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 method comprises: an acquisition step: acquiring a polycrystalline structure model; simulating the diffusion process of the polycrystalline structure model according to the initial concentration distribution information and microscopic diffusion performance data of the polycrystalline structure model to obtain macroscopic diffusion performance data of the polycrystalline structure model; simulating the electrochemical energy conversion device based on the macroscopic diffusion performance data and the polycrystalline structure model. 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 guide the design of the crystal material based on the analysis results.

[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] Acquisition step: acquiring a polycrystalline structure model;

[0007] According to the initial concentration distribution information and microscopic diffusion performance data of the polycrystalline structure model, the diffusion process of the polycrystalline structure model is simulated to obtain the macroscopic diffusion performance data of the polycrystalline structure model;

[0008] Electrochemical energy conversion device simulation is performed based on macroscopic characterization diffusion performance data and polycrystalline structure models.

[0009] In one embodiment, the macroscopic characterization diffusion performance data includes a target equivalent diffusion coefficient; based on the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data, the diffusion process of the polycrystalline structure model is simulated to obtain the macroscopic characterization diffusion performance data of the polycrystalline structure model, including:

[0010] Based on the initial concentration distribution information of the polycrystalline structure model and the microscopic diffusion performance data, as well as the preset boundary model, a concentration-time model is constructed. The concentration-time model is used to characterize the relationship between the concentration difference measurement value and time.

[0011] Based on the concentration-time model, a target concentration difference measurement value of the polycrystalline structure model in a diffusion equilibrium state is queried, and a target equivalent diffusion coefficient corresponding to the target concentration difference measurement value is detected.

[0012] In one embodiment, a concentration-time model is constructed based on initial concentration distribution information of a polycrystalline structure model and microscopic characterization diffusion performance data, as well as a preset boundary model, including:

[0013] Determining boundary conditions of the polycrystalline structure model based on initial concentration distribution information of the polycrystalline structure model and a preset boundary model;

[0014] Based on the bulk diffusion coefficient and the interface diffusion coefficient, querying a target concentration distribution change model from a plurality of preset concentration distribution change models;

[0015] A concentration-time model is constructed based on the initial concentration distribution information, boundary conditions and target concentration distribution change model of the polycrystalline structure model.

[0016] In one embodiment, the boundary condition includes a surface flux condition; based on the initial concentration distribution information of the polycrystalline structure model and the preset boundary model, the boundary condition of the polycrystalline structure model is determined, including:

[0017] Obtaining high-dimensional size information and low-dimensional size information of the polycrystalline structure model, and target operation time information corresponding to the electrochemical energy conversion device;

[0018] The surface flux condition of the polycrystalline structure model is detected based on the initial concentration distribution information, the high-dimensional size information, the low-dimensional size information, the target running time information and the preset boundary model.

[0019] In one embodiment, the target concentration distribution change model includes at least one of a first concentration distribution change model, a second concentration distribution change model, and a third concentration distribution change model; and based on the initial concentration distribution information of the polycrystalline structure model, boundary conditions, and the target concentration distribution change model, a concentration-time model is constructed, including at least one of the following items:

[0020] In the case where the bulk diffusion coefficient is greater than the interface diffusion coefficient and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a first concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the first concentration distribution change model, and the ion concentration difference on both sides of the grain boundary where ion exchange exists in the first concentration field model exceeds the preset concentration difference threshold; a concentration time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the first concentration field model;

[0021] When the bulk diffusion coefficient is less than the interface diffusion coefficient and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a second concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the second concentration distribution change model, and the ion concentration difference on both sides of the grain boundary in the second concentration field model does not exceed the preset concentration difference threshold; a concentration time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the second concentration field model;

[0022] When the difference between the bulk diffusion coefficient and the interface diffusion coefficient does not exceed the preset first difference threshold, a third concentration field model is constructed based on the bulk diffusion coefficient and the third concentration distribution change model; and a concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, boundary conditions and the third concentration field model.

[0023] In one embodiment, after performing the electrochemical energy conversion device simulation based on the macroscopic characterization diffusion performance data and the polycrystalline structure model, the method further includes:

[0024] Obtain device simulation performance data and performance requirement information of an electrochemical energy conversion device;

[0025] Based on the device simulation performance data and performance requirement information, the morphology data and size data of the polycrystalline structure model are adjusted, a new polycrystalline structure model is generated, and the process jumps to the acquisition step.

[0026] In a second aspect, the present application also provides an electrochemical energy conversion device simulation device, comprising:

[0027] An acquisition module is used to perform an acquisition step: acquiring a polycrystalline structure model;

[0028] A diffusion simulation module is used to simulate the diffusion process of the polycrystalline structure model according to the initial concentration distribution information and microscopic diffusion performance data of the polycrystalline structure model, and obtain the macroscopic diffusion performance data of the polycrystalline structure model;

[0029] The device simulation module is used to simulate electrochemical energy conversion devices based on macroscopic characterization diffusion performance data and polycrystalline structure models.

[0030] 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, and when the processor executes the computer program, the following steps are implemented:

[0031] Acquisition step: acquiring a polycrystalline structure model;

[0032] According to the initial concentration distribution information and microscopic diffusion performance data of the polycrystalline structure model, the diffusion process of the polycrystalline structure model is simulated to obtain the macroscopic diffusion performance data of the polycrystalline structure model;

[0033] Electrochemical energy conversion device simulation is performed based on macroscopic characterization diffusion performance data and polycrystalline structure models.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0035] Acquisition step: acquiring a polycrystalline structure model;

[0036] According to the initial concentration distribution information and microscopic diffusion performance data of the polycrystalline structure model, the diffusion process of the polycrystalline structure model is simulated to obtain the macroscopic diffusion performance data of the polycrystalline structure model;

[0037] Electrochemical energy conversion device simulation is performed based on macroscopic characterization diffusion performance data and polycrystalline structure models.

