Drying state prediction method for electrode sheet and drying state prediction device for electrode sheet

Abstract

The present application relates to a drying state prediction method for an electrode sheet and a drying state prediction device for an electrode sheet. The drying state prediction method for an electrode sheet comprises: under a plurality of drying parameters, respectively drying electrode sheets to be predicted, wherein in the drying process, when the mass of said electrode sheets at the Nth moment and the mass of said electrode sheets at the (N-1)th moment meet a preset threshold value, said electrode sheets are dried into dry electrode sheets; obtaining electrode sheet data of said electrode sheets corresponding to different moments in the drying process, and constructing corresponding models between the moments and the electrode sheet data under the plurality of drying parameters, respectively; and determining, on the basis of the corresponding models respectively corresponding to the plurality of drying parameters, target data sets corresponding to target times under at least two of the plurality of drying parameters. Embodiments of the present application can effectively predict the drying characteristics of electrode sheets in the drying process, and can improve the prediction precision.

Description

Drying state prediction method and drying state prediction device of electrode plate

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. 202410323423.0 filed on March 20, 2024, entitled “Drying state prediction method and drying state prediction device for electrode sheets,” the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application relates to the field of battery manufacturing, and in particular to a method and device for predicting the drying state of an electrode plate. Background Art

[0004] Batteries are widely used due to their reliable performance, pollution-free operation, and zero memory effect. For example, with increasing attention paid to environmental protection and the growing popularity of new energy vehicles, demand for batteries is expected to surge.

[0005] The electrode plates in the battery are an important component of the battery. The performance of the electrode plates has an important influence on the performance of the battery. During the preparation process of the electrode plates, especially the drying process, the drying characteristics have an important influence on the performance of the electrode plates. Therefore, how to predict the drying characteristics of the electrode plates during the drying process is an urgent problem to be solved in this field. Summary of the Invention

[0006] The embodiments of the present application provide a method and a device for predicting the drying state of an electrode plate. The drying state prediction method of the embodiments of the present application can effectively predict the drying characteristics of the electrode plate during the drying process and can improve the prediction accuracy.

[0007] In the first aspect, an embodiment of the present application proposes a method for predicting the drying state of an electrode plate, comprising: drying the electrode plate to be tested under multiple drying parameters, and when the quality of the electrode plate to be tested at the Nth moment and the quality at the N-1th moment during the drying process meet a preset threshold, the electrode plate to be tested is dried and processed into a dry electrode plate; obtaining electrode plate data of the electrode plate to be tested corresponding to different moments in the drying process, and constructing corresponding models between moments and electrode plate data under multiple drying parameters; based on the corresponding models corresponding to the multiple drying parameters, determining a target data set corresponding to the target time under at least two of the multiple drying parameters, the target data set includes multiple first target electrode plate data and multiple second target electrode plate data, the first target electrode plate data and the second target electrode plate data are respectively located in different corresponding models, the multiple first target electrode plate data respectively correspond to multiple first target moments in the target time, and the multiple second target electrode plate data respectively correspond to multiple second target moments in the target time.

[0008] Therefore, the drying state prediction method of the electrode plate of the embodiment of the present application obtains the plate data of the electrode plate to be tested during the drying process, and constructs a corresponding model of the time and plate data of the electrode plate to be tested during the drying process; by performing drying treatment under different drying parameters respectively, a corresponding model of the electrode plate to be tested under different drying parameters can be constructed; the corresponding model constructed above can predict the drying state of the electrode plate to be tested under multiple drying parameters in sequence; the above prediction method has high accuracy, which is conducive to guiding the optimization of process parameters in the drying process.

[0009] In some embodiments, the steps of obtaining electrode data of the electrode to be tested corresponding to different moments in the drying process, and constructing corresponding models between the moments and the electrode data under multiple drying parameters include: obtaining multiple sub-data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes multiple sub-data; and constructing sub-models of the moments and the sub-data under multiple drying parameters, wherein the corresponding models include multiple sub-models.

[0010] Therefore, the embodiment of the present application can obtain multiple sub-data and construct multiple sub-models respectively, so as to make a more comprehensive prediction of the drying state and improve the accuracy of the prediction.

[0011] In some embodiments, the steps of obtaining electrode data of the electrode to be tested corresponding to different moments in the drying process, and constructing corresponding models between the moments and the electrode data under multiple drying parameters include: obtaining electrode temperature data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes electrode temperature data; constructing temperature models of the moments and electrode temperature data under multiple drying parameters, wherein the corresponding models include temperature models; obtaining electrode quality data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes electrode quality data; and constructing quality models of the moments and electrode quality data under multiple drying parameters, wherein the corresponding models include quality models.

[0012] Therefore, in the embodiment of the present application, the electrode data includes electrode temperature data and electrode quality data. By monitoring the temperature and quality of the electrode, the drying state of the electrode can be effectively predicted through temperature and mass changes; and monitoring the temperature and quality of the electrode separately can simplify the test operation and improve the test accuracy.

[0013] In some embodiments, the step of obtaining the electrode temperature data of the electrode to be tested corresponding to different moments during the drying process includes: obtaining the electrode temperature data of the same electrode to be tested at multiple moments during the drying process.

[0014] Therefore, the embodiment of the present application can improve the accuracy of temperature detection by obtaining the electrode temperature data of the same electrode to be tested during the drying process.

[0015] In some embodiments, the step of obtaining the electrode quality data of the electrode to be tested corresponding to different moments during the drying process includes: obtaining different electrode to be tested at multiple moments during the drying process, and measuring the electrode quality data corresponding to each moment, wherein the number of electrode to be tested is multiple.

[0016] Therefore, the embodiment of the present application can improve the accuracy of quality detection by obtaining electrode quality data of different electrodes with the same specifications to be tested during the drying process, reduce the interference of quality detection on the drying state of the electrode, and reduce the impact on the accuracy of the quality detection results.

