion device, the electrochemical energy conversion device comprising a positive electrode, a negative electrode and a diaphragm region, the positive electrode being polycrystalline particles, and the diaphragm region being filled with an electrolyte; when the computer program is executed by a processor, the following steps are implemented:

[0032] Obtaining the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte, and the exchange current density data on the surface of the polycrystalline particles;

[0033] Based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data, a second electrochemical model at the interface between the polycrystalline particles and the electrolyte is constructed;

[0034] Based on the diffusion model, the first electrochemical model and the second electrochemical model, an electrochemical energy conversion device simulation is performed.

[0035] In a fifth aspect, the present application further provides a computer program product, including a computer program, the computer program is used for simulating an electrochemical energy conversion device, the electrochemical energy conversion device includes a positive electrode, a negative electrode and a diaphragm region, the positive electrode is a polycrystalline particle, and the diaphragm region is filled with an electrolyte; when the computer program is executed by a processor, the following steps are implemented:

[0036] Obtaining the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte, and the exchange current density data on the surface of the polycrystalline particles;

[0037] Based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data, a second electrochemical model at the interface between the polycrystalline particles and the electrolyte is constructed;

[0038] Based on the diffusion model, the first electrochemical model and the second electrochemical model, an electrochemical energy conversion device simulation is performed.

[0039] The above-mentioned electrochemical energy conversion device simulation method, device, computer equipment, computer-readable storage medium and computer program product, the electrochemical energy conversion device includes a positive electrode, a negative electrode and a diaphragm area, the positive electrode is a polycrystalline particle, and the diaphragm area is filled with an electrolyte. First, the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data of the electrolyte and the first electrochemical model, and the exchange current density data on the surface of the polycrystalline particles are obtained. The diffusion model can be used to simulate the diffusion behavior of the working ions in the polycrystalline particles, and the first electrochemical model can be used to simulate the working ions in the electrolyte under the action of the electric field. flow process; then, based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data, a second electrochemical model at the interface between the polycrystalline particles and the electrolyte is constructed, and the second chemical model can be used to simulate the flow process of the working ions at the interface between the polycrystalline particles and the electrolyte under the action of the electric field; then, based on the diffusion model, the first electrochemical model and the second electrochemical model, the electrochemical energy conversion device is simulated, and the simulation of the movement behavior of the working ions at various positions in the electrochemical energy conversion device under the influence of ion concentration and electric field can be achieved without the need to produce polycrystalline materials. In this way, through the electrochemical energy conversion device simulation method of the present application, the flow process of the working ions in the positive electrode, negative electrode and electrolyte of the electrochemical energy conversion device can be effectively simulated, and the obtained simulation results are directly related to the diffusion performance and electrochemical performance of the polycrystalline particles, so that the design of the polycrystalline material can be directly guided, so that the polycrystalline structure that meets the needs of the electrochemical energy conversion device can be directly designed in the polycrystalline structure design stage, reducing the process of repeated trial and error, improving the convenience of crystal material design, and reducing the cost of trial and error. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A schematic diagram of a flow chart of a simulation method for an electrochemical energy conversion device in one embodiment;

[0042] Figure 2 is a schematic structural diagram of an electrochemical energy conversion device in one embodiment;

[0043] Figure 3 is a schematic diagram of a simulation result of ion concentration in a diaphragm layer of an electrochemical energy conversion device in one embodiment;

[0044] Figure 4 A schematic diagram of a simulation result of ion concentration in a polycrystalline particle of an electrochemical energy conversion device in one embodiment;

[0045] Figure 5 is a schematic diagram of simulation results of voltage variation over time in an electrochemical energy conversion device in one embodiment;

[0046] Figure 6 A schematic diagram of a polycrystalline structure model in one embodiment;

[0047] Figure 7 A schematic diagram of a flow chart of a simulation method for an electrochemical energy conversion device in another embodiment;

[0048] Figure 8 A structural block diagram of an electrochemical energy conversion device simulation device in one embodiment;

[0049] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] In an exemplary embodiment, Figure 1 As shown, a simulation method for an electrochemical energy conversion device is provided. This embodiment uses the method applied to a terminal as an example, wherein the terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. It is understandable that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and may be implemented through the interaction between the terminal and the server. In this embodiment, the electrochemical energy conversion device includes a positive electrode, a negative electrode and a diaphragm region, the positive electrode is a polycrystalline particle, and the diaphragm region is filled with an electrolyte; the method includes the following steps S10-S30. Among them:

[0052] Step S10, obtaining the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte, and the exchange current density data on the surface of the polycrystalline particles.

[0053] It should be noted that crystalline materials are widely used in electrochemical energy conversion devices, such as electrode materials and solid electrolytes in ion batteries and fuel cells. Affected by the synthesis process, crystalline materials usually exist in a polycrystalline form composed of multiple single crystal particles. Polycrystalline particles can refer to crystalline materials in a polycrystalline form composed of multiple single crystal particles. The movement behavior of working ions in an electrochemical energy conversion device is one of the key factors that determine the electrochemical performance of the electrochemical energy conversion device.

