Formulation determination method, device, system and storage medium for double-layer coating

Abstract

This application discloses a method, apparatus, system, and storage medium for determining the formulation of a double-layer coating. The method includes: obtaining the target tortuosity and performance constraints of each individual layer in the double-layer coating; determining target formulation parameters for each individual layer in one or more execution rounds; one execution round includes: determining the current tortuosity and current performance parameters of the individual layer based on its current formulation parameters and under the constraints of the target performance parameters; determining whether the current tortuosity is within the target range defined by the target tortuosity, and whether the current performance parameters satisfy the performance constraints; if not, updating the current formulation parameters using at least a quantitative relationship model indicating the correspondence between tortuosity and formulation parameters, and executing the next round; if yes, determining the current formulation parameters as the target formulation parameters for the individual layer. This application achieves rapid formulation determination through a quantitative relationship model.

Description

Technical Field

[0001] This application relates to the field of lithium battery technology, and in particular to a method, apparatus, system and storage medium for determining the formulation of a double-layer coating based on a quantitative relationship model related to tortuosity. Background Technology

[0002] Bilayer coating technology has been widely used in new energy, electronics, and chemical industries due to its ability to optimize coating functions by region. Taking lithium-ion battery electrodes as an example, bilayer coatings are typically designed with the surface layer near the separator emphasizing rapid ion transport, and the bottom layer near the current collector emphasizing electron conduction and structural support. The electrochemical and mechanical properties of the coating are closely related to its porous microstructure. Tortuosity, as a key indicator characterizing the degree of curvature of the ion / fluid transport path in the pores, directly determines the ion transport efficiency of the coating. Therefore, achieving precise design and control of the coating's tortuosity is the core of optimizing the performance of bilayer coatings. Summary of the Invention

[0003] To achieve the above objectives, this application discloses a method, apparatus, system, and storage medium for determining the formulation of a double-layer coating. The formulation determination method utilizes a quantitative relationship model that indicates the correspondence between tortuosity and formulation parameters. This quantitative relationship model clearly demonstrates the connection, avoiding the blindness of trial and error based on experience, thus reducing the error in coating tortuosity control and significantly improving performance consistency.

[0004] A first aspect of this application provides a method for determining the formulation of a double-layer coating. The method may include obtaining a target tortuosity and target performance parameters for each individual layer in the double-layer coating; determining target formulation parameters for each individual layer in one or more execution rounds; wherein an execution round includes: determining the current tortuosity and current performance parameters of the individual layer based on its current formulation parameters and under the constraints of the target performance parameters; determining whether the current tortuosity is within a target range defined by the target tortuosity, and whether the current performance parameters satisfy the performance constraints; if not, updating the current formulation parameters using at least a quantitative relationship model indicating the correspondence between tortuosity and formulation parameters, and proceeding to the next execution round; if yes, determining the current formulation parameters as the target formulation parameters for the individual layer.

[0005] According to some embodiments of this application, the target performance parameters include at least one of coating thickness, coating areal density, and coating adhesion; the current formulation parameters include at least one of solid content, particle size distribution width, binder content, and mass fraction of each component constituting the single coating.

[0006] According to some embodiments of this application, the quantitative relationship model is constructed based on mathematical operations between the porosity term, the binder content normalization term, and the particle size distribution correction term.

[0007] According to some embodiments of this application, determining the porosity term includes: obtaining the packing efficiency coefficient of the active material constituting the single coating; and performing a first mathematical operation using the packing efficiency coefficient and the solid content to determine the porosity term.

[0008] According to some embodiments of this application, determining the adhesive content normalization term includes: obtaining a reference adhesive content, and performing a second mathematical operation using the adhesive content and the reference adhesive content to determine the adhesive content normalization term.

[0009] According to some embodiments of this application, determining the particle size distribution correction term includes: performing a third mathematical operation using the particle size distribution width to determine the particle size distribution correction term.

[0010] According to some embodiments of this application, constructing the quantitative relationship model includes: obtaining constant terms, and performing a summation operation based on the constant terms, as well as the products of the porosity term, the binder content normalization term, and the particle size distribution correction term with their respective individual coefficients, to obtain the quantitative relationship model.

[0011] A second aspect of this application provides a formulation determination apparatus for a double-layer coating. The apparatus includes: an acquisition module configured to acquire a target tortuosity and target performance parameters for each individual layer in the double-layer coating; and a determination module configured to determine target formulation parameters for each individual layer in one or more execution rounds. One execution round includes: determining the current tortuosity and current performance parameters of the individual layer based on its current formulation parameters and under the constraints of the target performance parameters; determining whether the current tortuosity is within a target range defined by the target tortuosity, and whether the current performance parameters satisfy the performance constraints; if not, updating the current formulation parameters using at least a quantitative relationship model indicating the correspondence between tortuosity and formulation parameters, and executing the next round; if yes, determining the current formulation parameters as the target formulation parameters for the individual layer.

[0012] A third aspect of this application provides a computing system, which may include: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it can implement the steps of the method for determining the formulation of a double-layer coating as described above.

[0013] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the method for determining the formulation of a double-layer coating as described above.

[0014] The fifth aspect of this application provides a computer program product comprising a computer program that, when executed by a processor, can implement the steps of the method for determining the formulation of a double-layer coating as described above.

