Shape and parameter optimization and electrode dry fabrication methods, systems, apparatuses, devices, media, and program product

Abstract

The application discloses a convex roller shape optimization method, a multi-stage continuous roller pressing electrode dry process manufacturing method, a process parameter optimization method, a multi-stage continuous roller pressing electrode dry process manufacturing system, a process parameter optimization device, a processing equipment, a storage medium and a computer program product, and relates to the technical field of electrode manufacturing. In the application, a first optimization function for optimizing the shape of the convex roller surface and minimizing the stress uniformity evaluation index of the convex roller surface is established, a first number of test shape parameters are extracted, and a first test stress uniformity evaluation index under the test shape parameters is calculated. Through the test shape parameters and the first test stress uniformity evaluation index, a first quantitative function relationship between the shape parameters of the convex roller surface and the stress uniformity evaluation index is fitted; finally, the optimal shape parameters when the stress uniformity evaluation index is the smallest are determined based on the first quantitative function relationship.

Description

Technical Field

[0001] This application relates to the technical field of electrode manufacturing, and in particular to a method for optimizing the shape of a convex roller, a method for dry manufacturing of multi-stage continuous rolling electrodes, a method for optimizing process parameters, a system for dry manufacturing of multi-stage continuous rolling electrodes, a device for optimizing process parameters, processing equipment, storage media, and computer program products. Background Technology

[0002] After coating and drying, the electrode sheets of lithium-ion batteries exhibit low peel strength between the active material and the current collector foil. Therefore, they need to be rolled to enhance the adhesion between the active material and the foil, preventing peeling during electrolyte immersion and battery use. Simultaneously, electrode rolling compresses the cell volume, increasing the cell's energy density, and reduces the porosity between the active material, conductive agent, and binder within the electrode, thereby lowering the battery's resistance and improving battery performance.

[0003] Currently, rolls are selected based on their basic compressive strength, hardness, wear resistance, impact resistance, and cutting performance. In some special designs, such as the roll structure with end diameters smaller than the middle diameter as described in patent CN110899336B (a roll pressing mechanism and roll design method), the roll structure can compensate for the deflection deformation of the roll during rolling, reducing the thickness reduction at both sides of the electrode sheet. Simultaneously, combined with the bending force of the bending roller, the thickness reduction at both sides of the electrode sheet can be further reduced, improving the lateral thickness consistency of the battery electrode sheet during rolling.

[0004] However, none of the existing technologies mention the design of the roll surface shape, or how to achieve the consistency and uniformity of the electrode sheet thickness through the designed roll.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this application is to provide a method for optimizing the shape of a convex roll, a method for manufacturing electrodes using a multi-stage continuous rolling process, a method for optimizing process parameters, a system for manufacturing electrodes using a multi-stage continuous rolling process, a device for optimizing process parameters, processing equipment, a storage medium, and a computer program product. The aim is to solve the technical problem of optimizing the roll surface shape of the roll in order to roll electrode sheets with consistent and uniform thickness.

[0007] To achieve the above objectives, this application proposes a method for optimizing the shape of a cam roller, the method comprising:

[0008] A first optimization function is established to optimize the shape of the convex roller surface, wherein the objective of the first optimization function is to minimize the stress uniformity evaluation index of the convex roller surface.

[0009] Extract a first number of test shape parameters from the first optimization function, and calculate the first test force uniformity evaluation index under the test shape parameters;

[0010] By fitting the test shape parameters and the first test force uniformity evaluation index, a first quantitative functional relationship between the shape parameters of the convex roller surface and the force uniformity evaluation index is obtained;

[0011] Based on the first quantitative function relationship, the optimal shape parameter is determined when the force uniformity evaluation index is minimized.

[0012] In addition, this application also proposes a multi-stage continuous rolling electrode dry manufacturing method, wherein the multi-stage continuous rolling electrode dry manufacturing method employs the convex rollers described above, and the multi-stage continuous rolling electrode dry manufacturing method includes:

[0013] During the film-forming stage of the roll forming process, at least one stage of pressure control is used to form a self-supporting film;

[0014] During the shaping stage of the roll forming process, at least one level of thickness control is employed to ensure that the coating thickness of the self-supporting film is uniform.

