Viscoelasticity measurement device, viscoelasticity measurement method, and computer-readable recording medium

Abstract

The invention relates to a viscoelasticity measuring device, a viscoelasticity measuring method, and a computer-readable recording medium. The viscoelasticity measurement method may include: collecting viscoelasticity characteristic data including a loss modulus and a storage modulus measured by applying shear strain to an active material slurry for a secondary battery at preset intervals; calculating a loss factor defined as a ratio between the storage modulus and the loss modulus, and calculating a difference between a current loss factor and a previous loss factor as a loss factor differential; generating time series data based on the loss factor differential; performing a sliding window analysis on the time series data to calculate an average value of a plurality of loss factor differentiations included in each window; and deriving, as a damping factor, any one average value among the results of calculating the average values of the plurality of loss factor differentiations included in each window.

Description

Technical Field

[0001] One or more embodiments of this disclosure relate to a viscoelastic measurement apparatus and method for deriving a standardized damping factor for an active material slurry used in secondary batteries. Background Technology

[0002] In industrial processes that handle slurries of active materials for secondary batteries, the design and determination of the entire processing facility (including the length, thickness, and angle of the production line piping) can be based on the viscoelastic properties of the slurry. Therefore, measuring the viscoelastic properties of the slurry becomes very important.

[0003] Rheological amplitude meters (a type of viscoelastic measurement device) can be used to evaluate the viscoelastic properties of slurries. However, due to the lack of consistent evaluation standards, there is a problem that the interpretation of the same viscoelastic measurement data output from the rheological amplitude meter can vary depending on the evaluator. This variability can make the interpretation of viscoelastic measurement data and the standardization of results difficult.

[0004] The above information is intended only to facilitate and improve the understanding of the background of this disclosure and may include information that does not constitute prior art. Summary of the Invention

[0005] One or more aspects of the embodiments of this disclosure relate to a method for accurately determining the viscoelastic properties of active material slurries for secondary batteries and standardizing the evaluation results.

[0006] One or more aspects of embodiments of this disclosure relate to a method for accurately measuring the viscoelastic properties of an active material slurry for a secondary battery to precisely determine whether the slurry is in a liquid or solid state.

[0007] However, the technical aspects of this disclosure are not limited to the above-described technical solutions, and additional aspects will be set forth in part in the following description and will be apparent in part from the description, or may be obtained by practice of the presented embodiments.

[0008] According to one or more embodiments of this disclosure, a viscoelastic measurement method executed by a processor of a viscoelastic measurement device is provided. The viscoelastic measurement method includes: collecting viscoelastic property data, including a loss modulus and a storage modulus measured by applying shear strain to an active material slurry for a secondary battery at preset intervals; calculating a loss factor defined as the ratio between the storage modulus and the loss modulus; calculating the difference between a current loss factor and a previous loss factor as a differential of the loss factor; generating time-series data based on the differential of the loss factor; performing a sliding window analysis on the time-series data to calculate an average of multiple differentials of the loss factor included in each window; and deriving any one of the averages of the results of calculating the averages of the multiple differentials of the loss factor included in each window as a damping factor.

[0009] According to one or more embodiments of this disclosure, a viscoelastic measurement device includes: one or more processors; and a memory operatively connected to the one or more processors and storing at least one piece of code to be executed by the one or more processors, wherein, when executed by the one or more processors, the at least one piece of code enables the one or more processors to: collect viscoelastic property data, including a loss modulus and a storage modulus measured by applying shear strain to an active material slurry for a secondary battery at preset intervals; calculate a loss factor defined as the ratio between the storage modulus and the loss modulus, and calculate the difference between the current loss factor and a previous loss factor as a differential of the loss factor; generate time series data based on the differential of the loss factor; perform a sliding window analysis on the time series data to calculate an average of a plurality of differentials of the loss factor included in each window; and derive any one of the averages of the results of calculating the averages of the plurality of differentials of the loss factor included in each window as a damping factor.

