Dry-method electrode active material pretreatment method based on multistage shearing fibrosis
By employing a dry electrode active material pretreatment method involving multi-stage shearing and fiberization, and utilizing real-time data acquisition and image recognition algorithms to dynamically adjust processing parameters, the problem of uneven morphology and instability in electrode active material processing was solved. This method achieves efficient and uniform material distribution and stability, thereby improving battery performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-04-10
Smart Images
Figure CN121837571A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of dry electrode technology, and more particularly to a method for pretreatment of active materials in dry electrodes based on multi-stage shearing and fiberization. Background Technology
[0002] In the field of new energy materials, the preparation technology of electrode active materials plays a crucial role in improving battery performance. Research in this area is directly related to the efficiency and stability of energy storage devices and is one of the core links in promoting the development of green energy. The morphology and uniformity of distribution of electrode active materials often determine the performance of materials in practical applications; therefore, how to precisely control their processing has become a focus of attention in the industry.
[0003] However, current methods for processing electrode active materials generally suffer from some insurmountable drawbacks. Many methods lack the ability to adapt to dynamic changes during processing when faced with material agglomeration or uneven morphology. They often only allow adjustments for a fixed state, failing to address the complex characteristics exhibited by the material at different stages. This limitation makes it difficult to achieve ideal uniformity and stability in the processing results, affecting the final performance of the material.
[0004] A deeper technical challenge lies in matching the dynamic adjustment of processing parameters with morphological evolution. The setting of processing parameters, such as strength and sequence, directly affects the changes in material morphology, which is a continuous and variable process. If parameter adjustments cannot keep pace with the trend of morphological changes, the material may experience increased agglomeration or morphological loss of control at certain stages. For example, in the early stages of processing, the material may become excessively fragmented due to excessive strength, resulting in overly small and irregular particles; while in the later stages, insufficient strength may fail to effectively disperse the agglomerated particles, leading to uneven distribution. This lack of real-time coordination between parameters and morphological changes makes the processing unstable, thus affecting the consistency of the material.
[0005] Therefore, how to dynamically adjust parameters during processing to adapt to real-time changes in material morphology and ensure the uniform distribution and stability of the final morphology has become a key issue in the preparation of electrode active materials. Summary of the Invention
[0006] To address the technical problems mentioned in the background section, this invention provides a dry electrode active material pretreatment method based on multi-stage shear fiberization, comprising: S1, collecting real-time data of the electrode active material during multi-stage processing, analyzing the real-time data to obtain initial characteristics of material dynamic changes, and if the initial characteristics show that agglomeration is dominant, adjusting the processing parameters to a low intensity level to determine the starting point of the deformation stage; S2, acquiring electrode active material data corresponding to the starting point, and obtaining intermediate transition characteristics of multi-stage morphological evolution by classifying the electrode active material data; S3, if the intermediate transition characteristics exceed a preset characteristic threshold, optimizing the processing conditions through a feedback mechanism, judging potential deviations in uniform distribution, and obtaining a corrected parameter set; S4, extracting highly correlated indicators from the parameter set, processing the indicators using time series analysis, and determining the optimized path for final morphological shaping; S5, obtaining the matching degree between real-time monitoring data and the optimized path, and if the matching degree is lower than the matching degree threshold, iteratively adjusting the intensity parameters to obtain an enhanced morphological evolution control sequence; S6, integrating sensor feedback loops according to the control sequence to determine the applicability of the control sequence in multi-stage processing.
[0007] Furthermore, step S1 includes: denoising and edge enhancement of the acquired real-time data.
[0008] Furthermore, step S1 also includes: step S11, extracting the distribution of agglomerated particles and the degree of fibrosis from the real-time data as initial features; step S12, if the initial features show that the agglomerated state is dominant, then adjusting the processing parameters to a low strength level to determine the starting point of the deformation stage.
[0009] Further step S11 includes:
[0010] Step S111: Acquire real-time images of the materials during the processing using a high-precision camera;
[0011] Step S112: Perform grayscale processing and binarization on the real-time image of the material to separate the particle region and the background region.
[0012] Step S113: Based on the boundary features of the particle region, calculate the area and perimeter of each particle, and then determine the distribution density and average size of the aggregated particles.
[0013] Furthermore, step S2 includes: step S21, constructing a classification model input feature vector for the electrode active material data corresponding to the starting point; step S22, outputting intermediate transition features through the classification model, wherein the intermediate transition features include morphological uniformity and fiber length distribution parameters.
[0014] Furthermore, step S3 includes: step S31, calculating the potential deviation vector based on the intermediate transition features; step S32, adjusting the intensity coefficient and processing sequence through a feedback mechanism to correct the compensation value of the associated potential deviation vector and obtain the corrected parameter set; step S33, verifying the convergence state of the uniformly distributed potential deviation based on the corrected parameter set.
[0015] Furthermore, step S32 includes:
[0016] Step S321: Calculate the directional component of the potential deviation vector based on the intensity coefficient;
[0017] Step S322: The compensation value of the processing sequence correction fusion potential deviation vector is used to generate a corrected parameter set;
[0018] Step S323: Verify the cyclic stability of the modified parameter set in the feedback mechanism.
[0019] Furthermore, step S4 includes: step S41, selecting the autocorrelation coefficient and volatility variance from the parameter set as indicators of high stability correlation; step S42, using a predictive model to analyze the trend of the indicators and outputting an optimized path for final shape determination.
[0020] Further, step S5 includes: step S51, calculating the matching degree based on real-time monitoring data obtained from the sensor; step S52, if the matching degree is lower than the matching degree threshold, gradually increasing the intensity parameter iteratively until the matching degree converges to obtain an enhanced morphological evolution control sequence; step S53, verifying the consistency of multi-level processing execution for the enhanced morphological evolution control sequence; and step S54, forming an incremental step size sequence of the control sequence by iteratively adjusting the intensity parameter.
[0021] Furthermore, step S6 includes: step S61, where the sensor feedback loop collects the execution data of the control sequence in real time; step S62, where the applicability is determined based on the statistical results of the deviation between the execution data and the control sequence.