[0015] In addition, this application also proposes a method for optimizing process parameters, wherein the process parameters to be optimized are those used in the multi-stage continuous roll pressing dry electrode manufacturing process as described above. The method for optimizing process parameters includes:

[0016] A second optimization function is established to optimize the process parameters, wherein the objective of the second optimization function is to minimize the porosity non-uniformity distribution index and the thickness non-uniformity distribution index;

[0017] Extract a second number of test process parameters, and calculate the test porosity non-uniform distribution index, test thickness non-uniform distribution index, and second test stress uniformity evaluation index under the test process parameters.

[0018] By using the experimental porosity non-uniformity distribution index, experimental thickness non-uniformity distribution index, and second experimental stress uniformity evaluation index, a second quantitative functional relationship between process parameters and porosity non-uniformity distribution index, thickness non-uniformity distribution index, and stress uniformity evaluation index is obtained.

[0019] Based on the second quantitative function relationship, the optimal process parameters are determined when the porosity non-uniform distribution index and the thickness non-uniform distribution index are minimized.

[0020] In one embodiment, the step of calculating the non-uniform distribution index of test porosity and the non-uniform distribution index of test thickness under the test process parameters includes:

[0021] Determine the electrode thickness and electrode porosity at each preset time step within the preset particle-filled three-dimensional space during the multi-stage continuous roll pressing dry electrode manufacturing process under the experimental process parameters;

[0022] By dividing the three-dimensional space filled with preset particles into subspaces, the non-uniform distribution index of test porosity and the non-uniform distribution index of test thickness within the three-dimensional space filled with preset particles are statistically obtained at each preset time step.

[0023] In one embodiment, the step of calculating the second test force uniformity evaluation index under the test process parameters includes:

[0024] The second test force uniformity evaluation index under the test process parameters is calculated using the first optimization function.

[0025] Furthermore, this application also proposes a multi-stage continuous rolling electrode dry manufacturing system, wherein the system applies the multi-stage continuous rolling electrode dry manufacturing method described above, including:

[0026] A pressure regulating device is used to form a self-supporting film by employing at least one level of pressure control during the film-forming stage of roller pressing.

[0027] A thickness adjustment device is used in the shaping stage of roll forming to employ at least one level of thickness control to ensure that the coating thickness of the self-supporting film is uniform.

[0028] Furthermore, this application also proposes a process parameter optimization apparatus, which applies the process parameter optimization method described above, including:

[0029] A module is established to establish a second optimization function for optimizing process parameters, wherein the objective of the second optimization function is to minimize the porosity non-uniformity distribution index and the thickness non-uniformity distribution index;

[0030] The calculation module is used to extract a second number of test process parameters and calculate the test porosity non-uniform distribution index, test thickness non-uniform distribution index and second test stress uniformity evaluation index under the test process parameters.

[0031] The fitting module is used to fit the second quantitative functional relationship between the process parameters and the non-uniform distribution index of porosity, the non-uniform distribution index of thickness, and the second evaluation index of stress uniformity by using the test porosity non-uniform distribution index, the test thickness non-uniform distribution index, and the second evaluation index of stress uniformity.

[0032] The determination module is used to determine the optimal process parameters when the porosity non-uniform distribution index and the thickness non-uniform distribution index are minimized, based on the second quantitative function relationship.

[0033] Furthermore, to achieve the above objectives, this application also proposes a processing apparatus, the apparatus comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the convex roller shape optimization method as described above, and / or the steps of the multi-stage continuous rolling electrode dry manufacturing method as described above, and / or the steps of the process parameter optimization method as described above.

[0034] Furthermore, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the above-described convex roller shape optimization method, and / or the steps of the above-described multi-stage continuous rolling electrode dry manufacturing method, and / or the steps of the above-described process parameter optimization method.

[0035] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the convex roller shape optimization method as described above, and / or the steps of the multi-stage continuous rolling electrode dry manufacturing method as described above, and / or the steps of the process parameter optimization method as described above.

[0036] One or more technical solutions proposed in this application have at least the following technical effects:

[0037] In this application, a first optimization function is established to optimize the shape of the convex roller surface, with the objective of minimizing the stress uniformity evaluation index of the convex roller surface. A first number of experimental shape parameters are extracted, and a first experimental stress uniformity evaluation index is calculated under these experimental shape parameters. Using these experimental shape parameters and the first experimental stress uniformity evaluation index, a first quantitative functional relationship between the shape parameters of the convex roller surface and the stress uniformity evaluation index is fitted. Finally, based on the first quantitative functional relationship, the optimal shape parameter that minimizes the stress uniformity evaluation index is determined.