[0015] The method for determining the formulation of the double-layer coating provided in this application adjusts parameters through a quantitative relationship model that indicates the correspondence between tortuosity and formulation parameters. This avoids the blindness of trial and error based on experience, reduces the error in coating tortuosity control, and significantly improves performance consistency. Furthermore, while optimizing tortuosity, it ensures that other properties such as coating mechanical strength and thickness meet standards, achieving synergistic optimization of multiple properties.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is an exemplary flowchart of a method for determining the formulation of a double-layer coating according to some embodiments of this application; Figure 2 This is an exemplary block diagram of a processing apparatus for implementing the recipe determination method according to some embodiments of this application; and, Figure 3 These are exemplary block diagrams of computing devices shown in some embodiments of this application. Detailed Implementation

[0018] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0019] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application and in its specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The terms "comprising" or "including," as used in this application, mean that an element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. The terms "and / or" or "and / or" as used in this application include any and all combinations of one or more of the associated listed items.

[0020] The terms “comprising,” “having,” and their cognates used in this application are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0021] Currently, the existing technology for formula design of double-layer coatings generally adopts an "experience-based trial-and-error method." This method relies on personal experience, repeatedly adjusting various parameters of the coating slurry and conducting numerous orthogonal experiments to prepare samples and test performance, ultimately deriving a feasible formula. However, this method has the following significant drawbacks: First, the research and development cycle is lengthy and inefficient; second, the relationship between formula parameters and tortuosity is unclear, making precise control difficult and easily leading to an imbalance between coating transport performance and mechanical properties; third, the lack of unified quantitative design standards results in poor consistency between different batches of products, making it difficult to achieve quality control for large-scale production.

[0022] Based on this, this application provides a method for determining the formulation of a double-layer coating. By using a quantitative relationship model to guide parameter adjustment, it avoids a large number of trial and error experiments and orthogonal experiments, thereby improving accuracy and efficiency.

[0023] The following describes some preferred embodiments of this application. It should be noted that the following description is for illustrative purposes only and is not intended to limit the scope of protection of this application. The steps involved in this application may be performed precisely in sequence, or various steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0024] The formula determination method provided in this application can be referenced. Figure 1 In some embodiments, Figure 1The process 100 shown can be implemented in a computing device, such as an industrial computer, server, computer, tablet, or smart mobile device. In some embodiments, the process 100 can be stored in a storage device (such as the built-in storage unit of the computing device or an external storage device) in the form of a program or instructions, which, when executed, can implement the process 100. Figure 1 As shown, process 100 may include the following operations.

[0025] Step 110: Obtain the target tortuosity and performance constraints of each single layer in the double coating.

[0026] In some embodiments, the double coating can be obtained using any suitable preparation process. Processes such as sequential coating (or step-by-step coating), simultaneous double coating (e.g., co-extrusion double coating, sliding double coating, curtain double coating, etc.), and gradient double coating can all be applied to this application.

[0027] The target tortuosity and performance constraints for each individual coating can be predetermined. Tortuosity is a crucial parameter for electrode electrical performance, affecting ion diffusion efficiency, polarization, active material utilization, and cycle life. Coating thickness, areal density, and adhesion also influence battery / electrode performance. Increased coating thickness elongates the ion transport path, increasing internal resistance and reducing rate performance. Insufficient coating thickness leads to insufficient active material and decreased energy density. Coating thickness also affects thermal management and mechanical stability. Areal density directly determines battery capacity and energy density, while adhesion affects interfacial resistance and cycle stability. Poor adhesion can cause internal short circuits, potentially triggering thermal runaway. These are all key factors to consider in battery design. Therefore, the target tortuosity, a critical indicator of ion transport efficiency in the coating, can be predetermined, serving as the core for battery / electrode performance optimization. For example, the target tortuosity can be predetermined as 1.1, 1.2, 1.3, 1.4, etc. The coating thickness, coating areal density, and coating adhesion will serve as performance constraints; while ensuring the tortuosity of each individual coating meets the requirements, other performance characteristics must also be satisfied. For example, the coating thickness can be set to 25μm, 30μm, 35μm, 40μm, 45μm, 50μm, 55μm, 60μm, etc., or other values, and the coating areal density can be set to 20mg / cm³. 2 25mg / cm 2 30mg / cm 2 Alternatively, the coating adhesion can be set to 1.2 N / cm, 1.3 N / cm, 1.4 N / cm, etc.

[0028] Step 120: Determine the target formulation parameters for each single coating in one or more execution rounds.

[0029] In some embodiments, each execution round can be a process of adjusting the formulation parameters for the single coating. In each execution round, the formulation parameters, also referred to as the current formulation parameters for that round, may include at least one of solid content, particle size distribution width, binder content, and the mass fraction of each component constituting the single coating. If the execution round is the first execution round, the current formulation parameters may be the initially set formulation parameters. If the execution round is not the first execution round, the formulation parameters may be the formulation parameters obtained after one or more adjustments to the initial formulation parameters.

[0030] In some embodiments, an execution round may include one or more of the following steps.

[0031] Step 121: Based on the current formulation parameters of the single coating, and under the constraints of the target performance parameters, determine the current tortuosity and current performance parameters of the single coating.