[0054] The working ions may include at least one of lithium ions, sodium ions, oxygen ions, etc. The movement behavior of the working ions in the electrochemical energy conversion device at least includes the flow behavior of the working ions under the action of the electric field and the diffusion behavior of the working ions under the action of the concentration field.

[0055] Electrochemical energy conversion devices may refer to devices that realize the mutual conversion between electrical energy and other forms of energy such as chemical energy, thermal energy, etc. through electrochemical reactions, such as batteries. Figure 2 As shown, the electrochemical energy conversion device at least includes a positive electrode 202, a negative electrode 204 and an electrolyte. The electrolyte is filled in the separator region 206 between the positive electrode 202 and the negative electrode 204. The electrochemical energy conversion device may also include a separator, a membrane, etc.

[0056] The diffusion model may refer to a mathematical framework that describes the transport behavior of ions in polycrystalline particles. The diffusion model may be derived based on technical principles or constructed through simulation after determining the properties, structure and other information of the polycrystalline particles. The transport behavior of ions in polycrystalline particles may be related to the microscopic characterization diffusion performance data of each grain in the polycrystalline particles, the concentration field in which the polycrystalline particles are located, the microstructure of the polycrystalline particles, and the like. In some feasible embodiments, the transport behavior of ions in polycrystalline particles may be characterized by macroscopic characterization diffusion performance data. Therefore, the diffusion model may be a relationship model between the macroscopic characterization diffusion performance data of polycrystalline particles and the microscopic characterization diffusion performance data, the concentration field in which the polycrystalline particles are located, the microstructure of the polycrystalline particles, and the like. The macroscopic characterization diffusion performance data includes at least one of the equivalent diffusion coefficient of the polycrystalline material in a diffusion equilibrium state, a diffusion model in which the concentration difference measurement value changes over time, and the like.

[0057] The electrochemical model may refer to a mathematical framework that describes the flow behavior of ions in a material under the action of an electric field. The electrochemical model may be derived based on technical principles or constructed through simulation after determining the properties, structure, and other information of the material. The first electrochemical model may refer to a mathematical framework that describes the flow behavior of ions in an electrolyte under the action of an electric field.

[0058] Electrochemical performance data may refer to data used to characterize the electrochemical performance of the material itself, and may include at least one of conductivity, intrinsic exchange current density, transfer coefficient, open circuit voltage, specific capacity, energy density, etc. Solid-phase electrochemical performance data may refer to electrochemical performance data of polycrystalline particles, and liquid-phase electrochemical performance data may refer to electrochemical performance data of electrolytes.

[0059] The intrinsic exchange current density value is an inherent property of the material itself, but the orientation of the grains on the surface of the polycrystalline particles will affect the surface atomic arrangement, the number of active sites, the reaction path and the surface energy, etc., thereby changing the reaction kinetics on the surface of the polycrystalline particles, so that the actual value of the exchange current density at the surface of the polycrystalline particles is different from the intrinsic exchange current density value of the polycrystalline particles. The exchange current density data can be in the form of a data set or a functional relationship, etc., which is used to characterize the actual value of the exchange current density at each site on the surface of the polycrystalline particles. The exchange current density data can be determined by principle deduction and simulation, etc., and this embodiment is not limited to this.

[0060] In some feasible embodiments, before simulating an electrochemical energy conversion device, a polycrystalline structure model can be created in advance through computer software based on the design information of the polycrystalline particles, such as element type, ratios between elements, size of single crystal structure, size of polycrystalline structure, etc. Then, a diffusion model of the polycrystalline particles can be constructed through principle deduction or simulation, and the solid-phase electrochemical performance data of the polycrystalline particles and the exchange current density data on the surface of the polycrystalline particles can be detected.

[0061] In some feasible embodiments, before simulating an electrochemical energy conversion device, relevant information about the electrolyte, such as the composition and concentration of the electrolyte, can be obtained first, and then the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte can be determined through principle deduction or simulation.

[0062] Exemplarily, during the simulation of an electrochemical energy conversion device, the diffusion model and solid-phase electrochemical performance data of polycrystalline particles, the liquid-phase electrochemical performance data of the electrolyte and the first electrochemical model, and the exchange current density data on the surface of the polycrystalline particles that are pre-stored or uploaded by the user can be first obtained.

[0063] In one possible implementation, the first electrochemical model can be expressed as:

[0064]

[0065]

[0066] Among them, J 1 is the current density in the electrolyte; is the effective ionic conductivity of the electrolyte; is the liquid phase potential; is the effective ionic conductivity of the polycrystalline particles; is the liquid phase concentration of lithium ions in the electrolyte; t is the time; t + is the working ion migration number; is the volume fraction of electrolyte; is the effective ion diffusion coefficient of the electrolyte; F is the Faraday constant.