[0022] The technical solution provided by this invention has the following beneficial effects:
[0023] This invention discloses a dry electrode active material pretreatment method based on multi-stage shearing and fiberization. Addressing common problems in electrode active material processing, such as agglomeration and morphological inhomogeneity, it integrates a logical framework for achieving precise morphological control and uniform distribution through dynamic data analysis and parameter optimization. To address this issue, this invention uses real-time sensor data acquisition and image recognition algorithms to extract agglomeration and fiberization features. Combined with support vector machine classification and a feedback mechanism, processing parameters are dynamically adjusted to progressively optimize the strength coefficient and processing sequence, ensuring the stability of morphological evolution. Furthermore, time series analysis is used to determine the optimized path for final morphological shaping, and real-time monitoring data is used to verify the path matching degree. The control sequence is iteratively adjusted to enhance applicability. Ultimately, this invention achieves efficient control and uniform distribution of electrode active material morphology, significantly improving the material's performance in subsequent applications and providing reliable technical support for the preparation of high-performance electrode materials. Attached Figure Description
[0024] Figure 1 This is a flowchart of the dry electrode active material pretreatment method based on multi-stage shearing and fiberization according to the present invention. Detailed Implementation
[0025] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.
[0026] This invention provides a pretreatment method for dry electrode active materials based on multi-stage shearing and fiberization. The aim is to achieve precise control and uniform distribution of the morphology of electrode active materials through dynamic data analysis and parameter optimization during multi-stage processing. The implementation process of this invention is described in detail below with reference to specific embodiments to make the objectives, technical solutions, and advantages of this invention clearer. In one embodiment, the method provided by this invention mainly addresses problems such as agglomeration and uneven morphology that may occur during the processing of dry electrode active materials. Through multi-stage feature extraction, parameter adjustment, and path optimization, the stability and consistency of the final pretreatment output are ensured. This method is applicable to various electrode active material processing scenarios, such as lithium-ion battery cathode materials and supercapacitor activated carbon materials, and can effectively improve the performance of materials in subsequent applications.
[0027] like Figure 1As shown, the method includes: S1, collecting real-time data of the electrode active material during multi-stage processing, analyzing the real-time data to obtain the initial characteristics of the material's dynamic changes, and if the initial characteristics show that the agglomeration state is dominant, adjusting the processing parameters to a low intensity level to determine the starting point of the deformation stage.
[0028] Specifically, in multi-stage processing, electrode active materials undergo different shearing and fibrosis stages, and agglomeration is often a key factor affecting subsequent morphological evolution. Real-time data acquisition via sensors allows for dynamic monitoring of material state changes, providing a basis for subsequent parameter adjustments. Sensors can include high-precision cameras, laser particle size analyzers, and other devices to capture particle distribution and morphological information. In one possible implementation, step S1 involves preprocessing the acquired real-time data, such as denoising and edge enhancement of image data, to improve the accuracy of subsequent analysis. Next, image recognition algorithms are used to extract particle distribution and agglomeration characteristics, reflecting the dynamic changes of the material at the current processing stage. For example, in lithium-ion battery cathode material processing, if image analysis shows that particle agglomeration exceeds 60%, it indicates that agglomeration is dominant. In this case, the shear rate of the processing equipment needs to be reduced to avoid excessive shearing leading to particle breakage or morphological loss of control. The adjusted low strength level typically refers to reducing the shear rate to below 50% of the initial value to ensure a smooth transition of the material into the deformation stage.
[0029] Optionally, this step may further include: step S11, extracting the distribution of aggregated particles and the degree of fibrosis from the real-time data as the initial features.
[0030] Specifically, the distribution of agglomerated particles can be determined using image analysis techniques to assess the degree of aggregation between particles, typically using the average diameter and distribution density of agglomerated particles as quantitative indicators. The degree of fibrosis is assessed by analyzing changes in particle shape, such as whether the aspect ratio of the particles has increased to a certain extent. In actual processing, the image data acquired by the sensors undergoes segmentation and feature extraction processing to generate quantitative data reflecting the agglomeration and fibrosis states. For example, in processing lithium-ion battery cathode materials, if the agglomerated particle distribution density exceeds 10 particles per square millimeter and the aspect ratio is less than 1.5, it indicates that the material is in a predominantly agglomerated state with insufficient fibrosis. In one embodiment, the extraction process of agglomerated particle distribution can be further refined into several sub-steps:
[0031] Step S111: Real-time images of the materials during the processing are acquired using a high-precision camera.
[0032] Step S112: Perform grayscale processing and binarization on the real-time image of the material to separate the particle region and the background region.
[0033] Step S113: Based on the boundary features of the particle region, calculate the area and perimeter of each particle, and then determine the distribution density and average size of the aggregated particles.
[0034] In another embodiment, the assessment of the degree of fibrosis relies on the geometric parameters of the particle shape, such as calculating the ratio of the major and minor axes of the particles to determine whether they exhibit fibrous characteristics. The extraction of these features provides data support for the subsequent adjustment of processing parameters. In step S12, if the initial features indicate a predominantly agglomerated state, the processing parameters are adjusted to a low strength level to determine the starting point of the deformation stage.
[0035] Specifically, when the agglomerated particle density and fibrosis degree indicate that the material is in a predominantly agglomerated state, the operating parameters of the processing equipment need to be adjusted promptly to prevent further agglomeration. For example, in the processing of lithium-ion battery cathode materials, if the agglomerated particle density exceeds a preset density threshold, the processing intensity can be adjusted to a low level by reducing the rotation speed of the shearing equipment or shortening the processing time. This adjustment can effectively slow down the mutual aggregation between particles, laying the foundation for the subsequent deformation stage. In one possible implementation, the process of adjusting processing parameters can be automatically completed by a preset control system. For example, the control system has a built-in mapping table between the agglomerated state and processing parameters. When agglomeration is detected as the dominant state, the system will automatically select the corresponding low-intensity parameter combination, such as reducing the shearing rate from 5000 revolutions per minute to 2000 revolutions per minute, while shortening the single processing cycle time to 70% of the original. This automatic adjustment method can quickly respond to changes in the material state, ensuring accurate determination of the starting point of the deformation stage. It should be noted that the starting point of the deformation stage usually refers to the critical point where the material transitions from an agglomerated state to a fibrous state, and the determination of this critical point is crucial for the subsequent processing effect. In one embodiment, to further verify whether the adjusted parameters are suitable for the current material state, a small-scale sampling analysis of the material can be performed after adjustment. For example, in the processing of lithium-ion battery cathode materials, after adjusting the processing parameters to a low strength level, image data of the material can be collected again using sensors to analyze whether the distribution density of agglomerated particles has decreased and whether the degree of fibrosis has begun to increase. If the sampling results show that the agglomeration state has been alleviated, the current parameter adjustment is confirmed to be effective, and this state is taken as the starting point of the deformation stage. Such a verification process can improve the reliability of parameter adjustment and avoid blind adjustment leading to poor processing results. S2, Obtain the electrode active material data corresponding to the starting point, and obtain the intermediate transition characteristics of multi-level morphological evolution by classifying the electrode active material data.
