A structural energy storage composite pixelated design method, device and medium

Abstract

The application discloses a kind of structural energy storage composite pixelated design method, device and medium, by establishing the database of different configurations of mesoscopic energy storage unit;According to the modeling of the structural requirement of composite material, and the finite element model is pixelated and divided to generate different regional units, different configurations in the mesoscopic energy storage unit database are given to different regional units;The finite element model to which different configurations in the mesoscopic energy storage unit database are given is subjected to mechanical properties and electrochemical performance analysis of macrostructure with boundary condition as constraint;And according to the structural requirement of composite material, the mechanical properties and electrochemical performance of macrostructure are optimized using multi-objective evolutionary algorithm;The process accessibility and convergence of the optimized macrostructure are judged, to obtain the macrostructure that meets the requirements, realize the cross-scale design of structural energy storage composite from mesoscopic to macroscopic, greatly improve the mechanical and electrochemical properties of structural energy storage composite.

Description

Technical Field

[0001] This invention belongs to the field of functionalized composite materials technology, specifically relating to a pixelated design method, device and medium for structural energy storage composite materials. Background Technology

[0002] In the existing technology, there are two major problems in the design of structural energy storage composite materials: (1) The existing design methods for structural energy storage composite materials are immature. The microstructure design of structural energy storage composite materials determines the mechanical and electrochemical properties of the microstructure energy storage unit, while the overall macrostructure design determines the macrostructure mechanical and electrochemical properties of the entire structural energy storage composite material structure. However, the current design methods for structural energy storage composite materials can only solve the design of microstructure energy storage units and cannot meet the requirements of macrostructure design. Practical applications require a cross-scale design method from micro to macro. (2) The existing pixelated design methods for functional composite materials are all designed for a specific scale, which also cannot meet the structural design requirements of structural energy storage composite materials from micro to macro. Summary of the Invention

[0003] The purpose of this invention is to provide a pixelated design method for structural energy storage composite materials, which solves the technical problem that the micro and macro structures of composite materials cannot be designed across scales in the prior art.

[0004] Another objective of this invention is to provide an apparatus for implementing a pixelated design method for structural energy storage composite materials.

[0005] Another objective of this invention is to provide a computer-readable storage medium for implementing a pixelated design method for structural energy storage composite materials.

[0006] The technical solution of this invention to solve its technical problem is as follows:

[0007] A pixelated design method for structural energy storage composite materials includes the following steps:

[0008] S1: Based on the mechanical and electrochemical performance requirements of micro-energy storage units, establish a database of micro-energy storage units with different configurations;

[0009] S2: Model the composite material according to its structural requirements, and divide the finite element model into pixelated partitions to generate different regional units. Assign different configurations to the microscopic energy storage unit database for each regional unit.

[0010] S3: Determine whether the finite element models with different configurations assigned to the microscopic energy storage unit database are in the first iteration. If yes, proceed to step S4; otherwise, generate an initial population and input it into the finite element models with different configurations assigned to the microscopic energy storage unit database, and proceed to step S4.

[0011] S4: For finite element models with different configurations in the database of microscopic energy storage units, analyze the mechanical and electrochemical performance of the macroscopic structure with boundary conditions as constraints;

[0012] S5: Based on the structural requirements of the composite material, the mechanical and electrochemical properties of the macrostructure are optimized using a multi-objective evolutionary algorithm. The optimized macrostructure is then judged to determine whether it meets the process attainability standard. If yes, proceed to step S6; otherwise, discard the current finite element model.

[0013] S6: Determine whether the convergence of the macroscopic structure meets the criteria. If yes, output the macroscopic structure. If no, generate a new population and input it into the finite element model with different configurations in the microscopic energy storage unit database, and execute step S4.

[0014] Preferred options also include:

[0015] S7: Perform detailed verification on the macro structure to determine whether the macro structure meets the detailed verification criteria. If yes, output the macro structure; otherwise, execute step S4.