[0067] Step S20, constructing a second electrochemical model at the interface between the polycrystalline particles and the electrolyte based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data.

[0068] The second electrochemical model may refer to a mathematical framework that describes the flow behavior of ions at the interface between polycrystalline particles and electrolyte under the action of an electric field. The flow behavior of ions at the interface between polycrystalline particles and electrolyte under the action of an electric field is related to the overpotential of the electrode surface and the actual value of the exchange current density, and the overpotential of the electrode surface is related to the electrochemical properties of the materials on both sides of the electrode surface.

[0069] For example, based on the solid-phase electrochemical performance data, liquid-phase electrochemical performance data and exchange current density data, the flow behavior of working ions at the interface between polycrystalline particles and the electrolyte under the action of the electric field can be simulated or the principle can be derived to construct a second electrochemical model at the interface between polycrystalline particles and the electrolyte.

[0070] In one feasible implementation, the solid phase electrochemical performance data may include the solid phase potential; the liquid phase electrochemical performance data may include the liquid phase potential. According to Ohm's law, the current density J 2 It can be expressed as:

[0071]

[0072] in, is the conductivity of the polycrystalline grains and is the reciprocal of the resistivity; s is the solid phase potential of polycrystalline particles.

[0073] When current is conserved or there is no current source, the divergence of current density is zero, that is:

[0074]

[0075] In this way, the solid phase potential of polycrystalline particles can be determined.

[0076] The second electrochemical model can be expressed as:

[0077]

[0078] Among them, i 0 is the exchange current density data on the surface of polycrystalline particles; a 1 is the electrochemical reaction transfer coefficient of polycrystalline particles; a 2 is the electrochemical reaction transfer coefficient of the electrolyte; a 1 and a 2 It can be taken as 0.5; R is the gas constant; T is the ambient temperature; It represents the overpotential on the surface of polycrystalline particles and characterizes the degree of electrode polarization, which can be expressed as:

[0079]

[0080] in, s is the solid phase potential of the polycrystalline particles; e is the liquid phase potential of the electrolyte; U is the open circuit voltage of the electrochemical energy conversion device.

[0081] Step S30, performing an electrochemical energy conversion device simulation based on the diffusion model, the first electrochemical model and the second electrochemical model.

[0082] Among them, electrochemical energy conversion device simulation refers to the process of using mathematical models and algorithms on computers to simulate the behavior and performance of electrochemical energy conversion devices.

[0083] Exemplarily, the movement behavior of working ions in an electrochemical energy conversion device under the action of an electric field and a concentration field can be simulated on a computer based on a diffusion model, a first electrochemical model, and a second electrochemical model, and a performance simulation can be performed based on the simulated electrochemical energy conversion device to obtain device simulation performance data. Among them, the device simulation performance data may include the working ion concentration distribution, the relationship between the change of voltage over time, etc. In this way, when the electrolyte is determined, the corresponding device simulation performance data can be obtained by adjusting at least one of the diffusion model, the solid-phase electrochemical performance data, and the exchange current density data, so that the electrochemical energy conversion device whose performance meets the actual needs can be quickly screened out, and the polycrystalline particles can be designed and produced according to the diffusion performance, electrochemical performance, and surface exchange current density data of the polycrystalline particles in the screened electrochemical energy conversion device.

[0084] In this way, the computer can be used to automatically complete the repeated debugging process, and each debugging process does not require actual production. Therefore, it can not only effectively simplify the manual operation process and improve the convenience of crystal material design, but also effectively save labor costs, time costs and economic costs.

[0085] In an exemplary embodiment, based on the diffusion model, the first electrochemical model and the second electrochemical model, the electrochemical energy conversion device simulation is performed to obtain the ion distribution in the electrolyte and the polycrystalline particles, the relationship between the voltage value and time, etc. The ion concentration distribution in the electrolyte is as follows: Figure 3 The ion concentration distribution in the polycrystalline particles is shown in Figure 4 As shown, Figure 4 The value in the right coordinate represents the ratio of the ion concentration at the point to the maximum ion concentration in the electrochemical energy conversion device. The relationship between the voltage value of the electrochemical energy conversion device and time is as follows: Figure 5 As shown, Figure 5 The figure shows the relationship between the voltage values ​​of the electrochemical energy conversion devices corresponding to the three random polycrystalline structures and their changes over time.

