A box exhaust optimization method, system and medium

Abstract

The embodiment of the present application provides a box airflow optimization method, system and medium, the method comprising: establishing a simulation analysis model for the design of a lithium-ion battery electrode drying box with an airflow duct; establishing boundary conditions for the lithium-ion battery electrode drying box with an airflow duct; establishing evaluation criteria for the lithium-ion battery electrode drying box with an airflow duct; using Latin square DOE to extract a preset number of samples, performing simulation analysis on each sample, and obtaining simulation results; using the Kriging model to analyze approximate optimization results based on the simulation results, and outputting optimal design variables based on the approximate optimization results; analyzing the airflow optimization results according to the simulation analysis model and the boundary conditions, thereby screening out the best optimization results and improving the airflow control accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of increasing and optimizing exhaust air, and in particular to a method, system and medium for increasing and optimizing exhaust air in a box. Background Art

[0002] Most of the existing lithium battery drying devices have a slow drying speed, which will affect production efficiency and battery quality. In addition, the placement racks of some drying devices are not convenient for workers to place and take out the electrode sheets, which will affect people's work efficiency. During the process of increasing the exhaust air in the box, different box structures will produce different increasing the exhaust air effects. The existing increasing the exhaust air optimization method does not have a simulation model designed, and it is difficult to simulate the air volume changes of the lithium-ion battery electrode drying box in the software, so it is impossible to accurately optimize the increasing the exhaust air, resulting in the total exhaust air volume not meeting the requirements. In response to the above problems, effective technical solutions are urgently needed. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a box exhaust optimization method, system and medium, analyze the exhaust optimization results according to the simulation analysis model and boundary conditions, so as to screen out the best optimization results and improve the exhaust control accuracy.

[0004] The present application also provides a box ventilation optimization method, including:

[0005] Establish a simulation analysis model for the design of a lithium-ion battery electrode drying oven with an additional exhaust duct;

[0006] Establish the boundary conditions of the lithium-ion battery electrode drying oven with an additional exhaust duct;

[0007] Establish evaluation criteria for lithium-ion battery electrode drying ovens with enhanced exhaust ducts;

[0008] Use Latin square DOE to extract a preset number of samples, perform simulation analysis on each sample, and obtain simulation results;

[0009] Based on the simulation results, the Kriging model is used to analyze the approximate optimization results, and the optimal design variables are output based on the approximate optimization results.

[0010] Optionally, in the box ventilation optimization method described in the embodiment of the present application, a simulation analysis model for the design of a lithium-ion battery electrode drying box with ventilation ducts is established, specifically including:

[0011] Obtain parameter information of a lithium-ion battery electrode drying oven, including size information and structure information;

[0012] Analyze the layout information of the additional exhaust duct and the inclined opening angle of the additional exhaust duct based on the structural information;

[0013] Based on the exhaust duct layout information and the inclined opening angle of the additional exhaust duct, the initial opening diameter value, the initial spacing value and the incremental value are analyzed to obtain the simulation parameters;

[0014] Design a simulation analysis model of the drying oven based on simulation parameters.

[0015] Optionally, in the box ventilation optimization method described in the embodiment of the present application, the boundary conditions of the lithium-ion battery electrode drying box with ventilation duct are established, specifically including:

[0016] Collect inlet and outlet flow rates based on exhaust duct layout information and the inclined opening angle of the increased exhaust duct;

[0017] Analyze the maximum and minimum flow rates based on the inlet and outlet flow rates, and establish flow boundary conditions;

[0018] Analyze the pressure values ​​of the exhaust pipe outlet at different time points to obtain pressure value information;

[0019] The maximum pressure value and the minimum pressure value are analyzed based on the pressure value change information, and the outlet pressure boundary condition is established based on the maximum pressure value and the minimum pressure value.