[0036] Specifically, after determining the starting point of the deformation stage, it is necessary to conduct an in-depth analysis of the material state corresponding to the starting point to understand the morphological evolution trend of the material during multi-stage processing. Support Vector Machines (SVMs), as a classification tool, can effectively distinguish the morphological characteristics of materials at different processing stages, providing a basis for optimizing subsequent processing conditions. Intermediate transition features typically include quantitative indicators of morphological uniformity and fibrosis degree, reflecting the intermediate state of the material transitioning from an agglomerated state to a fibrous state. In one possible implementation, step S2 involves feature extraction and classification of the electrode active material data corresponding to the starting point. Electrode active material data can include particle size distribution, shape parameters, and dynamic changes during processing. By classifying this data using SVMs, the morphological state of the material can be divided into multiple categories, such as agglomeration-dominated, transitional, and fibrosis-dominated categories. The classification results can intuitively reflect the morphological evolution of the material at the current stage, providing data support for adjusting subsequent processing conditions.
[0037] Optionally, this step may also include: step S21, constructing a classification model input feature vector for the electrode active material data corresponding to the starting point.
[0038] Specifically, the input feature vector typically includes multi-dimensional material characteristics, such as the average particle diameter, aspect ratio, distribution density, and parameters like shear rate and time during processing. These feature vectors comprehensively reflect the material's state at the starting point, providing sufficient information for support vector machine classification. For example, in lithium-ion battery cathode material processing, the input feature vector might include data such as an average particle diameter of 5 micrometers, an aspect ratio of 1.2, and a distribution density of 8 particles per square millimeter. In one embodiment, the process of constructing the input feature vector can be further refined into three stages: data acquisition, feature selection, and vector combination. First, material data corresponding to the starting point, including particle size and shape information, is acquired using sensors. Second, features highly correlated with morphological evolution, such as particle aspect ratio and distribution density, are selected from the acquired data as primary feature parameters. Finally, the selected feature parameters are combined into a multi-dimensional vector, which serves as the input to the support vector machine classification model. This multi-dimensional vector construction method improves the classification model's ability to distinguish material states. In one possible implementation, to improve the representativeness of the feature vector, the acquired data can be standardized. For example, in the processing of lithium-ion battery cathode materials, if the particle size distribution range is wide, the size data can be normalized to map it to the range of 0 to 1, avoiding the impact of different data units on the classification results. The standardized feature vector can more accurately reflect the true state of the material, thereby improving the accuracy of support vector machine classification. Step S22: The classification model outputs intermediate transition features, which include morphological uniformity and fiber length distribution parameters.
[0039] Specifically, after receiving the input feature vector, the Support Vector Machine (SVM) classification model classifies the material state into different categories according to pre-trained classification rules and outputs corresponding intermediate transition features. Morphological uniformity is typically quantified by the standard deviation of particle size distribution; a smaller standard deviation indicates higher uniformity. Fiber length distribution parameters are determined by analyzing the aspect ratio distribution of particles, reflecting the degree of material fibrosis. In one embodiment, the output process of intermediate transition features can be implemented through the decision boundary of the SVM model. For example, in the processing of lithium-ion battery cathode materials, if the classification model output shows that the standard deviation of the material's morphological uniformity is 0.8 micrometers and the average aspect ratio of the fiber length distribution parameters is 1.8, it indicates that the material is in an intermediate stage transitioning from an agglomerated state to a fibrous state. These feature parameters can provide important references for optimizing subsequent processing conditions, helping to determine whether the current processing parameters need adjustment. In one possible implementation, to further verify the accuracy of the intermediate transition features, manual sampling can be used to verify the classification results. For example, during processing, some material samples can be randomly selected, and their particle morphology and distribution can be observed under a microscope and compared with the feature parameters output by the support vector machine. If the sampling results are consistent with the model output, the intermediate transition features are confirmed to be reliable; if there is a large deviation, the classification model needs to be further optimized, such as by increasing training samples or adjusting model parameters. This verification method can improve the reliability of feature extraction. S3, if the intermediate transition features exceed the preset feature threshold, the processing conditions are optimized through a feedback mechanism to determine the potential deviation of uniform distribution and obtain the corrected parameter set.
[0040] Specifically, intermediate transition features reflect the morphological state of the material at the current processing stage. If the feature parameters exceed preset feature thresholds, such as a morphological uniformity standard deviation greater than 1.0 micrometer or a fiber length distribution parameter aspect ratio average less than 1.5, it indicates a deviation in the material's morphological evolution, requiring optimization of processing conditions through a feedback mechanism. The feedback mechanism typically includes dynamic adjustments to processing intensity, time, and equipment parameters to ensure uniform material distribution. In one embodiment, step S3 can be divided into two stages: deviation judgment and parameter correction. First, based on the comparison between the intermediate transition features and the preset feature thresholds, the degree of deviation in the material's morphological evolution is determined, such as insufficient uniformity or substandard fiberization. Second, the processing parameters that need adjustment are calculated through the feedback mechanism, such as increasing the shear rate or extending the processing time, and the adjusted parameters are applied to subsequent processing. For example, in the processing of lithium-ion battery cathode materials, if the intermediate transition features show a morphological uniformity standard deviation of 1.2 micrometers, exceeding the preset feature threshold of 1.0 micrometer, the shear rate can be increased by 10% through the feedback mechanism to promote uniform particle distribution.
[0041] Optionally, this step also includes: step S31, calculating the potential deviation vector based on the intermediate transition features.