[0086] In the above-mentioned electrochemical energy conversion device simulation method, the electrochemical energy conversion device includes a positive electrode, a negative electrode and a diaphragm area, the positive electrode is a polycrystalline particle, and the diaphragm area is filled with an electrolyte. First, a diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, liquid-phase electrochemical performance data of the electrolyte and a first electrochemical model, and exchange current density data on the surface of the polycrystalline particles are obtained. The diffusion model can be used to simulate the diffusion behavior of the working ions in the polycrystalline particles, and the first electrochemical model can be used to simulate the flow process of the working ions in the electrolyte under the action of the electric field; then, based on the solid-phase electrochemical Chemical performance data, liquid phase electrochemical performance data and exchange current density data are used to construct a second electrochemical model at the interface between polycrystalline particles and electrolyte. The second chemical model can simulate the flow process of working ions at the interface between polycrystalline particles and electrolyte under the action of electric field; then based on the diffusion model, the first electrochemical model and the second electrochemical model, the electrochemical energy conversion device is simulated, which can realize the simulation of the movement behavior of working ions at various positions in the electrochemical energy conversion device under the influence of ion concentration and electric field without the need to produce polycrystalline materials. In this way, through the electrochemical energy conversion device simulation method of the present application, the flow process of working ions in the positive electrode, negative electrode and electrolyte of the electrochemical energy conversion device can be effectively simulated, and the obtained simulation results are directly related to the diffusion performance and electrochemical performance of polycrystalline particles, so it can directly guide the design of polycrystalline materials, so that in the polycrystalline structure design stage, a polycrystalline structure that meets the needs of the electrochemical energy conversion device can be directly designed, reducing the process of repeated trial and error, improving the convenience of crystal material design, and reducing the cost of trial and error.

[0087] In an exemplary embodiment, obtaining the diffusion model of polycrystalline particles includes steps S111 to S112. In which:

[0088] Step S111, an acquisition step: acquiring microstructure characterization data of polycrystalline particles, and constructing a polycrystalline structure model based on the microstructure characterization data.

[0089] The microstructural characterization data may refer to information used to describe the microscopic structure of the polycrystalline particles, which may include at least one of material composition, crystal structure, phase distribution, defects, interface characteristics, size and morphology, etc. The diffusion process of ions inside polycrystalline particles depends not only on the chemical composition of the crystal, but also strongly on its microstructural characteristics. Therefore, the diffusion performance of polycrystalline particles is not only related to the microscopic characterization diffusion performance of the crystal, but also to the microstructure of the polycrystalline particles.

[0090] In an exemplary embodiment, the microstructure characterization data includes at least one of grain size data, grain crystallinity data, grain geometry data, and grain orientation data.

[0091] Among them, the difference in grain size will directly affect the diffusion path, diffusion rate and overall material properties of ions in polycrystalline particles. Generally speaking, grain boundary diffusion is faster than bulk diffusion. Smaller grains mean a higher grain boundary area ratio, which increases the possibility of grain boundary diffusion. The smaller the grain size, the easier the diffusion process is, and the faster the diffusion rate is.

[0092] The difference in grain crystallinity affects the defect density, atomic migration path and diffusion activation energy inside the polycrystalline particles, thus significantly changing the diffusion behavior. The higher the crystallinity of the grain, the more restricted the bulk diffusion path, and the diffusion mainly depends on grain boundary or surface diffusion. High crystallinity usually leads to slower but more orderly diffusion, while low crystallinity allows faster but more complex diffusion paths.

[0093] The geometric shape of the grains affects the diffusion path and diffusion rate inside the polycrystalline particles. For example, spherical or nearly spherical grains usually have a more uniform diffusion path because their surface curvature is relatively consistent, which reduces the difference in local diffusion rate. Irregularly shaped grains may form complex diffusion paths, resulting in different diffusion rates in different regions. If the grains are long strips or sheets, the diffusion path along the length direction may be longer than the path perpendicular to the length direction.

[0094] Grain orientation determines the arrangement of atoms in the material, which directly affects the choice of diffusion path, diffusion rate and overall diffusion behavior. Grains with different orientations may affect the distribution of elastic strain energy, produce activation energy differences, etc., thereby affecting the diffusion behavior of ions in polycrystalline materials. Differences in grain orientation determine the type of grain boundaries. High-angle grain boundaries usually serve as fast diffusion channels because they contain more defects and vacancies, while low-angle grain boundaries may hinder diffusion because they are closer to perfect lattice structures. Triple points or multiple points formed at the junction of multiple grains are also important diffusion locations. The geometric complexity and grain orientation differences at these points will affect the choice and efficiency of diffusion paths.

[0095] The polycrystalline structure model refers to a model created by computer software for characterizing the internal structure of polycrystalline materials, which can be a theoretical framework or visual representation of polycrystalline materials. Figure 6 As shown, Figure 6 The polycrystalline particles shown are composed of multiple anisotropic grains and multiple isotropic grains. The polycrystalline structure model includes multiple anisotropic grain regions 61 and multiple isotropic grain regions 62. The anisotropic grain regions 61, the isotropic grain regions 62, and the anisotropic grain regions 61 and the isotropic grain regions 62 are grain boundaries.