[0017] In some embodiments, the time intervals between any two adjacent moments in the multiple moments are the same. The same time intervals are beneficial to model construction and can improve the accuracy of model fitting.

[0018] In some embodiments, the electrode data of the electrode to be tested corresponding to different moments in the drying process are obtained, and the corresponding models between the moments and the electrode data are constructed under multiple drying parameters, respectively, including: obtaining the test parameters of multiple areas of the electrode to be tested corresponding to different moments in the drying process; calculating the average value of the test parameters of the multiple areas as the electrode data corresponding to each moment.

[0019] Therefore, the embodiment of the present application can improve the accuracy of obtaining the electrode data by collecting the test parameters of multiple areas corresponding to each moment and taking the average value of the test parameters of multiple areas as the electrode data corresponding to each moment, thereby further improving the prediction accuracy of the electrode drying state.

[0020] In some embodiments, based on the corresponding models corresponding to the multiple drying parameters, the step of determining the target data set corresponding to the target time under at least two of the multiple drying parameters includes: determining the first target data set corresponding to the first target time based on the corresponding model corresponding to one of the multiple drying parameters, the first target data set including multiple first target pole piece data, and the multiple first target pole piece data respectively correspond to multiple first target moments in the first target time; recording the end moment of the first target time as the first end moment, and determining the first end point pole piece data corresponding to the first end moment; based on the corresponding model corresponding to another one of the multiple drying parameters, determining the moment corresponding to the first end point pole piece data as the first starting moment; determining the second target data set corresponding to the second target time, the second target data set including multiple second target pole piece data, and the multiple second target pole piece data respectively correspond to multiple second target moments in the second target time, and the second target moment is determined according to the first starting moment and the time interval with the first starting moment.

[0021] In some embodiments, the step of providing a pole piece to be tested includes: providing a current collector; disposing a slurry coating on the current collector to form a pole piece to be tested, wherein the pole piece to be tested includes coating parameters of the slurry coating;

[0022] The steps of obtaining the electrode data of the electrode to be tested corresponding to different moments in the drying process, and constructing corresponding models between the moments and the electrode data under multiple drying parameters, include: obtaining the electrode data of the electrode to be tested corresponding to different moments in the drying process; and constructing corresponding models between the moments and the electrode data under multiple drying parameters, wherein the corresponding models correspond to the coating parameters.

[0023] Therefore, the embodiments of the present application can effectively predict the drying characteristics of the same electrode to be tested under various drying parameters by constructing corresponding models under various drying parameters.

[0024] In a second aspect, an embodiment of the present application proposes a drying state prediction device for an electrode plate, comprising a providing module, a drying module, a building module and a prediction module, wherein the providing module is configured to provide an electrode plate to be tested; the drying module is configured to dry the electrode plate to be tested under multiple drying parameters respectively, and when the quality of the electrode plate to be tested at the Nth moment and the quality at the N-1th moment during the drying process meet a preset threshold, the electrode plate to be tested is dried and processed into a dry electrode plate; the building module is configured to obtain electrode plate data of the electrode plate to be tested corresponding to different moments during the drying process, and respectively construct corresponding models between moments and electrode plate data under multiple drying parameters; the prediction module is configured to determine a target data set corresponding to a target time under at least two of the multiple drying parameters based on the corresponding models corresponding to the multiple drying parameters, the target data set including multiple first target electrode plate data and multiple second target electrode plate data, the first target electrode plate data and the second target electrode plate data are respectively located in different corresponding models, the multiple first target electrode plate data respectively correspond to multiple first target moments in the target time, and the multiple second target electrode plate data respectively correspond to multiple second target moments in the target time.

[0025] Therefore, the electrode plate drying state prediction device in the embodiment of the present application can effectively predict the drying state of the electrode plate to be tested under multiple drying parameters in sequence; and the prediction accuracy is high, which is conducive to guiding the optimization of process parameters in the drying process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the drawings without creative work.

[0027] FIG1 is a schematic flow chart of a method for predicting the dryness state of an electrode sheet according to an embodiment of the present application;

[0028] FIG2 is a flow chart of step S300 in a method for predicting the dryness state of an electrode sheet according to an embodiment of the present application;

[0029] FIG3 is a flow chart of step S300 in a method for predicting the dryness state of an electrode sheet according to another embodiment of the present application;

[0030] FIG4 is a flow chart of step S400 in a method for predicting the dryness state of an electrode sheet according to another embodiment of the present application;

[0031] FIG5 is a schematic structural diagram of a device for predicting the dryness state of an electrode sheet according to an embodiment of the present application;

[0032] FIG6 is a schematic structural diagram of a building block in a device for predicting the drying state of an electrode sheet according to an embodiment of the present application;

[0033] FIG7 is a schematic structural diagram of a prediction module in a device for predicting the drying state of an electrode plate provided in an embodiment of the present application.

[0034] The description of the accompanying drawings is as follows: 1. Drying state prediction device; 10. Providing module; 20. Drying module; 30. Building module; 31. Temperature acquisition module; 32. Temperature model building module; 33. Quality acquisition module; 34. Quality model building module, 40. Prediction module; 41. First prediction module; 42. First determination module; 43. Second determination module; 44. Second prediction module. DETAILED DESCRIPTION

[0035] Hereinafter, the embodiments of the electrode plate drying state prediction method and the electrode plate drying state prediction device of the present application will be described in detail with appropriate reference to the accompanying drawings. However, there may be cases where unnecessary detailed descriptions are omitted. For example, there are cases where detailed descriptions of well-known matters and repeated descriptions of actually the same structures are omitted. This is to avoid the following description from becoming unnecessarily lengthy and to facilitate the understanding of those skilled in the art. In addition, the drawings and the following description are provided for those skilled in the art to fully understand the present application and are not intended to limit the subject matter described in the claims.