[0010] Additionally, one or more other methods, systems for implementing this disclosure, and computer-readable recording media storing computer programs for performing the methods may be provided.

[0011] According to one or more embodiments of this disclosure, a viscoelastic measurement method executed by a processor of a viscoelastic measuring device includes: collecting viscoelastic property data (such as loss modulus and storage modulus) by applying shear strain to an active material slurry for a secondary battery at preset intervals. The method involves calculating a loss factor as a ratio between the storage modulus and the loss modulus, determining a differential of the loss factor by comparing the current loss factor with a previous loss factor, generating time-series data based on these differentials, performing a sliding window analysis on the time-series data to calculate the average value of the differentials of the loss factor in each window, thereby deriving a damping factor. Additionally, a viscoelastic measuring device includes a processor and a memory storing executable code to perform these measurements and calculations, and may also provide other methods, systems, and computer-readable media for implementing this disclosure.

[0012] In addition to the foregoing aspects, features and advantages, other aspects, features and advantages will become apparent or will be understood by those skilled in the art from the following drawings, claims and detailed description of this disclosure. Attached Figure Description

[0013] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the detailed description provided herein, serve to further understand the technical concept of the present disclosure; therefore, the present disclosure should not be construed as limited to the situations described in the drawings, in which:

[0014] Figure 1 The illustration shows the configuration of a viscoelasticity measuring device according to one or more embodiments of the present disclosure;

[0015] Figure 2 The illustration shows the configuration of the processor of a viscoelasticity measuring device according to one or more embodiments of the present disclosure;

[0016] Figure 3 This is an example of a table showing viscoelastic property data collected by a processor according to one or more embodiments of the present disclosure;

[0017] Figure 4 This is a diagram illustrating an example of sliding window analysis performed by a processor according to one or more embodiments of the present disclosure; and

[0018] Figure 5 This is a flowchart describing a viscoelasticity measurement method according to one or more embodiments of the present disclosure. Detailed Implementation

[0019] In the following description, one or more embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. The meaning of the terms used in this specification and claims should not be limited to their ordinary or literal meaning, but should be interpreted based on the principle that the inventor is able to define the terms in the most appropriate or suitable manner for describing his / her invention, and are not to depart from the meaning and concept of the present disclosure. Accordingly, the features disclosed in the embodiments and drawings of this specification are examples of embodiments of the present disclosure, and therefore it should be understood that at the time of filing of this application, there are alternative equivalents or variations that may replace the embodiments. It will be further understood that, if used in this disclosure (e.g., when used in this disclosure), the terms “comprising” and / or “including” and / or “having” and variations thereof specify the presence of the stated features, quantities, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, quantities, steps, operations, elements, components, and / or groups thereof. Additionally, the terms "comprising," "including," "having," or other similar terms include or support the terms "consisting of," "consisting of," and "substantially consisting of," which indicate the presence of the stated shapes, quantities, steps, operations, components, and / or parts, without or substantially without the presence of other shapes, quantities, steps, operations, components, and / or parts. Furthermore, if embodiments of this disclosure are described (e.g., when describing embodiments of this disclosure), the phrases "may," "may include," and "may be" refer to "one or more embodiments of this disclosure."

[0020] Additionally, to aid in understanding this disclosure, the accompanying drawings may not be drawn to scale, and the dimensions of some elements may be enlarged. Furthermore, in different embodiments, the same reference numerals may be assigned to the same elements.

[0021] When two things being compared are described as "identical," it means "substantially identical." Therefore, substantial equivalence can include deviations considered relatively low in the field, such as less than 5%. Furthermore, uniformity of parameters over a given region can mean uniformity from the perspective of the average value.

[0022] Although the terms “first” and / or “second” are used to describe one or more suitable elements, these elements are not limited by these terms. These terms are used only to distinguish one element from another, and unless otherwise specified, it should be understood that a first element may also be referred to as a second element.