[0032] In some embodiments, the current tortuosity can be determined using practical measurement methods. For example, the coating slurry for preparing each single coating layer can be configured using the current formulation parameters, and a double-layer coating sample can be prepared using any suitable double-layer coating process. This double-layer coating sample can be used to determine the current tortuosity of the single coating layer. Methods such as symmetric cell-electrochemical impedance spectroscopy (ESI layering method, which separates the interlayer interface impedance and bulk ion impedance of the double-layer electrode through symmetric cell construction and equivalent circuit analysis to achieve non-destructive measurement), physical exfoliation (separating the two layers of the double-layer electrode by mechanical or chemical methods, and performing EIS measurement on each separated single layer), X-ray computed tomography (μCT three-dimensional reconstruction method, which obtains the three-dimensional pore network structure of the double-layer electrode through high-resolution μCT scanning, reconstructs the ion transport path, and directly calculates the tortuosity of each layer), image analysis (obtaining cross-sectional and surface images of the double-layer coating through scanning electron microscopy (SEM), and calculating the tortuosity of each layer using open-source software), or other suitable methods can be used in this application and are not limited herein.

[0033] In conjunction with the foregoing description, the target performance parameters are used to ensure that the tortuosity of the single coating meets the standard while other properties also meet application requirements. For example, the target performance parameters will constrain the coating thickness, areal density, and / or adhesion of each coating layer when preparing a double-layer coating using a coating slurry, requiring them to meet relevant requirements. Therefore, the current performance parameters will be limited to be adapted to the target performance parameters. For example, the thickness, areal density, and / or adhesion of each single coating layer in the resulting double-layer coating will be the same as (e.g., consistent thickness and areal density) or better (e.g., better adhesion) as defined by the target performance parameters. The thickness of the single coating layer can be measured using a micrometer, the areal density can be measured using a weighing and weight reduction method, and the adhesion can be measured using a 180° peel test. Alternatively, other suitable methods can also be applied to this application.

[0034] Step 122: Determine whether the current tortuosity is within the target range defined by the target tortuosity, and whether the current performance parameters meet the performance constraints.

[0035] In some embodiments, the target range defined by the target tortuosity can refer to a numerical range that does not exceed the target tortuosity, with the target tortuosity as the endpoint. For example, if the target tortuosity is 1.3, then the target range can be (-∞, 1.3). Let the current tortuosity be τ, and the target tortuosity be τ. t To determine whether the current tortuosity is within the target range, it can be determined whether τ does not exceed 1.3, i.e., τ≤1.3. To determine whether the current performance parameters meet the performance constraints, it can be determined whether the current performance parameters are the same as or better than the parameters indicated by the performance constraints. For example, whether the thickness and areal density are consistent, or whether the adhesion is better, for example, exceeding the coating adhesion value given in the performance constraints, such as 1.2 N / cm.

[0036] The above two determinations are performed simultaneously. If the current tortuosity is not within the target range defined by the target tortuosity, and / or the current performance parameter does not meet the performance constraint, the process can proceed to step 123 to adjust the current recipe parameter in this execution round and proceed to the next execution round. If the current tortuosity is within the target range defined by the target tortuosity, and the current performance parameter meets the performance constraint, the process can proceed to step 124 to determine the current recipe parameter in this execution round as the target recipe parameter, and end process 100.

[0037] Step 123: When the current tortuosity is not within the target range defined by the target tortuosity, and / or the current performance parameter does not meet the performance constraint, at least the quantitative relationship model indicating the correspondence between tortuosity and formula parameters is used to update the current formula parameter and proceed to the next execution round.

[0038] In some embodiments, the quantitative relationship model can be constructed using porous media transport theory and based on mathematical operations between a porosity term, a binder content normalization term, and a particle size distribution correction term. To determine the porosity term, the packing efficiency coefficient of the active material constituting the single coating can be obtained, and then obtained by performing a first mathematical operation using the packing efficiency coefficient and the solid content. The packing efficiency coefficient can be an intrinsic parameter characterizing the degree of compaction of solid particles in a dry coating under a given standardized sample preparation process for a specific material system. It can be defined as the ratio of the actual volume occupied by the active material particles to the total volume of the coating, reflecting the degree of optimization of the arrangement of solid particles in a limited space and the space utilization rate. The compaction density ρ of the single coating is measured. e Based on the mass fraction w of the active material and the true density ρ of the active material obtained by querying, a The packing efficiency coefficient k can be obtained through the formula. That is, k = (ρ e ×w) / ρ a Let the porosity be n and the solid content be S. Multiply the packing efficiency coefficient by the solid content to obtain the relationship between the porosity and the solid content: n = 1 - S × k. Finally, the porosity term can be defined as (1 - n) = S × k.

[0039] To determine the normalized term for the adhesive content, a baseline adhesive content can be obtained, and it can be derived by performing a second mathematical operation between the current adhesive content and the baseline adhesive content. The baseline adhesive content refers to the typical amount required to achieve a basic bonding effect in a specific adhesive system. The value of the baseline adhesive content depends on the type of adhesive and its compatibility with the active material, and can be determined through routine experiments or by referring to industry experience values. Let the baseline adhesive content be B0, and the adhesive content be B. Then, by quotienting the adhesive content and the baseline adhesive content, the normalized term for the adhesive content can be obtained, denoted as B / B0.