[0036] " Range " disclosed in this application is limited in the form of lower limit and upper limit, and given range is limited by selecting a lower limit and an upper limit, and the selected lower limit and upper limit define the boundary of special range. The scope limited in this way can be inclusive or exclusive of end values, and can be arbitrarily combined, that is, any lower limit can form a range with any upper limit combination. For example, if the range of 60 to 120 and 80 to 110 is listed for a particular parameter, it is understood that the range of 60 to 110 and 80 to 120 is also expected. In addition, if the minimum range values ​​1 and 2 are listed, and if the maximum range values ​​3,4 and 5 are listed, then the following ranges can all be expected: 1 to 3, 1 to 4, 1 to 5, 2 to 3, 2 to 4 and 2 to 5. In this application, unless otherwise specified, the numerical range "a to b" represents an abbreviation of any real number combination between a and b, wherein a and b are real numbers. For example, a numerical range of "0 to 5" indicates that all real numbers between "0 and 5" are listed herein, and "0 to 5" is merely an abbreviation for a combination of these values. Furthermore, when a parameter is expressed as an integer ≥ 2, this is equivalent to disclosing that the parameter is, for example, an integer of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, etc.

[0037] Unless otherwise specified, all embodiments and optional embodiments of the present application can be combined with each other to form a new technical solution.

[0038] Unless otherwise specified, all technical features and optional technical features of this application can be combined with each other to form a new technical solution.

[0039] Unless otherwise specified, all steps S of the present application may be performed sequentially or randomly, preferably sequentially. For example, a method includes steps S(a) and (b), which means that the method may include steps S(a) and (b) performed sequentially, or may include steps S(b) and (a) performed sequentially. For example, a method may further include step S(c), which means that step S(c) may be added to the method in any order, for example, the method may include steps S(a), (b) and (c), or may include steps S(a), (c) and (b), or may include steps S(c), (a) and (b, etc.

[0040] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the embodiments of the present application.

[0041] In addition, the technical terms "first," "second," etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. In the description of the embodiments of the present application, the meaning of "plurality" is more than two, unless otherwise specifically defined.

[0042] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0043] In the embodiments of the present application, the terms "plurality" and "multiple" refer to two or more.

[0044] In the embodiments of the present application, battery cells may include lithium-ion secondary battery cells, lithium-ion primary battery cells, lithium-sulfur battery cells, sodium-lithium-ion battery cells, sodium-ion battery cells, or magnesium-ion battery cells, etc., and the embodiments of the present application do not limit this. Battery cells may be cylindrical, flat, rectangular, or other shapes, etc., and the embodiments of the present application do not limit this. Battery cells are generally divided into three types based on the packaging method: cylindrical battery cells, prismatic battery cells, and soft-pack battery cells, and the embodiments of the present application do not limit this.

[0045] The battery referred to in the embodiments of this application refers to a single physical module that includes one or more battery cells to provide higher voltage and capacity. For example, the battery referred to in this application may include a battery module or a battery pack. A battery generally includes a casing that encloses one or more battery cells. The casing prevents liquids or other foreign matter from affecting the charging or discharging of the battery cells.

[0046] A battery cell consists of a housing assembly, an electrode assembly, and an electrolyte. The electrode assembly includes a positive electrode sheet, a negative electrode sheet, and a separator. A battery cell primarily operates by allowing metal ions to migrate between the positive and negative electrode sheets through the electrolyte.

[0047] The battery cell manufacturing process includes electrode sheet fabrication, assembly, and post-injection steps. The electrode sheet preparation process includes mixing, coating, drying, and rolling. The coating process involves applying the slurry, which has been mixed evenly in the mixing process, to the current collector. After coating, the drying process involves heating the electrode sheet to remove solvents and other components from the slurry. The electrode sheet drying process primarily consists of two stages. The first is the "surface evaporation stage," in which the solvent evaporates from the electrode sheet surface, causing it to shrink continuously. When the active material solid particles reach their maximum packing density, the sheet shrinks and the surface evaporation stage ends. The second stage is the "internal diffusion stage," in which the evaporating surface descends into the coating. Under the influence of capillary forces and osmotic pressure, the solvent within the coating diffuses outward from the electrode sheet. Drying concludes when the moisture content reaches the equilibrium moisture content. The drying process plays a crucial role in the quality and lifespan of the electrode sheet. For example, the solvent release rate and drying temperature directly influence the rheological properties of the coating slurry.

[0048] In related technologies, the electrode plate drying process usually requires continuous drying in multiple oven sections. The status of the electrode plates inside each oven section is difficult to monitor in real time, and the drying status of the electrode plates cannot be effectively estimated and judged. The quality of the dried electrode plates is mainly evaluated based on experience.

[0049] In view of this, an embodiment of the present application proposes a method for predicting the drying state of an electrode plate. This method dries the electrode plate to be tested into a dry electrode plate during the drying process, tests the electrode plate data during the drying process, and constructs a corresponding model between the time and the electrode plate data. Based on the corresponding model, the drying state of the electrode plate can be predicted with high prediction accuracy.

[0050] The embodiment of the present application proposes a method for predicting the dryness state of an electrode plate.

[0051] As shown in FIG1 , the method for predicting the dry state of the electrode plate includes:

[0052] Step S100, providing a pole piece to be tested;

[0053] Step S200, drying the electrode to be tested under multiple drying parameters. When the mass of the electrode to be tested at the Nth moment and the mass at the N-1th moment during the drying process meet a preset threshold, the electrode to be tested is dried to be a dry electrode.

[0054] Step S300, obtaining electrode data of the electrode to be tested corresponding to different moments during the drying process, and constructing corresponding models between the moments and the electrode data under multiple drying parameters;

[0055] Step S400, based on the corresponding models corresponding to the multiple drying parameters, determines the target data set corresponding to the target time under at least two of the multiple drying parameters, the target data set includes multiple first target pole piece data and multiple second target pole piece data, the first target pole piece data and the second target pole piece data are respectively located in different corresponding models, the multiple first target pole piece data respectively correspond to multiple first target moments in the target time, and the multiple second target pole piece data respectively correspond to multiple second target moments in the target time.