[0023] Throughout this disclosure, unless otherwise specified, each element may be singular or plural. For example, as used herein, the singular forms “a” and “the (said)” are intended to also include the plural forms unless the context clearly indicates otherwise.

[0024] In this disclosure, any configuration arranged "above (or below)" or "on" an element can refer not only to any configuration arranged to contact the upper (or lower) surface of the element, but also to one or more other configurations that can be interposed between the element and any configuration arranged on (or below) the element. In contrast, if a configuration is referred to as "directly" on or "directly" below an element (e.g., when a configuration is referred to as "directly" on or "directly" below an element), then there is no intervening configuration.

[0025] Additionally, if an element is described as being “connected” or “coupled” to another element (e.g., when an element is described as being “connected,” “coupled,” or “connected to” another element), it should be understood that the element may be directly connected or directly coupled to another element, but may have one or more other elements “intercalated” therebetween, or each element may be “connected” or “coupled” to other elements. Moreover, if a component is described as being electrically connected to another component (e.g., when a component is described as being electrically connected to another component), this includes not only embodiments in which they are directly connected, but also embodiments in which they are connected to another element therebetween.

[0026] Throughout this disclosure, unless otherwise specified, references to “A and / or B”, “A or B”, or “A / B” mean A, B, or A and B. For example, “and / or”, “or”, and “ / ” can include all or any combination of the listed items. Unless otherwise specified, “C to D” means C or more and D or fewer. When preceding / following a list of elements, expressions such as “at least one of,” “one of,” and “selected from” modify the entire list of elements and do not modify individual elements in the list. For example, “at least one of a, b, and c,” “selected from at least one of a, b, and c,” and / or “selected from at least one of a to c” can indicate only a, only b, only c, both a and b (e.g., simultaneously), both a and c (e.g., simultaneously), both b and c (e.g., simultaneously), all a, b, and c, or variations thereof.

[0027] Figure 1 The illustration shows the configuration of a viscoelasticity measuring device according to one or more embodiments of the present disclosure. Reference Figure 1 The viscoelasticity measuring device 100 may include a rheological amplitude module 110, a sensing module 120, a memory 130, and a processor 140.

[0028] The rheological amplitude module 110 is rotatably arranged in a bearing within an active material slurry for a secondary battery. The rheological amplitude module 110 can control the magnitude of the shear strain applied to the active material slurry by controlling the rotation amplitude of the bearing during rotation. The rheological amplitude module 110 may further include units for controlling the amplitude and frequency to control the bearing rotation amplitude. The units for controlling the amplitude and frequency can electrically or mechanically control the bearing rotation amplitude and speed.

[0029] The sensing module 120 can measure the viscoelastic properties of the rotating slurry (including shear strain, shear stress, storage modulus, and loss modulus). Here, the storage modulus and loss modulus can be indicators of how similar the slurry is to a liquid or solid.

[0030] In this disclosure, shear strain can be a measure of the degree to which a slurry deforms due to shear force. Shear strain is a measure of the degree to which a portion of the slurry moves relative to another portion, and is typically expressed in radians. Mathematically, shear strain (γ) can be expressed as the displacement (Δx) at a given point divided by the thickness or reference length (h) of the layer (i.e., the layer containing that given point) (γ = Δx / h).

[0031] In this disclosure, shear stress can refer to the amount of force applied to each unit area of ​​the slurry. Shear stress represents the magnitude of the force per unit area of ​​the slurry and is typically measured in Pascals (Pa). Mathematically, shear stress (τ) can be expressed as the relationship between the force F applied to the slurry due to the rotation of the bearing and the fluid contact area A (τ = F / A).

[0032] In this disclosure, the storage modulus can be used as an indicator of the slurry's ability to elastically store energy. The storage modulus can indicate the degree of elastic response of the slurry to shear deformation. Mathematically, the storage modulus G' can be expressed as (τ' / γ)cos(δ), where τ' can represent the amplitude of the shear stress, γ can represent the amplitude of the shear strain, and δ can represent the response phase angle. This response phase angle can represent the time delay between the shear stress response and the shear strain input.

