Manufacturing method and device of battery pole piece and electronic equipment

Abstract

The invention discloses a manufacturing method and device of a battery pole piece and electronic equipment. The method comprises the following steps: obtaining a to-be-coated metal foil; coating parameters corresponding to the to-be-coated metal foil are determined, multiple coating index items corresponding to the coating parameters are determined, and coating index data corresponding to the multiple coating index items are determined in the process that the to-be-coated metal foil is coated with electrode cell slurry according to the initial coating process data; and according to the coating index data corresponding to the plurality of coating index items, updating the initial coating process data to obtain target coating process parameters until the coating of the to-be-coated metal foil is completed, and obtaining a target battery pole piece. According to the invention, the technical problem that the quality of the manufactured battery pole piece is difficult to ensure due to the fact that the coating parameters are adjusted by real-time data obtained by image processing when the battery pole piece is manufactured in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and device for manufacturing a battery pole piece and an electronic device. Background Art

[0002] The coating process is a key pre-step in the production of battery pole pieces, and its quality control directly affects the final quality of the entire batch of battery pole pieces. However, the processing method of most coating process data still relies on traditional manual analysis, such as the surface density measurement method, CCD detection method, and image processing method to perform real-time analysis on the data of the coating process. However, the surface density measurement method and CCD detection method are not only inefficient, but also difficult to ensure the accuracy and comprehensive representativeness of the data, which undoubtedly limits the precise control of the coating quality. When unqualified coated products enter the subsequent production process, it will cause unnecessary waste of manpower and resources. In the process of analyzing coating data, the image processing method relies on image processing to obtain real-time data to adjust the coating parameters, and it is also difficult to ensure the quality of the manufactured battery pole pieces.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and electronic device for manufacturing a battery pole piece, so as to at least solve the technical problem in the related art that when manufacturing battery pole pieces, the coating parameters are adjusted by relying on image processing to obtain real-time data, which makes it difficult to ensure the quality of the manufactured battery pole pieces.

[0005] According to one aspect of an embodiment of the present invention, a method for manufacturing a battery electrode sheet is provided, comprising: obtaining a metal foil to be coated; determining coating parameters corresponding to the metal foil to be coated, wherein the coating parameters include electrode core slurry and initial coating process data used in the coating process; determining a plurality of coating index items corresponding to the coating parameters, wherein the coating index items are used to evaluate the quality of the coating, the coating index items include index items that conform to a normal distribution, each coating index item corresponds to a plurality of subgroups, and each of the plurality of subgroups includes a plurality of continuous observation points; determining a plurality of coating index items corresponding to the coating parameters according to the initial coating parameters ... Initial coating process data is used to determine coating index data corresponding to multiple coating index items during the process of coating the electrode cell slurry on the metal foil to be coated, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to the full observation point, and the second coating index data is the index data corresponding to multiple subgroups; based on the coating index data corresponding to the multiple coating index items, the initial coating process data is updated to obtain the target coating process parameters until the coating of the metal foil to be coated is completed to obtain the target battery electrode.

[0006] Optionally, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, coating index data corresponding to multiple coating index items are determined, including: when the multiple coating index items include a process capability ratio item and the corresponding coating index data include a corresponding process capability ratio value, determining the target limit specification corresponding to the target coating index item, wherein the target limit specification includes an upper limit specification and a lower limit specification, and the target coating index item is any one of the multiple coating index items; determining the specification difference between the upper limit specification and the lower limit specification; determining the process capability ratio value corresponding to the target coating index item based on the specification difference and the combined standard deviation corresponding to the target coating index item, wherein the process capability ratio value represents the degree of deviation of the observation points included in multiple subgroups from the target limit specifications during the coating process of the target coating index item, and the combined standard deviation is the standard deviation corresponding to the multiple subgroups.

[0007] Optionally, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, the coating index data corresponding to the multiple coating index items are determined, and it also includes: when the multiple coating index items include process capability items and the corresponding coating index data include corresponding process capability indexes, the upper limit specification, lower limit specification and process average corresponding to the target coating index item are determined, wherein the process average represents the average value of all observation points corresponding to the coating index item; a first difference between the upper limit specification and the process average is determined, and a second difference between the process average and the lower limit specification is determined; based on the first difference, the second difference and the combined standard deviation corresponding to the target coating index item, the process capability index corresponding to the target coating index item is determined, wherein the process capability index represents the coating fluctuation degree of the observation points included in multiple subgroups during the coating process of the target coating index item.

[0008] Optionally, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, coating index data corresponding to multiple coating index items are determined, including: when the multiple coating index items include process performance items and the corresponding coating index data include corresponding process performance indices, the target limit specifications corresponding to the target coating index items are determined, wherein the target limit specifications include upper limit specifications and lower limit specifications; the specification difference between the upper limit specifications and the lower limit specifications is determined; based on the specification difference and the overall standard deviation corresponding to the target coating index item, the process performance index corresponding to the target coating index item is determined, wherein the process performance index represents the degree of deviation of the full amount of observation points from the target limit specifications during the coating process, and the overall standard deviation is the standard deviation of the full amount of observation points corresponding to the target coating index item.

[0009] Optionally, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, coating index data corresponding to multiple coating index items are determined, including: when the multiple coating index items include determining process index items and the corresponding coating index data include corresponding process index data, determining the upper limit limit specification, lower limit limit specification and process average value corresponding to the target coating index item; determining a first difference between the upper limit limit specification and the process average value, and determining a second difference between the process average value and the lower limit limit specification; determining the process index corresponding to the target coating index item based on the first difference, the second difference and the overall standard deviation corresponding to the target coating index item, wherein the process index represents the coating fluctuation degree of all observation points of the target coating index item during the coating process.

[0010] Optionally, based on coating index data corresponding to a plurality of coating index items, the initial coating process data is updated to obtain target coating process parameters until the coating of the metal foil to be coated is completed to obtain a target battery electrode, including: determining a linear regression model, wherein the linear regression model includes a plurality of independent variables, and the plurality of independent variables are variables corresponding to a plurality of coating index items; inputting the coating index data corresponding to the plurality of coating index items into the linear regression model to determine a performance score of the current battery; based on the performance score, updating the initial coating process data to obtain the target coating process parameters.

[0011] Optionally, based on coating index data corresponding to a plurality of coating index items, the initial coating process data is updated to obtain target coating process parameters until the coating of the metal foil to be coated is completed to obtain a target battery electrode, including: determining a first-order continuous system model, wherein the first-order continuous system model includes a coating process parameter item, so as to perform coating according to the data in the coating process parameter item; inputting the target coating process parameters into the first-order continuous system model, updating the data in the coating process parameter item to the target coating process parameters, so as to control and adjust the coating process, until the coating of the metal foil to be coated is completed to obtain a target battery electrode.

[0012] According to one aspect of an embodiment of the present invention, a coating layer manufacturing device for a battery is provided, comprising: an acquisition module, used to acquire a metal foil to be coated; a first determination module, used to determine coating parameters corresponding to the metal foil to be coated, wherein the coating parameters include electrode cell slurry and initial coating process data used in the coating process; a second determination module, used to determine a plurality of coating index items corresponding to the coating parameters, wherein the coating index items are used to evaluate the quality of the coating, the coating index items include index items that conform to a normal distribution, each coating index item corresponds to a plurality of subgroups, and each of the plurality of subgroups includes a plurality of continuous observation points; The third determination module is used to determine the coating index data corresponding to multiple coating index items in the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to the full observation point, and the second coating index data is the index data corresponding to multiple subgroups; the fourth determination module is used to update the initial coating process data according to the coating index data corresponding to the multiple coating index items, and obtain the target coating process parameters until the coating of the metal foil to be coated is completed and the target battery electrode is obtained.

[0013] According to one aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement any of the above-mentioned methods for manufacturing a battery electrode.

[0014] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any of the above-mentioned methods for manufacturing a battery electrode.