[0056] According to the drying state prediction method of the electrode plate of the embodiment of the present application, by obtaining the plate data of the electrode plate to be tested during the drying process, a corresponding model of the time and plate data of the electrode plate to be tested during the drying process is constructed; by performing drying treatment under different drying parameters respectively, a corresponding model of the electrode plate to be tested under different drying parameters can be constructed; the corresponding model constructed above can predict the drying state of the electrode plate to be tested under multiple drying parameters in sequence; the above prediction method has high accuracy, which is conducive to guiding the optimization of process parameters in the drying process.

[0057] [Step S100]

[0058] Provide the electrode to be tested.

[0059] In the embodiment of the present application, the electrode to be tested refers to an electrode electrode provided with a slurry coating. The slurry coating contains volatile substances such as solvents. During the drying process, the solvents and the like evaporate, and the slurry coating forms an active material layer.

[0060] In some implementations, step S100 may include:

[0061] Step S110, providing a current collector;

[0062] In step S120 , a slurry coating is provided on the current collector to form a pole piece to be tested, and the pole piece to be tested includes coating parameters of the slurry coating.

[0063] In the embodiment of the present application, the coating parameters may include the coating quality and coating density of the slurry coating. In the embodiment of the present application, by replacing the electrode to be tested, the electrode to be tested with different coating parameters can be tested separately, thereby predicting the drying state of the electrode under different coating parameters.

[0064] Corresponding to the above steps, step S300 may include: constructing corresponding models between the time and the electrode data under multiple drying parameters, wherein the corresponding models correspond to the coating parameters.

[0065] [Step S200]

[0066] The electrode to be tested is dried under multiple drying parameters. When the mass of the electrode to be tested at the Nth moment and the mass at the N-1th moment during the drying process meet the preset threshold, the electrode to be tested is dried to be a dry electrode.

[0067] The electrode to be tested is dried into a dry electrode. Drying the electrode to be tested under multiple drying parameters means that multiple electrode to be tested can be taken, and a part of the electrode to be tested is dried into a dry electrode under the same drying parameter; another part of the electrode to be tested is dried into a dry electrode under another drying parameter; and another part of the electrode to be tested is dried into a dry electrode under another drying parameter. In other words, the electrode to be tested needs to be dried into a dry electrode under the same drying parameter; then the drying parameter is changed, and the other electrode to be tested is dried into a dry electrode under the changed drying parameter.

[0068] In related technologies, the drying process of electrode plates usually requires drying in multi-section ovens. Specifically, during the process of transporting the electrode plates, they are dried in multiple sections of ovens in turn. The drying conditions of each section of the oven are different, making it difficult to monitor and predict the drying status of the dried plates in each oven.

[0069] In order to predict the drying state of the electrode plate, the embodiment of the present application places the electrode plate to be tested in the same oven for static drying treatment until the electrode plate to be tested is dried into a dry electrode plate. In this dry environment, it is easy to accurately obtain the electrode plate data of the drying process. During the drying process, due to the volatilization of solvents and the like, the mass of the electrode plate to be tested becomes smaller and smaller, showing as weight loss; when the solvent in the electrode plate to be tested is basically completely volatilized, the electrode plate to be tested gradually approaches the state of complete drying, and the mass change of the electrode plate to be tested is small. The electrode plate to be tested after drying is defined as a dry electrode plate.

[0070] When the mass of the electrode to be tested at the Nth moment and the mass at the N-1th moment during the drying process meet the preset threshold, the electrode to be tested at the Nth moment is determined to be the electrode to be tested after the drying is completed, that is, the dried electrode. Optionally, the preset threshold can be a weight loss rate or a difference. For example, the weight loss rate of the electrode to be tested between the Nth moment and the N-1th moment is (the mass of the electrode to be tested at the N-1th moment minus the mass at the Nth moment) / the mass of the electrode to be tested at the N-1th moment, and the weight loss rate is ≤1%, that is, the drying is completed when the electrode to be tested meets the weight loss rate ≤1%. N represents a positive number, and N-1 represents a positive number.

[0071] The drying parameters may include drying temperature, etc. For example, when hot air drying is used, the drying parameters may include the temperature of the hot air, the wind speed of the hot air, etc.

[0072] The electrode to be tested is dried under the same drying parameters, and electrode data of the electrode to be tested during the drying process is obtained. The drying parameters are changed, and the electrode to be tested is dried under different drying parameters, and electrode data of the electrode to be tested during the drying process is obtained. By testing the drying process under different drying parameters, the drying status of the electrode to be tested under different drying parameters can be obtained; this is conducive to predicting the drying status of the electrode during different drying processes.

[0073] For example, the electrode pieces to be tested are dried under first drying parameters (e.g., wind temperature of 110°C and wind speed of 2.1 m / s), and data related to the drying state of the electrode pieces to be tested under the first drying parameters is obtained. Another batch of electrode pieces to be tested, which have the same specifications as the electrode pieces to be tested (e.g., the same coating parameters), are taken and dried under second drying parameters (e.g., wind temperature of 110°C and wind speed of 7.4 m / s), and data related to the drying state of the electrode pieces to be tested under the second drying parameters is obtained.

[0074] Before the drying process, the drying parameters (such as wind speed, wind temperature, etc.) in the oven are stabilized before the electrode to be tested is dried; for example, after the wind speed and wind temperature in the oven are stabilized, the electrode to be tested is dried.

[0075] [Step S300]

[0076] The electrode data of the electrode to be tested corresponding to different moments in the drying process are obtained, and corresponding models between the moments and the electrode data are constructed under multiple drying parameters.