[0033] In this disclosure, the loss modulus can be used as an indicator of the viscous energy dissipation ability of a slurry. The loss modulus can represent the amount of energy loss that occurs in the slurry due to shear deformation. Mathematically, the loss modulus G'' can be expressed as (τ' / γ)sin(δ). Here, τ' can represent the amplitude of the shear stress. γ can represent the amplitude of the shear strain. δ can represent the response phase angle.

[0034] In one or more embodiments of this disclosure, the sensing module 120 may include and measure one or more selected from pressure sensors, strain gauges, and torque sensors to measure the shear strain and shear stress of the slurry. The sensing module 120 may further include a data processing unit that converts the measured values ​​into digital signals and performs necessary and desired calculations. For example, the sensing module 120 may include one or more sensors (such as pressure sensors, strain gauges, and / or torque sensors) to measure the shear strain and shear stress of the slurry. Additionally, the sensing module 120 may include a data processing unit that converts the measured values ​​into digital signals and performs necessary calculations.

[0035] The memory 130 can store data used in viscoelastic measurements. In one or more embodiments, the memory 130 can store viscoelastic property data detected by the sensing module 120. Additionally, the memory 130 can store the following results: results of calculating the loss factor processed by the processor 140, results of calculating the derivative of the loss factor, results of calculating the average of multiple derivatives of the loss factor, and results of deriving the damping factor. Furthermore, the sliding window analysis algorithm applied to the calculation of the derivative of the loss factor can be stored in the memory 130.

[0036] In this disclosure, memory 130 may be operatively connected to processor 140 and stores at least one piece of code associated with operations performed by processor 140.

[0037] Additionally, memory 130 can perform the function of temporarily or permanently storing data processed by processor 140. In one or more embodiments, memory 130 may include magnetic storage media and / or flash memory media, but embodiments of this disclosure are not limited thereto. Memory 130 may include internal memory and / or external memory, and may include one or more selected from volatile memory (such as dynamic random access memory (DRAM), static random access memory (SRAM) and / or synchronous dynamic random access memory (SDRAM)), non-volatile memory (such as one-time programmable read-only memory (OTPROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), mask read-only memory (ROM), flash ROM, NAND flash memory and / or NOR flash memory), flash drives (such as solid-state drives (SSD), compact flash (CF) cards, secure digital (SD) cards, micro SD cards, mini SD cards, xD cards and / or memory sticks), and storage devices (such as hard disk drives (HDD)).

[0038] Processor 140 can collect loss modulus and storage modulus from sensing module 120 by applying shear strain to the active material slurry for the secondary battery. Processor 140 can calculate loss factor and loss factor derivative using the collected loss modulus and storage modulus. Processor 140 can apply a sliding window analysis technique to the results of calculating the loss factor derivative to calculate the average value of the loss factor derivative for each window. Processor 140 can derive one of the average values ​​of the loss factor derivative for each window as the final damping factor.

[0039] In this disclosure, processor 140 can process instructions of a computer program by performing arithmetic, logical, and input / output operations. Additionally, processor 140 can typically control the operation of other components associated with rheometry module 110.

[0040] Processor 140 can perform at least some of the data analysis, processing, and result generation for performing the operations described above by using at least one of machine learning, neural network, and deep learning algorithms as rule-based or artificial intelligence algorithms. Non-limiting examples of neural networks may include models such as convolutional neural networks (CNNs), deep neural networks (DNNs), and recurrent neural networks (RNNs). For example, processor 140 can utilize machine learning, neural network, and / or deep learning algorithms (including models like convolutional neural networks (CNNs), deep neural networks (DNNs), and / or recurrent neural networks (RNNs)) to perform data analysis, processing, and result generation for the operations described above.