[0015] In an embodiment of the present invention, a metal foil to be coated is obtained; coating parameters corresponding to the metal foil to be coated are determined, wherein the coating parameters include electrode cell slurry and initial coating process data used in the coating process; multiple coating index items corresponding to the coating parameters are determined, wherein the coating index items are used to evaluate the quality of coating, the coating index items include index items that conform to a normal distribution, each coating index item corresponds to multiple subgroups, and each of the multiple subgroups includes a continuous plurality of observation points; in the process of coating the metal foil to be coated with the electrode cell slurry according to the initial coating process data, coating index data corresponding to the multiple coating index items are determined, wherein the coating index data include first coating index data and second coating index data, the first coating index data is the index data corresponding to all observation points, and the second coating index data is the index data corresponding to the multiple subgroups; based on the coating index data corresponding to the multiple coating index items, the initial coating process data is updated to obtain the target coating process parameters, until the coating of the metal foil to be coated is completed to obtain the target battery electrode. It can be seen that the embodiment of the present invention updates the coating process data based on the coating index data corresponding to the multiple coating index items, so as to achieve the purpose of producing an electrode plate that meets the quality standards. Since the establishment of multiple coating index items can comprehensively measure the coating quality, and the coating index data can evaluate the coating process capability in real time, multiple observation points corresponding to the multiple coating index items are collected in real time during the coating process, and the corresponding coating index data are calculated respectively. The coating process capability is evaluated using the coating index data, and real-time control is performed according to the evaluation results to ensure that the produced electrode plates meet the quality requirements, thereby solving the technical problem in the related art that when producing battery plates, it is difficult to ensure the quality of the manufactured battery plates by relying on image processing to obtain real-time data to adjust coating parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a method for manufacturing a battery electrode sheet according to an embodiment of the present invention;

[0018] Figure 2 is a normal distribution diagram of an embodiment provided by an optional embodiment of the present invention;

[0019] Figure 3 It is a curve fitting graph generated by the process capability algorithm model provided by an optional embodiment of the present invention;

[0020] Figure 4 is another curve fitting graph generated by the process capability algorithm model provided by an optional embodiment of the present invention;

[0021] Figure 5 It is another curve fitting diagram generated by the process capability algorithm model provided in an optional embodiment of the present invention;

[0022] Figure 6 It is another curve fitting graph generated by the process capability algorithm model provided by an optional embodiment of the present invention;

[0023] Figure 7 is a time series diagram of coating surface density provided by an optional embodiment of the present invention;

[0024] Figure 8 It is a structural block diagram of a device for manufacturing a battery electrode according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0028] CCD detection method: a non-contact, non-destructive automatic detection technology that mainly uses optical principles to focus the object to be tested on the charge-coupled device chip through the lens. The charge-coupled device chip converts the optical signal into an electrical signal, which is then processed to obtain a digital image. Different charge-coupled device detection equipment and technologies can be selected according to different application scenarios and detection requirements.

[0029] Python: A dynamic, object-oriented computer programming language.

[0030] sklearn library: A free software machine learning library for the Python programming language.

[0031] Example 1

[0032] According to an embodiment of the present invention, an embodiment of a method for manufacturing a coating layer of a battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] Figure 1 is a flow chart of a method for manufacturing a battery electrode according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0034] Step S102, obtaining the metal foil to be coated.

[0035] In step S102 provided in the present application, a metal foil to be coated is obtained.

[0036] Among them, the metal foil to be coated is involved. The metal foil to be coated refers to the battery pole piece on the battery production line that has not yet undergone the coating process, waiting for the electrode cell slurry to be coated on its surface to form the electrode pole piece.

[0037] In this step, the metal foil to be coated is obtained, that is, the battery pole piece that has not been coated with the electrode cell slurry is selected for use in the next coating process. This is a basic step in making battery pole pieces.

[0038] Step S104, determining coating parameters corresponding to the metal foil to be coated, wherein the coating parameters include electrode core slurry and initial coating process data used in the coating process.

[0039] In step S104 provided in the present application, coating parameters corresponding to the metal foil to be coated are determined.

[0040] Among them, coating parameters are involved. Coating parameters refer to a series of control parameters that need to be set in order to ensure coating quality during the lithium battery coating process. These parameters affect important characteristics such as coating uniformity, thickness, width, etc. For example, coating speed, coating pressure, slurry viscosity, slurry flow rate, etc.

[0041] Among them, electrode cell slurry is involved. Electrode cell slurry refers to a viscous liquid mixed with electrode active materials, binders, solvents, and possible thickeners, conductive agents, etc. It is an important raw material for making battery core components, and the battery electrodes are formed through coating, drying and other processes.

[0042] Among them, the initial coating process data is involved. The initial coating process data refers to a set of parameters set before the coating begins. They are determined based on factors such as battery design requirements and electrode cell slurry composition, and are used to guide the coating machine for coating.

[0043] In this step, the coating parameters corresponding to the metal foil to be coated are determined. That is, before the coating process begins, the coating process parameters need to be determined according to the specific type of metal foil to be coated. These parameters will serve as the basis for the operation of the coater to ensure that the coating process can proceed according to the predetermined goals.

[0044] By determining the corresponding coating parameters, it helps to complete the battery coating process.

[0045] Step S106, determining multiple coating index items corresponding to the coating parameters, wherein the multiple coating index items are used to evaluate the quality of coating, the multiple coating index items include index items that conform to the normal distribution, each coating index item corresponds to multiple subgroups, and each of the multiple subgroups includes multiple continuous observation points.

[0046] In step S106 provided in the present application, a plurality of coating index items corresponding to the coating parameters are determined.

[0047] Among them, coating index items are involved. Coating index items are multiple specific indicators used to measure coating quality, such as surface density, alignment, margin width, coating width, slurry distribution uniformity, coating thickness, etc. These indicators reflect the key quality attributes of the coating process.

[0048] Among them, it involves indicators that conform to normal distribution. Indicators that conform to normal distribution mean that the indicator data is concentrated near the mean, and gradually decreases on both sides, presenting a bell-shaped curve. When the distribution of the observed values ​​of the coating indicator items presents normal distribution characteristics, it means that the fluctuation of the data is within a certain range, and this fluctuation is random, predictable and controllable.

[0049] In this step, a plurality of coating index items corresponding to the coating parameters are determined. That is, in order to ensure the quality of the coating process, a series of coating index items for measuring the coating quality need to be determined according to the set coating parameters.

[0050] By determining multiple coating index items corresponding to the coating parameters, it is possible to collect observation values ​​of multiple specific indicators for measuring coating quality in real time during the coating process. Through statistical analysis, such as calculating the mean value, standard deviation, process capability ratio, etc., it is possible to deeply understand the fluctuation pattern of the indicators during the coating process, provide data support for the optimization of the coating parameters, and thus improve the quality of the obtained electrode plates.

[0051] Step S108, in the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, determine the coating index data corresponding to multiple coating index items respectively, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to the full amount of observation points, and the second coating index data is the index data corresponding to multiple subgroups.

[0052] In step S108 provided in the present application, coating index data corresponding to the plurality of coating index items are determined.

[0053] Among them, coating index data is involved. Coating index data refers to the data values ​​corresponding to the coating index items calculated according to multiple observation values ​​corresponding to the multiple coating index items collected during the coating process, such as process capability ratio value, process capability index, process performance index and process indicator data, etc., which are used for real-time evaluation of coating process capability.

[0054] Among them, the first coating index data is involved. The first coating index data refers to the data value corresponding to the coating index item obtained by calculating all observation points corresponding to each coating index item during the coating process, such as the process performance index and process index data.

[0055] Among them, the second coating index data is involved. The second coating index data refers to grouping the observation points corresponding to each coating index item during the coating process, and calculating the data values ​​of multiple subgroups corresponding to the coating index items, such as the process capability ratio value and the process capability index.

[0056] Among them, it involves all observation points. An observation point is a single data point collected during the coating process, which is the measurement value of a single coating indicator such as surface density, alignment, blank width, etc. All observation points refer to all observation points collected under the corresponding coating indicator.

[0057] In this step, the coating index data corresponding to the multiple coating index items are determined. Since the comprehensive multiple coating index items can comprehensively measure the coating quality, during the coating process, multiple observation values ​​corresponding to the multiple coating index items are collected and processed in real time to obtain the corresponding coating index data, which can monitor and evaluate the coating process capability in real time, facilitate the subsequent adjustment of the coating process data, and produce a better coating layer.