[0042] Specifically, the potential deviation vector is used to quantify the difference between the material's morphological evolution and its ideal state, typically including two dimensions: uniformity deviation and fibrous degree deviation. Uniformity deviation can be calculated by the difference between the standard deviation of morphological uniformity and a preset uniformity threshold, while fibrous degree deviation is determined by the difference between the fiber length distribution parameter and the target value. For example, in the processing of lithium-ion battery cathode materials, if the standard deviation of morphological uniformity is 1.2 micrometers and the preset uniformity threshold is 1.0 micrometer, then the uniformity deviation is 0.2 micrometers; if the average aspect ratio of the fiber length distribution parameter is 1.6 and the target value is 1.8, then the fibrous degree deviation is 0.2. In one possible implementation, the calculation process of the potential deviation vector can be combined with a comprehensive analysis of multi-dimensional features. For example, in addition to uniformity and fibrous degree, auxiliary indicators such as particle density and energy consumption during processing can be considered to construct a multi-dimensional deviation vector. This multi-dimensional analysis method can more comprehensively reflect the deviation of the material's state, providing a more accurate basis for subsequent parameter correction. It should be noted that each dimension of the deviation vector can be comprehensively evaluated through a weighted approach to highlight factors that have a greater impact on the processing effect. Step S32 involves adjusting the intensity coefficient and processing sequence through the aforementioned feedback mechanism to correct the compensation value of the associated potential deviation vector, thus obtaining the corrected parameter set.
[0043] Specifically, the core of the feedback mechanism lies in calculating the processing parameters that need adjustment based on the potential deviation vector. The strength coefficient typically refers to the operating rate or power of the shearing equipment, while the processing sequence includes the allocation of processing time and the order of processing stages. By adjusting these parameters, deviations in the evolution of material morphology can be effectively compensated, resulting in a corrected parameter set. In one embodiment, the process of adjusting the strength coefficient and processing sequence can be achieved through a preset feedback algorithm. For example, in the processing of lithium-ion battery cathode materials, if the potential deviation vector shows a uniformity deviation of 0.2 micrometers and a fiberization degree deviation of 0.2, the feedback mechanism can calculate that the strength coefficient needs to be increased by 15% based on the magnitude of the deviation, while simultaneously advancing the high-intensity shearing stage in the processing sequence to accelerate the fiberization process. The adjusted parameter set includes a new combination of strength coefficient and processing sequence, which can effectively improve the morphology of the material.
[0044] Optionally, this step may further include: step S321, calculating the directional component of the potential deviation vector based on the intensity coefficient.
[0045] Specifically, the directional component of the potential deviation vector is used to determine the direction and magnitude of the strength coefficient adjustment. For example, if the uniformity deviation is positive, it indicates insufficient uniformity, and the strength coefficient needs to be increased to improve the shearing effect; if the fibrous degree deviation is negative, it indicates excessive fibrousness, and the strength coefficient needs to be reduced to avoid excessive particle breakage. In the processing of lithium-ion battery cathode materials, if the uniformity deviation is 0.2 micrometers and the directional component is positive, the direction of the strength coefficient adjustment is to increase, and the magnitude can be determined according to the deviation size, ranging from 10% to 20%. In one possible implementation, the calculation of the directional component can be optimized by combining historical processing data. For example, by analyzing the relationship between the deviation vector and the strength coefficient adjustment effect in past processing, a mapping model can be constructed to predict the optimal adjustment direction and magnitude corresponding to the current deviation vector. This optimization method based on historical data can improve the response speed and adjustment accuracy of the feedback mechanism and avoid fluctuations in processing effect due to improper parameter adjustment. Step S322: The processing sequence corrects and fuses the compensation value of the potential deviation vector to generate a corrected parameter set.
[0046] Specifically, the correction of the processing sequence includes adjustments to processing time, stage sequence, and intensity allocation. The compensation value of the potential deviation vector refers to the parameter adjustment amount calculated based on the potential deviation vector. By incorporating the compensation value into the correction process of the processing sequence, a new parameter set can be generated to guide subsequent processing. For example, in the processing of lithium-ion battery cathode materials, if the compensation value of the potential deviation vector indicates a need to increase the time proportion of the high-intensity shearing stage, the processing sequence can be adjusted to first perform high-intensity shearing for 10 minutes, followed by low-intensity shearing for 5 minutes, forming a new parameter set. In one embodiment, the process of correcting the processing sequence can be further refined into multiple sub-steps. First, the time allocation of each stage in the processing sequence is determined based on the compensation value, for example, increasing the time proportion of the high-intensity shearing stage. Second, the stage sequence is adjusted, for example, advancing the high-intensity shearing stage to improve uniformity more quickly. Finally, the adjusted time allocation and stage sequence are combined into a new processing sequence, forming the corrected parameter set. This step-by-step correction method ensures the comprehensiveness and effectiveness of parameter adjustment. Step S323 verifies the cyclic stability of the corrected parameter set in the feedback mechanism.
[0047] Specifically, cycle stability refers to whether the modified parameter set can continuously improve the material morphology over multiple processing cycles, avoiding fluctuations in processing results due to improper parameter adjustments. The verification process typically involves small-scale experiments with the modified parameter set to observe its impact on material morphology. For example, in the processing of lithium-ion battery cathode materials, the modified parameter set can be applied to small batches of material, and morphological data after processing can be collected using sensors to analyze whether uniformity and fibrousness have improved. In one possible implementation, cycle stability verification can be accomplished through multiple iterative experiments. For example, after each processing cycle, the morphological characteristic parameters of the material are recorded and compared with the results of the previous cycle. If the characteristic parameters continuously approach the ideal state, it indicates that the modified parameter set has good cycle stability; if the characteristic parameters fluctuate or deteriorate, further adjustments to the parameter set are needed. This iterative verification method ensures the long-term effectiveness of the feedback mechanism. Step S33: Verify the convergence state of potential deviations in a uniform distribution based on the modified parameter set.