[0038] Therefore, before actually using the rolls for rolling, the optimal shape parameters are determined by constructing an optimization function, using experimental shape parameters and stress uniformity evaluation indicators, and fitting a quantitative functional relationship between the shape parameters and stress uniformity evaluation indicators, thereby minimizing the stress uniformity evaluation indicators. The roll surface shape is then optimized using these optimal shape parameters, and based on the optimized rolls, electrode sheets with consistent and uniform thickness can be rolled. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A schematic flowchart of an embodiment of the convex roller shape optimization method of this application;

[0042] Figure 2 A schematic diagram of a convex roller provided for an embodiment of the convex roller shape optimization method of this application;

[0043] Figure 3 A schematic flowchart is provided for an embodiment of the dry manufacturing method for multi-stage continuous rolling electrodes of this application;

[0044] Figure 4 A system schematic diagram provided for an embodiment of the multi-stage continuous rolling electrode dry manufacturing system of this application;

[0045] Figure 5 A schematic flowchart illustrating an embodiment of the method for optimizing process parameters in this application;

[0046] Figure 6 This is a schematic diagram of the module structure of the process parameter optimization device in an embodiment of this application;

[0047] Figure 7 This is a schematic diagram of the hardware operating environment involved in the processing device in the embodiments of this application.

[0048] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0050] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0051] This application provides a method for optimizing the shape of a convex roller, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the convex roller shape optimization method of this application.

[0052] In this embodiment, the method for optimizing the shape of the convex roller includes steps S10 to S40:

[0053] Step S10: Establish a first optimization function to optimize the shape of the cam roll surface, wherein the objective of the first optimization function is to minimize the stress uniformity evaluation index of the cam roll surface.

[0054] In one embodiment, the evaluation index ΔP for the uniformity of force distribution on the convex roller surface is as follows:

[0055]

[0056] Where L is the width of the roller surface, P(x) is the interaction force between the roller surface and the electrode surface, and the roller pressure line load (e.g., ... Figure 2 P(x) shown can be obtained from the finite element analysis model of the pressure on the convex roller. Let P(x) be the average value of P(x) over the width of the roller surface.

[0057] In another embodiment, the first optimization function for optimizing the shape of the convex roller surface is as follows:

[0058]

[0059] Where ΔP is the evaluation index of the uniformity of force on the convex roll surface, k1, k2, and k3 are the parameters to be optimized, Δh is the roll shaping parameter to be optimized, v is the Poisson's ratio of the roll, and R is the roll radius (e.g., ...). Figure 2 In the figure, R), P(x) is the interaction force between the roll surface and the electrode surface, E is the elastic modulus of the roll, x is the transverse coordinate, and L is the width of the roll surface.

[0060] It can be seen that the objective of the first optimization function is to minimize (min) the stress uniformity evaluation index ΔP on the convex roller surface.

[0061] Step S20: Extract a first number of test shape parameters from the first optimization function, and calculate the first test force uniformity evaluation index under the test shape parameters.

[0062] In one embodiment, a first number of N1 combined design variables (k1, k2, k3) are extracted using an experimental design method, and the uniformity evaluation index of the roll surface force under these N1 combined design variables is calculated using a finite element analysis model of the roll pressure, which is the first experimental uniformity evaluation index.

[0063] Step S30: By fitting the test shape parameters and the first test force uniformity evaluation index, the first quantitative functional relationship between the shape parameters of the convex roller surface and the force uniformity evaluation index is obtained;

[0064] In one embodiment, the first quantitative functional relationship between the fitted experimental shape parameters, i.e., the combined design variables (k1, k2, k3), and the roller surface force uniformity evaluation index, i.e., the first experimental force uniformity evaluation index, is as follows:

[0065] ΔP(k1,k2,k3)=f(k1,k2,k3)

[0066] The first quantitative function relationship f(k1,k2,k3) can be a combination of one or more models, such as neural networks, support vector machines, or Kriging spatial interpolation methods.

[0067] Step S40: Based on the first quantitative function relationship, determine the optimal shape parameter when the uniformity of force evaluation index is minimized.

[0068] In one embodiment, based on the first quantitative functional relationship f(k1,k2,k3), a genetic algorithm is used to obtain the optimal combination of design variables (k1, k2, k3) to minimize the stress uniformity evaluation index ΔP on the convex roller surface. At this point, the optimal combination of design variables, i.e., the optimal shape parameters, are output. Furthermore, the roller surface shape can be calculated and designed based on the optimal combination of design variables.