[0020] Optionally, in the box ventilation optimization method described in the embodiment of the present application, the evaluation criteria calculation formula for the lithium-ion battery electrode drying box with ventilation duct is established as follows:

[0021] ;

[0022] and Used to evaluate the extreme deviation and overall deviation of the uniformity of solvent evaporation on the pole surface. , and They represent the maximum mass transfer coefficient, average mass transfer coefficient and minimum mass transfer coefficient on the electrode surface respectively;

[0023] The mass transfer coefficient is calculated using the following formula:

[0024] ;

[0025] and They respectively represent the moisture concentration on the electrode surface and the moisture concentration at the drying box inlet.

[0026] Optionally, in the cabinet ventilation optimization method described in the embodiment of the present application, a Latin square DOE is used to extract a preset number of samples, and a simulation analysis is performed on each sample to obtain a simulation result, which specifically includes:

[0027] Use Latin square DOE sampling to extract 10*m samples, where m is the dimension of the design variable X, and call the simulation analysis model;

[0028] The simulation analysis of each sample is calculated based on the simulation analysis model, and the uniformity deviation of the solvent evaporation of each sample simulation is calculated using the evaluation criteria;

[0029] The simulation results are obtained based on the deviation of the uniformity of the solvent evaporation simulated for each sample.

[0030] Optionally, in the box ventilation optimization method described in the embodiment of the present application, the Kriging model is used to analyze the approximate optimization results based on the simulation results, and the optimal design variables are output based on the approximate optimization results, specifically including:

[0031] Define the optimization problem, which includes the objective function, constraint function and design variables;

[0032] Calculate the objective function and constraint function through DOE sampling;

[0033] A sample point library is established based on the objective function and the constraint function, a Kriging model is established based on the sample point library, the maximum expected improvement is analyzed based on the Kriging model, and the function value of the maximum expected improvement is calculated;

[0034] Determine whether the function value of the maximum expected improvement meets the convergence criterion;

[0035] If converged, the optimal design variables and corresponding function values ​​are output based on the Kriging model to generate approximate optimization results;

[0036] If it does not converge, the sample point at the maximum expectation is added to the sample point library.

[0037] In a second aspect, an embodiment of the present application provides a box ventilation optimization system, the system comprising: a memory and a processor, the memory comprising a program of a box ventilation optimization method, and the program of the box ventilation optimization method is executed by the processor to implement the following steps:

[0038] Establish a simulation analysis model for the design of a lithium-ion battery electrode drying oven with an additional exhaust duct;

[0039] Establish the boundary conditions of the lithium-ion battery electrode drying oven with an additional exhaust duct;

[0040] Establish evaluation criteria for lithium-ion battery electrode drying ovens with enhanced exhaust ducts;

[0041] Use Latin square DOE to extract a preset number of samples, perform simulation analysis on each sample, and obtain simulation results;

[0042] Based on the simulation results, the Kriging model is used to analyze the approximate optimization results, and the optimal design variables are output based on the approximate optimization results.

[0043] Optionally, in the box ventilation optimization system described in the embodiment of the present application, a simulation analysis model for the design of a lithium-ion battery electrode drying box with ventilation ducts is established, specifically including:

[0044] Obtain parameter information of a lithium-ion battery electrode drying oven, including size information and structure information;

[0045] Analyze the layout information of the additional exhaust duct and the inclined opening angle of the additional exhaust duct based on the structural information;

[0046] Based on the exhaust duct layout information and the inclined opening angle of the additional exhaust duct, the initial opening diameter value, the initial spacing value and the incremental value are analyzed to obtain the simulation parameters;

[0047] Design a simulation analysis model of the drying oven based on simulation parameters.

[0048] Optionally, in the box ventilation optimization system described in the embodiment of the present application, the boundary conditions of the lithium-ion battery electrode drying box with ventilation duct are established, specifically including:

[0049] Collect inlet and outlet flow rates based on exhaust duct layout information and the inclined opening angle of the increased exhaust duct;

[0050] Analyze the maximum and minimum flow rates based on the inlet and outlet flow rates, and establish flow boundary conditions;

[0051] Analyze the pressure values ​​of the exhaust pipe outlet at different time points to obtain pressure value information;

[0052] The maximum pressure value and the minimum pressure value are analyzed based on the pressure value change information, and the outlet pressure boundary condition is established based on the maximum pressure value and the minimum pressure value.