[0040] To determine the grain size distribution correction term, the grain size distribution width can be used to perform a third mathematical operation to obtain the grain size distribution correction term. In this application, the grain size distribution width can be D. 75 / D 25 Using D 75 / D 25As for the aforementioned particle size distribution width, on the one hand, extremely coarse particles (D) can be proposed. 90 (above) and extremely fine particles (D) 10 The interference (as described below) focuses only on the main range of particle size distribution, accurately reflecting the uniformity of particle packing and pore formation, thus meeting the core requirement of adjusting the tortuosity of porous coatings. Taking the reciprocal of the particle size distribution width yields the particle size distribution correction term, denoted as (D). 75 / D 25 ) -1 .

[0041] After the porosity, binder content normalization, and particle size distribution correction terms are determined, the constant terms can be obtained. Simultaneously, the individual coefficients corresponding to each of these terms will also be determined. Based on the constant terms, and the summation of the products of the porosity, binder content normalization, and particle size distribution correction terms with their respective individual coefficients, the quantitative relationship model can be obtained. The constant terms and their respective individual coefficients can be determined based on multiple linear regression fitting. Since there can be multiple material systems forming the single coating, the intrinsic characteristics of the material, the interactions between components, and the porosity formation mechanism within the coating all differ in different material systems. Therefore, the constant terms and individual coefficients corresponding to different material systems are all different. For each material system, multiple orthogonal experiments can be conducted (e.g., changing different formulation parameters) to obtain the corresponding sample coefficients, followed by multiple linear regression fitting to obtain different constant terms and individual coefficients corresponding to different material systems. As an example only, the quantitative relationship model can be expressed as: τ = 1 + 0.78 × (1 - n) + 0.05 × (B / B0) - 0.12 × (D 75 / D 25 ) - ¹, (R) 2 =0.92). Where, R 2 Indicates the confidence level.

[0042] The above quantitative relationship model predictively indicates the dynamic correspondence between tortuosity and formulation parameters. In step 122, when the current tortuosity is not within the target range defined by the target tortuosity, if the current tortuosity is greater than the target tortuosity, the formulation parameters can be adjusted by reducing the solid content and optimizing the particle size distribution width. As expressed in the above quantitative relationship model, reducing the solid content S will reduce the value of the porosity term (1-n), thereby reducing the tortuosity τ. Optimizing the particle size distribution width also reduces D. 75 / D 25 The value will increase (D) 75 / D 25 ) -The value of ¹, combined with a coefficient of -0.12, can also reduce the tortuosity τ. Additionally, the binder content can be adjusted, for example, by reducing the binder content. However, considering that reducing the binder content will lead to a decrease in adhesion, reducing the binder content is considered an adjustment option when the current performance parameters of the single coating meet the performance constraints. When the current tortuosity is within the target range (that is, the current tortuosity does not exceed the target tortuosity) but the current performance parameters do not meet the performance constraints, for example, when the adhesion is less than the coating adhesion, the formulation parameters can be adjusted by increasing the binder content. The adjustment range for each of the above formulation parameters can be determined based on the individual coefficients.

[0043] Step 124: When the current tortuosity is within the target range defined by the target tortuosity, and the current performance parameter satisfies the performance constraint, the current formulation parameter is determined to be the target formulation parameter for the single coating.

[0044] It can be seen that when all the above conditions are met, it means that the tortuosity and performance of the single coating prepared based on the current formula parameters in this execution round meet the requirements, and the coating can be prepared directly based on the current formula parameters.

[0045] It should be noted that the above explanation uses a single coating layer as an example. For a double-layer coating, each single coating layer needs to undergo the same process. For instance, the execution rounds for the two single coating layers can be performed simultaneously. That is, in one execution round, the current tortuosity and performance parameters of both single coating layers are simultaneously determined. If both requirements are met, the process can terminate. If at least one single coating layer's current tortuosity and / or performance parameters do not meet the requirements, the relevant parameters are adjusted, and the process continues to the next execution round. Even if the current tortuosity and performance parameters of one single coating layer meet the requirements in a certain round, they still need to be determined in the next round. This continues until both the tortuosity and performance parameters of the double-layer coating meet the requirements.

[0046] It should be noted that the above-mentioned Figure 1 The descriptions of the various steps in this specification are for illustrative purposes only and do not limit the scope of this specification. Those skilled in the art can, under the guidance of this specification, [perform certain tasks / activities]. Figure 1 Various modifications and changes have been made to the steps described herein. However, these modifications and changes remain within the scope of this specification.

[0047] The above process is illustrated below with specific implementation details. It should be noted that the following content is for illustrative purposes only and is not intended to limit the scope of this application.