[0069] Therefore, before actually using the rolls for rolling, the optimal shape parameters are determined by constructing an optimization function, using experimental shape parameters and stress uniformity evaluation indicators, and fitting a quantitative functional relationship between the shape parameters and stress uniformity evaluation indicators, thereby minimizing the stress uniformity evaluation indicators. The roll surface shape is then optimized using these optimal shape parameters, and based on the optimized rolls, electrode sheets with consistent and uniform thickness can be rolled.

[0070] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 The multi-stage continuous rolling electrode dry manufacturing method employs the convex roller as described in the first embodiment, and the multi-stage continuous rolling electrode dry manufacturing method includes:

[0071] Step D10: During the film-forming stage of the roll forming process, at least one level of pressure control is used to form a self-supporting film.

[0072] Step D20: In the shaping stage of the roll forming process, at least one level of thickness control is used to ensure that the coating thickness of the self-supporting film is uniform.

[0073] In one embodiment, reference is made to Figure 4The coating is compacted step-by-step through a multi-stage rolling process, employing pressure and thickness linkage control to avoid defects such as uneven coating deformation and cracking caused by excessively high single-stage compression ratios (the ratio of thickness before and after rolling). Pressure control is used for pre-thinning, while thickness control is used for precise thickness control. It should be noted that... Figure 4 The diagram only shows a scheme using three arc-shaped convex rollers, as well as the simplest single-stage pressure control and single-stage thickness control. Both pressure control and thickness control can be achieved using multiple convex rollers to achieve multi-stage control.

[0074] In one feasible implementation, a) an arc-shaped convex roller is used to pre-compensate for edge thickness differences, b) a reverse pre-bending moment is used to reduce the deflection deformation of the pressure roller, and c) hot roller pressing is used to reduce compaction resistance, so as to improve the uniformity of the roll gap and roll pressing load in the width direction.

[0075] In another embodiment, the rolls are rapidly heated to above the binder melting temperature using methods such as electromagnetic heating before rolling. An analytical model of the electromagnetic heating induction coil is established using a finite element CAE model or simulation analysis model to analyze the influence of the induction coil shape, heating current, and heating frequency on the uniformity of the roll surface temperature, thereby improving the uniformity of the bonding strength between coating particles and between the coating and the current collector.

[0076] The multi-stage rolling process includes preheating, film formation, and shaping. The preheating stage uses temperature control, such as the temperature control of the electromagnetic heating induction coil analysis model mentioned above, to rapidly heat the powder material composed of active particles, conductive agents, and binders to above the melting temperature of the binder, thereby increasing the bonding strength between the active particles. The film formation stage uses pressure control to form a self-supporting film through progressive compression. The shaping stage uses thickness control to ensure the consistency of the coating thickness.

[0077] In another feasible implementation, after the shaping stage, the hot roll composite forming process is continued. The influence of process parameters such as roll speed, roll load, and roll surface temperature on the bonding strength between coatings and between the coating and the current collector is analyzed. Orthogonal experiments are used to optimize the process parameters, thereby obtaining the optimal combination of hot roll composite process parameters and the optimal interfacial bonding strength. That is, in the hot roll composite forming process, the self-supporting film (coating) obtained by the multi-stage roll forming process is composited with the current collector.

[0078] The above-mentioned multi-stage continuous rolling electrode dry manufacturing method can solve the technical problem of difficulty in rolling electrode sheets with consistent and uniform thickness. By controlling the pressure in the rolling film forming stage and the thickness in the rolling shaping stage, electrode sheets with consistent and uniform thickness can be rolled.

[0079] Based on the second embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the second embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 The process parameters to be optimized are those used in the multi-stage continuous roll pressing dry electrode manufacturing process as described in claim 2, and the optimization method for the process parameters includes:

[0080] Step F10: Establish a second optimization function to optimize the process parameters, wherein the objective of the second optimization function is to minimize the porosity non-uniformity distribution index and the thickness non-uniformity distribution index.

[0081] In one embodiment, an optimization design variable X is established, which represents the process parameters to be optimized, including the initial electrode thickness h0, initial porosity ε0, final electrode thickness hfinal, final porosity εfinal, number of electrode layers N, roller load qL, roller speed v, and roller surface temperature T, etc., X = [h0, ε0, hfinal, εfinal, N, qL, v, T…]. Furthermore, the constraint functions are: the roller surface temperature must not exceed a specified temperature Tmax, the roller surface load uniformity ΔP is less than a set value ΔP0, etc.