[0053] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a box airflow optimization method program, and when the box airflow optimization method program is executed by a processor, the steps of the box airflow optimization method as described in any one of the above items are implemented.

[0054] As can be seen from the above, an embodiment of the present application provides a box airflow optimization method, system and medium, which establishes a simulation analysis model for the design of a lithium-ion battery electrode drying box with an airflow duct; establishes boundary conditions for the lithium-ion battery electrode drying box with an airflow duct; establishes evaluation criteria for the lithium-ion battery electrode drying box with an airflow duct; uses Latin square DOE to extract a preset number of samples, performs simulation analysis on each sample, and obtains simulation results; uses the Kriging model to analyze the approximate optimization results based on the simulation results, and outputs the optimal design variables based on the approximate optimization results; analyzes the airflow optimization results according to the simulation analysis model and boundary conditions, thereby screening out the best optimization results and improving the airflow control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flow chart of a box ventilation optimization method provided in an embodiment of the present application;

[0057] Figure 2 A flow chart of a method for establishing a simulation analysis model for a box ventilation optimization method provided in an embodiment of the present application;

[0058] Figure 3 A flow chart of a method for establishing boundary conditions for a box ventilation optimization method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0060] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0061] Please refer to Figure 1 , Figure 1 This is a flow chart of a cabinet ventilation optimization method in some embodiments of the present application. The cabinet ventilation optimization method is used in a terminal device, and the cabinet ventilation optimization method includes the following steps:

[0062] S101, establish a simulation analysis model for the design of a lithium-ion battery electrode drying oven with an additional exhaust duct;

[0063] S102, establishing boundary conditions of a lithium-ion battery electrode drying oven with an additional exhaust duct;

[0064] S103, establishing evaluation criteria for lithium-ion battery electrode drying ovens with exhaust ducts;

[0065] S104, using Latin square DOE to extract a preset number of samples, performing simulation analysis on each sample, and obtaining simulation results;

[0066] S105, analyzing the approximate optimization results using the Kriging model based on the simulation results, and outputting the optimal design variables based on the approximate optimization results.

[0067] It should be noted that the Kriging model is used to fit the design variables X and , The quantitative relationship between them is calculated, and the determination coefficient R2 is used to evaluate the fitting accuracy of the Kriging model. The calculation formula is as follows:

[0068] ;

[0069] The superscript ~ represents the Kriging fitting value, and the superscript - represents the average value of all samples. If R2 is greater than 0.9, the Kriging model is used to analyze the approximate optimization results. If R2 is less than or equal to 0.9, the number of samples is gradually increased until the requirements are met.

[0070] The sample increase criteria are as follows:

[0071] ;

[0072] δ represents the variance of the Kriging model prediction. The larger the variance, the greater the uncertainty of the model prediction at that location.

[0073] Please refer to Figure 2 , Figure 2 This is a flow chart of a simulation analysis model establishment method for a box ventilation optimization method in some embodiments of the present application. According to an embodiment of the present invention, a simulation analysis model for the design of a lithium-ion battery electrode drying box with ventilation ducts is established, specifically including:

[0074] S201, obtaining parameter information of a lithium-ion battery electrode drying box, the parameter information including size information and structure information;

[0075] S202, analyzing the layout information of the additional exhaust duct and the inclined opening angle of the additional exhaust duct based on the structural information;

[0076] S203, analyzing the initial opening diameter value, the initial spacing value, and the incremental value based on the exhaust duct layout information and the inclined opening angle of the additional exhaust duct to obtain simulation parameters;

[0077] S204, designing a simulation analysis model of the drying oven based on the simulation parameters.

[0078] It should be noted that a three-dimensional simulation analysis model is established in the simulation software based on the parameter information of the drying box, so that the three-dimensional simulation analysis model is consistent with the structure and layout of the actual drying box, so that the changing state of increased exhaust air can be simulated according to the simulation software, and the optimal distribution and size of the increased exhaust air ducts can be analyzed.