[0048] Specifically, the convergence state of potential deviations refers to whether the uniformity distribution deviation of the material has been reduced to an acceptable range after processing with the corrected parameter set. The verification process typically includes analyzing the morphological data of the processed material to determine whether the uniformity deviation and fibrous degree deviation are below preset deviation thresholds. For example, in the processing of lithium-ion battery cathode materials, if the standard deviation of uniformity decreases from 1.2 micrometers to 0.9 micrometers after processing, which is below the preset deviation threshold of 1.0 micrometer, it indicates that the potential deviation has converged to an acceptable level. In one embodiment, the verification of the convergence state of potential deviations can be combined with statistical analysis methods. For example, by statistically analyzing the morphological data from multiple processing cycles, the mean and variance of the uniformity deviation and fibrous degree deviation can be calculated to determine whether they are stable at a low level. If the statistical results show that the mean deviation continues to decrease and the variance is small, the convergence state of potential deviations is confirmed to be good. This statistical analysis method can improve the reliability of the verification results and provide assurance for subsequent processing. In one possible implementation, to further improve the comprehensiveness of the verification, the corrected parameter set can be tested under different processing conditions. For example, in the processing of lithium-ion battery cathode materials, a modified parameter set can be applied under high temperature, low temperature, and room temperature environments to observe its impact on material morphology. If the deviation converges under multiple conditions, it indicates that the parameter set has strong adaptability and can cope with complex processing environments. This multi-condition testing method provides more confidence in the practical application of the parameter set. It should be noted that the implementation process of steps S1 to S3 above is the first half of the electrode active material pretreatment, focusing on gradually improving the morphology of the material through real-time data acquisition, feature extraction, and feedback optimization. The implementation of these steps can effectively solve the problem of agglomeration-dominated state, laying the foundation for subsequent morphology shaping and path optimization. In subsequent embodiments, the complete process from extracting stability indicators from the parameter set to the final pretreatment output will be further described to ensure the effectiveness and consistency of the entire pretreatment method. In one embodiment, the implementation effect of the above method can be verified by testing the performance of the processed material. For example, in the processing of lithium-ion battery cathode materials, after applying the above steps S1 to S3 for pretreatment, the electrochemical performance of the material can be tested to observe whether its specific capacity and cycle stability have been improved. If the test results show that the specific capacity increases from 120 mAh / g to 130 mAh / g, and the capacity retention exceeds 90% after 100 cycles, it indicates that the pretreatment method significantly improves the application performance while improving the material morphology. This performance improvement can provide higher-quality raw materials for subsequent battery manufacturing. In one possible implementation, to accommodate different types of electrode active materials, the parameter thresholds and adjustment ranges in the above steps can be customized.For example, when processing activated carbon materials for supercapacitors, because their particle size and morphological characteristics differ from those of lithium-ion battery cathode materials, the threshold for determining agglomeration can be adjusted from 10 particles per square millimeter to 15 particles per square millimeter, while the adjustment range of the strength coefficient can be increased from 10% to 20%. This customized setting ensures the applicability of the method in processing different materials, broadening its application scope. In one embodiment, the implementation process of the above steps can be further optimized by combining an intelligent control system. For example, in steps S1 to S3, an artificial intelligence-based prediction model can be introduced to predict the trend of material state changes in advance and dynamically adjust the processing parameters based on the prediction results. This intelligent control method can reduce manual intervention, improve processing efficiency, and ensure the accuracy of parameter adjustment. In the processing of lithium-ion battery cathode materials, the intelligent control system can automatically reduce the shear rate when agglomeration is detected in its early stages, preventing further agglomeration. In one possible implementation, to improve the implementation effect of the method, a data recording and analysis step can be added after each step. For example, after step S1, the specific values of agglomerated particle distribution density and fibrosis degree are recorded, and their correlation with processing parameters is analyzed; after step S2, the classification results of intermediate transition characteristics are recorded, and their relationship with material properties is analyzed. These data recordings and analyses can provide valuable experience for optimizing subsequent processing conditions and lay the foundation for continuous improvement of the method. In one embodiment, the implementation of the above steps can also be adapted to the characteristics of different processing equipment. For example, when using a high-speed shearing machine to process lithium-ion battery cathode materials, the low intensity level in step S1 can be set to 60% of the minimum rotation speed of the equipment to avoid unstable equipment operation; when using a low-speed grinding machine, the low intensity level can be set to 80% of the minimum rotation speed to ensure shearing effect. This equipment adaptation method can improve the versatility of the method and ensure its effective implementation in different processing environments. In one possible implementation, to further improve the implementation accuracy of the method, multi-sensor fusion technology can be introduced in steps S1 to S3. For example, in step S1, in addition to using a high-precision camera to acquire image data, a laser particle size analyzer can be used to monitor particle size distribution in real time. By fusing data from both sensors, the accuracy of agglomeration determination can be improved. In step S3, data from temperature and pressure sensors can be combined to comprehensively evaluate the optimization effect of processing conditions. This multi-sensor fusion approach can provide more comprehensive data support for the implementation of the method. In one embodiment, the implementation process of the above steps can also be adjusted in accordance with dynamic changes in the processing environment.For example, in the processing of lithium-ion battery cathode materials, a sudden increase in ambient humidity may exacerbate material agglomeration. In this case, a humidity monitoring step can be added in step S1, and the threshold for determining the agglomeration state can be dynamically adjusted based on humidity changes. In step S3, the intensity coefficient of the feedback mechanism can be adjusted based on changes in ambient temperature. This environmentally adaptable implementation ensures the stability of the method under complex processing conditions. S4, highly relevant stability indicators are extracted from the parameter set, and time series analysis is used to process these indicators to determine the optimized path for final morphology shaping.
[0049] Specifically, after obtaining the corrected parameter set through the feedback mechanism, it is necessary to extract key indicators that reflect the stability of the processing process. These indicators are usually closely related to the long-term trend of material morphology evolution and the volatility of processing conditions. By performing time series analysis on these indicators, the future trend of the processing process can be predicted, thereby determining the optimal path for final morphology shaping and providing guidance for subsequent processing. In one embodiment, the implementation process of step S4 includes data screening and trend analysis of the corrected parameter set. First, indicators highly correlated with processing stability are extracted from the parameter set, such as the fluctuation range of processing intensity and the uniformity distribution of processing time. Then, these indicators are processed using time series analysis methods to identify their variation patterns and potential trends. For example, in the processing of lithium-ion battery cathode materials, if the fluctuation range of processing intensity remains within 5% for five consecutive processing cycles, it indicates that the processing conditions are relatively stable, and the optimal path for final morphology shaping can be determined based on this trend.