[0082] Step F20: Extract a second number of test process parameters and calculate the test porosity non-uniform distribution index, test thickness non-uniform distribution index, and second test stress uniformity evaluation index under the test process parameters.

[0083] In one embodiment, an experimental design method is used to extract a second set of N² design variables, namely experimental process parameters, as the training set for a neural network. The non-uniform distribution index ε of experimental porosity under the experimental process parameters is calculated using a pore-thickness co-evolution model. uniformity With the test thickness non-uniform distribution index h uniformity .

[0084] In one feasible implementation, step F20 includes:

[0085] The second test stress uniformity evaluation index under the test process parameters is calculated using the first optimization function.

[0086] Among them, the arc-shaped convex roller optimization model in the first embodiment is used to calculate the second test force uniformity evaluation index ΔP under the test process parameters.

[0087] Step F30: By using the test porosity non-uniformity distribution index, the test thickness non-uniformity distribution index, and the second test stress uniformity evaluation index, a second quantitative function relationship between the process parameters and the porosity non-uniformity distribution index, the thickness non-uniformity distribution index, and the stress uniformity evaluation index is obtained.

[0088] In one embodiment, the "design variable X," i.e., the experimental process parameters, is used as the model input, and the non-uniform distribution index ε of the experimental porosity under the experimental process parameters is calculated. uniformity With the test thickness non-uniform distribution index h uniformity The second test force uniformity evaluation index ΔP is used as the model output for model training.

[0089] Therefore, a neural network model is used to fit the design variable X with the objective function h. uniformity With ε uniformity And the quantitative relationship between the second test force uniformity evaluation index ΔP and the other.

[0090] In this neural network model, the number of input layers equals the number of design variables, and the number of output layers is 3. The number of intermediate layers is set to 4-6, and the number of neurons in each intermediate layer is set to 10-20. The ReLU activation function is used for neurons, and fully connected layers are used between them. The Adam optimizer is used to train the neural network, with 100 training epochs. The initial learning rate is set to 1e-3, and every 10 epochs, the learning rate is reduced to half of the previous epoch's learning rate.

[0091] In another embodiment, an experimental design method is used to extract N² / 10 design variables as the validation set for the neural network, and a pore-thickness co-evolution model is used to calculate h. uniformity With ε uniformity The uniformity of the roller surface load ΔP is calculated using an optimized model of an arc-shaped convex roller. The fitting accuracy R2 of the trained neural network is evaluated using validation set data. If the fitting accuracy R2 of the neural network is greater than 0.95, proceed to step F40; otherwise, proceed to step F30 and increase the value of the training set size N2 until the accuracy R2 of the neural network model is greater than 0.95.

[0092] Step F40: Based on the second quantitative function relationship, determine the optimal process parameters when the porosity non-uniform distribution index and the thickness non-uniform distribution index are minimized.

[0093] Based on a trained neural network, a multi-objective optimization algorithm is used to optimize process parameters such as electrode thickness and microstructure feature parameters, thereby achieving optimization of process parameters in the design and manufacturing process of multilayer dry electrodes.

[0094] In one feasible implementation, step F20 includes:

[0095] Determine the electrode thickness and electrode porosity at each preset time step within the preset particle-filled three-dimensional space during the multi-stage continuous roll pressing dry electrode manufacturing process under experimental process parameters;

[0096] By dividing the three-dimensional space filled with preset particles into subspaces, the non-uniform distribution index of test porosity and the non-uniform distribution index of test thickness within the three-dimensional space filled with preset particles are statistically obtained at each preset time step.

[0097] In one embodiment, firstly, a cubic region with a thickness of h0, a length of L (roller surface width), and a width of L (roller surface width) is defined. Electrode active material, conductive agent, and binder particles are generated to fill the cubic region using the Monte Carlo method. The number of active material, conductive agent, and binder particles is determined according to the mass of each component, and the particle size is determined according to the particle size distribution of each component. The thickness of the generated particle region is used as the initial thickness value h0, and the overall average porosity of the particle region is used as the initial porosity value ε0.