[0079] Please refer to Figure 3 , Figure 3 This is a flow chart of a method for establishing boundary conditions of a box ventilation optimization method in some embodiments of the present application. According to an embodiment of the present invention, establishing boundary conditions of a lithium-ion battery electrode drying box with ventilation ducts specifically includes:

[0080] S301, collecting inlet flow and outlet flow based on exhaust duct layout information and the inclined opening angle of the increased exhaust duct;

[0081] S302, analyzing the maximum flow rate and the minimum flow rate based on the inlet flow rate and the outlet flow rate, and establishing flow boundary conditions;

[0082] S303, analyzing the pressure values ​​of the exhaust pipe outlet at different time points to obtain pressure value information;

[0083] S304, analyzing the maximum pressure value and the minimum pressure value based on the pressure value change information, and establishing an air outlet pressure boundary condition based on the maximum pressure value and the minimum pressure value.

[0084] It should be noted that the opening slope angle is adapted to the wind speed angle affected by the movement of the pole piece and the negative pressure of the exhaust. The initial opening diameter value, initial spacing value and incremental value in the opening scheme are the optimal values ​​that meet the exhaust air volume distribution trend and the total exhaust air volume.

[0085] According to an embodiment of the present invention, the evaluation criteria calculation formula for a lithium-ion battery electrode drying oven with an exhaust duct is established as follows:

[0086] ;

[0087] and Used to evaluate the extreme deviation and overall deviation of the uniformity of solvent evaporation on the pole surface. , and They represent the maximum mass transfer coefficient, average mass transfer coefficient and minimum mass transfer coefficient on the electrode surface respectively;

[0088] The mass transfer coefficient is calculated using the following formula:

[0089] ;

[0090] and They respectively represent the moisture concentration on the electrode surface and the moisture concentration at the drying box inlet.

[0091] According to an embodiment of the present invention, a Latin square DOE is used to extract a preset number of samples, and a simulation analysis is performed on each sample to obtain a simulation result, which specifically includes:

[0092] Use Latin square DOE sampling to extract 10*m samples, where m is the dimension of the design variable X, and call the simulation analysis model;

[0093] The simulation analysis of each sample is calculated based on the simulation analysis model, and the uniformity deviation of the solvent evaporation of each sample simulation is calculated using the evaluation criteria;

[0094] The simulation results are obtained based on the deviation of the uniformity of the solvent evaporation simulated for each sample.

[0095] According to an embodiment of the present invention, the Kriging model is used to analyze the approximate optimization results based on the simulation results, and the optimal design variables are output based on the approximate optimization results, specifically including:

[0096] Define the optimization problem, which includes the objective function, constraint function and design variables;

[0097] Calculate the objective function and constraint function through DOE sampling;

[0098] A sample point library is established based on the objective function and the constraint function, a Kriging model is established based on the sample point library, the maximum expected improvement is analyzed based on the Kriging model, and the function value of the maximum expected improvement is calculated;

[0099] Determine whether the function value of the maximum expected improvement meets the convergence criterion;

[0100] If converged, the optimal design variables and corresponding function values ​​are output based on the Kriging model to generate approximate optimization results;

[0101] If it does not converge, the sample point at the maximum expectation is added to the sample point library.

[0102] According to an embodiment of the present invention, it also includes: establishing an optimization model for the exhaust duct structure, the formula is as follows:

[0103] ;

[0104] P is the negative pressure applied to the exhaust pipe connected to the end of the exhaust pipe, α and β are the inclination angles of the front and rear faces of the exhaust pipe, the front face is the end face upstream of the exhaust pipe axis in the belt running direction, the rear face is the end face downstream of the exhaust pipe axis in the belt running direction, h is the minimum vertical distance between the exhaust pipe and the pole piece. y is the coordinate vector in the width direction of the pole piece, and R represents the opening radius of each exhaust hole in the width direction.