[0016] Preferably, step S1 specifically includes:

[0017] S1.1: Based on the mechanical and electrochemical performance requirements of the micro-energy storage unit, parameters of different micro-energy storage unit configurations that meet the requirements are set. The macroscopic equivalent mechanical and electrochemical performance of the micro-energy storage unit is obtained by analyzing the micro-energy storage unit parameters. The different configurations that meet the requirements are combined into a micro-energy storage unit database.

[0018] Preferably, the microscopic energy storage unit parameters include the form of positive electrode carbon fiber, the thickness of positive electrode active layer, the form of separator, the thickness of negative electrode active layer, the form of negative electrode carbon fiber, and the form of resin structure electrolyte.

[0019] Preferably, the method for generating the initial population in step S3 includes random generation and manual assignment.

[0020] Preferably, the boundary conditions in step S4 include mechanical conditions, electrochemical conditions, and thermodynamic conditions.

[0021] An apparatus for implementing a pixelated design method for structural energy storage composite materials, comprising:

[0022] The microscale database creation module is used to create a database of microscale energy storage units with different configurations based on the mechanical and electrochemical performance requirements of the microscale energy storage units.

[0023] The macroscopic structure modeling module is used to model the structure requirements of composite materials and divide the finite element model into pixelated partitions to generate different regional units. Different configurations are assigned to different regional units from the microscopic energy storage unit database.

[0024] The judgment module is used to determine whether the finite element models with different configurations in the microscopic energy storage unit database are in the first iteration; to determine whether the optimized macroscopic structure meets the process accessibility standard; and to determine whether the convergence of the macroscopic structure meets the standard.

[0025] The analysis module is used to analyze the mechanical and electrochemical properties of macroscopic structures of finite element models with different configurations in the microscopic energy storage unit database, with boundary conditions as constraints.

[0026] The optimization module is used to optimize the macroscopic structure's mechanical and electrochemical properties using a multi-objective evolutionary algorithm based on the structural requirements of the composite material.

[0027] Preferably, the judgment module is also used to determine whether the macrostructure meets the detail verification criteria.

[0028] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the device containing the computer-readable storage medium to perform a pixelation design method for structural energy storage composite materials.

[0029] The beneficial effects of this invention are as follows: Based on the mechanical and electrochemical performance requirements of microscopic energy storage units, a database of microscopic energy storage units with different configurations is established; in macroscopic structural design, modeling is performed according to the structural requirements of composite materials, and the finite element model is pixelated to generate different regional units, which are then assigned different configurations from the microscopic energy storage unit database; the mechanical and electrochemical performance of the macroscopic structure is analyzed using boundary conditions as constraints on the finite element models assigned different configurations from the microscopic energy storage unit database; and based on the structural requirements of composite materials, a multi-objective evolutionary algorithm is used to optimize the macroscopic structure's mechanical and electrochemical performance; the process accessibility and convergence of the optimized macroscopic structure are judged to obtain a macroscopic structure that meets the requirements, thus realizing cross-scale design of structural energy storage composite materials from microscopic to macroscopic levels and significantly improving the mechanical and electrochemical performance of structural energy storage composite materials. Attached Figure Description

[0030] Figure 1 This is a flowchart of the pixelation design method for the structural energy storage composite material of the present invention;

[0031] Figure 2 yes Figure 1 Corresponding structural design flowchart;

[0032] Figure 3 This is a pixelated model diagram of a solid rectangular beam made of structural energy storage composite material, taken as an example. Detailed Implementation

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0034] Example 1:

[0035] like Figure 1 , Figure 2 As shown, this invention discloses a pixelated design method for structural energy storage composite materials, comprising the following steps:

[0036] S1: Based on the mechanical and electrochemical performance requirements of micro-energy storage units, establish a database of micro-energy storage units with different configurations;

[0037] S1.1: Based on the mechanical and electrochemical performance requirements of the micro-energy storage unit, parameters for different configurations of the micro-energy storage unit that meet the requirements are set. The micro-energy storage unit parameters include the form of positive electrode carbon fiber, the thickness of positive electrode active layer, the form of separator, the thickness of negative electrode active layer, the form of negative electrode carbon fiber, and the form of resin structure electrolyte. The macroscopic equivalent mechanical and electrochemical performance of the micro-energy storage unit is obtained by analyzing the micro-energy storage unit parameters, and the different configurations that meet the requirements are compiled into a micro-energy storage unit database.