[0098] Secondly, the particle distribution state at each time step was calculated using a DEM model, and the variation law of electrode thickness and porosity with external load was analyzed, thereby establishing a pore-thickness co-evolution model. The change process of thickness and porosity can be divided into several steps. For example, the electrode thickness can gradually change from the initial value h0 to h1, h2, h3..., h,... until the final thickness hfinal, and the electrode porosity can gradually change from the initial value ε0 to ε1, ε2, ε3..., ε,... until the final thickness εfinal.

[0099] Therefore, the electrode thickness and electrode porosity at each preset time step within the preset particle-filled three-dimensional space during the multi-stage continuous roller pressing dry electrode manufacturing process under experimental process parameters can be determined.

[0100] Next, the cubic region hixLxL is divided into KxK subregions hixL / KxL / K, where hi at any time step can take the value h0, h1, h2, h3… The distribution of active material, conductive agent, and binder particles in each subregion Vjk is statistically analyzed, and the porosity εjk of that region is calculated.

[0101]

[0102] Where the width direction is represented by j, and the vertical direction by k. and These represent the volumes occupied by the active material, conductive agent, and binder particles, respectively.

[0103] Then, after obtaining the porosity values ​​for each sub-region, the data are summarized to obtain the porosity distribution of the entire region. The average porosity of the entire region is then calculated based on the porosity of each sub-region. From the porosity distribution at each step, the final porosity non-uniformity distribution index ε is calculated. uniformity .

[0104] Meanwhile, with the aforementioned porosity non-uniform distribution index ε uniformity The calculation is similar, in calculating the thickness non-uniform distribution index h uniformity At that time, the thickness hjk in each sub-region Vjk is calculated:

[0105]

[0106]

[0107] in, and These represent the coordinates of the particle's surface and bottom along the thickness direction, respectively. After obtaining the thickness values ​​for each sub-region, the data is summarized to obtain the thickness distribution of the entire region, and the average thickness of the entire region is calculated based on the thickness of each sub-region. From the thickness distribution at each step, the final thickness non-uniformity distribution index h is calculated. uniformity .

[0108] The above-mentioned optimization methods for process parameters can solve the technical problem of optimizing the pressure roller process parameters in the electrode pressure roller process. In the optimization method of this application, a quantitative analysis model of deformation-microstructure evolution during the forming process is used, combined with a particle contact mechanics model of the electrode coating, to analyze the influence of process parameters such as solid content, particle morphology, and roller load on the evolution of pore structure, active particles, conductive agents, and binder size and spatial morphology, thereby obtaining the deformation-microstructure evolution law during the dry forming process of the electrode. Based on the evolution law, process parameters such as roller load are optimized to achieve precise control of deformation-microstructure. Using the uniformity of dry electrode thickness as the objective function, electrode manufacturing process as the constraint, and electrode layer number, thickness, and microstructure characteristic parameters as design variables, a deep neural network model is trained using electrode structure design-electrochemical performance simulation data. The multilayer electrode performance evaluation model and the deformation-microstructure evolution model are used to obtain the samples required for deep neural network training, achieving accurate prediction of the entire process of multilayer dry electrode design-manufacturing-performance. Finally, based on the trained deep neural network, a multi-objective optimization algorithm is used to optimize process parameters such as the number of electrode layers, thickness, and microstructure feature parameters.

[0109] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the methods of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0110] This application also provides a process parameter optimization device, please refer to... Figure 6 The process parameter optimization device applies the process parameter optimization method as described in the third embodiment, including:

[0111] Module 10 is established to establish a second optimization function for optimizing process parameters. The objective of the second optimization function is to minimize the porosity non-uniformity distribution index and the thickness non-uniformity distribution index.

[0112] The calculation module 20 is used to extract a second number of test process parameters and calculate the test porosity non-uniform distribution index, test thickness non-uniform distribution index and second test stress uniformity evaluation index under the test process parameters.

[0113] The fitting module 30 is used to fit the second quantitative functional relationship between the process parameters and the porosity non-uniformity distribution index, thickness non-uniformity distribution index and the stress uniformity evaluation index by using the test porosity non-uniformity distribution index, the test thickness non-uniformity distribution index and the second test stress uniformity evaluation index.

[0114] The determination module 40 is used to determine the optimal process parameters when the porosity non-uniform distribution index and the thickness non-uniform distribution index are minimized, based on the second quantitative function relationship.