[0048] Example 1: Design of a double-layer coating formulation for NCM955 cathode (1) The targets and constraints are set as shown in Table 1: the target tortuosity τ of the surface layer (close to the membrane side, ion transport layer) is ≤1.3, the thickness is 30μm, and the areal density is 25mg / cm²; the target tortuosity τ of the bottom layer (close to the current collector side, electron conduction layer) is ≤1.45, the thickness is 60μm, and the areal density is 25mg / cm²; the coating adhesion of both the surface layer and the bottom layer is required to be ≥1.2N / cm. The active material is NCM955 with a packing efficiency coefficient k=0.88; the binder is PVDF with a baseline content of B0=1.0wt.%; the conductive agent is a composite system of single-walled carbon nanotubes (SWCNTs) and conductive carbon black (SP), included in the total mass of the solid powder; the slurry solvent is NMP. Table 1. Target tortuosity and constraints set in Example 1 (2) Basic parameters of the initial formula (as shown in Table 2) and calculation of theoretical tortuosity Table 2. Basic parameters of the initial formulation in Example 1 The quantitative relationship model described above is τ = 1 + 0.78 × (1 - n) + 0.05 × (B / B0) - 0.12 × (D) 75 / D 25 ) -1 The relationship between porosity n and solid content S is given by the formula n = 1 - S × k. The calculated surface porosity n1 = 0.516 (51.6%) and bottom layer porosity n2 = 0.4896 (48.96%). Further calculations yield the theoretical tortuosity: surface theoretical τ1 = 1.47; bottom layer theoretical τ2 = 1.41. (3) Prepare samples according to the initial formula and measure the tortuosity using the EIS method. The top and bottom slurries were prepared according to the initial formula. The positive electrode active material, binder, and conductive agent were added to the solvent NMP according to the corresponding mass fraction ratio in Table 2 and mixed. The dispersion speed was controlled at 2500 r / min and the dispersion time was 45 min to ensure that the components were mixed evenly. The roller-to-roller doctor blade coating process was used. The bottom slurry was first coated onto the aluminum foil current collector and pre-dried at 60℃ for 30 min. Then the top slurry was coated and dried at 80℃ for 2 h. After being rolled under 8MPa pressure, the electrode sheet with a diameter of 12 mm was obtained after cutting. Using the obtained electrode as the working electrode, a CR2023 symmetrical battery was assembled. That is, two identical test electrodes were placed in the positive and negative electrode shells respectively, separated by a PP separator, and then LiPF6 / EC:DMC:EMC (volume ratio 1:1:1) electrolyte was injected and then encapsulated. Torque was measured using symmetrical cell-electrochemical impedance spectroscopy (EIS) under the following conditions: test temperature 25℃, frequency range 10 Hz. -2 -10 5 Hz, amplitude 5mV, combined with formula Calculate the tortuosity of the electrode. Where d is the electrode thickness, n is the electrode porosity, A is the electrode area, and r... ion Here, k is the ionic resistance, and k is the ionic conductivity of the electrolyte. Adhesion was measured using a 180° peel test, and the results are shown in Table 3. Table 3. Results of tortuosity test of the initial formulation (4) First iteration adjustment For surface τ exceeding the standard, reducing the solid content from 55% to 53% and optimizing the particle size distribution to 2.0 can reduce the tortuosity. For cases of insufficient adhesion to the substrate, the adhesive content was slightly increased from 1.2% to 1.4%, while the active material content was reduced to 97.54%. The adjustment results are shown in Table 4. Table 4 Formula parameters after the first adjustment After adjustment, theoretical calculations yielded the following results: the theoretical tortuosity of the surface layer was τ1 = 1.45; the theoretical tortuosity of the bottom layer was τ2 = 1.42. The tortuosity and adhesion of the electrodes prepared with the first adjusted formula were tested using the same method as before. The actual measurement results after the first adjustment are shown in Table 5. Table 5. Measured results after the first adjustment (5) Second iteration adjustment For the surface layer, the measured value τ = 1.38 still exceeds the target value, requiring further adjustment of the tortuosity. Combining the model influence coefficient, a combination of reducing the solid content by 1 vol.% and optimizing the particle size distribution to 1.8 was selected, which reduced the tortuosity to within the target range; the bottom layer parameters have already met the target and remain unchanged. The adjustment results are shown in Table 6. Table 6 Formula parameters after the second adjustment (6) Verification results The electrodes prepared according to the final formula were assembled into a three-electrode battery. Using lithium-ion batteries as both the counter and reference electrodes, constant current charging tests were conducted at 25°C. During the test, the battery was first charged at a constant current of 0.33C to 10% SOC, then charged at a constant current of 5C to 80% SOC, with the negative reference electrode potential monitored throughout the process. If the negative electrode potential remained at 0V (vs Li / Li) throughout the charging process... +The above results indicate that no lithium plating risk occurred, meaning the 5C fast charging capability requirement is met. The actual test results are shown in Table 7.