[0058] Step S110, based on the coating index data corresponding to the plurality of coating index items, the initial coating process data is updated to obtain target coating process parameters until the coating of the metal foil to be coated is completed to obtain the target battery electrode.

[0059] In step S110 provided in the present application, the initial coating process data is updated according to the coating index data respectively corresponding to the plurality of coating index items to obtain the target coating process parameters.

[0060] Among them, the target coating process parameters are involved. The target coating process parameters refer to the new coating process parameters obtained by optimizing and adjusting the coating index data feedback, which meet the coating process parameter conditions for making the coating layer of the metal foil to be coated.

[0061] In this step, the coating index data is calculated and analyzed in real time, and the initially set coating process data is optimized and adjusted to obtain new coating process parameters, which can optimize the coating process quality and produce coated electrode plates that meet the standards.

[0062] It should be noted that the target coating process parameters are adjusted in real time so that more accurate parameters that can produce a coating layer with better quality can be determined during the entire coating process.

[0063] Through the above steps S102-S110, the metal foil to be coated is obtained. The coating parameters corresponding to the metal foil to be coated are determined, wherein the coating parameters include initial coating process data used in the coating process. Multiple coating index items corresponding to the coating parameters are determined, wherein the multiple coating index items are used to evaluate the quality of coating, the multiple coating index items include index items that conform to the normal distribution, each subgroup in the multiple subgroups includes multiple continuous observation points, and each coating index item corresponds to multiple subgroups. In the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, the coating index data corresponding to the multiple coating index items are determined, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to the full amount of observation points, and the second coating index data is the index data corresponding to the multiple subgroups. According to the coating index data corresponding to the multiple coating index items, the initial coating process data is updated to obtain the target coating process parameters until the coating of the metal foil to be coated is completed and the target battery pole piece is obtained. It can be seen that the embodiment of the present invention updates the coating process data according to the coating index data corresponding to the multiple coating index items to achieve the purpose of producing an electrode pole piece that meets the quality standards. Since the establishment of multiple coating index items can comprehensively measure the coating quality, the coating index data can evaluate the coating process capability in real time. Therefore, during the coating process, multiple observation points corresponding to the multiple coating index items are collected in real time, and the corresponding coating index data are calculated respectively. The coating process capability is evaluated using the coating index data, and real-time closed-loop control is performed according to the evaluation results to ensure that the produced electrode pole piece meets the quality requirements, thereby solving the technical problem in the related art that when producing battery pole pieces, it is difficult to ensure the quality of the manufactured battery pole pieces by relying on image processing to obtain real-time data to adjust the coating parameters.

[0064] As an optional embodiment, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, coating index data corresponding to multiple coating index items are determined, including: when the multiple coating index items include a process capability ratio item and the corresponding coating index data include a corresponding process capability ratio value, the target limit specification corresponding to the target coating index item is determined, wherein the target limit specification includes an upper limit specification and a lower limit specification, and the target coating index item is any one of the multiple coating index items; the specification difference between the upper limit specification and the lower limit specification is determined; based on the specification difference and the combined standard deviation corresponding to the target coating index item, the process capability ratio value corresponding to the target coating index item is determined, wherein the process capability ratio value indicates the degree of deviation of the observation points included in multiple subgroups from the target limit specifications during the coating process of the target coating index item, and the combined standard deviation is the standard deviation corresponding to the multiple subgroups.

[0065] In this embodiment, specific steps are described for determining the process capability ratio value corresponding to the target coating index item when determining coating index data corresponding to multiple coating index items respectively during the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data.

[0066] Among them, the process capability ratio item is involved. The process capability ratio item refers to the statistical indicator of the intrinsic ability of the process to maintain product quality within the specification limit when the process is not affected by external factors. That is, the target coating index item is the degree of deviation of the observation points included in multiple subgroups from the target limit specifications during the coating process.

[0067] Among them, the process capability ratio value is involved. The process capability ratio value refers to the specific numerical value of the process capability ratio item. The larger the process capability ratio value is, the higher the proportion of observations of multiple subgroups corresponding to the target coating index item within the target limit specifications during the coating process.

[0068] Among them, a target coating index item is involved, and the target coating index item refers to any one of multiple coating index items.

[0069] Among them, the target limit specifications are involved. The target limit specifications refer to the expected specification ranges corresponding to multiple coating index items in the coating process, including upper limit specifications and lower limit specifications. They are used to define the qualified standards for coating quality and can be obtained by referring to the coating production process documents.

[0070] Among them, the specification difference is involved. The specification difference refers to the difference between the upper limit specification and the lower limit specification. It is a measure of the specification width and reflects the acceptable range of the coating index item.

[0071] Among them, the combined standard deviation is involved. The combined standard deviation refers to the within-group standard deviation obtained by combining the standard deviations of multiple subgroups when conducting data grouping analysis on the target coating index items. It reflects the overall variability of the index items during the coating process.

[0072] In the steps involved in this embodiment, the target limit specification corresponding to the target coating index item is first determined, and a clear qualified range is set for the coating index item. Then, the specification difference is calculated, which is the basic step for process capability assessment. Finally, the process capability ratio value corresponding to the target coating index item is determined based on the combined standard deviation of the specification difference and the target coating index item.

[0073] Taking area density as an example, the process capability ratio is used to evaluate whether the distribution of area density data can be completely covered within the set specification limits without exceeding these limits under normal operating conditions of the coating process.

[0074] The meanings of the process capability ratios are as follows:

[0075] If the process capability ratio is greater than one, it means that the natural variability of the process is well within the specification limits, indicating that the process is capable enough to produce product that meets specifications.

[0076] If the process capability ratio equals one, the natural variability of the process exactly matches the specification limits, with no additional buffer space; the process is in a marginal state.

[0077] If the process capability ratio is less than one, the natural variability of the process exceeds the specification limits, which means that a portion of the product may not meet the quality standards and the process is incapable of capability.

[0078] In this way, the process capability ratio value of the target coating index item can be obtained. The process capability ratio value is used as an indicator for coating process quality monitoring, reflecting the natural variability of the target coating index item, that is, the relationship between the observed values ​​in the corresponding subgroup of the target coating index item and the target limit specification. When the value is less than the predetermined value, it indicates that the observed values ​​in the multiple subgroups corresponding to the target coating index item have too large variability during the coating process, and some observation points are not within the target limit specification. Measures need to be taken to optimize so as to adjust the corresponding process parameters according to the indicator, such as adjusting the coating die movement, coating speed, pressure and other parameters of the coating machine.

[0079] As an optional embodiment, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, the coating index data corresponding to multiple coating index items are determined, and it also includes: when the multiple coating index items include process capability items and the corresponding coating index data include corresponding process capability indexes, the upper limit specification, the lower limit specification and the process average corresponding to the target coating index item are determined, wherein the process average represents the average value of all observation points corresponding to the coating index item; the first difference between the upper limit specification and the process average is determined, and the second difference between the process average and the lower limit specification is determined; based on the first difference, the second difference and the combined standard deviation corresponding to the target coating index item, the process capability index corresponding to the target coating index item is determined, wherein the process capability index represents the coating fluctuation degree of the observation points included in multiple subgroups during the coating process of the target coating index item.

[0080] In this embodiment, specific steps are described for determining the process capability index corresponding to the target coating index item when determining coating index data corresponding to multiple coating index items respectively during the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data.

[0081] Among them, the process capability item is involved. The process capability item refers to the degree of fluctuation of the observation points included in multiple subgroups during the coating process of the target coating index item when the process is not affected by external factors. It determines the positional relationship between the center of the target coating index item and the specification limit during the coating process. It measures whether the output of a process can stably fall within the pre-set specification limits and whether the center of the process is close to the midpoint of these specification limits.

[0082] Among them, the process capability index is involved. The process capability index is the specific value of the process capability item. The larger the process capability ratio value is, the higher the proportion of observations of multiple subgroups corresponding to the target coating index item within the target limit specification during the coating process, and the closer the center of the target coating index item is to the midpoint of the specification limit.