[0038] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0039] Acquisition step: acquiring a polycrystalline structure model;

[0040] According to the initial concentration distribution information and microscopic diffusion performance data of the polycrystalline structure model, the diffusion process of the polycrystalline structure model is simulated to obtain the macroscopic diffusion performance data of the polycrystalline structure model;

[0041] Electrochemical energy conversion device simulation is performed based on macroscopic characterization diffusion performance data and polycrystalline structure models.

[0042] The above-mentioned electrochemical energy conversion device simulation method, device, computer equipment, computer-readable storage medium and computer program product obtain a polycrystalline structure model, simulate the diffusion process of the polycrystalline structure model according to the initial concentration distribution information and microscopic characterization diffusion performance data of the polycrystalline structure model, obtain the macroscopic characterization diffusion performance data of the polycrystalline structure model, realize the simulation of polycrystalline materials, and characterize the macroscopic characterization diffusion performance of polycrystalline materials; then, based on the macroscopic characterization diffusion performance data and the polycrystalline structure model, simulate the electrochemical energy conversion device, and realize the simulation of the electrochemical energy conversion device without the need to produce polycrystalline materials. In this way, through the electrochemical energy conversion device simulation method of the present application, the macroscopic characterization of diffusion performance data can be used as a bridge to realize the cross-scale simulation process from atoms to polycrystalline structures and then to electrochemical energy conversion devices, and establish the relationship between atoms, polycrystalline materials and electrochemical energy conversion devices. The performance data of the electrochemical energy conversion device determined by the simulation can directly guide the design of polycrystalline materials, so that a polycrystalline structure that meets the needs of the electrochemical energy conversion device can be directly designed in the polycrystalline structure design stage, reducing the trial and error process, improving the convenience of crystal material design, and reducing the cost of trial and error. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] Figure 1 A schematic diagram of a flow chart of a simulation method for an electrochemical energy conversion device in one embodiment;

[0045] Figure 2 A schematic diagram of a polycrystalline structure model in one embodiment;

[0046] Figure 3 A schematic diagram of a flow chart of a simulation method for an electrochemical energy conversion device in another embodiment;

[0047] Figure 4 is a schematic diagram of an interpolation curve of the corresponding relationship between the concentration difference measurement value and the equivalent diffusion coefficient in one embodiment;

[0048] Figure 5 is a schematic diagram of a polycrystalline structure model image in one embodiment;

[0049] Figure 6 is a schematic diagram of a concentration-time model in one embodiment;

[0050] Figure 7 A schematic diagram of simulation results of the correlation between the voltage and the discharge capacity of an electrochemical energy conversion device in one embodiment;

[0051] Figure 8 A structural block diagram of an electrochemical energy conversion device simulation device in one embodiment;

[0052] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] 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.

[0054] In an exemplary embodiment, Figure 1 As shown, a method for simulating an electrochemical energy conversion device is provided. This embodiment takes 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 device may be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps S10-S30. Among them:

[0055] Step S10, acquisition step: acquiring a polycrystalline structure model.

[0056] 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. The diffusion performance of working ions (lithium ions, sodium ions, oxygen ions, etc.) in polycrystalline materials is a key factor in determining the performance of electrochemical energy conversion devices. The diffusion of substances in polycrystalline materials includes bulk diffusion and grain boundary diffusion. Bulk diffusion refers to the diffusion process of substances within the crystal of a single crystal, and grain boundary diffusion refers to the diffusion process of substances at the grain boundaries between different grains.

[0057] The polycrystalline structure model refers to a model created by computer software to characterize the internal structure of polycrystalline materials, which can be a theoretical framework or visual representation of polycrystalline materials. Figure 2 , Figure 2 What is shown is a polycrystalline material composed of a plurality of single crystals 21 . The atoms inside each single crystal 21 are arranged in an orderly manner, but the arrangement directions of different single crystals 21 are different, so grain boundaries are formed between the single crystals 21 .

[0058] For example, a designer or technician of polycrystalline materials can create a polycrystalline structure model through computer software based on the design information of the polycrystalline material, such as element type, ratio between each element, size of single crystal structure, size of polycrystalline structure, etc.

[0059] Step S20, simulating the diffusion process of the polycrystalline structure model according to the initial concentration distribution information and the microscopic diffusion performance data of the polycrystalline structure model, and obtaining the macroscopic diffusion performance data of the polycrystalline structure model.

[0060] Among them, the diffusion performance of the material is used to characterize the natural diffusion behavior of the substance inside the polycrystalline material without the influence of the externally applied chemical potential gradient or electric field. The diffusion performance of the polycrystalline material is determined by the properties of the polycrystalline material itself, rather than caused by external factors. The microscopic characterization diffusion performance data is used to characterize the concentration of the working ions in the polycrystalline material with time and space. It can be constructed in advance based on the actual situation. This embodiment is not limited to this. As an example, the microscopic characterization diffusion performance data can include at least one of the grain boundary diffusion coefficient, the bulk diffusion coefficient, and the diffusion model of the ion concentration changing with time and space. The macroscopic characterization diffusion performance data is used to characterize the diffusion performance of the polycrystalline material as a whole. The macroscopic characterization diffusion performance data includes at least one of the equivalent diffusion coefficient of the polycrystalline material in the diffusion equilibrium state and the diffusion model of the concentration difference measurement value changing with time. Microscopic characterization of diffusion performance data is usually difficult to directly apply to the simulation of electrochemical energy conversion devices. By using macroscopic characterization of diffusion performance data, the cross-scale conversion from atomic-level diffusion performance to material-level diffusion performance can be achieved, and then the material-level diffusion performance can be applied to the simulation of electrochemical energy conversion devices to achieve a larger-scale simulation process. Moreover, through the cross-scale conversion between atomic-level diffusion performance and material-level diffusion performance, the correlation between the performance of electrochemical energy conversion devices and atomic-level diffusion performance can be established, thereby reversely guiding the design of polycrystalline materials.