[0049] Table 7 Measured results after the second adjustment Example 2: Design of a double-layer coating for a graphite / silicon-carbon composite anode (1) Set the target tortuosity and constraints, as shown in Table 8; Table 8. Torque Target and Constraints for Example 2 The active material is a composite system of graphite and silicon carbon, with an equivalent packing efficiency coefficient of k=0.82; the binder is a composite system of polyacrylic acid (PAA) and sodium carboxymethyl cellulose (CMC), with a binder content of B0=4.0wt.% based on total mass; the conductive agent is a composite system of single-walled carbon nanotubes (SWCNT) and conductive carbon black (SP), and the solvent is deionized water. (2) Basic parameters of the initial formula (as shown in Table 9) and calculation of theoretical tortuosity Table 9. Initial formulation basic parameters for Example 2 The quantitative relationship model described above is τ = 1 + 0.78 × (1 - n) + 0.05 × (B / B0) - 0.12 × (D) 75 / D 25 ) -1 The relationship between porosity n and solid content S is given by the formula n = 1 - S × k. The calculated surface porosity n1 = 0.5736 (57.36%) and bottom layer porosity n2 = 0.5408 (54.08%). The tortuosity was calculated as follows: theoretical surface tortuosity τ1 = 1.33; theoretical bottom layer tortuosity τ2 = 1.362. (3) Prepare samples according to the initial formula and measure the tortuosity using the EIS method. The surface and bottom layer slurries were prepared according to the initial formula. First, graphite and silicon carbon were mixed, and deionized water was added and dispersed for 15 min. Then, CMC and SWCNT were added and dispersed for 30 min. Finally, PAA and SP were added. The dispersion speed was controlled at 2000 r / min and the dispersion time was 60 min to ensure that each component was evenly dispersed. The bottom layer slurry was coated onto the copper foil current collector using a roller-to-roller coating process. It was pre-dried at 50℃ for 40 min. Then, the surface layer slurry was coated and dried at 70℃ for 3 h. After being rolled under 4 MPa pressure, the electrode sheet with a diameter of 12 mm was obtained after cutting. Using the obtained electrode as the working electrode, a CR2023 symmetrical battery was assembled. That is, two identical test electrodes were placed in the positive and negative electrode shells respectively, separated by a PP separator, and then LiPF6 / EC:DMC:EMC (volume ratio 1:1:1) electrolyte was injected and then encapsulated. Torque was measured using symmetrical cell-electrochemical impedance spectroscopy (EIS) under the following conditions: test temperature 25℃, frequency range 10 Hz. -2 -10 5 Hz, amplitude 5mV, combined with formula Calculate the tortuosity of the electrode. Where d is the thickness of the electrode. R is the measured porosity of the electrode, A is the area of ​​the electrode, and r is the measured porosity of the electrode. ion Here, k is the ionic resistance, and k is the ionic conductivity of the electrolyte. Adhesion was measured using a 180° peel test; Using the obtained target electrode as the working electrode, a CR2032 type Li half-cell was assembled. Cyclic stability testing was conducted under the following conditions: constant temperature at 25℃, constant current charge-discharge at a 1C rate within a voltage range of 0.01-1.5V, for 100 cycles. The measured results are shown in Table 10. Table 10 Initial test results of Example 2 (4) First iteration adjustment For the bottom layer τ exceeding the standard by 0.02, the deviation is small. Reducing the solid content by 1 vol.% will bring the tortuosity down to the target range. All surface parameters have met the standards and will remain unchanged. The adjustment results are shown in Table 11. Table 11 Formula parameters after the first adjustment Adjusted theoretical calculation results: Surface theory τ1=1.33; Subsurface theory τ2=1.355; The electrodes prepared with the first adjusted formulation were tested for tortuosity, adhesion, and capacity retention, using the same methods as before. Table 12 shows that the standards were met after just one adjustment.

[0050] Table 12 Measured results after the first adjustment Comparative Example 1: For the positive electrode, the same initial formulation, target performance and boundary constraints as in Example 1 were used, but the quantitative model and parameter adjustment rules of this invention were not used. The formulation was optimized by traditional empirical trial and error method without clear adjustment logic. Any 1-2 parameters among solid content (±2 vol.%), particle size distribution (D75 / D25±0.2) and binder content (±0.3 wt.%) were randomly adjusted. After each adjustment, a sample was prepared and the performance test was completed. This process was repeated until the indicators met the standards. The trial-and-error process is as follows: Round 1 trial and error: Randomly reduce the surface solid content by 2 vol.% (S=53 vol.%), while keeping other parameters unchanged; Actual measured surface τ=1.48 (still exceeding the standard), bottom layer τ=1.43 (meets the standard), adhesion surface layer 1.5 N / cm (meets the standard), bottom layer 1.1 N / cm (does not meet the standard); Round 1 trial and error only focused on surface τ, without considering the bottom layer adhesion problem, and the adjustment direction was one-dimensional; Second round of trial and error: Based on the first round, the content of the bottom adhesive was randomly increased by 0.3 wt.% (B=1.5 wt.%); the measured surface τ=1.47 (exceeding the standard), the bottom adhesive τ=1.46 (slightly exceeding the standard), and the adhesion of the bottom adhesive was 1.3 N / cm (meeting the standard); the second round of trial and error made up for the bottom adhesive adhesion problem that was not addressed in the previous round, but blindly increasing the adhesive content caused the bottom adhesive τ to exceed the standard; Rounds 3-8 of trial and error: Parameters such as "surface particle size distribution reduced to 2.0", "bottom solid content reduced by 2 vol.%", "surface adhesive reduced by 0.3 wt.%", and "bottom particle size distribution reduced to 2.3" were randomly adjusted in turn. During this period, repeated problems such as "surface τ meets the standard but adhesion decreases" occurred, and no pattern for synergistic optimization of parameters was found. Round 9 of trial and error: Based on the experience of the previous 8 rounds, the surface layer S=52 vol.% and D75 / D25=1.8 were fine-tuned, and the bottom layer B=1.4 wt.% were adjusted; the measured surface layer τ=1.29 (meets the standard), the bottom layer τ=1.43 (meets the standard), and the adhesion was ≥1.2 N / cm (meets the standard), thus completing the trial and error. Comparative Example 1 took 9 rounds of trial and error to find a feasible formula by chance, and the process lacked theoretical guidance, resulting in multiple misjudgments of adjustment direction and repeated performance fluctuations. The development cycle was long, the efficiency was low, and batch consistency could not be guaranteed.