[0096] Exemplarily, the designer or technician of the polycrystalline particles can design the polycrystalline particles in advance, determine one or more sets of microstructure characterization data of the polycrystalline particles, and then upload each set of determined microstructure characterization data separately, and use computer software, Voronoi tessellation method, random geometric model, etc., to build a polycrystalline structure model based on each uploaded set of microstructure characterization data. For each set of microstructure characterization data, a corresponding electrochemical energy conversion device simulation model can be built, and the corresponding device simulation performance data can be determined by simulation. Finally, the microstructure characterization data whose performance meets the actual needs can be determined based on the device simulation performance data to guide the design and processing of polycrystalline particles. The entire process does not require actual processing and production, so the cost can be effectively reduced.

[0097] Step S112, obtaining microscopic diffusion performance data corresponding to the polycrystalline structure model, and constructing a diffusion model of polycrystalline particles based on the microscopic diffusion performance data and the polycrystalline structure model.

[0098] The diffusion model of polycrystalline particles is used to characterize the natural diffusion behavior of substances inside polycrystalline particles without the influence of externally applied chemical potential gradients or electric fields. The diffusion performance of polycrystalline particles is determined by the properties of the polycrystalline particles themselves, rather than by external factors. The microscopic characterization diffusion performance data is used to characterize the variation of the concentration of working ions in polycrystalline particles over time and space. It can be constructed in advance based on actual conditions. This embodiment does not limit this. As an example, the microscopic characterization diffusion performance data can include at least one of the grain boundary diffusion coefficient, bulk diffusion coefficient, and diffusion model of ion concentration variation over time and space.

[0099] For example, after constructing the polycrystalline structure model, the microscopic characterization diffusion performance data uploaded by the user can be queried or obtained according to the information of each grain defined in the polycrystalline structure model. Then, based on the microscopic characterization diffusion performance data and the polycrystalline structure model, the diffusion process of ions inside the polycrystalline particles at different initial concentrations can be determined through simulation or principle deduction, and a diffusion model of the polycrystalline particles can be constructed.

[0100] In some feasible implementations, the diffusion model can be represented as macroscopic diffusion performance data at different initial concentrations. The macroscopic diffusion performance data characterizes the diffusion performance of the polycrystalline particles as a whole, and the macroscopic diffusion performance data includes at least one of an equivalent diffusion coefficient of the polycrystalline particles in a diffusion equilibrium state, a diffusion model in which the concentration difference measurement value changes with time, and the like.

[0101] In this embodiment, the diffusion model of polycrystalline particles is constructed by combining microstructure characterization data and microscopic characterization diffusion performance data. This can combine the microstructure and diffusion performance to more accurately simulate and characterize the diffusion process of ions in polycrystalline particles, improve the accuracy of the diffusion model, and thus improve the accuracy of the simulation of the electrochemical energy conversion device.

[0102] In an exemplary embodiment, the polycrystalline structure model includes at least one anisotropic grain region and at least one isotropic grain region; the microscopic characterization diffusion performance data includes a first diffusion coefficient corresponding to each anisotropic grain, a second diffusion coefficient corresponding to each isotropic grain, and a third diffusion coefficient at each grain boundary;

[0103] Based on the microscopic characterization diffusion performance data and the polycrystalline structure model, a diffusion model of polycrystalline particles is constructed, including:

[0104] A diffusion model of the polycrystalline particles is constructed based on the polycrystalline structure model, the first diffusion coefficients, the second diffusion coefficients and the third diffusion coefficients.

[0105] Among them, Figure 6As shown, the polycrystalline structure model includes at least one anisotropic grain region 61 and at least one isotropic grain region 62, and the grain boundaries are between the anisotropic grain regions 61, between the isotropic grain regions 62, and between the anisotropic grain regions 61 and the isotropic grain regions 62. The diffusion properties of ions inside anisotropic grains, inside isotropic grains, between anisotropic grains, between isotropic grains, and between anisotropic grains and isotropic grains are all different.

[0106] The first diffusion coefficient may refer to the diffusion coefficient of anisotropic grains. It is understood that the specific values ​​of the first diffusion coefficients of different anisotropic grains may be different. The second diffusion coefficient may refer to the diffusion coefficient of isotropic grains. It is understood that the specific values ​​of the second diffusion coefficients of different isotropic grains may be different. The third diffusion coefficient may refer to the diffusion coefficient at the grain boundary. It is understood that if any one of the two grains corresponding to the grain boundary is different, the specific values ​​of the corresponding third diffusion coefficients may be different.

[0107] For example, after constructing the polycrystalline structure model, the diffusion coefficient of each grain and the diffusion coefficient at the grain boundary between every two adjacent grains can be obtained, and then based on the diffusion coefficient of each grain and the diffusion coefficient at the grain boundary between every two adjacent grains, the macroscopic characterization diffusion performance data of the polycrystalline particles at different initial concentrations are calculated to generate a diffusion model in which the macroscopic characterization diffusion performance data of the polycrystalline particles changes with the change of the initial concentration. Among them, the calculation method of the macroscopic characterization diffusion performance data is similar to the prior art, and this embodiment will not be repeated in detail here.

