Lithium battery component drying control method and system based on drying defect data analysis

Abstract

The invention provides a lithium battery component drying control method and system based on drying defect data analysis, and the method comprises the steps: carrying out multiple times of lithium battery component drying tests, and detecting the water content parameter, the drying power parameter and the drying defect characteristic of each time node; performing first control optimization based on the variation amplitude of the water content parameter curve, pushing through a curve scanning analysis frame, recording a relative position, comparing a curve curvature group with a standard interval, if the curvature is lower than a lower limit, increasing the power, and if the curvature is higher than an upper limit, reducing the power; and performing secondary optimization based on the defect blocks, types, volumes and change rates, setting a part position corresponding graph and a defect feature mapping graph, determining drying power abnormity explanation, optimizing time duration and amplitude according to the explanation, and performing secondary test verification and correction to obtain an optimized drying power curve for controlling the drying process. The defects are effectively reduced through double optimization, and the drying efficiency and the battery performance are improved.

Description

Technical Field

[0001] This invention relates to the field of lithium battery production technology, and in particular to a method and system for controlling the drying of lithium battery components based on drying defect data analysis. Background Technology

[0002] In lithium-ion battery manufacturing, the drying process, as a crucial step in electrode coating preparation, directly impacts the battery's electrochemical performance, safety, and lifespan. Current technologies typically employ solvent evaporation or vacuum drying to control the drying process using fixed power or empirical parameters, aiming to remove moisture or residual solvent from the electrode materials. However, these traditional methods have significant drawbacks: on the one hand, excessively high drying power can lead to localized overheating, stress accumulation, and crack formation, such as mud cracking or structural damage; on the other hand, insufficient drying power can cause residual moisture retention, resulting in uniformity issues and potential safety hazards, such as short circuits or capacity decay. Furthermore, existing drying control strategies largely rely on static parameter settings, making it difficult to respond in real-time to changes in moisture content and the dynamic evolution of defect characteristics. This leads to uneven coating, binder migration, and frequent microstructural defects, thereby increasing battery failure risk and production costs. Although some studies have introduced drying model simulations or sensor monitoring, these methods still lack a comprehensive optimization mechanism based on defect data analysis. They cannot effectively quantify the feedback adjustment of drying power based on defect blocks, types, sizes, and rates of change, thus limiting the accuracy and efficiency of the lithium-ion battery component drying process. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system that can improve the accuracy and efficiency of the drying process for lithium battery components.

[0004] This invention discloses a method for controlling the drying of lithium battery components based on drying defect data analysis, including:

[0005] Step S100: Establish several lithium battery component drying tests. Each lithium battery component drying test includes detecting the water content parameters of the lithium battery component at different time points, determining the drying power parameters at different time points, and the drying defect characteristics at different time points.

[0006] Step S200: Establish a time reference axis and set a parameter reference axis relative to the time reference axis. Configure the moisture content parameters and drying power at different time nodes as mapping points between the time reference axis and the parameter reference axis. Connect the mapping points of the same type with a smooth line to obtain the moisture content parameter curve and the drying power curve.

[0007] Step S300: Based on the variation range of the moisture content parameter curve, the drying power curve is optimized for the first time.

[0008] In step S400, based on the defect block, defect type, defect volume, and defect change rate of the drying defect characteristics, the drying power curve is optimized for the second time to obtain the optimized drying power curve. The drying power of the lithium battery component is controlled based on the optimized drying power curve.

[0009] In some embodiments disclosed in this invention, the method for first-stage control optimization of the drying power curve based on the variation range of the moisture content parameter curve includes:

[0010] Step S301: A curve scanning analysis frame is set, and the curve scanning analysis frame is gradually moved on the water content parameter curve. During each movement, the relative position of the curve scanning analysis frame relative to the water content parameter curve is recorded, and the characteristic range of the standard curve change amplitude corresponding to the curve scanning analysis frame is determined based on the correspondence of the relative position in the preset relative position table.

[0011] Step S302: During each shift of the curve scanning analysis frame, the curve change amplitude characteristics in the curve scanning analysis frame are determined, and the curve change amplitude characteristics are compared with the standard curve change amplitude characteristic range. Based on the comparison results, the drying power parameters at the corresponding time nodes are adjusted.