[0051] Comparative Example 2: For the negative electrode, the initial formulation, target performance and boundary constraints are exactly the same as those in the corresponding embodiment. The quantitative model and parameter adjustment rules of this invention are not used. Instead, optimization is carried out through traditional empirical trial and error methods. There is no clear adjustment logic. Any 1-2 parameters among solid content (±2 vol.%), particle size distribution (D75 / D25±0.2) and binder content (±0.3 wt.%) are randomly adjusted. After each adjustment, a sample is prepared and a full performance test is completed. This process is repeated until all indicators meet the standards. The trial-and-error process is as follows: Round 1 trial and error: Randomly reduce the solid content of the bottom layer by 2 vol.% (S=54 vol.%), while keeping other parameters unchanged; the measured τ of the bottom layer was 1.45 (meets the standard), but the τ of the surface layer was 1.36 (slightly exceeds the standard), and the adhesion was up to standard; the first round of trial and error only adjusted the τ of the bottom layer and did not anticipate the indirect impact of the reduction of the solid content of the bottom layer on the surface layer performance; Second round of trial and error: To solve the problem of excessive surface τ, the surface particle size distribution was randomly increased to 2.5; the measured surface τ=1.38 (still exceeding the standard), and the bottom layer τ=1.46 (meeting the standard); the second round of trial and error mistakenly believed that relaxing the particle size distribution could reduce tortuosity, so the surface particle size distribution was increased from 2.3 to 2.5, and the measured surface τ increased from 1.36 to 1.38, but the tortuosity increased instead; Rounds 3-6 of trial and error: The parameters such as "reducing the surface adhesive by 0.3 wt.%", "reducing the particle size distribution of the bottom layer to 2.4", "reducing the surface solid content by 1 vol.%" and "increasing the bottom adhesive by 0.3 wt.%" were adjusted one after another. Contradictory problems such as "the surface τ meets the standard but the cycle stability decreases" and "the bottom adhesion meets the standard but the τ exceeds the standard" occurred, and it was impossible to accurately balance multiple performance indicators. Round 7 trial and error: Based on the experience of the previous 6 failed rounds, the bottom layer S=55vol.% was adjusted, and the surface layer parameters remained unchanged; the measured surface layer τ=1.34 (meets the standard) and the bottom layer τ=1.49 (meets the standard) met the requirements of all indicators, and the trial and error was completed.

[0052] The comparison results between the above embodiments and comparative examples are shown in Table 13.

[0053] Table 13 Project Comparison The method for determining the formulation of a double-layer coating provided in this application adjusts parameters through a quantitative relationship model that indicates the correspondence between tortuosity and formulation parameters. This avoids the blindness of trial and error based on experience, reduces the error in coating tortuosity control, and significantly improves performance consistency. Furthermore, it eliminates the need for numerous orthogonal experiments, allowing the initial formulation to be calculated directly through the model, reducing the number of iterations to 1-3 and shortening the development cycle by more than 60%. In addition, while optimizing tortuosity, it ensures that other properties such as coating mechanical strength and thickness meet standards, achieving synergistic optimization of multiple properties.

[0054] This application also discloses a processing apparatus for implementing the aforementioned formula determination method. This processing apparatus can be used to perform, for example... Figure 1 For details on each step shown, please refer to the corresponding accompanying drawings. Figure 2 These are exemplary block diagrams of a processing apparatus shown according to some embodiments of this application, such as... Figure 2 As shown, the processing system 200 may include an acquisition module 210 and a determination module 220.

[0055] The acquisition module 210 can be configured to acquire the target tortuosity and target performance parameters of each single layer in the double coating.

[0056] The determination module 220 can be configured to determine target formulation parameters for each single coating layer in one or more execution rounds; wherein an execution round may include: determining the current tortuosity and current performance parameters of the single coating layer based on the current formulation parameters of the single coating layer and under the constraints of the target performance parameters; determining whether the current tortuosity is within the target range defined by the target tortuosity, and whether the current performance parameters meet the performance constraints; if not, updating the current formulation parameters at least using a quantitative relationship model indicating the correspondence between tortuosity and formulation parameters, and executing the next round; if yes, determining the current formulation parameters as the target formulation parameters for the single coating layer.

[0057] Further descriptions of the aforementioned components can be found in this application. Figure 1 part.

[0058] It should be understood that Figure 2 The systems and modules shown can be implemented in various ways. For example, in some embodiments, the systems and modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the methods and systems described above can be implemented using computer-executable instructions and / or included in processor control code, for example, on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this application can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the aforementioned hardware circuits and software (e.g., firmware).

[0059] It should be noted that the above description of the modules is for ease of description only and should not be construed as limiting this application to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principle of the system, may arbitrarily combine the modules or construct subsystems connected to other modules without departing from this principle. For example, the modules may share a single storage module, or each module may have its own separate storage module. Such modifications are all within the scope of protection of this application.

[0060] This application also provides a computing device. (See reference...) Figure 3 The diagram shown is an exemplary block diagram of a computing device according to some embodiments of this application. The computing device 300 may include components for implementing the processes described in the embodiments of this application (e.g., Figure 1 The content shown) or system (e.g., Figure 2 Any component of the contents shown. For example, computing device 300 can be implemented using hardware, software programs, firmware, or a combination thereof. For convenience, Figure 3 Only one computing device is shown in the figure, but the computing functions related to the process and / or system / device described in the embodiments of this application can be implemented in a distributed manner by a set of similar platforms to distribute the processing load of the system.