[0050] Optionally, this step may further include: step S41, selecting the autocorrelation coefficient and volatility variance from the parameter set as indicators of high stability correlation.
[0051] Specifically, the autocorrelation coefficient measures the correlation between processing parameters at different time points, reflecting the continuity and stability of processing conditions; while the variance of fluctuation quantifies the dispersion of processing parameters, reflecting the drastic changes in conditions during processing. These two indicators comprehensively reflect the stability of the processing process, providing a data foundation for subsequent trend analysis. For example, in the processing of lithium-ion battery cathode materials, an autocorrelation coefficient close to 0.9 indicates strong continuity of processing parameters; a variance of fluctuation less than 0.1 indicates relatively small changes in processing conditions. In one possible implementation, the extraction of the autocorrelation coefficient and variance of fluctuation can be accomplished through statistical analysis of historical data in the parameter set. For example, in the processing of lithium-ion battery cathode materials, shear rate and processing time data from the past 10 processing cycles can be collected, the correlation between these data at different time points can be calculated to obtain the autocorrelation coefficient, and simultaneously, the dispersion of these data can be calculated to obtain the variance of fluctuation. This statistical analysis method ensures that the extracted indicators have high representativeness, laying the foundation for subsequent analysis. Step S42: Analyze the trend of the indicator using a predictive model and output the optimized path for the final form determination. The optimized path includes a parameter adjustment sequence.
[0052] Specifically, the predictive model analyzes historical data on autocorrelation coefficients and variance to predict the changing trends of processing parameters over several periods, and generates an optimized path for final morphology shaping based on this trend. The optimized path typically includes a series of parameter adjustment sequences, such as increasing or decreasing the shear rate at specific time points, or adjusting the processing time allocation. For example, in the processing of lithium-ion battery cathode materials, if the predictive model shows that the variance may increase over the next three periods, the optimized path may include an adjustment sequence that gradually reduces the shear rate to ensure processing stability. In one embodiment, the predictive model's analysis process can be further refined into two stages: data fitting and trend inference. First, a trend curve of processing parameter changes is constructed by fitting historical data on autocorrelation coefficients and variance. Second, the possible range of future processing parameter changes is inferred based on the trend curve, and a corresponding parameter adjustment sequence is generated. For example, in the processing of lithium-ion battery cathode materials, if the trend curve shows that the autocorrelation coefficient may decrease to below 0.7 in the future, the parameter adjustment sequence may include stability control measures such as increasing processing time to avoid deviations during morphology shaping. In one possible implementation, to improve the accuracy of the prediction model, a comprehensive analysis combining multiple data sources can be performed. For example, in the processing of lithium-ion battery cathode materials, in addition to the autocorrelation coefficient and variance, environmental factor data, such as temperature and humidity changes in the processing workshop, can be introduced to analyze their impact on processing stability. If the analysis results show that increased temperature may lead to increased variance, the optimization path can include adjustments to reduce the shear rate in high-temperature environments. This multi-data source analysis approach can improve the adaptability of the optimization path. S5, the matching degree between real-time monitoring data and the optimization path is obtained. If the matching degree is lower than the matching degree threshold, the intensity parameters are iteratively adjusted to obtain an enhanced morphological evolution control sequence.
[0053] Specifically, after determining the optimized path for final morphology, the applicability of this path in actual processing needs to be verified through real-time monitoring data. The matching degree is used to measure the similarity between real-time processing data and the preset parameters of the optimized path. If the matching degree is lower than the preset matching degree threshold, it indicates a deviation between the optimized path and the actual processing situation, requiring iterative adjustment of the intensity parameters to enhance the control effect of morphological evolution. In one embodiment, step S5 includes three stages: real-time data acquisition, matching degree calculation, and parameter iterative adjustment. First, real-time data during the processing is acquired through sensors, such as shear rate, processing time, and material morphology characteristics. Second, the similarity between this real-time data and the preset parameters of the optimized path is calculated to obtain the matching degree. Finally, if the matching degree is lower than the matching degree threshold, iterative optimization is performed by gradually adjusting the intensity parameters until the matching degree reaches an acceptable level. For example, in the processing of lithium-ion battery cathode materials, if the matching degree is lower than 0.8, the shear rate can be gradually increased until the matching degree is improved to above 0.9.
[0054] Optionally, this step further includes: step S51, calculating the matching degree based on real-time monitoring data obtained from the sensor.
[0055] Specifically, the matching degree is the path deviation similarity, calculated by comparing the difference between real-time monitoring data and the preset parameters of the optimized path, usually expressed as a percentage. The smaller the difference, the higher the similarity, indicating a better match between the optimized path and the actual processing conditions. For example, in the processing of lithium-ion battery cathode materials, if the preset shear rate of the optimized path is 3000 revolutions per minute, while the real-time monitoring data is 3200 revolutions per minute, the path deviation similarity can be calculated as 0.93 based on the ratio of the difference between the two. In one possible implementation, the calculation process of path deviation similarity can be combined with multi-dimensional data for comprehensive evaluation. For example, in the processing of lithium-ion battery cathode materials, in addition to shear rate, processing time and material morphology characteristics can also be considered, and the deviation ratio of each dimension can be calculated separately, and then a weighted average can be used to obtain the comprehensive similarity. This multi-dimensional evaluation method can more comprehensively reflect the degree of matching between the optimized path and the actual processing conditions, providing a more accurate basis for subsequent adjustments. Step S52: If the matching degree is lower than the matching degree threshold, the intensity parameter is gradually increased iteratively until the matching degree converges, obtaining an enhanced morphological evolution control sequence. Specifically, if the path deviation similarity is lower than a preset matching degree threshold, such as below 0.85, the intensity parameter needs to be gradually adjusted to improve the matching degree. The adjustment process typically employs an iterative approach, increasing the intensity parameter by a certain percentage each time and recalculating the matching degree until it reaches above the matching degree threshold, forming an enhanced morphological evolution control sequence. For example, in the processing of lithium-ion battery cathode materials, if the initial matching degree is 0.82, the shear rate can be increased by 5% each time, and after three iterations, the matching degree increases to 0.88, forming a new control sequence. In one embodiment, the iterative adjustment process of the intensity parameter can be further refined into two stages: amplitude determination and effect verification. First, the amplitude of each adjustment is determined based on the magnitude of the matching degree deviation; for example, increasing the intensity by 10% when the deviation is large and by 3% when the deviation is small. Second, real-time data is collected using sensors after each adjustment to verify whether the matching degree has improved. If the verification results show a continuous improvement in the matching degree, iterative adjustments continue until convergence; if the matching degree does not improve significantly, the adjustment strategy needs to be re-evaluated. For example, in the processing of lithium-ion battery cathode materials, if the matching degree still does not improve after two consecutive adjustments, the processing time can be adjusted instead of the strength parameter. Step S53 verifies the consistency of multi-level processing execution for the enhanced morphological evolution control sequence.