[0077] In some implementations, step S300 may include:

[0078] Step S310, obtaining test parameters of multiple regions of the electrode to be tested corresponding to different moments during the drying process;

[0079] Step S320 , calculating the average value of the test parameters of the multiple regions as the electrode data corresponding to each moment.

[0080] By collecting the test parameters of multiple areas corresponding to each moment and taking the average value of the test parameters of multiple areas as the electrode data corresponding to each moment, the accuracy of obtaining the electrode data can be improved, thereby further improving the prediction accuracy of the electrode drying state.

[0081] Multiple areas can be selected as drying areas; for example, the oven uses a rectangular air nozzle for drying, and the air nozzle affects the central area of ​​the electrode. The central area can be a certain size extending to both sides of the center line of the electrode, such as 50μm, and multiple places in the central area are taken as multiple areas.

[0082] As shown in FIG. 2 , in some embodiments, step S300 may include:

[0083] Step S330, acquiring a plurality of sub-data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes a plurality of sub-data;

[0084] In step S340 , sub-models of time and sub-data are constructed respectively under multiple drying parameters, wherein the corresponding model includes multiple sub-models.

[0085] Electrode data can include multiple sub-data, and accordingly, each sub-data can be used to construct a sub-model at each moment. By acquiring multiple sub-data and constructing multiple sub-models, a more comprehensive prediction of the drying state can be made, improving the accuracy of the prediction. Multiple sub-data can include electrode temperature data and electrode quality data. By monitoring the temperature and quality of the electrode, the drying state of the electrode can be effectively predicted based on temperature and quality changes. Moreover, monitoring the temperature and quality of the electrode separately can simplify the test operation and improve the test accuracy.

[0086] As shown in FIG3 , optionally, step S300 may include:

[0087] Step S301, obtaining electrode temperature data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes electrode temperature data;

[0088] Step S302 : constructing temperature models of the time and electrode temperature data respectively under a plurality of drying parameters, wherein the corresponding models include temperature models.

[0089] The temperature model can be understood as a model of how the temperature of the electrode to be tested changes with time during the drying process, which can characterize the temperature change state of the electrode to be tested.

[0090] Optionally, step S301 may include: obtaining electrode temperature data of the same electrode to be tested at multiple times during the drying process, which can improve the accuracy of temperature detection.

[0091] Specifically, a temperature sensor can be installed on the electrode to be tested, and the electrode temperature data of the electrode to be tested can be collected by the temperature sensor. During the drying process, the electrode to be tested does not need to be replaced, and the temperature of the electrode to be tested can be collected in real time by the temperature sensor. To further improve the accuracy of data collection, temperature sensors can be installed in multiple areas of the same electrode to be tested. The temperatures collected by multiple temperature sensors at the same time are averaged and calculated as the electrode temperature data.

[0092] The setting of multiple zones can be flexibly adjusted according to production. For example, three zones, four zones or five zones can be set, and each zone can be provided with a temperature sensor. The spacing between zones can be the same.

[0093] Optionally, step S300 may further include:

[0094] Step S303, obtaining electrode quality data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes electrode quality data;

[0095] Step S304 , constructing quality models of the time and electrode quality data respectively under multiple drying parameters, wherein the corresponding models include quality models.

[0096] The mass model can be understood as a model of how the mass of the electrode to be tested changes with time during the drying process. It can characterize the mass change state of the electrode to be tested, indicating the weight loss state of the electrode to be tested as the solvent in the electrode to be tested evaporates.

[0097] The quality of the electrode to be tested can be monitored inside the oven. For example, the electrode to be tested is placed on a weighing device, and both the electrode to be tested and the weighing device are placed together in the oven. The weighing device can then collect real-time data on the electrode's mass during the drying process. To further improve data collection accuracy, weighing devices can be installed on different electrode to be tested. The mass data collected by multiple weighing devices at the same time is averaged and used as the electrode quality data.

[0098] The quality inspection of the electrode to be tested can be carried out outside the oven. The environmental conditions outside the oven (such as temperature) are different from the drying conditions inside the oven (such as temperature). The environmental conditions outside the oven may cause the temperature of the electrode to be tested to drop. If the electrode to be tested is moved back into the oven to continue drying after the quality inspection, the data changes of the electrode to be tested during the drying process cannot accurately reflect the data changes of the electrode to be tested under the same drying parameters.

[0099] In an embodiment of the present application, the number of electrode pieces to be tested may be set to multiple, and step S303 may include: obtaining different electrode pieces to be tested at multiple times during the drying process, and measuring the electrode quality data corresponding to each time. By obtaining electrode quality data of electrode pieces to be tested with the same specifications but different specifications during the drying process, the accuracy of quality testing can be improved, and the interference caused by quality testing on the drying state of the electrode pieces, which affects the accuracy of the quality test results, can be reduced.

[0100] Optionally, the time intervals between any two adjacent moments in the multiple moments are the same. In other words, the time interval between one of the multiple moments and the previous moment is a first interval, and the time interval between one of the multiple moments and the next moment is a second interval, and the first interval and the second interval are the same. For example, the multiple moments include a first moment, a second moment, and a third moment, and the time intervals between the second moment and the first moment and between the third moment and the second moment are the same. Identical time intervals facilitate model construction and can improve the accuracy of model fitting.

[0101] For example, the number of electrode pieces to be tested is M, where M is a positive integer greater than 1. The specifications of the M electrode pieces to be tested, such as coating parameters, are the same, such as the mass of the electrode pieces to be tested before drying. The drying parameters of the M electrode pieces to be tested are the same, which can be understood as the M electrode pieces to be tested being dried under the same drying conditions.