[0038] S2: Model the composite material according to its structural requirements, and divide the finite element model into pixelated partitions to generate different regional units. Assign different configurations to the microscopic energy storage unit database for each regional unit.

[0039] The micro-level energy storage unit assignment can be random or manual. For example, in the random assignment, the micro-level energy storage units in the micro-level energy storage unit database are numbered 001, 002, 003, etc. When assigning micro-level energy storage units, pixel 1 in the finite element model of the macro structure is assigned the macro-level properties of energy storage unit 001, pixel 2 is assigned the macro-level properties of 008, and pixel N is assigned the macro-level properties of 006.

[0040] S3: Determine whether the finite element models with different configurations assigned to the microscopic energy storage unit database are in the first iteration. If yes, proceed to step S4; otherwise, generate an initial population and input it into the finite element models with different configurations assigned to the microscopic energy storage unit database, and proceed to step S4.

[0041] The initial population can be generated in two ways: random generation and artificial assignment. For example, when the random generation method is selected, in the actual operation of finite element method, the macroscopic equivalent mechanical and electrochemical properties of the microscopic energy storage unit number 001 are assigned to the pixel partition of pixel number 001. By analogy, different material properties are assigned to different parts, so that different pixel units are given different microscopic energy storage unit properties.

[0042] S4: For finite element models with different configurations in the database of microscopic energy storage units, analyze the mechanical and electrochemical performance of the macroscopic structure with boundary conditions as constraints;

[0043] Boundary conditions include mechanical, electrochemical, and thermodynamic conditions, which can be set for different structures according to actual needs. Parallel computing strategies can also be incorporated into the analysis. These strategies can address the problem of low accuracy caused by large amounts of computational data and numerous distributed computing steps in optimization analysis. For example, when the calculation requires iterating through 100 individuals each time, 100 calculations are needed, resulting in a large amount of computational data and cumbersome distributed computing. Using a parallel computing strategy, it is possible to simultaneously calculate 50 or 25 individuals, improving computational efficiency and accuracy. Optimization software typically offers several parallel computing strategy options, which can be selected based on experience or simultaneous test results.

[0044] S5: Based on the structural requirements of the composite material, the mechanical and electrochemical properties of the macrostructure are optimized using a multi-objective evolutionary algorithm. The optimized macrostructure is then judged to determine whether it meets the process attainability standard. If yes, proceed to step S6; otherwise, discard the current finite element model.

[0045] The formula for the multi-objective optimization objective function will not be elaborated here. For example, NSGA-II can be selected. For different needs, the relevant multi-objective optimization objective function can be adopted. If mechanical performance is important, add weight to the mechanical index; if energy storage performance is important, add weight to the energy storage index.

[0046] S6: Determine whether the convergence of the macroscopic structure meets the criteria. If yes, output the macroscopic structure. If no, generate a new population and input it into the finite element model with different configurations in the microscopic energy storage unit database, and execute step S4.

[0047] Convergence can be defined as reaching the maximum number of iterations N, or the difference between the result of the Mth iteration and the result of the (M-1)th iteration being less than 0.1%, and the convergence condition is set according to the actual situation.

[0048] S7: Perform detailed verification on the macro structure to determine whether the macro structure meets the detailed verification criteria. If yes, output the macro structure; otherwise, execute step S4.

[0049] Detailed design process:

[0050] Taking a solid rectangular beam made of structural energy storage composite material as an example, which is required to provide 2 kWh of energy storage and bear a 3-ton load at the same time;

[0051] S1: As shown in Table 1, establish a database of microscopic energy storage units with different configurations; number the databases.