[0083] Among them, the process average is involved. The process average is the center of the target coating index item, that is, the average value of all observation points corresponding to the target coating index item. It can reflect the average level of the target coating index item in the coating process and is one of the key parameters required to calculate the process capability index.

[0084] Among them, the first difference and the second difference are involved. The first difference refers to the difference between the upper limit specification corresponding to the target coating index item and the process average value, and the second difference refers to the difference between the process average value corresponding to the target coating index item and the lower limit specification.

[0085] In the steps involved in this embodiment, first determine the upper limit specification, lower limit specification and process average corresponding to the target coating index item, clarify the qualified range of the target coating index item, and determine the average level of the target coating index item in the coating process. Secondly, determine the first difference and the second difference, which can reflect the relationship between the center and specification limit of the target coating index item in the coating process. Finally, based on the first difference, the second difference and the combined standard deviation corresponding to the target coating index item, determine the process capability index corresponding to the target coating index item.

[0086] Taking areal density as an example, the process capability index can indicate whether the areal density value produced by the coating process is within the specified upper specification limit (USL) and lower specification limit (LSL), and whether the output of the process is offset or aligned with the target areal density value.

[0087] If the process capability index is greater than one, it indicates that most (at least 99.73%) of the process output falls within the specification limits and the process center is close to the midpoint of the specification limits, indicating that the process has good capability.

[0088] If the process capability index is equal to one, the output of the process just covers the specification limit, the process is in a boundary state, and there may be certain quality risks.

[0089] If the process capability index is less than one, it means that the natural variability or center position deviation of the process has caused part of the output to exceed the specification limit, the process capability is insufficient, and improvement is needed.

[0090] A high process capability index value means that the fluctuation of the areal density is small, the output of the process is concentrated near the target value, and very few products exceed the specification limit, which directly reflects the superiority of the areal density quality.

[0091] In this way, the process capability index of the target coating index item can be obtained. As an indicator of coating process quality monitoring, the process capability index can measure the degree of fluctuation of the observation points included in multiple subgroups of the target coating index item and determine the relationship between the center of the target coating index item and the specification limit. When the value is less than the predetermined value, it indicates that the observation values ​​in the multiple subgroups corresponding to the target coating index item fluctuate greatly during the coating process, and the center of the target coating index item deviates from the specification center. Measures need to be taken to optimize it so that the corresponding process parameters, such as the coating head movement, coating speed, pressure and other parameters, can be adjusted according to the indicator.

[0092] As an optional embodiment, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, coating index data corresponding to multiple coating index items are determined, including: when the multiple coating index items include process performance items and the corresponding coating index data include corresponding process performance indices, the target limit specifications corresponding to the target coating index items are determined, wherein the target limit specifications include upper limit specifications and lower limit specifications; the specification difference between the upper limit specifications and the lower limit specifications is determined; based on the specification difference and the overall standard deviation corresponding to the target coating index item, the process performance index corresponding to the target coating index item is determined, wherein the process performance index represents the degree of deviation of the target coating index item from the target limit specifications during the coating process, and the overall standard deviation is the standard deviation of the full amount of observation points corresponding to the target coating index item.

[0093] In this embodiment, specific steps are described for determining the process performance index corresponding to the target coating index item when determining coating index data corresponding to multiple coating index items respectively during the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data.

[0094] Among them, process performance items are involved. Process performance items refer to the proportion of all observation points within the target limit specifications during the coating process of the target coating index items.

[0095] Among them, the process performance index is involved. The process performance index is the specific value of the process performance item. The larger the value of the process performance index is, the higher the proportion of the total observed values ​​corresponding to the target coating index item within the target limit specification during the coating process.

[0096] Among them, the overall standard deviation is involved. The overall standard deviation refers to the standard deviation of all observation points corresponding to the target coating index item during the coating process, which is used to measure the degree of dispersion of data distribution in the entire coating process.

[0097] In the steps involved in this embodiment, first, the target limit specification corresponding to the target coating index item is determined to set a clear qualified range for the coating index item. Secondly, the target limit specification corresponding to the target coating index item is determined. Finally, based on the specification difference and the overall standard deviation corresponding to the target coating index item, the process performance index corresponding to the target coating index item is determined.

[0098] In this way, the process performance index of the target coating index item is determined. As an indicator for quality monitoring of the coating process, the process performance index can measure whether the target coating index item is controllable during a long coating process, that is, the relationship between the full observed value of the target coating index item and the target limit specification. A high process performance index value means that the surface density distribution width of the coating process is moderate, and the product will not exceed USL or LSL due to excessive variability, which directly reflects the stability of the surface density quality. However, when the value is less than the predetermined value, it indicates that the full observed value corresponding to the target coating index item has too large variability during the coating process, and some observation points are not within the target limit specifications, and measures need to be taken to optimize it.

[0099] As an optional embodiment, in the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, coating index data corresponding to multiple coating index items are determined, including: when the multiple coating index items include determining process index items and the corresponding coating index data include corresponding process index data, determining the upper limit limit specification, lower limit limit specification and process average value corresponding to the target coating index item; determining a first difference between the upper limit limit specification and the process average value, and determining a second difference between the process average value and the lower limit limit specification; determining the process index corresponding to the target coating index item based on the first difference, the second difference and the overall standard deviation corresponding to the target coating index item, wherein the process index indicates the coating fluctuation degree of all observation points of the target coating index item during the coating process.

[0100] In this embodiment, specific steps are described for determining the process indicator index corresponding to the target coating indicator item when determining the coating indicator data corresponding to multiple coating indicator items respectively during the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data.

[0101] Among them, process indicators are involved. Process indicators refer to the degree of fluctuation of the total number of observation points corresponding to the target coating indicators during the long coating process, as well as the positional relationship between the center of the target coating indicators and the specification limit.

[0102] Among them, process indicator data is involved. Process indicator data is the specific value of process indicator items. The larger the value of process indicator data, the higher the proportion of the total observation values ​​corresponding to the target coating indicator items within the target limit specifications, and the closer the center of the target coating indicator item is to the midpoint of the specification limit.

[0103] In the steps involved in this embodiment, first, the upper limit specification, lower limit specification and process average corresponding to the target coating index item are determined, the qualified range of the target coating index item is clarified, and the average level of the target coating index item in the coating process is determined. Secondly, the first difference and the second difference are determined, and the first difference and the second difference can reflect the relationship between the center and the specification limit of the target coating index item in the coating process. Finally, based on the first difference, the second difference and the overall standard deviation corresponding to the target coating index item, the process indicator data corresponding to the target coating index item is determined.

[0104] In this way, the process indicator data of the target coating index item can be obtained. As an indicator of coating process quality monitoring, the process indicator data can measure the degree of fluctuation of the full amount of observation points corresponding to the target coating index item and determine the relationship between the center of the target coating index item and the specification limit. When the value is less than the predetermined value, it indicates that the full amount of observation values ​​corresponding to the target coating index item fluctuates greatly during the coating process, and the center of the target coating index item deviates from the specification center, and measures need to be taken to optimize it.

[0105] As an optional embodiment, based on coating index data corresponding to multiple coating index items, the initial coating process data is updated to obtain target coating process parameters until the coating of the metal foil to be coated is completed to obtain a coating layer corresponding to the metal foil to be coated, including: determining a linear regression model, wherein the linear regression model includes multiple independent variables, and the multiple independent variables are variables corresponding to multiple coating index items; inputting the coating index data corresponding to the multiple coating index items into the linear regression model to determine the performance score of the current battery; based on the performance score, updating the initial coating process data to obtain the target coating process parameters.

[0106] In this embodiment, the steps of updating the initial coating process data based on coating index data corresponding to a plurality of coating index items to obtain target coating process parameters are described until the coating of the metal foil to be coated is completed and a coating layer corresponding to the metal foil to be coated is obtained, determining a linear regression model, updating the initial coating process data, and obtaining the target coating process parameters are described.

[0107] Among them, the linear regression model is involved. The linear regression model is a model that uses statistical methods to evaluate the linear relationship between multiple coating indicators and battery performance scores. The linear regression model describes the relationship between the coating indicator and the battery performance score by finding a best fit line.