[0012] In some embodiments disclosed in this invention, the method for comparing the curve variation amplitude feature and the standard curve variation amplitude feature range includes:

[0013] Step S3021, the standard curve variation amplitude feature range includes the curve curvature range corresponding to each preset interval point within the curve scanning analysis frame, forming a curve curvature range group; the curve variation amplitude feature includes the curve curvature corresponding to each preset interval point within the curve scanning analysis frame, forming a curve curvature group.

[0014] Step S3022: Compare the curve curvature group and the curve curvature interval group. If the curve curvature corresponding to the preset interval point is less than or equal to the lower limit of the curve curvature interval, the drying power parameter of the corresponding time node is increased. If the curve curvature corresponding to the preset interval point is greater than or equal to the upper limit of the curve curvature interval, the drying power parameter of the corresponding time node is decreased.

[0015] In some embodiments disclosed in this invention, the method for a second control optimization of the drying power curve based on the defect block, defect type, defect volume, and defect change rate of the drying defect characteristics includes:

[0016] Step S401: A component position correspondence diagram is set for the lithium battery component, and several defect blocks are set on the component position correspondence diagram;

[0017] Step S402: Based on the sequential relationship between defect blocks, defect types, defect volumes, and defect change rates, a drying defect feature mapping map is set, including several defect block nodes. Each defect block node is connected to several defect type nodes, each defect type node is connected to several defect volume interval nodes, each defect volume interval node is connected to a defect change rate interval node, and each defect change rate interval node is connected in parallel to each other and connected to different comprehensive analysis nodes. Each comprehensive analysis node is set with an explanation of drying power anomalies.

[0018] Step S403: Based on the determination of the drying defect characteristics on the lithium battery component, the drying defect characteristics are mapped onto the drying defect characteristic map to determine the explanation of the drying power anomaly corresponding to the lithium battery component, and based on the explanation of the drying power anomaly, the drying power is optimized for the second time.

[0019] In some embodiments disclosed in this invention, the defect volume includes the average defect area and the number of independent defects.

[0020] In some embodiments disclosed in this invention, the method for mapping drying defect features onto a drying defect feature mapping map includes:

[0021] Step S4031: Delineate the defect sub-blocks corresponding to the drying defect features, determine the defect block to which the defect sub-block belongs, and determine the defect type of the defect sub-block based on the defect block to which the defect sub-block belongs, its shape, and its area.

[0022] Step S4032: Determine the average defect area and the number of independent defects of the same type of defect sub-blocks. Substitute the defect block, defect type, average defect area, number of independent defects and defect change rate of several defect sub-blocks of the same type into the drying defect feature mapping map to determine the comprehensive analysis node to which it is mapped.

[0023] In some embodiments disclosed in this invention, the explanations for abnormal drying power include: local overheating defects caused by excessively high drying power, residual moisture defects caused by excessively low drying power, uniformity defects caused by fluctuations in drying power, structural damage defects caused by mismatch in drying power, and accelerated crack propagation or abnormal volume accumulation caused by abnormal defect change rate.

[0024] In some embodiments disclosed in this invention, the method for second control optimization of drying power based on anomaly interpretation includes:

[0025] Step S4033: Determine the time node when the drying power anomaly explanation occurs, and based on the content of the drying power explanation, determine the optimization time length and control optimization range that need to be carried out in advance.

[0026] Step S4034: Based on the determined optimization time length and control optimization range, and based on the first corrected drying power curve, a secondary drying test of the lithium battery components is conducted. Based on the test results, the accuracy of the optimization time length and control optimization range is determined, and then it is determined whether the optimization time length and control optimization range should be corrected. The corrected optimization time length and control optimization range are then updated and saved.

[0027] In some embodiments disclosed in this invention, a lithium battery component drying control system based on drying defect data analysis is also disclosed, including:

[0028] The first module is used to establish several drying tests for lithium battery components. Each drying test for lithium battery components includes detecting the moisture content parameters of lithium battery components at different time points, determining the drying power parameters at different time points, and the drying defect characteristics at different time points.

[0029] The second module is used to establish a time reference axis and set a parameter reference axis relative to the time reference axis. The moisture content parameters and drying power at different time nodes are configured as mapping points between the time reference axis and the parameter reference axis. The mapping points of the same type are connected by a smooth line to obtain the moisture content parameter curve and the drying power curve.