[0102] Take the first electrode to be tested among the M electrodes to be tested, and record the mass of the electrode to be tested before drying as m1; after the electrode to be tested is placed in an oven and dried for a time interval Δt, take out the electrode to be tested and measure its mass as m 1-Δt , the time is recorded as Δt;

[0103] Take the second electrode to be tested from the M electrodes to be tested, and record the mass of the electrode to be tested before drying as m2; after the electrode to be tested is placed in an oven and dried for a time interval of 2Δt, take out the electrode to be tested and measure its mass as m 2-2Δt , the time is recorded as 2Δt;

[0104] Take the Pth electrode to be tested among the M electrodes to be tested, and record the mass of the electrode to be tested before drying as m p ; After the electrode to be tested is placed in an oven and dried for a time interval of pΔt, the electrode to be tested is taken out and its mass is measured as m p-pΔt, the time is recorded as pΔt; P<M, P is a positive integer.

[0105] Take the P+1th electrode to be tested among the M electrodes to be tested, and record the mass of the electrode to be tested before drying as m p+1 ; After the electrode to be tested is placed in an oven and dried for a time interval of (p+1)Δt, the electrode to be tested is taken out and its mass is measured to be m(p+1)*(1-Δt), and the time is recorded as (p+1)Δt; where m(p+1)*(1-Δt) is equal to m p-pΔt If the preset threshold is met, for example, the weight loss rate is ≤1%, in this case, the electrode to be tested with a mass of m(p+1)*(1-Δt) is a dry electrode.

[0106] A quality model corresponding to each moment and the pole piece quality data is constructed by using the multiple moments recorded above and the pole piece quality data corresponding to each moment.

[0107] [Step S400]

[0108] Based on corresponding models corresponding to the plurality of drying parameters, a target data set corresponding to the target time under at least two of the plurality of drying parameters is determined.

[0109] Electrode plates usually need to be dried in multiple oven sections in sequence, and the drying parameters set in each oven section are different; the implementation method of the present application constructs corresponding models under multiple drying parameters through the above steps, which can predict the drying characteristics of the same electrode plate to be tested under multiple drying parameters. Of course, it can also predict the drying characteristics of electrode plates with the same coating parameters as the electrode plate to be tested.

[0110] For example, the electrode plate needs to be dried in six sections of the oven, and different drying parameters are set in each of the six sections; or, for example, the electrode plate needs to be dried in two sections of the oven, and different drying parameters are set in each of the two sections of the oven;

[0111] As shown in FIG4 , specifically, step S400 may include:

[0112] Step S410, based on the corresponding model corresponding to one of the multiple drying parameters, determine the first target data set corresponding to the first target time, the first target data set includes multiple first target pole piece data, and the multiple first target pole piece data respectively correspond to multiple first target moments in the first target time.

[0113] The first target time is a time period, specifically a time period consisting of multiple first target moments; the first target data set is a collection of multiple first target electrode piece data. Based on the drying parameters of the electrode piece in the first drying oven section, a corresponding model corresponding to the drying parameters is determined. This corresponding model serves as the first corresponding model. Based on the first corresponding model, the first target data set at the first target time is determined, thereby accurately predicting the drying state of the electrode piece under test in the first drying oven section.

[0114] Step S420 , recording the end time of the first target time as the first end time, and determining the first end pole piece data corresponding to the first end time.

[0115] Based on the first corresponding model, first endpoint pole piece data corresponding to the first endpoint moment is determined.

[0116] Step S430 : Based on a corresponding model corresponding to another one of the plurality of drying parameters, determine a time corresponding to the first endpoint pole piece data as a first starting time.

[0117] After the electrode to be tested completes the drying process in the first section of the oven, it needs to enter the second section of the oven for drying. The corresponding model under the drying parameters in the second section of the oven is the second corresponding model. The electrode state corresponding to the end moment (first end moment) of the completion of the drying process in the first section of the oven is used as the starting electrode state of the electrode to be tested for drying in the second section of the oven. Specifically, based on the first corresponding model, the first end point electrode data corresponding to the first end point is determined. Based on the second corresponding model, the moment corresponding to the first end point electrode data is determined as the starting moment in the second section of the oven, that is, the second starting moment.

[0118] Step S440, determine a second target data set corresponding to the second target time, the second target data set includes multiple second target pole piece data, the multiple second target pole piece data respectively correspond to multiple second target moments in the second target time, and the second target moment is determined according to the first starting moment and the time interval with the first starting moment.

[0119] The second target time is a time period, specifically a time period consisting of multiple second target moments. Since the start time in the second oven section is non-zero and is the second start time, the second target time can be determined based on the second start time and the second target time. For example, if the second start time is 20 minutes and the second target time is 30 minutes, the second target time corresponds to any time between (20+0) minutes and (20+30) minutes, that is, any time between 20 minutes and 50 minutes.

[0120] The second target data set is a collection of multiple second target electrode piece data. Based on the second corresponding model in the second oven section, the second target electrode piece data corresponding to each second target moment in the second target time is determined, thereby accurately predicting the drying state of the electrode piece to be tested in the second oven section.

[0121] The electrode plate can be dried in two sections of the oven, or in more sections of the oven. When dried in more sections of the oven, the drying state prediction steps are such as steps S420 to S440, which will not be repeated here.

[0122] For example, based on the corresponding models corresponding to multiple drying parameters, a target data set corresponding to the target time under three of the multiple drying parameters is determined, and the target data set includes multiple first target pole piece data, multiple second target pole piece data, and multiple third target pole piece data. The first target pole piece data, the second target pole piece data, and the third target pole piece data are respectively located in different corresponding models. The multiple first target pole piece data correspond to multiple first target moments in the target time, the multiple second target pole piece data correspond to multiple second target moments in the target time, and the multiple third target pole piece data correspond to multiple third target moments in the target time.

[0123] In some embodiments, the method for predicting the dryness state of an electrode plate may further include, after step S100 , step S500 of sealing the electrode plate to be tested.