[0108] Among them, independent variables are involved. Independent variables refer to variables used to predict dependent variables in a linear regression model. In this optional embodiment, the independent variables refer to multiple coating index items determined during the coating process.

[0109] Among them, the performance score is involved. The performance score refers to the score calculated by the linear regression model, which reflects the current performance level of the electrode plate.

[0110] In the steps involved in this embodiment, firstly, multiple coating index items are used as independent variables to determine a linear regression model. The addition of multiple coating index items can identify the different degrees of influence of different coating index items on the performance score of the electrode plate, such as the degree of influence. Secondly, the corresponding coating index data is input into the linear regression model to determine the performance score of the current battery, quantify the quality of the electrode plate, and finally update the initial coating process data based on the performance score to obtain the target coating process parameters.

[0111] Through this method, coating index data can be received in real time, the electrode plate performance score can be determined, the impact of multiple coating index items on the electrode plate performance score can be determined, and the future electrode plate performance score can be predicted based on the current coating index item observation point data. During the coating process, dynamic adjustments can be made according to the prediction results, and the initial coating process data can be updated to obtain the target coating process parameters and achieve closed-loop control.

[0112] As an optional embodiment, based on coating index data corresponding to a plurality of coating index items, the initial coating process data is updated to obtain target coating process parameters until the coating of the metal foil to be coated is completed and a coating layer corresponding to the metal foil to be coated is obtained, including: determining a first-order continuous system model, wherein the first-order continuous system model includes a coating process parameter item, so as to perform coating according to the data in the coating process parameter item; inputting the target coating process parameters into the first-order continuous system model, updating the data in the coating process parameter item to the target coating process parameters, so as to control and adjust the coating process, until the coating of the metal foil to be coated is completed and a target battery electrode is obtained.

[0113] In this embodiment, the specific steps of updating the initial coating process data based on the coating index data corresponding to the plurality of coating index items to obtain the target coating process parameters until the coating of the metal foil to be coated is completed and the target battery electrode is obtained, determining the first-order continuous system model, and using the first-order continuous system model to update the data in the coating process parameter items to the target coating process parameters are explained.

[0114] Among them, the first-order continuous system model is involved. The first-order continuous system model refers to a mathematical model used to describe dynamic processes, especially linear dynamic systems in control theory and system engineering. It is usually expressed as a first-order differential equation that can capture the characteristics of the process changing over time.

[0115] Among them, coating process parameter items are involved, various process parameter items involved in the coating process, such as coating speed items, slurry flow items, etc.

[0116] In the steps involved in this embodiment, first, a first-order continuous system model is determined based on the coating process parameter items and the coating index data, and the dynamic relationship between the coating process parameter items and the coating index data is determined. Secondly, the target coating process parameters are input into the first-order continuous system model, and the first-order continuous system model is adjusted according to the target coating process parameters. Finally, the data in the coating process parameter items is updated to the target coating process parameters until the coating of the metal foil to be coated is completed to obtain the target battery electrode.

[0117] In this way, the coating process can be closed-loop controlled by using a first-order continuous system model to control the data in the coating process parameter item, so that the data in the coating process parameter item is the target coating process parameter, and the coating process of the electrode plate is monitored and adjusted in real time.

[0118] Based on the above embodiments and optional embodiments, an optional implementation is provided, which is described in detail below.

[0119] In an optional embodiment of the present invention, a method for manufacturing a battery electrode is provided, which can also be called an intelligent coating closed-loop control method based on lithium battery coating process data. It makes full use of the intelligent diagnostic algorithm model library, analyzes the collected real-time data, compares the standard range of the corresponding parameters in the control plan to determine the results and mark the problem areas, and transmits the parameters back to the coating equipment to perform closed-loop control of the manufacturing process quality.

[0120] The optional implementation of the present invention focuses on the quality control of the coating production process. Aiming at the various quality risk factors existing in current production, an intelligent diagnosis algorithm model is designed. This model can quickly locate and mark the problem points in the coating process by analyzing the production data in real time, and feedback to the equipment to form a closed-loop control, thereby reducing the quality risk of the manufacturing process. The intelligent diagnosis algorithm model has the following core functions:

[0121] Intelligent analysis and positioning: The model can receive various data from the coating production process in real time, and use advanced algorithms for rapid analysis to accurately identify potential quality problems, such as uneven coating, bubbles, impurities, etc.

[0122] Automatic data processing: The system not only automatically collects data, but also automatically cleans the data to remove noise and erroneous values ​​to ensure the accuracy of the analysis results. In addition, the system can automatically generate charts to display production data and analysis results in an intuitive way.

[0123] Automatic push of analysis results: Once a problem is found, the system can automatically push the analysis results to relevant personnel so that they can take corrective measures in time to reduce production losses.

[0124] Linkage with post-process equipment: In order to realize the automation and intelligence of the production process, the present invention also designs a linkage mechanism with post-process equipment. When a problematic coil is detected, the system can automatically trigger a rejection mechanism to remove the problematic product from the production line to prevent it from entering the subsequent process, thereby further improving product quality.

[0125] Continuous optimization and improvement: With the continuous accumulation of production data, the intelligent diagnosis algorithm model of the present invention can also be continuously optimized and improved through advanced technologies such as machine learning, so as to continuously improve its diagnostic accuracy and efficiency.

[0126] The optional implementation of the present invention generally includes the following steps:

[0127] Confirm the key process parameters of coating, such as surface density, alignment, blank width, coating width, etc.; use the Internet of Things platform to create equipment types and corresponding data models, and configure collection parameters and collection frequency; use network communication protocols (TCP / IP), message queue telemetry transmission (MQTT), open platform communication unified architecture (OPC UA) and other communication methods to realize automatic data collection, and use high-performance storage platforms for big data storage; use big data platforms to obtain process data for data cleaning; configure process standards and set upper and lower parameter thresholds; establish a diagnostic algorithm model library service layer to classify, label and index models, quickly find the model that best matches the current data, and generate graphics and charts in combination with process standards; use big data and host-side (PC-side) data analysis and reporting platforms to realize data anomaly marking and early warning notifications for points in the graphics that exceed the threshold through algorithm model operations; obtain the ratio between the historical defect change and the normal coating change, determine the parameter change, and feed back the change in real time to the equipment host system for control and adjustment.

[0128] The specific steps involved in the optional implementation manner of the present invention are introduced below.

[0129] S1. Obtain metal foil to be coated. The metal foil to be coated refers to the battery pole piece on the battery production line that has not yet been subjected to the coating process and is waiting to be coated with electrode cell slurry on its surface to form an electrode pole piece.

[0130] S2. Determine coating parameters corresponding to the metal foil to be coated, wherein the coating parameters include initial coating process data used in the coating process.

[0131] Among them, coating parameters refer to a series of control parameters that need to be set in order to ensure the coating quality during the lithium battery coating process. These parameters affect important characteristics such as coating uniformity, thickness, width, etc. For example, coating speed, coating pressure, slurry viscosity, slurry flow, etc. Initial coating process data refers to a set of parameters set before the start of coating. They are determined based on factors such as battery design requirements and electrode cell slurry composition, and are used to guide the coating machine for coating.

[0132] S3. Determine a plurality of coating index items corresponding to the coating parameters, wherein the plurality of coating index items are used to evaluate the coating quality, and the plurality of coating index items include index items that conform to a normal distribution.

[0133] It should be noted that the coating index items are based on historical coating process data to confirm the key process parameters that affect quality, such as surface density, alignment, margin width, coating width, slurry distribution uniformity, coating thickness, etc. These indicators reflect the key quality attributes in the coating process.

[0134] S4. Collect observation point data of multiple coating index items.

[0135] Specifically, the following steps may be included:

[0136] S402. Use the Internet of Things platform to create the coating process equipment type and corresponding data model of the metal foil to be coated, and configure the collection parameters and collection frequency according to the equipment type and data model. The parameters include the type of data to be collected, data format, data unit, etc. To ensure the real-time nature of the data, the collection frequency adopts the change-as-you-go mode.