[0030] The third module is used to perform the first control optimization of the drying power curve based on the change range of the moisture content parameter curve.

[0031] The fourth module is used to perform a second control optimization on the drying power curve based on the defect block, defect type, defect size, and defect change rate of the drying defect characteristics, to obtain an optimized drying power curve, and to control the drying power of the lithium battery components based on the optimized drying power curve.

[0032] This invention proposes a drying control method and system for lithium battery components based on drying defect data analysis. The method includes: conducting multiple drying tests on lithium battery components to detect moisture content parameters, drying power parameters, and drying defect characteristics at each time point; performing a first optimization based on the variation range of the moisture content parameter curve, recording the relative position by moving the curve scanning analysis frame and comparing the curve curvature group with a standard interval; increasing power if the curvature is below the lower limit and decreasing power if it is above the upper limit; and performing a second optimization based on defect blocks, types, sizes, and change rates, setting a component position correspondence map and a defect feature mapping map to determine the explanation for abnormal drying power, optimizing the time length and amplitude accordingly, and verifying and correcting through two rounds of experiments to obtain an optimized drying power curve for controlling the drying process. This invention effectively reduces defects and improves drying efficiency and battery performance through dual optimization.

[0033] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0034] Figure 1 This diagram illustrates the steps of a method for controlling the drying of lithium battery components based on drying defect data analysis. Detailed Implementation

[0035] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. It should be understood that the preferred embodiments described herein are only for illustration and explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Those skilled in the art can make some non-essential improvements and adjustments based on the following content of the present invention. In the present invention, unless otherwise expressly specified and limited, the technical terms used in the present invention should have the ordinary meaning understood by those skilled in the art.

[0037] Example:

[0038] This invention discloses a drying control method for lithium battery components based on drying defect data analysis, see reference. Figure 1 ,include:

[0039] Step S100: Establish several drying tests for lithium battery components. Each drying test includes detecting the water content parameters of the lithium battery components at different time points, determining the drying power parameters at different time points, and identifying the drying defect characteristics at different time points.

[0040] The principle of step S100 is to systematically collect experimental data by conducting multiple drying tests on lithium battery components. This includes monitoring the moisture content parameters (such as moisture percentage) of the lithium battery components at different time points, and simultaneously recording the corresponding drying power parameters (such as heating power level) and drying defect characteristics (such as the morphology and location of defects like cracks and bubbles). This data constitutes a dynamic profile of the drying process. The purpose of this step is to establish an empirical database for subsequent analysis of the relationship between moisture content changes and drying power, as well as how defects evolve over time. This provides fundamental data for optimizing drying strategies, avoiding the blind spots caused by empirical parameter settings in traditional drying methods, and ensuring that data-driven control optimization can specifically reduce defect occurrence.

[0041] Step S200: Establish a time reference axis and set a parameter reference axis relative to the time reference axis. Configure the moisture content parameters and drying power at different time nodes as mapping points between the time reference axis and the parameter reference axis, and connect the mapping points of the same type with a smooth line to obtain the moisture content parameter curve and the drying power curve.

[0042] The principle of step S200 is to construct a coordinate system framework, where the time reference axis represents the temporal evolution of the drying process, and the parameter reference axis is used to quantify the numerical changes in moisture content and drying power. By mapping the moisture content parameters and drying power parameters at each time point to coordinate points, and connecting similar points with smooth curves, moisture content parameter curves and drying power curves are formed. This step aims to visualize and quantify the dynamic trends of the drying process, facilitating the identification of the rate and pattern of moisture content decrease, as well as the distribution of power input. This provides an intuitive curve analysis basis for subsequent optimization, ensuring that control adjustments are based on the mathematical characteristics of the curve shape rather than isolated data points.

[0043] Step S300: Based on the variation range of the moisture content parameter curve, the drying power curve is optimized for the first time.

[0044] The principle of step S300 is to use the change range of the moisture content parameter curve (such as changes in curvature or slope) as a feedback indicator to perform preliminary optimization and adjustment of the drying power curve. For example, if the curve change range is too slow, it indicates low drying efficiency and the power needs to be increased, while if it is too steep, it may lead to overheating and the power needs to be reduced. This step achieves real-time fine-tuning of the power parameters by gradually moving the curve scanning analysis box and comparing it with standard feature intervals. It aims to initially correct the macroscopic imbalance in the drying process, improve the uniformity and efficiency of moisture content reduction, lay the foundation for subsequent defect-oriented fine optimization, and avoid the power setting deviating from the actual moisture content dynamics.