[0108] In this embodiment, when the multiple grains constituting the polycrystalline particles are different, each grain may affect the diffusion performance of the polycrystalline particles. By accurately defining the diffusion coefficient of each grain, the characterization accuracy of the diffusion model can be improved, thereby improving the accuracy of the simulation of the electrochemical energy conversion device.

[0109] In an exemplary embodiment, after simulating the electrochemical energy conversion device based on the diffusion model, the first electrochemical model and the second electrochemical model, the method further includes steps S40 to S50. In which:

[0110] Step S40, obtaining device simulation performance data and performance requirement information of the electrochemical energy conversion device.

[0111] Among them, the device simulation performance data refers to the performance data of the electrochemical energy conversion device obtained by performing simulation tests on the electrochemical energy conversion device, and the performance requirement information is used to characterize the actual requirements for the performance of the electrochemical energy conversion device.

[0112] Exemplarily, after simulating an electrochemical energy conversion device, the device simulation performance data obtained by the simulation can be recorded and stored, and the pre-set performance requirement information can be obtained. In the case of multiple device simulation performance data, the device simulation performance data corresponds to the performance requirement information one by one. For example, when performing a discharge capacity test, the discharge capacity simulation data and the discharge capacity requirement information will be obtained. If the discharge capacity requirement information is that the discharge capacity is greater than the preset discharge capacity threshold, and if the discharge capacity simulation data is less than or equal to the preset discharge capacity threshold, it means that the current polycrystalline structure model may not meet the actual needs and parameter adjustment is required.

[0113] Step S50, based on the device simulation performance data and the performance requirement information, adjust the microstructure characterization data of the polycrystalline structure model, generate a new polycrystalline structure model, and jump to the acquisition step.

[0114] Exemplarily, it is determined whether the device simulation performance data meets the actual requirements of the electrochemical energy conversion device based on the performance requirement information. If it meets the requirements, the current polycrystalline structure model can be used as the target polycrystalline structure model, and the polycrystalline material processing and production can be carried out based on the target polycrystalline structure model; if it does not meet the requirements, the microstructure characterization data and microscopic characterization diffusion performance data of the polycrystalline structure model can be adjusted based on the difference between the device simulation performance data and the target performance data in the performance requirement information, and then the polycrystalline structure model and the exchange current density data of the polycrystalline particle surface are updated based on the adjusted microstructure characterization data, and the diffusion model is updated based on the microscopic characterization diffusion performance data and the regenerated polycrystalline structure model, and the acquisition step is jumped to, and the cycle is repeated until the target polycrystalline structure model that meets the actual requirements of the electrochemical energy conversion device is obtained. Among them, the specific adjustment method can be set according to the actual situation, and this embodiment is not limited to this.

[0115] In this embodiment, by automatically guiding the adjustment of the microstructure characterization data, microscopic characterization diffusion performance data, and interaction current density data of the polycrystalline structure model based on the simulation results, automatic correction of the polycrystalline structure material can be achieved, so that the polycrystalline structure model that meets the actual needs of the electrochemical energy conversion device can be automatically obtained, effectively simplifying the design process of polycrystalline materials.

[0116] In an exemplary embodiment, Figure 7 As shown, the exchange current density data includes the point exchange current density value of at least one surface point on the surface of the polycrystalline particle; the polycrystalline particle includes a plurality of surface layer grains located on the outer surface of the polycrystalline particle; obtaining the exchange current density data on the surface of the polycrystalline particle includes steps S121 to S122. Wherein:

[0117] Step S121, obtaining the intrinsic exchange current density value of the polycrystalline particle, the external normal vector of each surface point, and the surface layer grain orientation data of the surface layer grains at each surface point.

[0118] Among them, the intrinsic exchange current density value is an inherent property of the material itself, which is used to characterize the equilibrium rate between oxidation and reduction reactions in the absence of net current flow. After determining the basic characteristics of polycrystalline particles, the intrinsic exchange current density value of polycrystalline particles can be determined by database query, principle deduction, simulation, etc. However, the orientation and surface properties of different grains may cause changes in the local exchange current density value, and the exchange current density value on the surface of polycrystalline particles will affect the electrochemical reaction between polycrystalline particles and electrolyte, so it has an important impact on the accuracy of the second electrochemical model.

[0119] Exemplarily, at least one surface point is selected from the surface of a polycrystalline particle, and the surface layer grain corresponding to each surface point and the surface layer grain orientation data of the surface layer grain are determined, and the surface layer grain orientation data corresponds one-to-one to the surface point; simultaneously, the external normal vector of each surface point can also be determined based on the surface morphology of the grain at each surface point.