[0105] After the above processing, the approximate optimization results are converted into optimization problems based on the Kriging model based on the simulation results:

[0106] ;

[0107] The maximum expectation improved algorithm is used to solve the approximate optimization problem and obtain the Pareto frontier of the multi-objective optimization problem. Three typical sample points are selected from the Pareto frontier. The selection principles of the three typical sample points are as follows:

[0108] ;

[0109] Check the accuracy of the approximate optimization results of Sampling1, Sampling2 and Sampling3. The calculation formula is as follows:

[0110] ;

[0111] ε1 and ε2 are the allowable tolerances. If the allowable tolerances are met, the final Pareto frontier is output; otherwise, three typical sample points are added to the sample point library.

[0112] In a second aspect, an embodiment of the present application provides a box ventilation optimization system, the system comprising: a memory and a processor, the memory comprising a program of a box ventilation optimization method, and when the program of the box ventilation optimization method is executed by the processor, the following steps are implemented:

[0113] Establish a simulation analysis model for the design of a lithium-ion battery electrode drying oven with an additional exhaust duct;

[0114] Establish the boundary conditions of the lithium-ion battery electrode drying oven with an additional exhaust duct;

[0115] Establish evaluation criteria for lithium-ion battery electrode drying ovens with enhanced exhaust ducts;

[0116] Use Latin square DOE to extract a preset number of samples, perform simulation analysis on each sample, and obtain simulation results;

[0117] Based on the simulation results, the Kriging model is used to analyze the approximate optimization results, and the optimal design variables are output based on the approximate optimization results.

[0118] It should be noted that the Kriging model is used to fit the design variables X and , The quantitative relationship between them is calculated, and the determination coefficient R2 is used to evaluate the fitting accuracy of the Kriging model. The calculation formula is as follows:

[0119] ;

[0120] The superscript ~ represents the Kriging fitting value, and the superscript - represents the average value of all samples. If R2 is greater than 0.9, the Kriging model is used to analyze the approximate optimization results. If R2 is less than or equal to 0.9, the number of samples is gradually increased until the requirements are met.

[0121] The sample increase criteria are as follows:

[0122] ;

[0123] δ represents the variance of the Kriging model prediction. The larger the variance, the greater the uncertainty of the model prediction at that location.

[0124] According to an embodiment of the present invention, a simulation analysis model for the design of a lithium-ion battery electrode drying box with an exhaust duct is established, specifically including:

[0125] Obtain parameter information of a lithium-ion battery electrode drying oven, including size information and structure information;

[0126] Analyze the layout information of the additional exhaust duct and the inclined opening angle of the additional exhaust duct based on the structural information;

[0127] Based on the exhaust duct layout information and the inclined opening angle of the additional exhaust duct, the initial opening diameter value, the initial spacing value and the incremental value are analyzed to obtain the simulation parameters;

[0128] Design a simulation analysis model of the drying oven based on simulation parameters.

[0129] It should be noted that a three-dimensional simulation analysis model is established in the simulation software based on the parameter information of the drying box, so that the three-dimensional simulation analysis model is consistent with the structure and layout of the actual drying box, so that the changing state of increased exhaust air can be simulated according to the simulation software, and the optimal distribution and size of the increased exhaust air ducts can be analyzed.

[0130] According to an embodiment of the present invention, the boundary conditions of a lithium-ion battery electrode drying box with an exhaust duct are established, specifically including:

[0131] Collect inlet and outlet flow rates based on exhaust duct layout information and the inclined opening angle of the increased exhaust duct;

[0132] Analyze the maximum and minimum flow rates based on the inlet and outlet flow rates, and establish flow boundary conditions;

[0133] Analyze the pressure values ​​of the exhaust pipe outlet at different time points to obtain pressure value information;

[0134] The maximum pressure value and the minimum pressure value are analyzed based on the pressure value change information, and the outlet pressure boundary condition is established based on the maximum pressure value and the minimum pressure value.