[0045] In step S400, based on the defect block, defect type, defect volume, and defect change rate of the drying defect characteristics, the drying power curve is optimized for the second time to obtain the optimized drying power curve. The drying power of the lithium battery component is controlled based on the optimized drying power curve.

[0046] The principle of step S400 lies in comprehensively considering the multi-dimensional attributes of drying defect characteristics, including the defect's location (a specific area of ​​the component), defect type (such as cracks or residual moisture), defect size (average area and number), and defect change rate (the rate at which it increases or decreases over time). This is then used to match the drying power anomaly explanation (such as overheating defects caused by excessive power) through a mapping map. The drying power curve is then further optimized and adjusted to generate the final optimized curve for actual control. This step emphasizes a feedback closed-loop mechanism for defect data, ensuring that optimization not only considers changes in moisture content but also makes targeted corrections based on the spatiotemporal evolution of defects, thereby minimizing defect risks and improving the overall drying quality of lithium battery components and battery performance.

[0047] In some embodiments disclosed in this invention, the method for first-stage control optimization of the drying power curve based on the variation range of the moisture content parameter curve includes:

[0048] Step S301: A curve scanning analysis frame is set, and the curve scanning analysis frame is gradually moved on the water content parameter curve. During each movement, the relative position of the curve scanning analysis frame with respect to the water content parameter curve is recorded. Based on the correspondence of the relative position in the preset relative position table, the characteristic range of the standard curve change amplitude corresponding to the curve scanning analysis frame is determined.

[0049] The principle of step S301 is to set a curve scanning analysis frame as a moving window, which slides gradually on the moisture content parameter curve to capture the characteristics of local curve segments. Each time the frame slides, its relative position (e.g., starting point, middle segment, or ending segment) relative to the overall curve is recorded. A pre-defined relative position table, which predefines standard variation ranges (e.g., standard ranges of curvature or slope) for different positions, is used to map the corresponding expected range based on the current position. This step aims to decompose the curve into manageable local segments, ensuring that the optimization process considers the global context of the curve and avoids ignoring local variations in a single global analysis. This provides a position-sensitive standard benchmark for subsequent comparisons, promoting accurate initial adjustments to drying power.

[0050] Step S302: During each shift of the curve scanning analysis frame, the curve change amplitude characteristics in the curve scanning analysis frame are determined, and the curve change amplitude characteristics are compared with the standard curve change amplitude characteristic range. Based on the comparison results, the drying power parameters at the corresponding time nodes are adjusted.

[0051] The principle of step S302 is to extract the actual change amplitude characteristics of the curve within the frame (such as local curvature or slope changes) during each shift of the curve scanning analysis frame, and compare it point by point with the standard feature interval determined in S301. If the actual characteristics deviate from the interval (e.g., too low curvature indicates slow drying or too high curvature indicates rapid change), the drying power parameters at the corresponding time points are dynamically adjusted according to the direction and degree of deviation. For example, the power is increased to accelerate drying or decreased to prevent overheating. This step achieves closed-loop optimization through a feedback mechanism, ensuring that the drying power curve initially adapts to the dynamic characteristics of moisture content changes, improving the stability and efficiency of the overall process, and laying the foundation for subsequent defect-oriented secondary optimization.

[0052] In some embodiments disclosed in this invention, the method for comparing the curve variation amplitude feature and the standard curve variation amplitude feature range includes:

[0053] Step S3021, the standard curve variation amplitude feature range includes the curve curvature range corresponding to each preset interval point within the curve scanning analysis frame, forming a curve curvature range group; the curve variation amplitude feature includes the curve curvature corresponding to each preset interval point within the curve scanning analysis frame, forming a curve curvature group.

[0054] Step S3022: Compare the curve curvature group and the curve curvature interval group. If the curve curvature corresponding to the preset interval point is less than or equal to the lower limit of the curve curvature interval, the drying power parameter of the corresponding time node is increased. If the curve curvature corresponding to the preset interval point is greater than or equal to the upper limit of the curve curvature interval, the drying power parameter of the corresponding time node is decreased.