[0061] During the design and production stage of polycrystalline materials, usually only microscopic characterization diffusion performance data can be used as the design and production target. However, in the actual application process of polycrystalline materials, the actual diffusion performance will be affected by the actual application scenario, and the impact of the actual application scenario on the diffusion performance is unknown and not necessarily regular. This will result in the electrochemical energy conversion device being unable to meet the needs after the produced crystalline materials are provided to the manufacturer of the electrochemical energy conversion device, and there are barriers and differences between the needs of the electrochemical energy conversion device and the microscopic characterization diffusion performance data. Polycrystalline material designers cannot directly convert the needs of the electrochemical energy conversion device into microscopic characterization diffusion performance data, and can only redesign and debug based on experience. Therefore, it is usually necessary to go through repeated tedious processing, analysis, parameter adjustment and reprocessing to obtain crystalline materials that meet the needs of the electrochemical energy conversion device. The whole process is time-consuming, labor-intensive and costly.

[0062] The initial concentration distribution information is used to characterize the concentration distribution of working ions at various locations in the polycrystalline material at the initial moment of operation of the electrochemical energy conversion device.

[0063] For example, initial concentration distribution information and microscopic characterization diffusion performance data of a polycrystalline structure model that is preset or input by a user may be acquired.

[0064] As an example, the concentration field of working ions in polycrystalline materials satisfies Fick's second law, that is, the rate of change of the concentration of working ions in polycrystalline materials is proportional to the quadratic spatial derivative of the concentration of working ions in polycrystalline materials, and the proportionality coefficient is the diffusion coefficient. Therefore, the microscopic characterization diffusion performance data can be expressed as:

[0065]

[0066] Where C is the concentration field of the working ion in the polycrystalline material, t is the time, is the gradient operator, D b is the bulk diffusion coefficient of a single crystal in a polycrystalline material.

[0067] As an example, after creating a polycrystalline structure model, before designing a polycrystalline material, the diffusion process of the polycrystalline material can be simulated based on the initial concentration distribution information and microscopic characterization diffusion performance data of the created polycrystalline structure model, and the intrinsic diffusion performance of the polycrystalline material can be tested to obtain the macroscopic characterization diffusion performance data corresponding to the polycrystalline structure model. Furthermore, when actually designing a polycrystalline material, the selected polycrystalline structure model can be selected based on the detected polycrystalline structure model selection operation to obtain the selected polycrystalline structure model and the macroscopic characterization diffusion performance data of the polycrystalline structure model determined by simulation. As an example, if the macroscopic characterization diffusion performance data corresponding to the polycrystalline structure model is not obtained by simulation test before designing a polycrystalline material, the diffusion process of the polycrystalline material can be simulated based on the initial concentration distribution information and microscopic characterization diffusion performance data of the created polycrystalline structure model after the polycrystalline structure model is selected in the process of designing a polycrystalline material, and the intrinsic diffusion performance of the polycrystalline material can be tested to obtain the macroscopic characterization diffusion performance data corresponding to the polycrystalline structure model.

[0068] Step S30, performing electrochemical energy conversion device simulation based on the macroscopic characterization diffusion performance data and the polycrystalline structure model.

[0069] 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. Before simulating an electrochemical energy conversion device, it is necessary to first determine the key parameters of each module in the electrochemical energy conversion device. The diffusion performance of working ions (lithium ions, sodium ions, oxygen ions, etc.) in polycrystalline materials is a key factor in determining the performance of electrochemical energy conversion devices.

[0070] Exemplarily, after determining the macroscopic characterization diffusion performance data of the polycrystalline structure model, other key parameters required for the simulation of the electrochemical energy conversion device can also be obtained, and based on the polycrystalline structure model, the macroscopic characterization diffusion performance data of the polycrystalline structure model and other key parameters, the electrochemical energy conversion device simulation model is created, and then based on the electrochemical energy conversion device simulation model, a simulation performance test is performed to obtain the device simulation performance data of the electrochemical energy conversion device. The device simulation performance data obtained by the simulation test can be used to guide the design of polycrystalline materials. For example, a plurality of design schemes can be formulated, and corresponding polycrystalline structure models can be created according to the design information of each design scheme, and simulation can be performed to obtain a plurality of device simulation performance data, thereby screening out the design scheme that meets the actual needs of the electrochemical energy conversion device, and then the design information of the final design scheme is produced; or after each device simulation performance data is obtained, the design information can be dynamically adjusted, a new polycrystalline structure model can be created, and simulation can be performed again based on the new polycrystalline structure model until the design information that meets the actual needs of the electrochemical energy conversion device is determined, and then the design information is finally determined. The design information is produced.

[0071] 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.

[0072] In the above-mentioned electrochemical energy conversion device simulation method, by obtaining a polycrystalline structure model, according to the initial concentration distribution information and microscopic characterization diffusion performance data of the polycrystalline structure model, the diffusion process of the polycrystalline structure model is simulated, and the macroscopic characterization diffusion performance data of the polycrystalline structure model is obtained, so as to realize the simulation of the polycrystalline material, and obtain the macroscopic characterization diffusion performance data of the polycrystalline material through the simulation; and then based on the macroscopic characterization diffusion performance data and the polycrystalline structure model, the electrochemical energy conversion device is simulated, so as to realize the simulation of the electrochemical energy conversion device without the need to produce the polycrystalline material. In this way, through the electrochemical energy conversion device simulation method of the present application, the cross-scale simulation process from atoms to polycrystalline structures and then to the electrochemical energy conversion device can be realized, and the relationship between atoms, polycrystalline materials and electrochemical energy conversion devices can be established, so that the performance data of the electrochemical energy conversion device determined by the simulation can directly guide the design of the polycrystalline material, so that the polycrystalline structure design stage can directly design a polycrystalline structure that meets the needs of the electrochemical energy conversion device, reduce the process of repeated trial and error, improve the convenience of crystal material design, and reduce the cost of trial and error.

[0073] In an exemplary embodiment, Figure 3As shown, the macroscopic characterization diffusion performance data includes the target equivalent diffusion coefficient; according to the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data, the diffusion process of the polycrystalline structure model is simulated to obtain the macroscopic characterization diffusion performance data of the polycrystalline structure model, including steps S21 to S22. Wherein:

[0074] Step S21, based on the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data, as well as the preset boundary model, a concentration-time model is constructed, and the concentration-time model is used to characterize the relationship between the change of the concentration difference measurement value with time.