[0135] It should be noted that the opening slope angle is adapted to the wind speed angle affected by the movement of the pole piece and the negative pressure of the exhaust. The initial opening diameter value, initial spacing value and incremental value in the opening scheme are the optimal values ​​that meet the exhaust air volume distribution trend and the total exhaust air volume.

[0136] According to an embodiment of the present invention, the evaluation criteria calculation formula for a lithium-ion battery electrode drying oven with an exhaust duct is established as follows:

[0137] ;

[0138] and Used to evaluate the extreme deviation and overall deviation of the uniformity of solvent evaporation on the pole surface. , and They represent the maximum mass transfer coefficient, average mass transfer coefficient and minimum mass transfer coefficient on the electrode surface respectively;

[0139] Among them, the mass transfer coefficient is calculated using the following formula:

[0140] ;

[0141] and They respectively represent the moisture concentration on the electrode surface and the moisture concentration at the drying box inlet.

[0142] According to an embodiment of the present invention, a Latin square DOE is used to extract a preset number of samples, and a simulation analysis is performed on each sample to obtain a simulation result, which specifically includes:

[0143] Use Latin square DOE sampling to extract 10*m samples, where m is the dimension of the design variable X, and call the simulation analysis model;

[0144] The simulation analysis of each sample is calculated based on the simulation analysis model, and the uniformity deviation of the solvent evaporation of each sample simulation is calculated using the evaluation criteria;

[0145] The simulation results are obtained based on the deviation of the uniformity of the solvent evaporation simulated for each sample.

[0146] According to an embodiment of the present invention, the Kriging model is used to analyze the approximate optimization results based on the simulation results, and the optimal design variables are output based on the approximate optimization results, specifically including:

[0147] Define the optimization problem, which includes the objective function, constraint function and design variables;

[0148] Calculate the objective function and constraint function through DOE sampling;

[0149] A sample point library is established based on the objective function and the constraint function, a Kriging model is established based on the sample point library, the maximum expected improvement is analyzed based on the Kriging model, and the function value of the maximum expected improvement is calculated;

[0150] Determine whether the function value of the maximum expected improvement meets the convergence criterion;

[0151] If converged, the optimal design variables and corresponding function values ​​are output based on the Kriging model to generate approximate optimization results;

[0152] If it does not converge, the sample point at the maximum expectation is added to the sample point library.

[0153] According to an embodiment of the present invention, it also includes: establishing an optimization model for the exhaust duct structure, the formula is as follows:

[0154] ;

[0155] P is the negative pressure applied to the exhaust pipe connected to the end of the exhaust pipe, α and β are the inclination angles of the front and rear faces of the exhaust pipe, the front face is the end face upstream of the exhaust pipe axis in the belt running direction, the rear face is the end face downstream of the exhaust pipe axis in the belt running direction, h is the minimum vertical distance between the exhaust pipe and the pole piece. y is the coordinate vector in the width direction of the pole piece, and R represents the opening radius of each exhaust hole in the width direction.

[0156] After the above processing, the approximate optimization results are converted into optimization problems based on the Kriging model based on the simulation results:

[0157] ;

[0158] The maximum expectation improved algorithm is used to solve the approximate optimization problem and obtain the Pareto frontier of the multi-objective optimization problem. Three typical sample points are selected from the Pareto frontier. The selection principles of the three typical sample points are as follows:

[0159] ;

[0160] Check the accuracy of the approximate optimization results of Sampling1, Sampling2 and Sampling3. The calculation formula is as follows:

[0161] ;

[0162] ε1 and ε2 are the allowable tolerances. If the allowable tolerances are met, the final Pareto frontier is output; otherwise, three typical sample points are added to the sample point library.

[0163] A third aspect of the present invention provides a computer-readable storage medium, which includes a box airflow optimization method program. When the box airflow optimization method program is executed by a processor, the steps of the box airflow optimization method as described in any one of the above items are implemented.