[0120] Step S122, based on the external normal vector corresponding to each surface point, the surface layer grain orientation data and the intrinsic exchange current density value, the point exchange current density value of each surface point is detected.

[0121] For example, for each surface point, the intrinsic exchange current density value can be converted into the point exchange current density value at the surface point according to the angle between the external normal vector of the surface point and the surface layer grain orientation according to the preset conversion rule. In some feasible implementations, the preset conversion rule can be expressed as:

[0122]

[0123] Among them, i 0 is the point exchange current density value of the surface point; i c is the intrinsic exchange current density value of the polycrystalline particles; θ is the angle between the external normal vector at the surface point and the grain orientation of the surface layer.

[0124] In this embodiment, the point exchange current density values ​​at different points on the surface of the polycrystalline particles can be more accurately characterized based on the grain orientation of the surface layer, so that the electrochemical reaction process at the interface between the polycrystalline particles and the electrolyte can be more accurately simulated, thereby improving the accuracy of the second electrochemical model and thus improving the accuracy of the simulation of the electrochemical energy conversion device.

[0125] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0126] Based on the same inventive concept, the embodiment of the present application also provides an electrochemical energy conversion device simulation device for implementing the electrochemical energy conversion device simulation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more electrochemical energy conversion device simulation device embodiments provided below can refer to the limitations of the electrochemical energy conversion device simulation method above, and will not be repeated here.

[0127] In an exemplary embodiment, Figure 8 As shown, a simulation device for an electrochemical energy conversion device is provided, the electrochemical energy conversion device includes a positive electrode, a negative electrode and a diaphragm region, the positive electrode is a polycrystalline particle, and the diaphragm region is filled with an electrolyte; the simulation device for the electrochemical energy conversion device includes: an acquisition module 802, a construction module 804 and a simulation module 806, wherein:

[0128] An acquisition module 802 is used to acquire a diffusion model and solid-phase electrochemical performance data of polycrystalline particles, liquid-phase electrochemical performance data and a first electrochemical model of an electrolyte, and exchange current density data on the surface of polycrystalline particles;

[0129] A construction module 804 is used to construct a second electrochemical model at the interface between the polycrystalline particles and the electrolyte based on the solid phase electrochemical performance data, the liquid phase electrochemical performance data and the exchange current density data;

[0130] The simulation module 806 is used to simulate the electrochemical energy conversion device based on the diffusion model, the first electrochemical model and the second electrochemical model.

[0131] In an exemplary embodiment, the acquisition module 802 is further configured to:

[0132] Acquisition step: acquiring microstructure characterization data of polycrystalline particles, and constructing a polycrystalline structure model based on the microstructure characterization data;

[0133] The microscopic characterization diffusion performance data corresponding to the polycrystalline structure model is obtained, and the diffusion model of the polycrystalline particles is constructed based on the microscopic characterization diffusion performance data and the polycrystalline structure model.

[0134] In an exemplary embodiment, the polycrystalline structure model includes at least one anisotropic grain region and at least one isotropic grain region; the microscopic characterization diffusion performance data includes a first diffusion coefficient corresponding to each anisotropic grain, a second diffusion coefficient corresponding to each isotropic grain, and a third diffusion coefficient at each grain boundary; the acquisition module 802 is further used to:

[0135] A diffusion model of the polycrystalline particles is constructed based on the polycrystalline structure model, the first diffusion coefficients, the second diffusion coefficients and the third diffusion coefficients.

[0136] In an exemplary embodiment, the microstructure characterization data includes at least one of grain size data, grain crystallinity data, grain geometry data, and grain orientation data.

[0137] In an exemplary embodiment, the electrochemical energy conversion device simulation device further includes an optimization module. After simulating the electrochemical energy conversion device based on the diffusion model, the first electrochemical model, and the second electrochemical model, the optimization module is used to:

[0138] Obtain device simulation performance data and performance requirement information of an electrochemical energy conversion device;

[0139] Based on the device simulation performance data and performance requirement information, the microstructure characterization data of the polycrystalline structure model is adjusted, a new polycrystalline structure model is generated, and the process jumps to the acquisition step.

[0140] In an exemplary embodiment, the exchange current density data includes a point exchange current density value of at least one surface point on the surface of a polycrystalline particle; the polycrystalline particle includes a plurality of surface layer grains located on the outer surface of the polycrystalline particle; the acquisition module 802 is further used to:

[0141] Obtaining intrinsic exchange current density values ​​of polycrystalline particles, external normal vectors of each surface point, and surface layer grain orientation data of surface layer grains at each surface point;

[0142] Based on the external normal vector corresponding to each surface point, the surface layer grain orientation data and the intrinsic exchange current density value, the point exchange current density value of each surface point is detected.