[0061] In some embodiments, computing device 300 may include processor 310, memory 320, input / output 330, and communication port 340. In some embodiments, the processor (e.g., CPU) 310 may execute program instructions as one or more processors. In some embodiments, the memory 320 may include different forms of program memory and data memory, such as hard disk, read-only memory (ROM), random access memory (RAM), etc., for storing various data files processed and / or transmitted by the computer. In some embodiments, the input / output 330 may be used to support input / output between computing device 300 and other components. In some embodiments, the communication port 340 may be connected to a network for data communication. Exemplary computing devices may include program instructions executed by processor 310 stored in read-only memory (ROM), random access memory (RAM), and / or other types of non-transitory storage media. The methods and / or processes of the embodiments of this application may be implemented in the form of program instructions. Computing device 300 may also receive programs and data disclosed in this application via network communication.

[0062] For ease of understanding, Figure 3 Only one processor is illustrated in the illustration. However, it should be noted that the computing device 300 in this embodiment may include multiple processors. Therefore, the operations and / or methods implemented by one processor as described in this embodiment may also be implemented jointly or independently by multiple processors. For example, if, in this application, the processor of the computing device 300 executes operations A and B, it should be understood that operations A and B may also be executed jointly or independently by two different processors of the computing device 300 (e.g., the first processor executes operation A, the second processor executes operation B, or the first and second processors jointly execute operations A and B).

[0063] This application has described the basic concepts. Obviously, for those skilled in the art, the above detailed disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0064] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this application do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0065] Similarly, it should be noted that, in order to simplify the description of this application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of this application sometimes combines multiple features into one embodiment or its description. However, this disclosure method does not imply that the subject matter of this application requires more features than those mentioned in the claims. In fact, the embodiments have fewer features than all the features of the single embodiments disclosed above.

[0066] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other modifications may also fall within the scope of this application. Therefore, alternative configurations of the embodiments of this application are considered as examples and not limitations, and are regarded as consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly described and illustrated in this application.

Claims

1. A method for determining the formulation of a double-layer coating, characterized in that, The method includes: Obtain the target tortuosity and performance constraints of each single layer in the double coating process; Determine the target formulation parameters for each single coating layer in one or more execution rounds; wherein, one execution round includes: Based on the current formulation parameters of the single coating, and under the constraints of the target performance parameters, the current tortuosity and current performance parameters of the single coating are determined. Determine whether the current tortuosity is within the target range defined by the target tortuosity, and whether the current performance parameters meet the performance constraints. If not, at least use a quantitative relationship model that indicates the correspondence between tortuosity and formula parameters to update the current formula parameters and proceed to the next execution round; If so, determine that the current formulation parameters are the target formulation parameters for the single coating.

2. The formula determination method according to claim 1, characterized in that, The performance constraints include at least one of coating thickness, coating areal density, and coating adhesion; the current formulation parameters include at least one of solid content, particle size distribution width, binder content, and mass fraction of each component constituting the single coating.

3. The formula determination method according to claim 2, characterized in that, The quantitative relationship model is constructed based on mathematical operations between the porosity term, the binder content normalization term, and the particle size distribution correction term.

4. The formula determination method according to claim 3, characterized in that, Determining the porosity term includes: Obtain the stacking efficiency coefficient of the active material constituting the single coating; The porosity term is determined by performing a first mathematical operation using the packing efficiency coefficient and the solid content.

5. The formula determination method according to claim 3, characterized in that, Determining the normalization term for the adhesive content includes: Obtain the baseline adhesive content, and perform a second mathematical operation using the adhesive content and the baseline adhesive content to determine the adhesive content normalization term.

6. The formula determination method according to claim 3, characterized in that, Determine the particle size distribution correction term, including: The particle size distribution width is used to perform a third mathematical operation to determine the particle size distribution correction term.

7. The formula determination method according to claim 3, characterized in that, Constructing the quantitative relationship model includes: Obtain the constant term, and sum the products of the constant term, the porosity term, the binder content normalization term, and the particle size distribution correction term with their respective individual coefficients to obtain the quantitative relationship model.

8. A device for determining the formulation of a double-layer coating, characterized in that, The device includes: The acquisition module is configured to acquire the target tortuosity and target performance parameters of each single layer in the double coating; The determination module is configured to determine the target formulation parameters for each single coating layer in one or more execution rounds; wherein, one execution round includes: Based on the current formulation parameters of the single coating, and under the constraints of the target performance parameters, the current tortuosity and current performance parameters of the single coating are determined. Determine whether the current tortuosity is within the target range defined by the target tortuosity, and whether the current performance parameters meet the performance constraints. If not, at least use a quantitative relationship model that indicates the correspondence between tortuosity and formula parameters to update the current formula parameters and proceed to the next execution round; If so, determine that the current formulation parameters are the target formulation parameters for the single coating.

9. A computing system, characterized in that, The computing system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it can implement the steps of the method for determining the formulation of a double-layer coating as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for determining the formulation of a double-layer coating as described in any one of claims 1-7.

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