[0124] Before the drying process, the electrode to be tested is sealed, which reduces the risk of inaccurate electrode data detection due to volatilization of solvents before the drying process, and can improve the accuracy of electrode data detection.

[0125] Specifically, the surface of the electrode to be tested can be sealed by placing a plastic wrap or a sealing bag.

[0126] As a specific embodiment of the present application, a method for predicting the dryness state of an electrode sheet may include:

[0127] providing a current collector;

[0128] A slurry coating is provided on the current collector to form a pole piece to be tested, and the pole piece to be tested includes coating parameters of the slurry coating;

[0129] Drying the electrode to be tested under multiple drying parameters to obtain a dry electrode, wherein the mass of the electrode to be tested at the Nth moment in the drying process and the mass of the dry electrode meet a preset threshold;

[0130] Acquire electrode temperature data of the electrode to be tested corresponding to different moments during the drying process, wherein the electrode data includes electrode temperature data;

[0131] Constructing temperature models of the moment and electrode temperature data under multiple drying parameters, wherein the corresponding models include temperature models;

[0132] Acquire electrode quality data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes electrode quality data;

[0133] Quality models of the moment and electrode quality data are constructed respectively under multiple drying parameters, wherein the corresponding models include a quality model.

[0134] Determining a first target data set corresponding to a first target time based on a corresponding model corresponding to one of the plurality of drying parameters, the first target data set including a plurality of first target pole piece data, the plurality of first target pole piece data respectively corresponding to a plurality of first target moments in the first target time;

[0135] Recording the end time of the first target time as a first end time, and determining first end pole piece data corresponding to the first end time based on a corresponding model corresponding to one of the plurality of drying parameters;

[0136] Based on a corresponding model corresponding to another one of the plurality of drying parameters, determining a time corresponding to the first endpoint pole piece data as a first starting time;

[0137] Based on a corresponding model corresponding to another one of the multiple drying parameters, a second target data set corresponding to the second target time is determined, the second target data set includes multiple second target pole piece data, and the multiple second target pole piece data respectively correspond to multiple second target moments in the second target time. The second target moment is determined based on the first starting moment and the second target time.

[0138] An embodiment of the present application further provides a device for predicting the drying state of an electrode plate, which can be used to implement the method for predicting the drying state of an electrode plate according to any of the above embodiments of the present application.

[0139] As shown in FIG5 , the electrode sheet drying state prediction device 1 includes a providing module 10, a drying module 20, a building module 30, and a prediction module 40. The providing module 10 is configured to provide an electrode sheet to be tested; the drying module 20 is configured to dry the electrode sheet to be tested under multiple drying parameters. When the mass of the electrode sheet to be tested at the Nth moment and the mass at the N-1th moment during the drying process meet a preset threshold, the electrode sheet to be tested is dried to be a dry electrode sheet; the building module 30 is configured to obtain electrode sheet data of the electrode sheet to be tested corresponding to different moments during the drying process, and to build corresponding models between the moments and the electrode sheet data under multiple drying parameters; the prediction module 40 is configured to determine a target data set corresponding to a target time under at least two of the multiple drying parameters based on the corresponding models corresponding to the multiple drying parameters, the target data set including a plurality of first target electrode sheet data and a plurality of second target electrode sheet data, the first target electrode sheet data and the second target electrode sheet data being located in different corresponding models, the plurality of first target electrode sheet data corresponding to a plurality of first target moments in the target time, and the plurality of second target electrode sheet data corresponding to a plurality of second target moments in the target time.

[0140] The above-mentioned electrode plate drying state prediction device 1 can effectively predict the drying state of the electrode plate to be tested under multiple drying parameters in sequence; and the prediction accuracy is high, which is conducive to guiding the optimization of process parameters in the drying process.

[0141] As shown in Figure 6, in some embodiments, the construction module 30 includes a temperature acquisition module 31, a temperature model construction module 32, a quality acquisition module 33 and a quality model construction module 34, the temperature acquisition module 31 is configured to obtain the electrode temperature data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes electrode temperature data; the temperature model construction module 32 is configured to respectively construct temperature models of the moment and electrode temperature data under multiple drying parameters, wherein the corresponding model includes a temperature model; the quality acquisition module 33 is configured to obtain the electrode quality data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes electrode quality data; the quality model construction module 34 is configured to respectively construct quality models of the moment and electrode quality data under multiple drying parameters, wherein the corresponding model includes a quality model.

[0142] As shown in Figure 7, in some embodiments, the prediction module 40 includes a first prediction module 41, a first determination module 42, a second determination module 43 and a second prediction module 44. The first prediction module 41 is configured to determine a first target data set corresponding to the first target time based on a corresponding model corresponding to one of a plurality of drying parameters, the first target data set including a plurality of first target pole piece data, and the plurality of first target pole piece data respectively correspond to a plurality of first target moments in the first target time; the first determination module 42 is configured to record the end moment of the first target time as the first end moment, and determine the first end pole piece data corresponding to the first end moment; the second determination module 43 is configured to determine the moment corresponding to the first end pole piece data as the first starting moment based on a corresponding model corresponding to another one of the plurality of drying parameters; the second prediction module 44 determines a second target data set corresponding to the second target time, the second target data set including a plurality of second target pole piece data, the plurality of second target pole piece data respectively correspond to a plurality of second target moments in the second target time, and the second target moment is determined based on the first starting moment and the second target time.

[0143] Although illustrative embodiments have been shown and described, those skilled in the art should understand that the above embodiments should not be construed as limitations on the present application, and that changes, substitutions, and modifications may be made to the embodiments without departing from the spirit, principles, and scope of the present application.