[0075] Among them, the concentration-time model is used to characterize the relationship between the change of the concentration difference measurement value and time. It can be a relationship equation for the change of the concentration difference measurement value and time, or an interpolation table of the concentration difference measurement value and time. The concentration difference measurement value is used to characterize the difference between the maximum concentration value and the minimum concentration value in the polycrystalline material.

[0076] As an example, the ratio of the difference between the maximum concentration value and the minimum concentration value in the polycrystalline material to the initial concentration can be used as the concentration difference measure. For example, the concentration difference measure is C * It can be expressed as:

[0077]

[0078] in, C is the difference between the maximum concentration value max(c) in the polycrystalline material and the minimum concentration value min(c) in the polycrystalline material, and C0 is the initial concentration of the polycrystalline material.

[0079] For example, a simulation method can be used to detect the relationship between the concentration difference measurement value of the polycrystalline material and time based on the initial concentration distribution information, microscopic diffusion performance data and a preset boundary model, and a concentration-time model can be constructed based on the detection results.

[0080] Step S22, based on the concentration-time model, querying the target concentration difference measurement value when the polycrystalline structure model is in a diffusion equilibrium state, and detecting the target equivalent diffusion coefficient corresponding to the target concentration difference measurement value.

[0081] Among them, the equivalent diffusion coefficient is used to characterize the diffusion behavior of working ions in polycrystalline materials and to reflect the complexity of the actual diffusion process.

[0082] Exemplarily, after determining the concentration-time model, the time range and target concentration difference measurement value corresponding to the polycrystalline structure model being in a diffusion equilibrium state can be located from the concentration-time model; then, the correspondence between the preset concentration difference measurement value in a diffusion equilibrium state and the equivalent diffusion coefficient is queried to obtain the target equivalent diffusion coefficient corresponding to the target concentration difference measurement value.

[0083] Among them, the corresponding relationship between the concentration difference measurement value and the equivalent diffusion coefficient in the diffusion equilibrium state can be in the form of a relational expression or an interpolation table, etc., and can be determined in advance through simulation, experimental testing, etc., and this embodiment does not limit this. As an example, based on the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data, by setting different equivalent diffusion coefficients, the concentration difference measurement value of the polycrystalline structure model in the diffusion equilibrium state under different equivalent diffusion coefficients can be tested through simulation testing, and then the corresponding relationship between the concentration difference measurement value and the equivalent diffusion coefficient in the diffusion equilibrium state is constructed based on the test results. As an example, refer to Figure 4 , Figure 4 In one embodiment, an interpolation curve of the corresponding relationship between the concentration difference measurement value and the equivalent diffusion coefficient in a diffusion equilibrium state detected and fitted by simulation, the horizontal axis D eff is the equivalent diffusion coefficient, the ordinate C ** is the concentration difference measurement value in the diffusion equilibrium state. By constructing the corresponding relationship between the concentration difference measurement value and the equivalent diffusion coefficient, the cross-scale conversion from atomic-level diffusion performance to material-level diffusion performance can be realized, and then the material-level diffusion performance can be applied to the simulation of the electrochemical energy conversion device to achieve a larger-scale simulation process. Moreover, through the cross-scale conversion between the atomic-level diffusion performance and the material-level diffusion performance, the correlation between the electrochemical energy conversion device performance and the atomic-level diffusion performance can also be established, thereby reversely guiding the design of polycrystalline materials.

[0084] As an example, a method for locating the time range and concentration difference measurement value corresponding to the diffusion equilibrium state from the concentration-time model includes detecting the rate of change of the concentration difference measurement value at each moment. The moment when the rate of change of the concentration difference measurement value is less than a preset change rate threshold is in the diffusion equilibrium state, and the concentration difference measurement value corresponding to this moment is the concentration difference measurement value corresponding to the diffusion equilibrium state; the moment when the rate of change of the concentration difference measurement value is greater than or equal to the preset change rate threshold is in the non-diffusion equilibrium state, and the concentration difference measurement value corresponding to this moment is the concentration difference measurement value corresponding to the non-diffusion equilibrium state.

[0085] In this embodiment, by constructing a concentration-time model, it is possible to simulate the relationship between the concentration difference measurement value of the working example in the polycrystalline material and time, so as to find the concentration difference measurement value of the diffusion equilibrium state, and determine the target equivalent diffusion coefficient of the polycrystalline material based on the concentration difference measurement value of the diffusion equilibrium state, thereby achieving a simplified characterization of the complex diffusion behavior of the working ions in the polycrystalline material, saving the computing power resources required for the simulation of the electrochemical energy conversion device, and shortening the time spent on the simulation of the electrochemical energy conversion device.

[0086] In an exemplary embodiment, the microscopic characterization diffusion performance data includes bulk diffusion coefficient and interface diffusion coefficient; based on the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data, and the preset boundary model, a concentration-time model is constructed, including steps S211 to S213. Wherein:

[0087] Step S211 , determining the boundary conditions of the polycrystalline structure model based on the initial concentration distribution information of the polycrystalline structure model and a preset boundary model.

[0088] It should be noted that the concentration-time model can be obtained through simulation testing, but during the simulation process, at least the initial conditions and boundary conditions need to be determined. The initial conditions refer to the concentration distribution at the initial moment, which can be set based on the initial concentration distribution information; the boundary conditions refer to the concentration distribution or surface flux conditions at the boundary of the polycrystalline structure model.

[0089] The boundary model is used to characterize the concentration distribution or ion flux at the boundary of the polycrystalline material, and can be determined in advance according to actual conditions, which is not limited in this embodiment.

[0090] Exemplarily, the initial concentration distribution information is brought into a preset boundary model for solution to obtain the concentration distribution or surface flux condition at the boundary of the polycrystalline structure model. The concentration distribution or surface flux condition at the boundary of the polycrystalline structure model can be used as the boundary condition of the polycrystalline structure model.

[0091] Step S212: querying a target concentration distribution change model from a plurality of preset concentration distribution change models based on the bulk diffusion coefficient and the interface diffusion coefficient.