[0056] Specifically, multi-level processing execution consistency refers to whether the enhanced morphological evolution control sequence can maintain a consistent degree of matching across different processing stages, reflecting the stability and applicability of the control sequence. The verification process typically involves applying the control sequence over multiple processing cycles and observing changes in the degree of matching. For example, in the processing of lithium-ion battery cathode materials, if the degree of matching of the control sequence remains above 0.9 for five consecutive processing cycles, it indicates good execution consistency. In one possible implementation, the verification of multi-level processing execution consistency can be accomplished through phased testing. For example, in the processing of lithium-ion battery cathode materials, the processing can be divided into an initial shearing stage, a mid-term fibrosis stage, and a late-stage shaping stage, and the degree of matching of the control sequence can be tested at each stage. If the degree of matching at each stage reaches a preset degree of matching threshold, the control sequence is confirmed to have high execution consistency; if the degree of matching at a certain stage is significantly low, the control sequence needs further optimization for that stage. This phased testing method improves the comprehensiveness of the verification. Step S54: An incremental step size sequence of the control sequence is formed by iteratively adjusting the intensity parameters.
[0057] Specifically, the incremental step size sequence refers to the magnitude and order of each adjustment during the iterative adjustment of the intensity parameter, forming an ordered sequence of parameter changes. This sequence reflects the gradual adjustment process of the intensity parameter from its initial value to its final value, providing a reference for subsequent processing. For example, in the processing of lithium-ion battery cathode materials, if the initial shear rate is 3000 revolutions per minute, and after three iterations of adjustment, it increases by 5%, 8%, and 10% respectively, then the incremental step size sequence is 3000 revolutions, 3150 revolutions, 3402 revolutions, and 3742 revolutions. In one embodiment, the formation process of the incremental step size sequence can be dynamically optimized in conjunction with the adjustment effect. For example, in the processing of lithium-ion battery cathode materials, if the matching degree improves significantly after a certain adjustment, the step size of the next adjustment can be appropriately increased to accelerate convergence; if the matching degree improves only slightly, the step size can be decreased to avoid over-adjustment leading to processing instability. This dynamic optimization method can improve the generation efficiency of the control sequence and ensure the stability of the adjustment process. S6, Based on the control sequence, integrate the sensor feedback loop to determine the applicability of the control sequence in multi-stage processing.
[0058] Specifically, after forming the enhanced morphology evolution control sequence, a comprehensive evaluation of the control sequence's performance in actual multi-stage processing is required through a sensor feedback loop to determine its applicability. The applicability evaluation result directly affects the quality of the final preprocessing output, ensuring that the morphology of the processed electrode active material meets the expected target. In one embodiment, step S6 includes three stages: feedback data acquisition, applicability judgment, and preprocessing output generation. First, data such as material morphology characteristics and processing parameter changes are acquired in real time through sensors during the execution of the control sequence. Second, based on this data, it is determined whether the control sequence can maintain stability and consistency throughout multi-stage processing. Finally, the final preprocessing output is generated based on the judgment result, such as the processed material morphology data and parameter records. For example, in the processing of lithium-ion battery cathode materials, if the control sequence performs stably in multi-stage processing, the preprocessing output may include material morphology data with a uniformity standard deviation of 0.8 micrometers.
[0059] Optionally, this step further includes: step S61, whereby the sensor feedback loop collects the execution data of the control sequence in real time.
[0060] Specifically, the sensor feedback loop monitors the execution of the control sequence in real time during processing using high-precision equipment. The collected data includes dimensions such as shear rate, processing time, material particle size distribution, and degree of fibrosis. This data comprehensively reflects the execution effect of the control sequence, providing a basis for subsequent applicability judgment. For example, in the processing of lithium-ion battery cathode materials, the sensor can collect shear rate data once per minute to ensure a complete record of the control sequence's execution process. In one possible implementation, the sensor feedback loop's data acquisition process can be combined with multi-device collaborative work to improve data coverage. For example, in the processing of lithium-ion battery cathode materials, a high-precision camera can be used simultaneously to collect material morphology image data, a laser particle size analyzer can be used to collect particle size distribution data, and a pressure sensor can be used to collect the operating status data of the processing equipment. Through multi-device collaborative acquisition, the comprehensiveness and accuracy of the control sequence execution data can be ensured, providing a more reliable basis for applicability judgment. Step S62: Determine the applicability based on the statistical results of the deviation between the execution data and the control sequence.