[0052]

[0053]

[0054] Table 1

[0055] S2: As Figure 3 As shown, a pixelated model of a rectangular beam of structural energy storage composite material is established;

[0056] Among them, the model input is: pixelated model micro-energy storage unit assignment: each pixel unit is randomly assigned a micro-energy storage unit configuration.

[0057] Model input: pixelated model. Boundary conditions: fixed support on the left side section, temperature 25℃.

[0058] Model input: Mechanical load: A concentrated force of 3 tons is applied to the right section.

[0059] Model input: Electrochemical load: 50Ω resistive load.

[0060] S3: Determine whether the finite element models with different configurations assigned to the microscopic energy storage unit database are in the first iteration; if yes, proceed to step S4; if no, generate an initial population and input it into the finite element models with different configurations assigned to the microscopic energy storage unit database, and proceed to step S4.

[0061] When it is not the first iteration, the initial population is generated manually as shown in Table 2, where each pixel number corresponds to the mesoscopic energy storage unit number.

[0062]

[0063] Table 2

[0064] S4: Analysis of the mechanical and electrochemical properties of the macroscopic structure;

[0065] Pixelated model boundary conditions: follow S22 boundary: left cross section is fixed, temperature 25℃.

[0066] Parallel computing strategy: Up to 512 models can be submitted simultaneously.

[0067] Model output results: stress distribution under this load, and determination of whether the energy storage capacity is damaged.

[0068] S5: Multi-objective evolutionary algorithm optimizes macroscopic structure;

[0069] Design parameters: As shown in Table 2, the microscopic energy storage unit numbers corresponding to each pixel number.

[0070] Objective function:

[0071]

[0072] Where K1 represents the stress weighting coefficient; K2 represents the energy storage capacity weighting coefficient; and σ represents the stress.

[0073] Constraints: 1. The structure is intact; 2. Energy storage capacity ≥ 2kWh.

[0074] Process accessibility assessment: 1. Adjacent pixels in the same layer use the same carbon fiber form; 2. The difference in active layer thickness between adjacent units is no greater than 2μm.

[0075] S6: Convergence determination;

[0076] Convergence conditions: 1. Maximum number of iterations: 1000; 2. The difference between the result of the Mth iteration and the result of the (M-1)th iteration is less than 0.1%.

[0077] S6: Detailed checks;

[0078] Manually add details such as packaging method and electrode lead-out method, and check compatibility.

[0079] Final result:

[0080] 1. Output the microscopic energy storage unit number corresponding to each pixel number, as shown in Table 2.

[0081] 2. Output the stress distribution of the overall composite material structure under this load to determine whether the energy storage capacity is damaged.

[0082] 3. The output can provide 2 kWh of energy storage while supporting a 3-ton load on the macro structure.

[0083] Example 2:

[0084] An apparatus for implementing a pixelated design method for structural energy storage composite materials, comprising:

[0085] The microscale database creation module is used to create a database of microscale energy storage units with different configurations based on the mechanical and electrochemical performance requirements of the microscale energy storage units.

[0086] The macroscopic structure modeling module is used to model the structure requirements of composite materials and divide the finite element model into pixelated partitions to generate different regional units. Different configurations are assigned to different regional units from the microscopic energy storage unit database.

[0087] The judgment module is used to determine whether the finite element models with different configurations in the microscopic energy storage unit database are in the first iteration; to determine whether the optimized macroscopic structure meets the process accessibility standard; to determine whether the convergence of the macroscopic structure meets the standard; and to determine whether the macroscopic structure meets the detail verification standard.

[0088] The analysis module is used to analyze the mechanical and electrochemical properties of macroscopic structures of finite element models with different configurations in the microscopic energy storage unit database, with boundary conditions as constraints.

[0089] The optimization module is used to optimize the macroscopic structure's mechanical and electrochemical properties using a multi-objective evolutionary algorithm based on the structural requirements of the composite material.