[0092] It should be noted that the grain boundaries in polycrystalline materials may promote or hinder the diffusion process of working ions in polycrystalline materials. When the bulk diffusion coefficient is greater than the interface diffusion coefficient, the grain boundaries hinder the diffusion process of working ions in polycrystalline materials; and when the bulk diffusion coefficient is less than the interface diffusion coefficient, the grain boundaries promote the diffusion process of working ions in polycrystalline materials. Under the action of different grain boundaries, the concentration of working ions in polycrystalline materials will change differently over time and space. Therefore, different concentration distribution change models can be constructed in advance for different situations. The concentration distribution change model refers to a mathematical model used to characterize the basic change law of the concentration of working ions in polycrystalline materials over time and space. When the bulk diffusion coefficient and interface diffusion coefficient of a single crystal in a polycrystalline structure are different, the adapted concentration distribution change model may be different; therefore, when constructing a concentration-time model, a suitable target concentration distribution change model can be selected based on the actual bulk diffusion coefficient and interface diffusion coefficient.

[0093] Among them, the bulk diffusion coefficient is used to characterize the diffusion behavior of working ions within the single crystal of a polycrystalline material, and the interface diffusion coefficient is used to characterize the diffusion behavior of working ions at the grain boundaries between different grains. The bulk diffusion coefficient and the interface diffusion coefficient can be calculated in advance using the first principles method.

[0094] Exemplarily, the target concentration distribution change model may be selected from a plurality of preset concentration distribution change models based on the bulk diffusion coefficient and the interface diffusion coefficient.

[0095] Step S213, constructing a concentration-time model based on the initial concentration distribution information of the polycrystalline structure model, boundary conditions and the target concentration distribution change model.

[0096] Exemplarily, a simulation method can be used to detect the relationship between the concentration difference measurement value of the polycrystalline material and time based on the initial concentration distribution information, boundary conditions, and target concentration distribution change model of the polycrystalline structure model, and a concentration-time model can be constructed based on the detection results.

[0097] In this embodiment, based on the bulk diffusion coefficient and the interface diffusion coefficient, a suitable target concentration distribution change model can be dynamically matched to improve the accuracy of the simulation, thereby improving the accuracy of the concentration-time model, thereby improving the accuracy of the crystal material design and enhancing the performance of the electrochemical energy conversion device.

[0098] In an exemplary embodiment, the boundary conditions include surface flux conditions; based on the initial concentration distribution information of the polycrystalline structure model and the preset boundary model, the boundary conditions of the polycrystalline structure model are determined, including steps S2111 to S2112. Wherein:

[0099] Step S1211, obtaining high-dimensional size information and low-dimensional size information of the polycrystalline structure model, and target operating time information corresponding to the electrochemical energy conversion device.

[0100] Among them, the boundary condition can be a surface flux condition, and the surface flux refers to the number of working ions passing through a unit area per unit time. When the polycrystalline structure model is a three-dimensional structure, the high-dimensional size information refers to the volume information of the polycrystalline structure model, and the low-dimensional size information refers to the surface area information of the surface of the polycrystalline structure model; when the polycrystalline structure model is a two-dimensional structure, the high-dimensional size information refers to the surface area information of the polycrystalline structure model, and the low-dimensional size information refers to the circumference information of the surface of the polycrystalline structure model. The target operating time information is used to characterize the operating time of the electrochemical energy conversion device, and can be a preset value or input by the user, which is not limited in this embodiment.

[0101] For example, after the polycrystalline structure model is determined, at least one size information of the volume, surface area, perimeter, length, height and width of the polycrystalline structure model can be detected, and then the high-dimensional size information and low-dimensional size information of the polycrystalline structure model can be directly detected or calculated. Simultaneously, based on user operation, the preset running time or the running time input by the user can be set as the target running time.

[0102] Step S2112, detecting the surface flux condition of the polycrystalline structure model based on the initial concentration distribution information, the high-dimensional size information, the low-dimensional size information, the target running time information and the preset boundary model.

[0103] Exemplarily, initial concentration distribution information, high-dimensional size information, low-dimensional size information and target running time information are brought into a preset boundary model for solution to obtain the surface flux condition at the boundary of the polycrystalline structure model.

[0104] As an example, the preset boundary model may be:

[0105]

[0106] Among them, j is the flux of working ions on the surface of polycrystalline materials, n is the external normal vector of the surface of polycrystalline materials, V is the high-dimensional size information of polycrystalline materials, S is the low-dimensional size information of polycrystalline materials, T0 is the operating time under typical working conditions, and C0 is the initial concentration distribution information. And j=-D b C. After determining the surface flux conditions, combined with the microscopic characterization diffusion performance data, the relationship between the concentration difference measurement value in the polycrystalline material and time can be calculated or simulated.

[0107] In this embodiment, the surface flux is used as the boundary condition, which can reflect the diffusion process more intuitively, simplify the model, improve the flexibility of control, and be more stable in numerical simulation.

[0108] In an exemplary embodiment, the target concentration distribution change model includes at least one of a first concentration distribution change model, a second concentration distribution change model, and a third concentration distribution change model; based on the initial concentration distribution information of the polycrystalline structure model, boundary conditions, and the target concentration distribution change model, a concentration-time model is constructed, including at least one of steps S2131 to S2133. Wherein:

[0109] Step S2131, when the bulk diffusion coefficient is greater than the interface diffusion coefficient, and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a first concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the first concentration distribution change model, and the ion concentration difference on both sides of the grain boundary where ion exchange exists in the first concentration field model exceeds the preset concentration difference threshold; a concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the first concentration field model.

[0110] It should be noted that the microscopic characterization diffusion performance data is used to characterize the variation of the concentration of working ions in polycrystalline materials over time and space. Under different grain boundary effects, the variation of the concentration of working ions in polycrystalline materials over time and space will also be different. Therefore, it is necessary to select appropriate microscopic characterization diffusion performance data based on the actual bulk diffusion coefficient and interface diffusion coefficient. The concentration field model is used to characterize the variation of the concentration of working ions in the polycrystalline structure model over time and space.