Claims

1. A method for predicting the drying state of an electrode plate, comprising: Provide the electrode to be tested; Drying the electrode piece to be tested under multiple drying parameters respectively, and when the mass of the electrode piece to be tested at the Nth moment and the mass at the N-1th moment during the drying process meet a preset threshold, the electrode piece to be tested is dried to be a dry electrode piece; Acquire electrode data of the electrode to be tested corresponding to different moments during the drying process, and respectively construct corresponding models between the moments and the electrode data under a plurality of drying parameters; Based on the corresponding models corresponding to the multiple drying parameters, a target data set corresponding to the target time under at least two of the multiple drying parameters is determined, and the target data set includes multiple first target pole piece data and multiple second target pole piece data. The first target pole piece data and the second target pole piece data are respectively located in different corresponding models. The multiple first target pole piece data respectively correspond to multiple first target moments in the target time, and the multiple second target pole piece data respectively correspond to multiple second target moments in the target time.

2. The method for predicting the dry state of an electrode sheet according to claim 1, wherein: The step of obtaining the electrode data of the electrode to be tested corresponding to different moments during the drying process, and constructing corresponding models between the moments and the electrode data under a plurality of drying parameters, comprises: Acquire multiple sub-data of the electrode to be tested corresponding to different moments in the drying process, wherein the electrode data includes multiple sub-data; Sub-models of the time instants and the sub-data are respectively constructed under the plurality of drying parameters, wherein the corresponding model includes a plurality of sub-models.

3. The method for predicting the dry state of an electrode sheet according to claim 1 or 2, wherein: The step of obtaining the electrode data of the electrode to be tested corresponding to different moments during the drying process, and constructing corresponding models between the moments and the electrode data under a plurality of drying parameters, comprises: Acquiring electrode temperature data of the electrode to be tested corresponding to different moments during the drying process, wherein the electrode data includes electrode temperature data; Constructing temperature models of the time and the electrode temperature data respectively under a plurality of the drying parameters, wherein the corresponding models include temperature models; Acquiring electrode quality data of the electrode to be tested corresponding to different moments during the drying process, wherein the electrode data includes electrode quality data; A quality model of the moment and the pole piece quality data is constructed respectively under a plurality of the drying parameters, wherein the corresponding model includes a quality model.

4. The method for predicting the dry state of an electrode sheet according to claim 3, wherein: The step of obtaining the electrode temperature data of the electrode to be tested corresponding to different moments during the drying process includes: During the drying process, the electrode temperature data of the same electrode to be tested is obtained at multiple time points.

5. The method for predicting the dry state of an electrode sheet according to claim 3 or 4, wherein: The step of obtaining the electrode quality data of the electrode to be tested corresponding to different moments during the drying process includes: During the drying process, different electrodes to be tested are obtained at multiple moments, and electrode quality data corresponding to each of the moments are measured, wherein the number of the electrode to be tested is multiple.

6. The method for predicting the dry state of an electrode sheet according to claim 5, wherein: The time intervals between any two adjacent moments in the multiple moments are the same.

7. The method for predicting the dryness state of an electrode sheet according to any one of claims 1 to 6, wherein: The step of obtaining the electrode data of the electrode to be tested corresponding to different moments during the drying process, and constructing corresponding models between the moments and the electrode data under a plurality of drying parameters, comprises: Acquiring test parameters of multiple areas of the electrode to be tested corresponding to different moments during the drying process; The average value of the test parameters of the plurality of regions is calculated as the electrode data corresponding to each of the moments.

8. The method for predicting the dryness state of an electrode sheet according to any one of claims 1 to 7, wherein: The step of determining a target data set corresponding to a target time under at least two of the plurality of drying parameters based on the corresponding models corresponding to the plurality of drying parameters respectively includes: Determining a first target data set corresponding to a first target time based on the corresponding model corresponding to one of the plurality of drying parameters, wherein the first target data set includes a plurality of first target pole piece data, and the plurality of first target pole piece data respectively correspond to a plurality of first target moments in the first target time; Recording the end time of the first target time as a first end time, and determining first end pole piece data corresponding to the first end time; Based on the corresponding model corresponding to another one of the plurality of drying parameters, determining a time corresponding to the first endpoint pole piece data as a first starting time; Determine a second target data set corresponding to a second target time, wherein the second target data set includes a plurality of second target pole piece data, and the plurality of second target pole piece data respectively correspond to a plurality of second target moments in the second target time, and the second target moment is determined based on the first starting moment and the time interval with the first starting moment.

9. The method for predicting the dryness state of an electrode sheet according to any one of claims 1 to 8, wherein: The step of providing a pole piece to be tested comprises: providing a current collector; A slurry coating is provided on the current collector to form a pole piece to be tested, wherein the pole piece to be tested includes coating parameters of the slurry coating; The step of obtaining the electrode data of the electrode to be tested corresponding to different moments during the drying process, and constructing corresponding models between the moments and the electrode data under a plurality of drying parameters, comprises: Acquiring electrode data of the electrode to be tested corresponding to different moments during the drying process; A corresponding model between the moment and the electrode data is constructed under the plurality of drying parameters, wherein the corresponding model corresponds to the coating parameter.

10. A device for predicting the drying state of an electrode plate, comprising: A providing module is configured to provide a pole piece to be tested; A drying module is configured to dry the electrode to be tested under a plurality of drying parameters. When the mass of the electrode to be tested at the Nth moment and the mass at the N-1th moment during the drying process meet a preset threshold, the electrode to be tested is dried to be a dry electrode. A construction module is configured to obtain electrode data of the electrode to be tested corresponding to different moments during the drying process, and respectively construct a correspondence model between the moments and the electrode data under a plurality of drying parameters; The prediction module is configured to determine a target data set corresponding to the target time under at least two of the multiple drying parameters based on the corresponding models corresponding to the multiple drying parameters, the target data set including multiple first target pole piece data and multiple second target pole piece data, the first target pole piece data and the second target pole piece data are respectively located in different corresponding models, the multiple first target pole piece data respectively correspond to multiple first target moments in the target time, and the multiple second target pole piece data respectively correspond to multiple second target moments in the target time.

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