[0143] Each module in the electrochemical energy conversion device simulation device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a simulation method of an electrochemical energy conversion device is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0145] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0148] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0150] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0151] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0152] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for simulating an electrochemical energy conversion device, characterized in that: The electrochemical energy conversion device comprises a positive electrode, a negative electrode and a diaphragm region, the positive electrode comprises polycrystalline particles, and the diaphragm region is filled with an electrolyte; the method comprises: Acquiring a diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, liquid-phase electrochemical performance data and a first electrochemical model of the electrolyte, and exchange current density data on the surface of the polycrystalline particles; constructing a second electrochemical model at the interface between the polycrystalline particles and the electrolyte based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data; Performing an electrochemical energy conversion device simulation based on the diffusion model, the first electrochemical model and the second electrochemical model; The exchange current density data includes a point exchange current density value of at least one surface point on the surface of the polycrystalline particle; the polycrystalline particle includes a plurality of surface layer grains located on the outer surface of the polycrystalline particle; obtaining the exchange current density data on the surface of the polycrystalline particle includes: Acquire the intrinsic exchange current density value of the polycrystalline particles, the external normal vector of each surface point, and the surface layer grain orientation data of the surface layer grains at each surface point, wherein each surface layer grain orientation data corresponds to each external normal vector; According to the one-to-one correspondence between each of the surface layer grain orientation data and each of the external normal vectors, the angle between the external normal vector and the surface layer grain orientation at each surface point is determined, and according to the angle, the intrinsic exchange current density value is converted into a point exchange current density value at the surface point.

2. The method according to claim 1, characterized in that The obtaining of the diffusion model of the polycrystalline particles comprises: Acquisition step: acquiring microstructure characterization data of the polycrystalline particles, and constructing a polycrystalline structure model based on the microstructure characterization data; The microscopic diffusion performance data corresponding to the polycrystalline structure model is obtained, and a diffusion model of the polycrystalline particles is constructed based on the microscopic diffusion performance data and the polycrystalline structure model.

3. The method according to claim 2, characterized in that The polycrystalline structure model includes at least one anisotropic grain region and at least one isotropic grain region; the microscopic diffusion performance characterization data includes a first diffusion coefficient corresponding to each of the anisotropic grains, a second diffusion coefficient corresponding to each of the isotropic grains, and a third diffusion coefficient at each grain boundary; The step of constructing a diffusion model of the polycrystalline particles based on the microscopic characterization diffusion performance data and the polycrystalline structure model comprises: A diffusion model of the polycrystalline grains is constructed based on the polycrystalline structure model, the first diffusion coefficients, the second diffusion coefficients and the third diffusion coefficients.

4. The method according to claim 2, characterized in that: The microstructure characterization data includes at least one of grain size data, grain crystallinity data, grain geometry data, and grain orientation data.

5. The method according to claim 2, characterized in that: After simulating the electrochemical energy conversion device based on the diffusion model, the first electrochemical model and the second electrochemical model, the method further includes: Acquiring device simulation performance data and performance requirement information of the electrochemical energy conversion device; Based on the device simulation performance data and the performance requirement information, the microstructure characterization data of the polycrystalline structure model is adjusted, a new polycrystalline structure model is generated, and the process jumps to the acquisition step.

6. An electrochemical energy conversion device simulation device, characterized in that: The electrochemical energy conversion device comprises a positive electrode, a negative electrode and a diaphragm region, the positive electrode is a polycrystalline particle, and the diaphragm region is filled with an electrolyte; the device comprises: An acquisition module, used to acquire the diffusion model and solid-phase electrochemical performance data of the polycrystalline particles, the liquid-phase electrochemical performance data and the first electrochemical model of the electrolyte, and the exchange current density data on the surface of the polycrystalline particles; A construction module, for constructing a second electrochemical model at the interface between the polycrystalline particles and the electrolyte based on the solid-phase electrochemical performance data, the liquid-phase electrochemical performance data and the exchange current density data; A simulation module, configured to simulate an electrochemical energy conversion device based on the diffusion model, the first electrochemical model and the second electrochemical model; The exchange current density data includes a point exchange current density value of at least one surface point on the surface of the polycrystalline particle; the polycrystalline particle includes a plurality of surface layer grains located on the outer surface of the polycrystalline particle; the acquisition module is further used to: Acquire the intrinsic exchange current density value of the polycrystalline particles, the external normal vector of each of the surface points, and the surface layer grain orientation data of the surface layer grains at each of the surface points; Based on the external normal vector, surface layer grain orientation data and intrinsic exchange current density value corresponding to each of the surface points, the point exchange current density value of each of the surface points is detected.

7. The device according to claim 6, characterized in that The acquisition module is also used for: Acquisition step: acquiring microstructure characterization data of the polycrystalline particles, and constructing a polycrystalline structure model based on the microstructure characterization data; The microscopic diffusion performance data corresponding to the polycrystalline structure model is obtained, and a diffusion model of the polycrystalline particles is constructed based on the microscopic diffusion performance data and the polycrystalline structure model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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