[0111] According to the different effects of grain boundaries, the microscopic characterization of diffusion performance data can be divided into three types. When the bulk diffusion coefficient is equivalent to the interface diffusion coefficient, the diffusion process of the working ions in the polycrystalline material is less affected by the interface. Therefore, the concentration of the working ions in the polycrystalline material gradually changes from the center to the surrounding, such as gradually increasing or decreasing. Therefore, the third concentration distribution change model corresponding to this situation is used to characterize the gradual change of the concentration of the working ions from the center to the surrounding in the polycrystalline material; when the bulk diffusion coefficient is significantly greater than the interface diffusion coefficient, the grain boundary hinders the diffusion process of the working ions in the polycrystalline material. At the grain boundary where ion exchange exists, the ion concentration changes discontinuously. Along the diffusion direction, the ion concentration at the grain boundary decreases significantly. Therefore, the first concentration corresponding to this situation The distribution change model is used to characterize that the concentration of working ions changes gradually from the center to the surrounding in polycrystalline materials, but at the grain boundaries where ion exchange exists, the ion concentration changes discontinuously, and the ion concentration decreases significantly at the grain boundaries along the diffusion direction; and when the bulk diffusion coefficient is significantly smaller than the interface diffusion coefficient, the grain boundaries promote the diffusion process of working ions in polycrystalline materials, and at the grain boundaries where ion exchange exists, the rate of change of ion concentration increases along the diffusion direction. Therefore, the second concentration distribution change model corresponding to this situation is used to characterize that the concentration of working ions changes gradually from the center to the surrounding in polycrystalline materials, but at the grain boundaries where ion exchange exists, the rate of change of ion concentration increases along the diffusion direction.

[0112] Exemplarily, after determining the bulk diffusion coefficient and the interface diffusion coefficient, the numerical values ​​of the bulk diffusion coefficient and the interface diffusion coefficient can be compared, and the difference between the bulk diffusion coefficient and the interface diffusion coefficient can be calculated. If it is detected that the bulk diffusion coefficient is greater than the interface diffusion coefficient, and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a first concentration distribution change model is obtained, and a first concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the first concentration distribution change model; further, a simulation method can be used to detect the change relationship between the concentration difference measurement value of the polycrystalline material and time based on the initial concentration distribution information, boundary conditions and the first concentration field model of the polycrystalline structure model, and a concentration-time model is constructed based on the detection results.

[0113] As an example, the first concentration distribution change model may be:

[0114]

[0115] Where C is the concentration field of the working ion in the polycrystalline material, t is the time, is the gradient operator, D b is the bulk diffusion coefficient of a single crystal in a polycrystalline material, D i is the interfacial diffusion coefficient at the grain boundary in polycrystalline materials, Cu is the concentration on the grain boundary side, n u For C u The external normal vector of the grain boundary on the same side, C d is the concentration on the other side of the grain boundary, n d For C d The external normal vector of the grain boundary on the same side, h is the thickness of the grain boundary.

[0116] The above-mentioned first concentration distribution change model is used for simulation, and a polycrystalline structure model image at a certain moment in the equilibrium state is obtained from the first concentration field model obtained by simulation, such as Figure 5 The polycrystalline structure model image located in the middle is shown.

[0117] Step S2132, when the bulk diffusion coefficient is less than the interface diffusion coefficient, and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds the preset first difference threshold, a second concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the second concentration distribution change model, and the ion concentration difference on both sides of the grain boundary in the second concentration field model does not exceed the preset concentration difference threshold; a concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the second concentration field model.

[0118] Exemplarily, after determining the bulk diffusion coefficient and the interface diffusion coefficient, the numerical values ​​of the bulk diffusion coefficient and the interface diffusion coefficient can be compared, and the difference between the bulk diffusion coefficient and the interface diffusion coefficient can be calculated. If it is detected that the bulk diffusion coefficient is smaller than the interface diffusion coefficient, and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a second concentration distribution change model is obtained, and a second concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the second concentration distribution change model; further, a simulation method can be used to detect the change relationship between the concentration difference measurement value of the polycrystalline material and time based on the initial concentration distribution information, boundary conditions and the second concentration field model of the polycrystalline structure model, and a concentration-time model is constructed based on the detection results.

[0119] As an example, the second concentration distribution change model may be:

[0120]

[0121] Where C is the concentration field of the working ion in the polycrystalline material, t is the time, is the gradient operator, D b is the bulk diffusion coefficient of a single crystal in a polycrystalline material, D i is the interfacial diffusion coefficient at the grain boundary in polycrystalline materials, t is the tangential differential of the physical field along the grain boundary.

[0122] The above second concentration distribution change model is used for simulation, and a polycrystalline structure model image at a certain moment in the equilibrium state is obtained from the second concentration field model obtained by simulation, such as Figure 5 The polycrystalline structure model image on the right is shown in the figure.

[0123] Step S2133, when the difference between the bulk diffusion coefficient and the interface diffusion coefficient does not exceed the preset first difference threshold, construct a third concentration field model based on the bulk diffusion coefficient and the third concentration distribution change model; and construct a concentration-time model based on the initial concentration distribution information of the polycrystalline structure model, boundary conditions and the third concentration field model.

[0124] Exemplarily, after determining the bulk diffusion coefficient and the interface diffusion coefficient, the numerical values ​​of the bulk diffusion coefficient and the interface diffusion coefficient can be compared, and the difference between the bulk diffusion coefficient and the interface diffusion coefficient can be calculated. If it is detected that the difference between the bulk diffusion coefficient and the interface diffusion coefficient does not exceed the preset first difference threshold, the third concentration distribution change model is obtained. Since the third concentration distribution change model is less affected by interface diffusion, a third concentration field model can be constructed based on the bulk diffusion coefficient and the third concentration distribution change model; furthermore, a simulation method can be used to detect the change relationship between the concentration difference measurement value of the polycrystalline material and time based on the initial concentration distribution information, boundary conditions, and the third concentration field model of the polycrystalline structure model, and a concentration-time model can be constructed based on the detection results.