[0137] S404. Configure communication methods such as network communication protocol (TCP / IP), message queue telemetry transmission (MQTT), open platform communication unified architecture (OPC UA), etc. to realize automatic data collection, send device data to the central server or cloud platform, and use high-performance storage platforms such as distributed file systems or relational databases (MYSQL databases) to store big data.

[0138] S406. Use the big data platform to obtain process data for data cleaning, identify and correct errors, remove duplications, fill missing values, convert data types and formats to obtain accurate, consistent and easy-to-analyze data sets.

[0139] S408. According to the coating production process document, the manufacturing system is configured with process standards to set upper and lower parameter thresholds.

[0140] S5. Establish a diagnostic algorithm model library service layer, including: process capability algorithm model library, process control algorithm model library. Classify, label and index the models, quickly find the model that best matches the current data, and automatically generate graphics and charts in combination with process standards.

[0141] Specifically, the following steps may be included:

[0142] S502. Construct a process capability algorithm model library: In the process of coating the electrode cell slurry on the metal foil to be coated based on the initial coating process data, determine the coating index data corresponding to the multiple coating index items respectively, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to all observation points, the second coating index data is the index data corresponding to multiple subgroups, each of the multiple subgroups includes multiple continuous observation points, and each coating index item corresponds to multiple subgroups.

[0143] It should be noted that the application of normal distribution diagram in the process capability algorithm model library realizes big data model analysis by collecting data for extraction. If the process parameter X obeys a normal distribution with a mathematical expectation of μ and a variance of δ, it is recorded as N(μ, δ). Its probability density function is the expected value μ of the normal distribution, which determines its position, and its standard deviation δ determines the amplitude of the distribution. When μ=0, δ=1, the normal distribution is the standard normal distribution.

[0144] The specific steps to build a process capability algorithm model library are as follows:

[0145] A1. Calculate the combined standard deviation:

[0146] In an optional embodiment of the present invention, normal capability analysis estimates the within-group standard deviation, and the method used to estimate the within-group standard deviation δ within the subgroup depends on the subgroup size.

[0147] When the subgroup size is > 1, the within-group standard deviation is estimated using one of the following methods:

[0148] The combined standard deviation (δ within ):

[0149]

[0150] in:

[0151]

[0152] If the default method is changed and you choose not to use an unbiasing constant, δ will be estimated from SP.

[0153] Where d represents the degree of freedom of SP, which is calculated using ∑(ni-1); x ij represents the jth observation in the i-th subgroup; represents the mean of the ith subgroup, x i Represents the value of each observation point; n i represents the number of observations in the ith subgroup; C4(D+1) is an unbiasing constant, and Γ(·) is the Euler second integral (Gamma) function.

[0154] When subgroup size = 1, the within-group standard deviation is estimated using one of the following methods:

[0155] Moving range average (δ xbar ):

[0156]

[0157] in:

[0158] R i =Max[x i ...,x i-w+1 ]-Min[x i ...,x i-w+1 ],i=w

[0159]

[0160] Ri represents the i-th moving range; w represents the number of observations used in the moving range. By default, w = 2; d2(w) is the unbiasing constant read from the table, and d2(w) is the expected value of the total number of observations (N) in the normal population distribution (standard deviation = 1).

[0161] Therefore, if r is the range (E(r)) of a sample of a total number (N) of observations from a normal distribution (standard deviation = δ), then:

[0162] E(r)=d2(N)δ

[0163] The resulting within-group standard deviation is the combined standard deviation.

[0164] A2. Normal capability analysis for estimating overall standard deviation δ in an optional implementation of the present invention overall :

[0165]

[0166] in:

[0167]

[0168] By default, no unbiasing constant is used when estimating δ overall, and the overall standard deviation is estimated by the sample standard deviation S; represents the process mean; C4(N) is the unbiasing constant (defined for the combined standard deviation); N (or ∑n i) represents the total number of observations.

[0169] A3. Analyze the potential of coating indicators in the coating process:

[0170] Methods and formulas for potential capability measures in normal capability analysis:

[0171] Process capability ratio CP:

[0172]

[0173] Where USL is the upper specification limit, LSL is the lower specification limit, and δ within is the standard deviation within the subgroup. The higher the CP value, the smaller the variability of the process and the stronger the process capability.

[0174] According to the process standards, the target surface density is set at 90g / m 2 , the USL of the surface density is set to 100g / m 2 , LSL is set to 80g / m 2 50 samples were automatically collected, and the surface density data were distributed in the range of 80 to 100 g / m 2 Assuming the calculated CP value is between 90g / m 2 The deviation is large, and improvement measures need to be taken to adjust the coating machine's coating head movement, coating speed, pressure and other parameters.

[0175] Process capability index CPK:

[0176]

[0177] Process average u:

[0178]

[0179] Where USL is the upper specification limit, LSL is the lower specification limit, and δ within It is the standard deviation within the subgroup. The larger the CPK value is, the stronger the process capability is, that is, the smaller the variability of the process is, and the closer the mean value of the process is to the specification center.

[0180] According to the process standard, the USL of the surface density is set to 100g / m 2 , LSL is set to 80g / m 2 50 samples were automatically collected, and the surface density data were distributed in the range of 80 to 100 g / m 2 If the calculated result CPK=1.2, it means that the process capability is good, but there is still room for improvement. If the calculated result CPK=1.33, improvement measures need to be taken to adjust parameters such as the movement of the coating die, coating speed, and pressure.

[0181] A4. Analyze the overall capability of coating index items during the coating process:

[0182] In more specific technical scenarios, the methods and formulas used to measure overall capability in normal capability analysis are:

[0183] Process performance Pp:

[0184]

[0185] Where USL is the upper specification limit, LSL is the lower specification limit, and δ overall is the overall standard deviation. Pp refers to the proportion of process performance within the specification limit. The larger the Pp value, the smaller the variability of the process within the specification range and the stronger the capability.

[0186] According to the process standards, the upper limit of the specification for the surface density of lithium battery coating is USL 100g / m 2 , the lower specification limit LSL is 80g / m 2 The overall standard deviation of the automatic acquisition parameters is 2g / m 2 , Pp=(100-80) / (6*2)=10 / 12=0.83. In the optional implementation manner of the present invention, the Pp value is 0.83, indicating that the variability of the process within the specification range is relatively large, and improvement measures need to be taken.

[0187] Process indicator PpK:

[0188]

[0189] Process average u:

[0190]

[0191] Where USL is the upper specification limit, LSL is the lower specification limit, and δ overall is the overall standard deviation.

[0192] According to the process standards, the upper limit of the specification for the surface density of lithium battery coating is USL 100g / m 2 , the lower specification limit LSL is 80g / m 2 , automatically collect n samples, the average value u is 90g / m 2 , the overall standard deviation is 2g / m 2 Ppk = min(100-90,90-80) / (3*2) = min(10,10) / 6 = 10 / 6≈1.67. The Ppk value is 1.67, indicating that the process performs well in meeting the specification requirements.

[0193] A5. In a more specific technical solution, Pz represents the proportion of process output exceeding a special specified value (such as an engineering specification limit), which is separated from the mean of the process by z standard deviation units. When z takes the value of -2.17, it means that the specified limit value is 2.17 standard deviations below the mean of the process. In a normal distribution, this area (i.e., 2.17 standard deviations below the mean) contains approximately 1.5% of the data points.

[0194] Figure 2 is a normal distribution diagram of an embodiment provided by an optional embodiment of the present invention, such as Figure 2 As shown, the average surface density (μ) of the lithium battery coating process is 90g / m 2 , standard deviation is 2g / m 2 . A lower limit is set so that only 1.5% of the product surface density will be below this limit. According to the normal distribution, the area below 2.17 standard deviations from the mean contains approximately 1.5% of the data points. Therefore, the lower limit (LSL) can be calculated by the following formula: LSL = u-2.17*δ, inserting the given value: LSL = 90-2.17×2 = 85.66g / m 2 , USL = u + 2.17 * δ, insert the given value: USL = 90 + 2.17 × 2 = 94.34 g / m 2 .