[0090] Example 3:

[0091] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the device containing the computer-readable storage medium to perform a pixelation design method for structural energy storage composite materials.

[0092] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

Claims

1. A pixelated design method for structural energy storage composite materials, characterized in that, Includes the following steps: S1: Based on the mechanical and electrochemical performance requirements of the micro-energy storage unit, parameters for different configurations of the micro-energy storage unit that meet the requirements are set. The macroscopic equivalent mechanical and electrochemical performance of the micro-energy storage unit is obtained by analyzing the micro-energy storage unit parameters. The different configurations that meet the requirements are compiled into a micro-energy storage unit database. The micro-energy storage unit parameters include the form of positive electrode carbon fiber, the thickness of positive electrode active layer, the form of separator, the thickness of negative electrode active layer, the form of negative electrode carbon fiber, and the form of resin structure electrolyte. S2: Model the composite material according to its structural requirements, and divide the finite element model into pixelated partitions to generate different regional units. Assign different configurations to the microscopic energy storage unit database for each regional unit. S3: Determine whether the finite element models with different configurations assigned to the microscopic energy storage unit database are in the first iteration. If yes, proceed to step S4; otherwise, generate an initial population and substitute it into the finite element models with different configurations assigned to the microscopic energy storage unit database, and proceed to step S4. S4: For finite element models with different configurations in the database of microscopic energy storage units, analyze the mechanical and electrochemical performance of the macroscopic structure with boundary conditions as constraints; S5: Based on the structural requirements of the composite material, the mechanical and electrochemical properties of the macrostructure are optimized using a multi-objective evolutionary algorithm. The optimized macrostructure is then judged to determine whether it meets the process attainability standard. If yes, proceed to step S6; otherwise, discard the current finite element model. S6: Determine whether the convergence of the macroscopic structure meets the criteria. If yes, output the macroscopic structure. If no, generate a new population and substitute it into the finite element models of different configurations in the microscopic energy storage unit database, and execute step S4.

2. The pixelated design method for structural energy storage composite materials according to claim 1, characterized in that, Also includes: S7: Perform detailed verification on the macro structure to determine whether the macro structure meets the detailed verification criteria. If yes, output the macro structure; otherwise, execute step S4.

3. The pixelated design method for structural energy storage composite materials according to claim 1, characterized in that: The methods for generating the initial population in step S3 include random generation and manual assignment.

4. The pixelated design method for structural energy storage composite materials according to claim 1, characterized in that: The boundary conditions in step S4 include mechanical conditions, electrochemical conditions, and thermodynamic conditions.

5. An apparatus for implementing the pixelated design method for structural energy storage composite materials according to any one of claims 1-4, characterized in that, include: The microscale database creation module is used to create a database of microscale energy storage units with different configurations based on the mechanical and electrochemical performance requirements of the microscale energy storage units. The macroscopic structure modeling module is used to model the structure requirements of composite materials and divide the finite element model into pixelated partitions to generate different regional units. Different configurations are assigned to different regional units from the microscopic energy storage unit database. The judgment module is used to determine whether the finite element models with different configurations in the microscopic energy storage unit database are in the first iteration; to determine whether the optimized macroscopic structure meets the process accessibility standard; and to determine whether the convergence of the macroscopic structure meets the standard. The analysis module is used to analyze the mechanical and electrochemical properties of macroscopic structures of finite element models with different configurations in the microscopic energy storage unit database, with boundary conditions as constraints. The optimization module is used to optimize the macroscopic structure's mechanical and electrochemical properties using a multi-objective evolutionary algorithm based on the structural requirements of the composite material.

6. The apparatus for realizing the pixelated design method of structural energy storage composite materials according to claim 5, characterized in that, The judgment module is also used to determine whether the macrostructure meets the detail verification criteria.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the device containing the computer-readable storage medium to perform the design method according to any one of claims 1-4.

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