[0125] As an example, the third concentration distribution change model may be:

[0126]

[0127] Where C is the concentration field of the working ion in the polycrystalline material, t is the time, is the gradient operator, D b is the bulk diffusion coefficient of a single crystal in a polycrystalline material.

[0128] The third concentration distribution change model is used for simulation, and a polycrystalline structure model image at a certain moment in the equilibrium state is obtained from the third concentration field model obtained by simulation, such as Figure 5 The polycrystalline structure model image on the left is shown in the figure.

[0129] As an example, in the above three different cases, the concentration-time model constructed according to the above three microscopic diffusion performance data is as follows: Figure 6 As shown, the horizontal axis is time and the vertical axis is the concentration difference measurement value C / C0, C is the difference between the maximum concentration and the minimum concentration in the polycrystalline material, and C0 is the initial concentration of the polycrystalline material. Then, the target equivalent diffusion coefficients corresponding to the three microscopic diffusion performance data are determined respectively, and applied to the simulation of the electrochemical energy conversion device to test the correlation between the voltage and discharge capacity of the electrochemical energy conversion device. The simulation results are as follows: Figure 7 shown.

[0130] In this embodiment, according to the different numerical size relationships between the bulk diffusion coefficient and the interface diffusion coefficient, different microscopic characterization diffusion performance data are adapted to more accurately characterize the relationship between concentration changes with time and space, improve the accuracy of the simulation, thereby improving the accuracy of the concentration-time model, thereby improving the accuracy of the crystal material design and enhancing the performance of the electrochemical energy conversion device.

[0131] In an exemplary embodiment, after the electrochemical energy conversion device simulation is performed based on the macroscopic characterization diffusion performance data and the polycrystalline structure model, steps S40 to S50 are also included. Among them:

[0132] Step S40, obtaining device simulation performance data and performance requirement information of the electrochemical energy conversion device.

[0133] 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.

[0134] 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.

[0135] Step S50, based on the device simulation performance data and performance requirement information, adjust the morphology data and size data of the polycrystalline structure model, generate a new polycrystalline structure model, and jump to the acquisition step.

[0136] 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 morphology data and size 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 a new polycrystalline structure model is regenerated based on the adjusted morphology data and size data, 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, for example, the first adjustment amplitude of the morphology data and the second adjustment amplitude of the size data can be calculated based on the algorithm, and then the adjustment is made based on the first adjustment amplitude and the second adjustment amplitude, or it can be adjusted step by step according to the preset adjustment amplitude.

[0137] In this embodiment, by automatically guiding the adjustment of the morphology data and size data of the polycrystalline structure model based on the simulation results, automatic correction of the polycrystalline structure material can be achieved, so that a 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.

[0138] 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.

[0139] 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.

[0140] In an exemplary embodiment, Figure 8As shown, a simulation device for an electrochemical energy conversion device is provided, including: an acquisition module 802 and a device simulation module 804, wherein:

[0141] The acquisition module 802 is used to perform the acquisition step: acquiring a polycrystalline structure model;

[0142] The diffusion simulation module 804 is used to simulate the diffusion process of the polycrystalline structure model according to the initial concentration distribution information and the microscopic diffusion performance data of the polycrystalline structure model, so as to obtain the macroscopic diffusion performance data of the polycrystalline structure model;

[0143] The device simulation module 806 is used to simulate the electrochemical energy conversion device based on the macroscopic characterization diffusion performance data and the polycrystalline structure model.

[0144] In an exemplary embodiment, the macroscopic characterization diffusion performance data includes a target equivalent diffusion coefficient; the diffusion simulation module 804 is further used to:

[0145] Based on the initial concentration distribution information of the polycrystalline structure model and the microscopic diffusion performance data, as well as the preset boundary model, a concentration-time model is constructed. The concentration-time model is used to characterize the relationship between the concentration difference measurement value and time.

[0146] Based on the concentration-time model, a target concentration difference measurement value of the polycrystalline structure model in a diffusion equilibrium state is queried, and a target equivalent diffusion coefficient corresponding to the target concentration difference measurement value is detected.

[0147] In an exemplary embodiment, the microscopic characterization diffusion performance data includes bulk diffusion coefficient and interface diffusion coefficient; the diffusion simulation module 804 is further used to:

[0148] Determining boundary conditions of the polycrystalline structure model based on initial concentration distribution information of the polycrystalline structure model and a preset boundary model;

[0149] Based on the bulk diffusion coefficient and the interface diffusion coefficient, querying a target concentration distribution change model from a plurality of preset concentration distribution change models;

[0150] A concentration-time model is constructed based on the initial concentration distribution information, boundary conditions and target concentration distribution change model of the polycrystalline structure model.

[0151] In an exemplary embodiment, the boundary conditions include surface flux conditions; the diffusion simulation module 804 is further configured to:

[0152] Obtaining high-dimensional size information and low-dimensional size information of the polycrystalline structure model, and target operation time information corresponding to the electrochemical energy conversion device;

[0153] The surface flux condition of the polycrystalline structure model is detected based on the initial concentration distribution information, the high-dimensional size information, the low-dimensional size information, the target running time information and the preset boundary model.

[0154] In an exemplary embodiment, the target concentration distribution change model includes at least one of a first concentration distribution change model, a second concentration distribution change model, and a third concentration distribution change model; the diffusion simulation module 804 is further configured to perform at least one of the following:

[0155] In the case where the bulk diffusion coefficient is greater than the interface diffusion coefficient and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a first concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the first concentration distribution change model, and the ion concentration difference on both sides of the grain boundary where ion exchange exists in the first concentration field model exceeds the preset concentration difference threshold; a concentration time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the first concentration field model;

[0156] When the bulk diffusion coefficient is less than the interface diffusion coefficient and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a second concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the second concentration distribution change model, and the ion concentration difference on both sides of the grain boundary in the second concentration field model does not exceed the preset concentration difference threshold; a concentration time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the second concentration field model;

[0157] When the difference between the bulk diffusion coefficient and the interface diffusion coefficient does not exceed the preset first difference threshold, a third concentration field model is constructed based on the bulk diffusion coefficient and the third concentration distribution change model; and a concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, boundary conditions and the third concentration field model.