[0115] In one embodiment, the computing module 20 is further configured to:

[0116] Determine the electrode thickness and electrode porosity at each preset time step within the preset particle-filled three-dimensional space during the multi-stage continuous roll pressing dry electrode manufacturing process under experimental process parameters;

[0117] By dividing the three-dimensional space filled with preset particles into subspaces, the non-uniform distribution index of test porosity and the non-uniform distribution index of test thickness within the three-dimensional space filled with preset particles are statistically obtained at each preset time step.

[0118] In one embodiment, the computing module 20 is further configured to:

[0119] The second test stress uniformity evaluation index under the test process parameters is calculated using the first optimization function.

[0120] The process parameter optimization apparatus provided in this application, employing the process parameter optimization method described in the above embodiments, can solve the technical problem of difficulty in optimizing the process parameters of the electrode pressure roller. Compared with the prior art, the beneficial effects of the process parameter optimization apparatus provided in this application are the same as those of the process parameter optimization method provided in the above embodiments, and other technical features in the process parameter optimization apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0121] This application provides a processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the processing method in Embodiment 1 above.

[0122] The following is for reference. Figure 7 The diagram illustrates a structural schematic of a processing device suitable for implementing embodiments of this application. The processing device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The processing device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0123] like Figure 7 As shown, the processing device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the processing device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the processing device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a processing device with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0124] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0125] The processing equipment provided in this application employs the convex roll shape optimization method, and / or the multi-stage continuous roll electrode dry manufacturing method, and / or the process parameter optimization method described in the above embodiments. This solves the technical problems of difficulty in optimizing the roll surface shape to roll electrode sheets of consistent and uniform thickness, and / or difficulty in rolling electrode sheets of consistent and uniform thickness, and / or difficulty in optimizing the roll pressing process parameters during the electrode pressing process. Compared with the prior art, the beneficial effects of the processing equipment provided in this application are the same as those of the convex roll shape optimization method, and / or the multi-stage continuous roll electrode dry manufacturing method, and / or the process parameter optimization method provided in the above embodiments. Furthermore, other technical features of this processing equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0126] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0128] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the convex roller shape optimization method in the above embodiments, and / or, the multi-stage continuous rolling electrode dry manufacturing method, and / or, the process parameter optimization method.

[0129] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0130] The aforementioned computer-readable storage medium may be included in the processing device or may exist independently and not assembled into the processing device.

[0131] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by a processing device, cause the processing device to perform the convex roller shape optimization method, and / or the multi-stage continuous rolling electrode dry manufacturing method, and / or the process parameter optimization method in the above embodiments.

[0132] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0134] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0135] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for performing the above-described method for optimizing the shape of the convex roll, and / or the method for manufacturing electrodes using a multi-stage continuous rolling process, and / or the method for optimizing process parameters. This solves the technical problems of difficulty in optimizing the roll surface shape to roll electrode sheets of uniform thickness, and / or difficulty in rolling electrode sheets of uniform thickness, and / or difficulty in optimizing the rolling process parameters during the electrode pressing process. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the method for optimizing the shape of the convex roll, and / or the method for manufacturing electrodes using a multi-stage continuous rolling process, and / or the method for optimizing process parameters provided in the above embodiments, and will not be elaborated upon here.

[0136] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for optimizing the shape of a convex roller, and / or, a method for manufacturing a multi-stage continuous roller-pressed electrode using a dry process, and / or, a method for optimizing process parameters.

[0137] The computer program product provided in this application can solve the technical problems of difficulty in optimizing the roll surface shape of the rolling mill to roll electrode sheets with consistent and uniform thickness, and / or difficulty in rolling electrode sheets with consistent and uniform thickness, and / or difficulty in optimizing the rolling mill process parameters during the electrode pressing process. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the convex roll shape optimization method, and / or, multi-stage continuous rolling electrode dry manufacturing method, and / or process parameter optimization method provided in the above embodiments, and will not be elaborated here.

[0138] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for optimizing the shape of a convex roller, characterized in that, The method for optimizing the shape of the convex roller includes: A first optimization function is established to optimize the shape of the convex roller surface, wherein the objective of the first optimization function is to minimize the stress uniformity evaluation index of the convex roller surface. Extract a first number of test shape parameters from the first optimization function, and calculate the first test force uniformity evaluation index under the test shape parameters; By fitting the test shape parameters and the first test force uniformity evaluation index, a first quantitative functional relationship between the shape parameters of the convex roller surface and the force uniformity evaluation index is obtained; Based on the first quantitative function relationship, the optimal shape parameter is determined when the force uniformity evaluation index is minimized.