[0055] In some embodiments disclosed in this invention, the method for a second control optimization of the drying power curve based on the defect block, defect type, defect volume, and defect change rate of the drying defect characteristics includes:

[0056] Step S401: A component position correspondence diagram is set for the lithium battery component, and several defect blocks are set on the component position correspondence diagram.

[0057] The principle of step S401 is to establish a component location mapping map for the lithium battery components. This map divides the surface or internal area of ​​the component into several predefined defect blocks (such as edge areas, central areas, or specific functional areas) to achieve spatial location and classification of defects. This step aims to provide a reference framework by standardizing the geometric mapping of the components to accurately identify and classify defects that occur during the drying process, avoiding arbitrariness in defect analysis, and ensuring that subsequent optimization processes can be adjusted for the sensitivity of specific blocks. This improves the targeting and effectiveness of drying control and reduces uneven drying caused by location differences.

[0058] Step S402: Based on the sequential relationship between defect blocks, defect types, defect volumes, and defect change rates, a drying defect feature mapping map is set, including several defect block nodes. Each defect block node is connected to several defect type nodes, each defect type node is connected to several defect volume interval nodes, each defect volume interval node is connected to a defect change rate interval node, and each defect change rate interval node is connected in parallel to each other and connected to different comprehensive analysis nodes. Each comprehensive analysis node is set with an explanation of drying power anomalies.

[0059] The principle of step S402 lies in constructing a hierarchical drying defect feature mapping map. This map starts with defect block nodes and sequentially connects defect type nodes (such as cracks, bubbles, or residual moisture types), defect volume range nodes (such as size classification ranges), and defect change rate range nodes (such as rate classifications). These nodes are linked in parallel to different comprehensive analysis nodes, and each analysis node is pre-defined with a corresponding explanation for abnormal drying power (such as overheating caused by excessive power). This step captures the sequential relationships and multi-path combinations of defect attributes through the tree structure of the map, realizing a systematic modeling of defect features. This facilitates rapid matching and diagnosis of drying power problems, provides a logically rigorous decision-making basis for secondary optimization, and ensures that control adjustments are based on multi-dimensional quantification of defects rather than a single indicator.

[0060] Step S403: Based on the determination of the drying defect characteristics on the lithium battery component, the drying defect characteristics are mapped onto the drying defect characteristic map to determine the explanation of the drying power anomaly corresponding to the lithium battery component, and based on the explanation of the drying power anomaly, the drying power is optimized for the second time.

[0061] The principle of step S403 is to first detect and quantify the actual drying defect characteristics of the lithium battery components, including the delineation of defect sub-blocks, determination of the block to which they belong, type identification, volume calculation (such as average area and number), and rate of change assessment. These characteristics are then input into a drying defect feature mapping map for path matching, locating the corresponding comprehensive analysis node, thereby outputting an explanation of drying power anomalies (such as overheating defects or residual moisture defects). Based on this, the drying power curve is further optimized and adjusted, for example, by extending or shortening the optimization time and modifying the magnitude of the change according to the anomaly type. This step emphasizes real-time feedback of defect data and a map-driven interpretation mechanism, forming a closed-loop optimization cycle to ensure that the final drying power curve can dynamically adapt to specific defects, improving the drying quality of lithium battery components, reducing the defect incidence rate, and enhancing the overall performance and safety of the battery.

[0062] In some embodiments disclosed in this invention, the defect volume includes the average defect area and the number of independent defects.

[0063] In some embodiments disclosed in this invention, the method for mapping drying defect features onto a drying defect feature mapping map includes:

[0064] Step S4031: Delineate the defect sub-blocks corresponding to the drying defect features, determine the defect block to which the defect sub-block belongs, and determine the defect type of the defect sub-block based on the defect block to which the defect sub-block belongs, its shape, and its area.