[0195] A6. Use the normal distribution graph to apply the process capability algorithm model, combined with the process standards, to generate graphs and charts. Compare the overall solid curve with the group's dashed curves to determine how closely they align. If the curves differ little, Figure 3 is a curve fitting diagram generated by the process capability algorithm model provided by an optional embodiment of the present invention, such as Figure 3 If there are large differences between the curves, Figure 4 is another curve fitting diagram generated by the process capability algorithm model provided by an optional embodiment of the present invention, such as Figure 4 , the process may not be stable or there may be significant variation between subgroups.

[0196] Compare the overall solid curve to the bars of the histogram to assess whether the data are approximately normal. If the bars fit the curve well, Figure 5 is another curve fitting diagram generated by the process capability algorithm model provided in an optional embodiment of the present invention, such as Figure 5 If the bar is very different from the curve, the fit is poor. Figure 6 is another curve fitting diagram generated by the process capability algorithm model provided by an optional embodiment of the present invention, such as Figure 6 , your data may not be normal and the capability estimates for your process may not be reliable.

[0197] S504, construct a process control algorithm model library.

[0198] It should be noted that in the process control algorithm model library, big data model analysis is achieved by collecting and refining data. The library contains linear regression algorithms, time series analysis, continuous and discrete system models, etc.

[0199] The specific types of building process control algorithm model libraries are as follows:

[0200] B1. Use linear regression algorithm to predict coating quality.

[0201] A linear regression model is trained based on stored historical data, including key coating quality parameters (area density, alignment, margin width, coating width) and one or more target variables (such as battery performance score).

[0202] The coating index data corresponding to the multiple coating index items are respectively input into the linear regression model to determine the performance score of the current battery.

[0203] It should be noted that the linear regression class in Python's sklearn library can be used to train the model. The model parameters (intercept and slope) and the predicted battery performance score based on the new data points are obtained. Table 1 is a historical data table of the coating process, as shown in Table 1 (the data is fictitious and is only used for example):

[0204] Table 1

[0205] Alignment (0-10) Blank width(mm) Coating width(mm) Battery performance score (0-100) 8.5 2 1000 85 9.0 1.5 1010 90 7.8 2.2 990 80 … … … …

[0206] The general form of a linear regression model is:

[0207] y=\beta_0+\beta_1x_1+\beta_2x_2+\beta_3x_3+\beta_4x_4]

[0208] Among them, y is the target variable (battery performance score), (x_1, x_2, x_3, x_4) are surface density, alignment, margin width, and coating width respectively, and (\beta_0,\beta_1,\beta_2,\beta_3,\beta_4) are the parameters of the model (intercept and slope).

[0209] B2. Time series analysis is used to analyze data arranged in chronological order to discover its patterns and trends.

[0210] In the coating process, time series analysis is used to monitor and predict the changing trends of key quality parameters (such as surface density, alignment, margin width, coating width), and the moving average model is used for time series analysis to smooth short-term fluctuations and highlight long-term trends or cycles.

[0211] The formula for the moving average model is:

[0212] [MA_t=\frac{1}{n}\sum_{i=0}^{n-1}Y_{ti}],

[0213] Where (MA_t) is the moving average at time (t), (Y_{ti}) is the observation at time (ti), and (n) is the window size of the moving average.

[0214] Figure 7 : is a coating surface density time series diagram provided by an optional embodiment of the present invention, wherein the curve in the sequence diagram has a horizontal coordinate of the scanning time, and a vertical coordinate of the net coating amount partition data of the AB surface surface density of the positive electrode coating, the blue curve represents the mean value of the AB surface surface density net coating amount partition data 1, the red curve represents the mean value of the AB surface surface density net coating amount partition data 2, the green curve represents the mean value of the AB surface surface density net coating amount partition data 3, the purple curve represents the mean value of the AB surface surface density net coating amount partition data 4, the orange curve represents the mean value of the AB surface surface density net coating amount partition data 5, and the cyan curve represents the mean value of the AB surface surface density net coating amount partition data 6, and the mean range of the partition data is 406.5 to 415.5. Figure 7 The coating surface density time series diagram shows the original surface density time series data and the predicted number based on a simple exponential smoothing model, so as to adjust the process parameters in time to ensure product quality.

[0215] B3. Determine the first-order continuous system model to perform closed-loop control on coating index items.

[0216] A first-order continuous system model is determined, wherein the first-order continuous system model includes coating process parameter items.

[0217] In an optional embodiment of the present invention, the continuous system model controls the coating surface density, and the coating surface density is a continuously changing parameter, which is controlled by adjusting continuous variables such as coating speed, coating pressure and coating flow rate.

[0218] A simple first-order continuous system model is used to describe the change in coating surface density. The model can be expressed as:

[0219] [\frac{dD}{dt}=k_1\cdot(FD)]

[0220] Where: D represents the coating surface density (unit: g / cm 2); t represents time (unit: s); F represents target surface density (unit: g / cm 2 ); k_1 represents the system gain, which reflects the response speed of the system to the control input (unit: 1 / s).

[0221] The model describes the relationship between the rate of change of coating surface density over time and the difference between the current surface density and the target surface density. By adjusting parameters such as coating flow rate and coating speed, the system gain (k_1) can be changed to control the coating surface density to reach the target value.

[0222] S6. According to steps S5 and S6, big data and PC data analysis and reporting platform are used to automatically generate normal distribution, discrete and other graphs through algorithm model operations, calculate coating index items, generate diagnostic reports, etc., and mark data anomalies and issue warning notifications for points in the graph that exceed the threshold.

[0223] S7. Update the initial coating process data according to the coating index data corresponding to the plurality of coating index items, and obtain the target coating process parameters, until the coating of the metal foil to be coated is completed, and the target battery electrode is obtained.

[0224] According to steps S6 and S7, key process parameter data processing is implemented through the process capability algorithm model library and the process control algorithm model library, the ratio between the historical defect change and the normal coating change is obtained, the parameter change is determined, and the change is fed back to the equipment host system in real time for control and adjustment.

[0225] The process capability algorithm model library and process control algorithm model library classify, label and index the models, quickly find the model that best matches the current data, and automatically generate graphics and charts in combination with process standards. The intelligent diagnosis algorithm model library is used to analyze the collected real-time data, compare the standard range of the corresponding parameters in the control plan to determine the results and mark the problem areas, and send the parameters back to the coating equipment for closed-loop control of the manufacturing process quality.

[0226] Through the above optional implementation, at least the following beneficial effects can be achieved:

[0227] (1) Determine multiple coating index items corresponding to coating parameters, and collect observation values ​​of multiple specific indicators measuring coating quality in real time during the coating process. Through statistical analysis, such as calculating the mean value, standard deviation, process capability ratio, etc., the fluctuation law of the indicators during the coating process can be deeply understood, providing data support for the optimization of coating parameters, thereby improving the quality of the obtained electrode sheet;

[0228] (2) Establishing a process capability algorithm model, which can determine the process capability ratio value, process capability index, process performance index, process indicator data and other coating indicator data corresponding to the coating indicator items according to the collected coating indicator item observation values, determine the fluctuation of the coating indicator item observation values ​​within the specification limit during the coating process, and the position relationship between the coating indicator item center and the specification center, and evaluate the coating process capability in real time, so as to facilitate the subsequent adjustment of the coating process data;

[0229] (3) Establish a process capability algorithm model, use linear regression algorithm, time series analysis, continuous and discrete system models and other methods, perform closed-loop control of the coating process based on coating index items and coating index data, update the initial coating process data, obtain the target coating process parameters, and produce a coating layer of a battery that meets the standards.

[0230] (4) The optional implementation of the present invention constructs an intelligent diagnostic algorithm model for the quality control of the lithium battery coating process, which can accurately respond to various quality risk factors. Through automated data collection, cleaning, chart generation and result push, the model can quickly locate and mark problem points in the coating process, achieve real-time feedback and closed-loop control, thereby greatly improving the efficiency of coating surface density and CCD detection data analysis, significantly improving CPK consistency, and achieving a significant improvement in product quality.