2. A method for dry manufacturing of multi-stage continuous rolling electrodes, characterized in that, The multi-stage continuous rolling electrode dry manufacturing method employs the convex roller as described in claim 1, and the multi-stage continuous rolling electrode dry manufacturing method includes: During the film-forming stage of the roll forming process, at least one stage of pressure control is used to form a self-supporting film; During the shaping stage of the roll forming process, at least one level of thickness control is employed to ensure that the coating thickness of the self-supporting film is uniform.

3. A method for optimizing process parameters, characterized in that, The process parameters to be optimized are those used in the multi-stage continuous roll pressing dry electrode manufacturing process as described in claim 2. The optimization method for these process parameters includes: A second optimization function is established to optimize the process parameters, wherein the objective of the second optimization function is to minimize the porosity non-uniformity distribution index and the thickness non-uniformity distribution index; Extract a second number of test process parameters, and calculate the test porosity non-uniform distribution index, test thickness non-uniform distribution index, and second test stress uniformity evaluation index under the test process parameters. By using the experimental porosity non-uniformity distribution index, experimental thickness non-uniformity distribution index, and second experimental stress uniformity evaluation index, a second quantitative functional relationship between process parameters and porosity non-uniformity distribution index, thickness non-uniformity distribution index, and stress uniformity evaluation index is obtained. Based on the second quantitative function relationship, the optimal process parameters are determined when the porosity non-uniform distribution index and the thickness non-uniform distribution index are minimized.

4. The method for optimizing process parameters as described in claim 3, characterized in that, The steps for calculating the non-uniform distribution index of test porosity and the non-uniform distribution index of test thickness under the test process parameters include: Determine the electrode thickness and electrode porosity at each preset time step within the preset particle-filled three-dimensional space during the multi-stage continuous roll pressing dry electrode manufacturing process under the experimental process parameters; By dividing the three-dimensional space filled with preset particles into subspaces, the non-uniform distribution index of test porosity and the non-uniform distribution index of test thickness within the three-dimensional space filled with preset particles are statistically obtained at each preset time step.

5. The method for optimizing process parameters as described in claim 3, characterized in that, The step of calculating the second test force uniformity evaluation index under the test process parameters includes: The second test force uniformity evaluation index under the test process parameters is calculated using the first optimization function.

6. A multi-stage continuous rolling electrode dry manufacturing system, characterized in that, The system employs the multi-stage continuous rolling electrode dry manufacturing method as described in claim 2, including: A pressure regulating device is used to form a self-supporting film by employing at least one level of pressure control during the film-forming stage of roller pressing. A thickness adjustment device is used in the shaping stage of roll forming to employ at least one level of thickness control to ensure that the coating thickness of the self-supporting film is uniform.

7. A device for optimizing process parameters, characterized in that, The apparatus employs the process parameter optimization method as described in claim 3, comprising: A module is established to establish a second optimization function for optimizing process parameters, wherein the objective of the second optimization function is to minimize the porosity non-uniformity distribution index and the thickness non-uniformity distribution index; The calculation module is used to extract a second number of test process parameters and calculate the test porosity non-uniform distribution index, test thickness non-uniform distribution index and second test stress uniformity evaluation index under the test process parameters. The fitting module is used to fit the second quantitative functional relationship between the process parameters and the non-uniform distribution index of porosity, the non-uniform distribution index of thickness, and the second evaluation index of stress uniformity by using the test porosity non-uniform distribution index, the test thickness non-uniform distribution index, and the second evaluation index of stress uniformity. The determination module is used to determine the optimal process parameters when the porosity non-uniform distribution index and the thickness non-uniform distribution index are minimized, based on the second quantitative function relationship.

8. A processing device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the convex roller shape optimization method as claimed in claim 1, and / or the steps of the multi-stage continuous rolling electrode dry manufacturing method as claimed in claim 2, and / or the steps of the process parameter optimization method as claimed in any one of claims 3 to 5.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the convex roller shape optimization method as described in claim 1, and / or the steps of the multi-stage continuous rolling electrode dry manufacturing method as described in claim 2, and / or the steps of the process parameter optimization method as described in any one of claims 3 to 5.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the convex roller shape optimization method as described in claim 1, and / or the steps of the multi-stage continuous rolling electrode dry manufacturing method as described in claim 2, and / or the steps of the process parameter optimization method as described in any one of claims 3 to 5.

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