[0065] The principle of step S4031 lies in performing fine-grained analysis of the drying defect characteristics on lithium battery components. First, defect sub-blocks (i.e., local areas of defects) are delineated using image recognition or sensor scanning techniques. Then, based on a pre-defined component location correspondence map, the larger defect block to which the sub-block belongs (such as an edge or central region) is determined. Finally, the defect type (such as stress cracks or residual moisture) is classified by combining the sub-block's belonging block, geometry (such as linear cracks or circular bubbles), and area size. This step aims to transform abstract defect characteristics into quantifiable attributes, ensuring the accuracy and location relevance of defect identification, avoiding the neglect of the influence of component structural differences on defect formation, thereby providing accurate input data for subsequent mapping and facilitating targeted diagnosis and optimization of abnormal drying power.

[0066] Step S4032: Determine the average defect area and the number of independent defects of the same type of defect sub-blocks. Substitute the defect block, defect type, average defect area, number of independent defects and defect change rate of several defect sub-blocks of the same type into the drying defect feature mapping map to determine the comprehensive analysis node to which it is mapped.

[0067] The principle of step S4032 is that, after classification, the defect sub-blocks of the same type are statistically summarized, and their average defect area (total area divided by number) and the number of independent defects (deduplicated defect count) are calculated. These summarized indicators, along with the defect block, defect type, and defect change rate (the rate of change of defect size over time), are then input as a multi-dimensional parameter set into the drying defect feature mapping map. By traversing and matching block nodes, size interval nodes, and change rate interval nodes through the map's node paths, the corresponding comprehensive analysis node is finally located. This step, through aggregated statistics and map mapping, achieves dimensionality reduction and correlation reasoning of defect data, ensuring the comprehensiveness of anomaly interpretation and avoiding analysis dominated by a single defect. This provides a reliable decision-making basis for the secondary optimization of the drying power curve, improving control robustness and defect minimization.

[0068] In some embodiments disclosed in this invention, the explanations for abnormal drying power include: local overheating defects caused by excessively high drying power, residual moisture defects caused by excessively low drying power, uniformity defects caused by fluctuations in drying power, structural damage defects caused by mismatch in drying power, and accelerated crack propagation or abnormal volume accumulation caused by abnormal defect change rate.

[0069] In some embodiments disclosed in this invention, the method for second control optimization of drying power based on anomaly interpretation includes:

[0070] Step S4033: Determine the time node when the drying power anomaly explanation occurs, and based on the content of the drying power explanation, determine the optimization time length and control optimization range that need to be carried out forward.

[0071] The principle of step S4033 is as follows: First, by tracking the occurrence of anomalies in drying power (such as localized overheating due to excessively high power or residual moisture caused by excessively low power) at specific time points, this point typically corresponds to the first appearance or peak point of defect characteristics during the drying process. Then, based on the specific content of the anomaly explanation (such as defect type and severity), reverse reasoning is performed to determine the forward-looking optimization time length (i.e., how many time periods to adjust the power from the anomaly point to cover the potential inducing period of defect formation) and the control optimization magnitude (i.e., the percentage or absolute value of power adjustment, such as reducing it by 10% to alleviate overheating), thereby achieving preventative correction of the drying power curve. This step aims to transform anomaly diagnosis into actionable parameters, ensuring that optimization is not limited to the current node but covers the causal chain of defect evolution, improving the foresight and targeting of secondary optimization, and preventing the recurrence of defects.

[0072] Step S4034: Based on the determined optimization time length and control optimization range, and based on the first corrected drying power curve, a secondary drying test of the lithium battery components is conducted. Based on the test results, the accuracy of the optimization time length and control optimization range is determined, and then it is determined whether the optimization time length and control optimization range should be corrected. The corrected optimization time length and control optimization range are then updated and saved.

[0073] The principle of step S4034 is to modify the drying power curve after the first optimization using the optimization time length and control optimization range determined from S4033. Then, a second drying test of the lithium battery components is carried out to simulate and verify the adjustment effect. The accuracy of the initial parameters is evaluated by comparing the test results (such as moisture content distribution and defect reduction rate). If the results show deviations (such as defects not being significantly improved), it is decided whether to correct the time length (extend or shorten) and optimization range (increase or decrease). The corrected parameters are then updated and saved to the system database, forming an iterative learning mechanism. This step emphasizes closed-loop feedback of experimental verification to ensure the dynamic adaptability and reliability of the optimized parameters. By accumulating empirical data through repeated experiments, the robustness and long-term effectiveness of the overall drying control are improved, and the trial-and-error costs in production are reduced.