[0061] Specifically, applicability is determined by comparing the deviation between the preset parameters of the control sequence and the actual execution data. If the deviation is within an acceptable range, the control sequence is considered to have high applicability; if the deviation exceeds the range, further optimization of the control sequence is required. For example, in the processing of lithium-ion battery cathode materials, if the preset shear rate of the control sequence is 3500 revolutions per minute, and the average value of the actual execution data is 3450 revolutions per minute, the deviation ratio is 1.4%, which is lower than the preset deviation threshold of 5%, thus confirming that the control sequence has good applicability. In one embodiment, the deviation statistics judgment process can be further refined into two stages: multi-dimensional analysis and comprehensive evaluation. First, the deviation ratio is calculated for each parameter dimension of the control sequence, such as shear rate deviation, processing time deviation, and material morphology deviation. Second, the deviations of each dimension are comprehensively evaluated using a weighted method to obtain the overall deviation statistics result. For example, in the processing of lithium-ion battery cathode materials, if the shear rate deviation is 1.4%, the processing time deviation is 2%, and the material morphology deviation is 3%, the comprehensive deviation statistics result is 2.1%, which is lower than the deviation threshold of 5%, thus confirming that the control sequence meets the applicability standard. In one possible implementation, to improve the reliability of applicability judgment, historical data can be used for comparative analysis. For example, in the processing of lithium-ion battery cathode materials, the execution data of the current control sequence can be compared with the data of previously successfully applied control sequences to analyze whether the deviation statistics are within a similar range. If the comparison results show that the deviation statistics of the current sequence are close to those of historically successful sequences, its applicability is further confirmed; if the deviation is significantly higher, the cause needs to be analyzed and the sequence optimized. This historical comparison method can improve the scientific nature of the judgment. In one embodiment, the results of the applicability judgment can also be used to guide the parameter settings of subsequent processing batches. For example, in the processing of lithium-ion battery cathode materials, if the applicability judgment result of the current control sequence shows a deviation statistics of 2.1%, which is far below the deviation threshold of 5%, the sequence can be used as a reference template for the next batch of processing and directly applied to the pretreatment of similar materials. This experience reuse method based on applicability judgment can reduce the workload of repeated adjustments and improve processing efficiency. In one possible implementation, to further verify the applicability of the control sequence, tests can be conducted under different processing conditions. For example, in the processing of lithium-ion battery cathode materials, control sequences can be applied under high-temperature, low-temperature, and room-temperature environments, and the statistical results of deviations in the execution data can be observed. If the deviations are all controlled within the threshold under various conditions, it indicates that the control sequence has strong environmental adaptability and can cope with complex processing scenarios; if the deviation is significantly higher under a certain condition, the sequence needs to be locally optimized for that condition. In one possible implementation, to further improve the reliability of the method, a redundant verification mechanism can be introduced in steps S4 to S6.For example, in step S4, the trends of stability indicators can be analyzed using multiple prediction models to cross-validate the generated results of the optimized path; in step S6, deviation statistics can be calculated using multiple sets of sensor data to cross-validate the accuracy of the applicability judgment. This redundant verification method can reduce errors from a single data source or analysis method and improve the reliability of the method implementation.
[0062] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.
Claims
1. A dry electrode active material pretreatment method based on multi-stage shearing and fiberization, characterized in that, include: S1. Collect real-time data of the electrode active material during multi-stage processing, analyze the real-time data to obtain the initial characteristics of the material's dynamic changes. If the initial characteristics show that agglomeration is dominant, adjust the processing parameters to a low intensity level to determine the starting point of the deformation stage. S2. Obtain the electrode active material data corresponding to the starting point, and obtain the intermediate transition characteristics of multi-stage morphological evolution by classifying the electrode active material data. S3. If the intermediate transition characteristics exceed a preset characteristic threshold, optimize the processing conditions through a feedback mechanism, judge the potential deviation of uniform distribution, and obtain a corrected parameter set. S4. Extract highly correlated indicators with high stability from the parameter set, process the indicators using time series analysis, and determine the optimized path for final morphological shaping. S5. For the optimized path, obtain the matching degree between real-time monitoring data and the optimized path. If the matching degree is lower than the matching degree threshold, iteratively adjust the intensity parameter to obtain an enhanced morphological evolution control sequence. S6. Based on the control sequence, integrate the sensor feedback loop to determine the applicability of the control sequence in multi-level processing.
2. The method as described in claim 1, characterized in that, Step S1 includes: denoising and edge enhancement of the acquired real-time data.
3. The method as described in claim 2, characterized in that, Step S1 further includes: Step S11, extracting the distribution of agglomerated particles and the degree of fibrosis from the real-time data as initial features; Step S12, if the initial features show that the agglomerated state is dominant, adjusting the processing parameters to a low strength level to determine the starting point of the deformation stage.
4. The method as described in claim 3, characterized in that, Step S11 includes: Step S111: Acquire real-time images of the materials during the processing using a high-precision camera; Step S112: Perform grayscale processing and binarization on the real-time image of the material to separate the particle region and the background region. Step S113: Based on the boundary features of the particle region, calculate the area and perimeter of each particle, and then determine the distribution density and average size of the aggregated particles.
5. The method as described in claim 1, characterized in that, Step S2 includes: Step S21, constructing a classification model input feature vector for the electrode active material data corresponding to the starting point; Step S22, outputting intermediate transition features through the classification model, wherein the intermediate transition features include morphological uniformity and fiber length distribution parameters.
6. The method as described in claim 1, characterized in that, Step S3 includes: Step S31, calculating the potential deviation vector based on the intermediate transition features; Step S32, adjusting the intensity coefficient and processing sequence through a feedback mechanism to correct the compensation value of the associated potential deviation vector and obtain the corrected parameter set; Step S33, verifying the convergence state of the uniformly distributed potential deviation based on the corrected parameter set.
7. The method as described in claim 6, characterized in that, Step S32 includes: Step S321: Calculate the directional component of the potential deviation vector based on the intensity coefficient; Step S322: The compensation value of the processing sequence correction fusion potential deviation vector is used to generate a corrected parameter set; Step S323: Verify the cyclic stability of the modified parameter set in the feedback mechanism.
8. The method as described in claim 1, characterized in that, Step S4 includes: Step S41, selecting the autocorrelation coefficient and volatility variance from the parameter set as indicators of high stability correlation; Step S42, using a predictive model to analyze the trend of the indicators and outputting an optimized path for final shape determination.
9. The method as described in claim 1, characterized in that, Step S5 includes: Step S51, calculating the matching degree based on real-time monitoring data obtained from the sensor; Step S52, if the matching degree is lower than the matching degree threshold, gradually increasing the intensity parameter iterating until the matching degree converges to obtain an enhanced morphological evolution control sequence; Step S53, verifying the consistency of multi-level processing execution for the enhanced morphological evolution control sequence; Step S54, forming an incremental step size sequence of the control sequence by iteratively adjusting the intensity parameter.
10. The method as described in claim 1, characterized in that, Step S6 includes: Step S61, the sensor feedback loop collects the execution data of the control sequence in real time; Step S62, the applicability is determined based on the statistical results of the deviation between the execution data and the control sequence.