[0164] The present invention discloses a box air-increase and exhaust optimization method, system and medium, which establish a simulation analysis model for the design of a lithium-ion battery electrode drying box with an air-increase and exhaust duct; establish boundary conditions of the lithium-ion battery electrode drying box with an air-increase and exhaust duct; establish evaluation criteria for the lithium-ion battery electrode drying box with an air-increase and exhaust duct; use Latin square DOE to extract a preset number of samples, perform simulation analysis on each sample, and obtain simulation results; use Kriging model to analyze approximate optimization results based on the simulation results, and output optimal design variables based on the approximate optimization results; analyze the air-increase and exhaust optimization results according to the simulation analysis model and boundary conditions, so as to screen out the best optimization results and improve the air-increase and exhaust control accuracy.

[0165] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0166] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0167] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0168] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0169] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A box ventilation optimization method, characterized in that: include: Establish a simulation analysis model for the design of a lithium-ion battery electrode drying oven with an additional exhaust duct; Establish the boundary conditions of the lithium-ion battery electrode drying oven with an additional exhaust duct; Establish evaluation criteria for lithium-ion battery electrode drying ovens with enhanced exhaust ducts; Use Latin square DOE to extract a preset number of samples, perform simulation analysis on each sample, and obtain simulation results; Based on the simulation results, the Kriging model is used to analyze the approximate optimization results, and the optimal design variables are output based on the approximate optimization results; A simulation analysis model for the design of a lithium-ion battery electrode drying oven with an exhaust duct is established, including: Obtain parameter information of a lithium-ion battery electrode drying oven, including size information and structure information; Analyze the layout information of the additional exhaust duct and the inclined opening angle of the additional exhaust duct based on the structural information; Based on the exhaust duct layout information and the inclined opening angle of the additional exhaust duct, the initial opening diameter value, the initial spacing value and the incremental value are analyzed to obtain the simulation parameters; Design a simulation analysis model of the drying oven based on simulation parameters; Establish the boundary conditions of the lithium-ion battery electrode drying oven with an exhaust duct, including: Collect inlet and outlet flow rates based on exhaust duct layout information and the inclined opening angle of the exhaust duct; Analyze the maximum and minimum flow rates based on the inlet and outlet flow rates, and establish flow boundary conditions; Analyze the pressure values ​​of the exhaust pipe outlet at different time points to obtain pressure value information; Analyze the maximum pressure value and the minimum pressure value based on the pressure value change information, and establish the air outlet pressure boundary condition based on the maximum pressure value and the minimum pressure value; The evaluation criteria calculation formula for establishing a lithium-ion battery electrode drying oven with an additional exhaust duct is as follows: ； and Used to evaluate the extreme deviation and overall deviation of the uniformity of solvent evaporation on the pole surface. , and They represent the maximum mass transfer coefficient, average mass transfer coefficient and minimum mass transfer coefficient on the electrode surface respectively; The mass transfer coefficient is calculated using the following formula: ； and They represent the moisture concentration on the electrode surface and the moisture concentration at the inlet of the drying box respectively; The box exhaust optimization method also includes: establishing an optimization model for the exhaust duct structure, the formula is as follows: ； P is the negative pressure applied to the exhaust duct connected to the end of the exhaust duct, α and β are the inclination angles of the front and rear faces of the exhaust duct respectively, the front face is the end face upstream of the axis of the exhaust duct in the belt running direction, the rear face is the end face downstream of the axis of the exhaust duct in the belt running direction, h is the minimum vertical distance between the exhaust duct and the pole piece; y is the coordinate vector in the width direction of the pole piece, and R represents the opening radius of each exhaust hole in the width direction.

2. The box exhaust optimization method according to claim 1, characterized in that: A preset number of samples are extracted using Latin square DOE, and each sample is simulated and analyzed to obtain simulation results, including: Use Latin square DOE sampling to extract 10*m samples, where m is the dimension of the design variable X, and call the simulation analysis model; The simulation analysis of each sample is calculated based on the simulation analysis model, and the uniformity deviation of the solvent evaporation of each sample simulation is calculated using the evaluation criteria; The simulation results are obtained based on the deviation of the uniformity of the solvent evaporation simulated for each sample.