[0074] In some embodiments disclosed in this invention, a lithium battery component drying control system based on drying defect data analysis is also disclosed, including:

[0075] The first module is used to establish several drying tests for lithium battery components. Each drying test includes detecting the moisture content parameters of the lithium battery components at different time points, determining the drying power parameters at different time points, and identifying the drying defect characteristics at different time points.

[0076] The second module is used to establish a time reference axis and set a parameter reference axis relative to the time reference axis. The moisture content parameters and drying power at different time points are configured as mapping points between the time reference axis and the parameter reference axis. The mapping points of the same type are connected by a smooth line to obtain the moisture content parameter curve and the drying power curve.

[0077] The third module is used to perform the first control optimization of the drying power curve based on the variation range of the moisture content parameter curve.

[0078] The fourth module is used to perform a second control optimization on the drying power curve based on the defect block, defect type, defect size, and defect change rate of the drying defect characteristics, to obtain an optimized drying power curve, and to control the drying power of the lithium battery components based on the optimized drying power curve.

[0079] This invention proposes a drying control method and system for lithium battery components based on drying defect data analysis. The method includes: conducting multiple drying tests on lithium battery components to detect moisture content parameters, drying power parameters, and drying defect characteristics at each time point; performing a first optimization based on the variation range of the moisture content parameter curve, recording the relative position by moving the curve scanning analysis frame and comparing the curve curvature group with a standard interval; increasing power if the curvature is below the lower limit and decreasing power if it is above the upper limit; and performing a second optimization based on defect blocks, types, sizes, and change rates, setting a component position correspondence map and a defect feature mapping map to determine the explanation for abnormal drying power, optimizing the time length and amplitude accordingly, and verifying and correcting through two rounds of experiments to obtain an optimized drying power curve for controlling the drying process. This invention effectively reduces defects and improves drying efficiency and battery performance through dual optimization.

[0080] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling the drying of lithium battery components based on drying defect data analysis, characterized in that, include: Step S100: Establish several lithium battery component drying tests. Each lithium battery component drying test includes detecting the water content parameters of the lithium battery component at different time points, determining the drying power parameters at different time points, and the drying defect characteristics at different time points. Step S200: Establish a time reference axis and set a parameter reference axis relative to the time reference axis. Configure the moisture content parameters and drying power at different time nodes as mapping points between the time reference axis and the parameter reference axis. Connect the mapping points of the same type with a smooth line to obtain the moisture content parameter curve and the drying power curve. Step S300: Based on the variation range of the moisture content parameter curve, the drying power curve is optimized for the first time. In step S400, based on the defect block, defect type, defect volume, and defect change rate of the drying defect characteristics, the drying power curve is optimized for the second time to obtain the optimized drying power curve. The drying power of the lithium battery component is controlled based on the optimized drying power curve.

2. The method for controlling the drying of lithium battery components based on drying defect data analysis according to claim 1, characterized in that, The methods for first-stage control optimization of the drying power curve based on the variation range of the moisture content parameter curve include: Step S301: A curve scanning analysis frame is set, and the curve scanning analysis frame is gradually moved on the water content parameter curve. During each movement, the relative position of the curve scanning analysis frame relative to the water content parameter curve is recorded, and the characteristic range of the standard curve change amplitude corresponding to the curve scanning analysis frame is determined based on the correspondence of the relative position in the preset relative position table. Step S302: During each shift of the curve scanning analysis frame, the curve change amplitude characteristics in the curve scanning analysis frame are determined, and the curve change amplitude characteristics are compared with the standard curve change amplitude characteristic range. Based on the comparison results, the drying power parameters at the corresponding time nodes are adjusted.

3. The method for controlling the drying of lithium battery components based on drying defect data analysis according to claim 2, characterized in that, Methods for comparing the range of variation of the curve with the range of variation of the standard curve include: Step S3021, the standard curve variation amplitude feature range includes the curve curvature range corresponding to each preset interval point within the curve scanning analysis frame, forming a curve curvature range group; the curve variation amplitude feature includes the curve curvature corresponding to each preset interval point within the curve scanning analysis frame, forming a curve curvature group. Step S3022: Compare the curve curvature group and the curve curvature interval group. If the curve curvature corresponding to the preset interval point is less than or equal to the lower limit of the curve curvature interval, the drying power parameter of the corresponding time node is increased. If the curve curvature corresponding to the preset interval point is greater than or equal to the upper limit of the curve curvature interval, the drying power parameter of the corresponding time node is decreased.