[0158] In an exemplary embodiment, the electrochemical energy conversion device simulation device further includes an adjustment module. After performing the electrochemical energy conversion device simulation based on the macroscopic characterization diffusion performance data and the polycrystalline structure model, the adjustment module is further used to:

[0159] Obtain device simulation performance data and performance requirement information of an electrochemical energy conversion device;

[0160] Based on the device simulation performance data and performance requirement information, the morphology data and size data of the polycrystalline structure model are adjusted, a new polycrystalline structure model is generated, and the process jumps to the acquisition step.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 method comprises: Acquisition step: acquiring a polycrystalline structure model; According to the initial concentration distribution information and the microscopic diffusion performance data of the polycrystalline structure model, a diffusion process simulation is performed on the polycrystalline structure model to obtain the macroscopic diffusion performance data of the polycrystalline structure model; Based on the macroscopic characterization diffusion performance data and the polycrystalline structure model, performing electrochemical energy conversion device simulation; The macroscopic characterization diffusion performance data includes a target equivalent diffusion coefficient; the diffusion process of the polycrystalline structure model is simulated according to the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data to obtain the macroscopic characterization diffusion performance data of the polycrystalline structure model, including: Based on the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data, as well as the preset boundary model, a concentration-time model is constructed, wherein the concentration-time model is used to characterize the relationship between the concentration difference measurement value and time; Based on the concentration-time model, a target concentration difference measure value of the polycrystalline structure model in a diffusion equilibrium state is queried, and a target equivalent diffusion coefficient corresponding to the target concentration difference measure value is detected.

2. The method according to claim 1, characterized in that: The microscopic characterization diffusion performance data includes a bulk diffusion coefficient and an interface diffusion coefficient; the initial concentration distribution information and the microscopic characterization diffusion performance data based on the polycrystalline structure model, as well as a preset boundary model, are used to construct a concentration-time model, including: Determining boundary conditions of the polycrystalline structure model based on initial concentration distribution information of the polycrystalline structure model and a preset boundary model; Based on the bulk diffusion coefficient and the interface diffusion coefficient, querying a target concentration distribution change model from a plurality of preset concentration distribution change models; A concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the target concentration distribution change model.

3. The method according to claim 2, characterized in that The boundary conditions include surface flux conditions; the boundary conditions of the polycrystalline structure model are determined based on the initial concentration distribution information of the polycrystalline structure model and the preset boundary model, including: Obtaining high-dimensional size information and low-dimensional size information of the polycrystalline structure model, and target operating time information corresponding to the electrochemical energy conversion device; Based on the initial concentration distribution information, the high-dimensional size information, the low-dimensional size information, the target running time information and a preset boundary model, a surface flux condition of the polycrystalline structure model is detected.

4. The method according to claim 2, characterized in that: The target concentration distribution change model includes at least one of a first concentration distribution change model, a second concentration distribution change model, and a third concentration distribution change model; the concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions, and the target concentration distribution change model, including at least one of the following items: In the case where the bulk diffusion coefficient is greater than the interface diffusion coefficient and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a first concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the first concentration distribution change model, wherein the ion concentration difference on both sides of the grain boundary where ion exchange exists in the first concentration field model exceeds the preset concentration difference threshold; a concentration time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the first concentration field model; In the case where the bulk diffusion coefficient is less than the interface diffusion coefficient and the difference between the bulk diffusion coefficient and the interface diffusion coefficient exceeds a preset first difference threshold, a second concentration field model is constructed based on the bulk diffusion coefficient, the interface diffusion coefficient and the second concentration distribution change model, wherein the ion concentration difference on both sides of the grain boundary in the second concentration field model does not exceed the preset concentration difference threshold; a concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the second concentration field model; When the difference between the bulk diffusion coefficient and the interface diffusion coefficient does not exceed a preset first difference threshold, a third concentration field model is constructed based on the bulk diffusion coefficient and the third concentration distribution change model; and a concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the third concentration field model.

5. The method according to any one of claims 1 to 4, characterized in that: After performing the electrochemical energy conversion device simulation based on the macroscopic characterization diffusion performance data and the polycrystalline structure 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 morphology data and size data of the polycrystalline structure model are adjusted to generate a new polycrystalline structure model, and then the process jumps to the acquisition step.

6. An electrochemical energy conversion device simulation device, characterized in that: The device comprises: An acquisition module is used to perform an acquisition step: acquiring a polycrystalline structure model; A diffusion simulation module, used to simulate the diffusion process of the polycrystalline structure model according to the initial concentration distribution information and the microscopic diffusion performance data of the polycrystalline structure model, so as to obtain the macroscopic diffusion performance data of the polycrystalline structure model; A device simulation module, for simulating an electrochemical energy conversion device based on the macroscopic characterization diffusion performance data and the polycrystalline structure model; Wherein, the macroscopic characterization diffusion performance data includes a target equivalent diffusion coefficient; and the diffusion simulation module is further used for: Based on the initial concentration distribution information of the polycrystalline structure model and the microscopic characterization diffusion performance data, as well as the preset boundary model, a concentration-time model is constructed, wherein the concentration-time model is used to characterize the relationship between the concentration difference measurement value and time; Based on the concentration-time model, a target concentration difference measure value of the polycrystalline structure model in a diffusion equilibrium state is queried, and a target equivalent diffusion coefficient corresponding to the target concentration difference measure value is detected.

7. The device according to claim 6, characterized in that The microscopic diffusion performance data includes bulk diffusion coefficient and interface diffusion coefficient; the diffusion simulation module is also used for: Determining boundary conditions of the polycrystalline structure model based on initial concentration distribution information of the polycrystalline structure model and a preset boundary model; Based on the bulk diffusion coefficient and the interface diffusion coefficient, querying a target concentration distribution change model from a plurality of preset concentration distribution change models; A concentration-time model is constructed based on the initial concentration distribution information of the polycrystalline structure model, the boundary conditions and the target concentration distribution change 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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