[0231] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0232] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0233] Example 2

[0234] According to an embodiment of the present invention, there is also provided a device for implementing the above-mentioned method for manufacturing a battery electrode sheet. Figure 8 is a structural block diagram of a device for manufacturing a battery electrode according to an embodiment of the present invention, such as Figure 8 As shown, the device includes: an acquisition module 802, a first determination module 804, a second determination module 806, a third determination module 808 and a fourth determination module 810. The device is described in detail below.

[0235] An acquisition module 802 is used to acquire the metal foil to be coated; a first determination module 804 is connected to the acquisition module 802 and is used to determine coating parameters corresponding to the metal foil to be coated, wherein the coating parameters include initial coating process data used in the coating process; a second determination module 806 is connected to the first determination module 804 and is used to determine a plurality of coating index items corresponding to the coating parameters, wherein the plurality of coating index items are used to evaluate the quality of coating, the plurality of coating index items include index items that conform to a normal distribution, each of the plurality of subgroups includes a plurality of continuous observation points, and each coating index item corresponds to a plurality of subgroups; a third determination module 808 is connected to the second determination module 806 and is used to determine a plurality of coating index items corresponding to the coating parameters, wherein the plurality of coating index items are used to evaluate the quality of coating, the plurality of coating index items include index items that conform to a normal distribution, each of the plurality of subgroups includes a plurality of continuous observation points, and each coating index item corresponds to a plurality of subgroups; A determination module 806 is used to determine coating index data corresponding to multiple coating index items in the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to the full amount of observation points, and the second coating index data is the index data corresponding to multiple subgroups; a fourth determination module 810 is connected to the above-mentioned third determination module 808, and is used to update the initial coating process data according to the coating index data corresponding to the multiple coating index items, and obtain the target coating process parameters until the coating of the metal foil to be coated is completed and the target battery electrode is obtained.

[0236] It should be noted here that the above-mentioned acquisition module 802, the first determination module 804, the second determination module 806, the third determination module 808 and the fourth determination module 810 correspond to steps S102 to S110 in the method for implementing the manufacturing method of the battery electrode, and the examples and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0237] Example 3

[0238] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the instructions to implement any of the above-mentioned methods for manufacturing a battery electrode.

[0239] Example 4

[0240] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute any of the above-mentioned methods for manufacturing a battery electrode.

[0241] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0242] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0243] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0244] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0245] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0246] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for manufacturing a battery electrode, characterized in that: include: Obtaining a metal foil to be coated; Determining coating parameters corresponding to the metal foil to be coated, wherein the coating parameters include electrode core slurry and initial coating process data used in the coating process; Determine a plurality of coating index items corresponding to the coating parameters, wherein the coating index items are used to evaluate the quality of coating, the coating index items include index items that conform to a normal distribution, each coating index item corresponds to a plurality of subgroups, and each of the plurality of subgroups includes a plurality of continuous observation points; In the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, coating index data corresponding to the multiple coating index items are determined, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to the full amount of observation points, and the second coating index data is the index data corresponding to multiple subgroups; The initial coating process data is updated according to the coating index data respectively corresponding to the plurality of coating index items to obtain target coating process parameters until the coating of the metal foil to be coated is completed to obtain a target battery electrode.

2. The method according to claim 1, characterized in that In the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, coating index data corresponding to the plurality of coating index items are determined, including: In the case where the plurality of coating index items include a process capability ratio item and the corresponding coating index data include a corresponding process capability ratio value, determining a target limit specification corresponding to the target coating index item, wherein the target limit specification includes an upper limit specification and a lower limit specification; Determine a specification difference between the upper limit specification and the lower limit specification; Based on the specification difference and the combined standard deviation corresponding to the target coating index item, the process capability ratio value corresponding to the target coating index item is determined, wherein the process capability ratio value represents the degree of deviation of multiple observation points respectively included in multiple subgroups from the target limit specifications during the coating process of the target coating index item, and the combined standard deviation is the standard deviation when multiple subgroups are combined.

3. The method according to claim 1, characterized in that In the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, determining coating index data corresponding to the plurality of coating index items respectively, further comprising: In the case where the multiple coating index items include a process capability item and the corresponding coating index data include a corresponding process capability index, determining an upper limit specification, a lower limit specification and a process average corresponding to the target coating index item, wherein the process average represents the average value of all observation points corresponding to the coating index item; determining a first difference between the upper limit specification and the process mean, and determining a second difference between the process mean and the lower limit specification; Based on the first difference and the second difference, the process capability index corresponding to the target coating index item is determined, wherein the process capability index represents the coating fluctuation degree of multiple observation points within a subgroup during the coating process of the target coating index item.

4. The method according to claim 1, characterized in that: In the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, coating index data corresponding to the plurality of coating index items are determined, including: In the case where the plurality of coating index items include a process performance item and the corresponding coating index data include a corresponding process performance index, determining an upper limit specification and a lower limit specification corresponding to the target coating index item; Determine a specification difference between the upper limit specification and the lower limit specification; Based on the specification difference and the overall standard deviation corresponding to the target coating index item, the process performance index corresponding to the target coating index item is determined, wherein the process performance index represents the degree of deviation of the total number of observation points from the target limit specifications during the coating process of the target coating index item, and the overall standard deviation is the standard deviation of the total number of observation points corresponding to the target coating index item.

5. The method according to claim 1, characterized in that In the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, coating index data corresponding to the plurality of coating index items are determined, including: In the case where the plurality of coating index items include a determination process index item, and the corresponding coating index data include corresponding process index data, determining an upper limit specification, a lower limit specification, and a process average value corresponding to the target coating index item; determining a first difference between the upper limit specification and the process mean, and determining a second difference between the process mean and the lower limit specification; Based on the first difference and the second difference, the process indicator index corresponding to the target coating index item is determined, wherein the process indicator index represents the coating fluctuation degree of the corresponding full amount of observation points during the coating process of the target coating index item.

6. The method according to claim 1, characterized in that According to the coating index data respectively corresponding to the plurality of coating index items, the initial coating process data is updated to obtain target coating process parameters until the coating of the metal foil to be coated is completed to obtain a target battery electrode, including: Determine a linear regression model, wherein the linear regression model includes a plurality of independent variables, and the plurality of independent variables are variables corresponding to the plurality of coating index items respectively; Inputting the coating index data corresponding to the plurality of coating index items into the linear regression model to determine the performance score of the current battery; According to the performance score, the initial coating process data is updated to obtain the target coating process parameters.

7. The method according to any one of claims 1 to 6, characterized in that According to the coating index data respectively corresponding to the plurality of coating index items, the initial coating process data is updated to obtain target coating process parameters until the coating of the metal foil to be coated is completed to obtain a target battery electrode, including: Determining a first-order continuous system model, wherein the first-order continuous system model includes a coating process parameter item, so as to perform coating according to data in the coating process parameter item; The target coating process parameters are input into the first-order continuous system model, and the data in the coating process parameter item is updated to the target coating process parameters to control and adjust the coating process until the coating of the metal foil to be coated is completed to obtain the target battery electrode.

8. A battery electrode manufacturing device, characterized in that: include: An acquisition module, used for acquiring the metal foil to be coated; A first determination module is used to determine coating parameters corresponding to the metal foil to be coated, wherein the coating parameters include electrode core slurry and initial coating process data used in the coating process; A second determination module is used to determine a plurality of coating index items corresponding to the coating parameters, wherein the coating index items are used to evaluate the quality of coating, the coating index items include index items that conform to a normal distribution, each coating index item corresponds to a plurality of subgroups, and each of the plurality of subgroups includes a plurality of continuous observation points; A third determination module is used to determine coating index data corresponding to the plurality of coating index items respectively in the process of coating the electrode cell slurry on the metal foil to be coated according to the initial coating process data, wherein the coating index data includes first coating index data and second coating index data, the first coating index data is the index data corresponding to the full amount of observation points, and the second coating index data is the index data corresponding to the plurality of subgroups; The fourth determination module is used to update the initial coating process data according to the coating index data corresponding to the multiple coating index items, and obtain the target coating process parameters until the coating of the metal foil to be coated is completed to obtain the target battery electrode.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the instructions to implement the method for manufacturing a battery electrode sheet as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for manufacturing a battery electrode sheet as claimed in any one of claims 1 to 7.

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