4. The method for controlling the drying of lithium battery components based on drying defect data analysis according to claim 1, characterized in that, The methods for secondary control optimization of the drying power curve based on the characteristics of drying defects, including the defect block, defect type, defect volume, and defect change rate, include: Step S401: A component position correspondence diagram is set for the lithium battery component, and several defect blocks are set on the component position correspondence diagram; Step S402: Based on the sequential relationship between defect blocks, defect types, defect volumes, and defect change rates, a drying defect feature mapping map is set, including several defect block nodes. Each defect block node is connected to several defect type nodes, each defect type node is connected to several defect volume interval nodes, each defect volume interval node is connected to a defect change rate interval node, and each defect change rate interval node is connected in parallel to each other and connected to different comprehensive analysis nodes. Each comprehensive analysis node is set with an explanation of drying power anomalies. Step S403: Based on the determination of the drying defect characteristics on the lithium battery component, the drying defect characteristics are mapped onto the drying defect characteristic map to determine the explanation of the drying power anomaly corresponding to the lithium battery component, and based on the explanation of the drying power anomaly, the drying power is optimized for the second time.

5. The method for controlling the drying of lithium battery components based on drying defect data analysis according to claim 4, characterized in that, Defect size includes average defect area and number of independent defects.

6. The method for controlling the drying of lithium battery components based on drying defect data analysis according to claim 4, characterized in that, Methods for mapping drying defect features onto a drying defect feature map include: Step S4031: Delineate the defect sub-blocks corresponding to the drying defect features, determine the defect block to which the defect sub-block belongs, and determine the defect type of the defect sub-block based on the defect block to which the defect sub-block belongs, its shape, and its area. Step S4032: Determine the average defect area and the number of independent defects of the same type of defect sub-blocks. Substitute the defect block, defect type, average defect area, number of independent defects and defect change rate of several defect sub-blocks of the same type into the drying defect feature mapping map to determine the comprehensive analysis node to which it is mapped.

7. The method for controlling the drying of lithium battery components based on drying defect data analysis according to claim 4, characterized in that, Explanations for abnormal drying power include: local overheating defects caused by excessively high drying power, residual moisture defects caused by excessively low drying power, uniformity defects caused by fluctuations in drying power, structural damage defects caused by mismatch in drying power, and accelerated crack propagation or abnormal volume accumulation caused by abnormal defect change rate.

8. The method for controlling the drying of lithium battery components based on drying defect data analysis according to claim 4, characterized in that, Based on the explanation of drying power anomalies, the methods for second-level control optimization of drying power include: Step S4033: Determine the time node when the drying power anomaly explanation occurs, and based on the content of the drying power explanation, determine the optimization time length and control optimization range that need to be carried out in advance. Step S4034: Based on the determined optimization time length and control optimization range, and based on the first corrected drying power curve, a secondary drying test of the lithium battery components is conducted. Based on the test results, the accuracy of the optimization time length and control optimization range is determined, and then it is determined whether the optimization time length and control optimization range should be corrected. The corrected optimization time length and control optimization range are then updated and saved.

9. A drying control system for lithium battery components based on drying defect data analysis, characterized in that, A method for controlling the drying of lithium battery components according to any one of claims 1-8, comprising: The first module is used to establish several drying tests for lithium battery components. Each drying test for lithium battery components includes detecting the moisture content parameters of lithium battery components at different time points, determining the drying power parameters at different time points, and the drying defect characteristics at different time points. The second module is used to establish a time reference axis and set a parameter reference axis relative to the time reference axis. The moisture content parameters and drying power at different time nodes are configured as mapping points between the time reference axis and the parameter reference axis. The mapping points of the same type are connected by a smooth line to obtain the moisture content parameter curve and the drying power curve. The third module is used to perform the first control optimization of the drying power curve based on the change range of the moisture content parameter curve. The fourth module is used to perform a second control optimization on the drying power curve based on the defect block, defect type, defect size, and defect change rate of the drying defect characteristics, to obtain an optimized drying power curve, and to control the drying power of the lithium battery components based on the optimized drying power curve.

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