3. The box ventilation optimization method according to claim 2, characterized in that: Based on the simulation results, the Kriging model is used to analyze the approximate optimization results, and the optimal design variables are output based on the approximate optimization results, including: Define the optimization problem, which includes the objective function, constraint function and design variables; Calculate the objective function and constraint function through DOE sampling; A sample point library is established based on the objective function and the constraint function, a Kriging model is established based on the sample point library, the maximum expected improvement is analyzed based on the Kriging model, and the function value of the maximum expected improvement is calculated; Determine whether the function value of the maximum expected improvement meets the convergence criterion; If converged, the optimal design variables and corresponding function values ​​are output based on the Kriging model to generate approximate optimization results; If it does not converge, the sample point at the maximum expectation is added to the sample point library.

4. A box ventilation optimization system, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a box ventilation optimization method, and when the program of the box ventilation optimization method is executed by the processor, the following steps are implemented: Establish a simulation analysis model for the design of a lithium-ion battery electrode drying oven with an additional exhaust duct; Establish the boundary conditions of the lithium-ion battery electrode drying oven with an additional exhaust duct; Establish evaluation criteria for lithium-ion battery electrode drying ovens with enhanced exhaust ducts; Use Latin square DOE to extract a preset number of samples, perform simulation analysis on each sample, and obtain simulation results; Based on the simulation results, the Kriging model is used to analyze the approximate optimization results, and the optimal design variables are output based on the approximate optimization results; A simulation analysis model for the design of a lithium-ion battery electrode drying oven with an exhaust duct is established, including: Obtain parameter information of a lithium-ion battery electrode drying oven, including size information and structure information; Analyze the layout information of the additional exhaust duct and the inclined opening angle of the additional exhaust duct based on the structural information; Based on the exhaust duct layout information and the inclined opening angle of the additional exhaust duct, the initial opening diameter value, the initial spacing value and the incremental value are analyzed to obtain the simulation parameters; Design a simulation analysis model of the drying oven based on simulation parameters; Establish the boundary conditions of the lithium-ion battery electrode drying oven with an exhaust duct, including: Collect inlet and outlet flow rates based on exhaust duct layout information and the inclined opening angle of the exhaust duct; Analyze the maximum and minimum flow rates based on the inlet and outlet flow rates, and establish flow boundary conditions; Analyze the pressure values ​​of the exhaust pipe outlet at different time points to obtain pressure value information; Analyze the maximum pressure value and the minimum pressure value based on the pressure value change information, and establish the air outlet pressure boundary condition based on the maximum pressure value and the minimum pressure value; The evaluation criteria calculation formula for establishing a lithium-ion battery electrode drying oven with an additional exhaust duct is as follows: ； and Used to evaluate the extreme deviation and overall deviation of the uniformity of solvent evaporation on the pole surface. , and They represent the maximum mass transfer coefficient, average mass transfer coefficient and minimum mass transfer coefficient on the electrode surface respectively; The mass transfer coefficient is calculated using the following formula: ； and They represent the moisture concentration on the electrode surface and the moisture concentration at the inlet of the drying box respectively; The box exhaust optimization method also includes: establishing an optimization model for the exhaust duct structure, the formula is as follows: ； P is the negative pressure applied to the exhaust duct connected to the end of the exhaust duct, α and β are the inclination angles of the front and rear faces of the exhaust duct respectively, the front face is the end face upstream of the axis of the exhaust duct in the belt running direction, the rear face is the end face downstream of the axis of the exhaust duct in the belt running direction, h is the minimum vertical distance between the exhaust duct and the pole piece; y is the coordinate vector in the width direction of the pole piece, and R represents the opening radius of each exhaust hole in the width direction.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a box ventilation optimization method program, and when the box ventilation optimization method program is executed by a processor, the steps of the box ventilation optimization method according to any one of claims 1 to 3 are implemented.

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