[0041] In one or more embodiments, processor 140 may be implemented as an array of logic gates, or as a combination of a general-purpose microprocessor and a memory storing the program executed on the microprocessor. For example, in one or more embodiments, processor 140 may include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, and / or a state machine, etc. In one or more embodiments, processor 140 may include an application-specific integrated circuit (ASIC), a programmable logic device (PLD), and / or a field-programmable gate array (FPGA), etc. For example, in one or more embodiments, processor 140 may refer to a combination of processing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled to a DSP core, or any other such combination configured.

[0042] In one or more embodiments, the viscoelasticity measuring device 100 may further include a communication unit. The communication unit may be linked to a network and transmit data processed by the processor 140 to an external device (e.g., a user terminal). In one or more embodiments, under the control of the processor 140, the communication unit may transmit viscoelastic property data detected by the sensing module 120, the result of calculating the loss factor, the result of calculating the derivative of the loss factor, the result of calculating the average of multiple loss factor derivatives, and the result of calculating the damping factor to the user terminal.

[0043] Figure 2 The illustration shows the configuration of the processor of a viscoelasticity measuring device according to one or more embodiments of the present disclosure. Figure 3 This is an example of a table showing viscoelastic property data collected by a processor according to one or more embodiments of the present disclosure. Figure 4 This is a diagram illustrating an example of sliding window analysis performed by a processor according to one or more embodiments of the present disclosure. In the following description, for the sake of brevity, references to other methods will not be provided. Figure 1 The description repeats the details.

[0044] refer to Figures 2 to 4 The processor 140 may include a collector 141, a first calculator 142, a generator 143, a second calculator 144, and an output unit 145.

[0045] Collector 141 can collect viscoelastic property data, including loss modulus and storage modulus measured by applying shear strain to an active material slurry for a secondary battery at preset intervals. Collector 141 can arrange the viscoelastic property data measured by applying shear strain to the active material slurry at preset intervals in... Figure 3 In the table.

[0046] For example, from Figure 3 The preset shear strain 310 in the table can be applied to the active material slurry. Here, the shear strain applied to the active material slurry can refer to the shear deformation generated within the active material slurry via a rotary bearing. In one or more embodiments, the preset shear strain 310 can follow a logarithmic scale interval starting from an initial value of 0.01% and increasing at each step by approximately 1.58 times (10 to the power of 0.2) the previous value until a final value of 100% is reached.

[0047] Additionally, in one or more embodiments, the shear strain 310 can be expressed as Figure 3The logarithmic scale value is 320. Because using a logarithmic scale can effectively handle a wide range of data, from very small to very large values, the shear strain 310 is represented as a logarithmic scale value 320. Furthermore, by using a logarithmic scale, the changing values ​​can be distributed proportionally. For example, the data can be examined while maintaining a uniform relative difference between each data point. With a conventional linear scale, relatively small changes may not be noticeable if the differences between values ​​are large (e.g., when the differences between values ​​are large). However, by using a logarithmic scale, the visibility of the data can be improved because even these small changes can be clearly observed.

[0048] in addition, Figure 3 The table shows the shear stress 330, loss modulus 340, and storage modulus 350 collected by collector 141 from sensing module 120 at preset intervals in response to shear strain 310.

[0049] In one or more embodiments, including Figure 3 The data in the tables are results obtained through experiments, and they may include values ​​measured under specific environments and conditions. Therefore, these data are not fixed values ​​and may show different results depending on variations in the test environment or conditions.

[0050] The first calculator 142 can be used by Figure 3 The table discloses loss modulus 340 and energy storage modulus 350 to calculate loss factor 360 and loss factor derivative. In this disclosure, loss factor 360 can be calculated as the ratio between energy storage modulus 350 and loss modulus 340 (energy storage modulus / loss modulus). Additionally, loss factor derivative can be calculated as the difference between the current (t) loss factor and the previous (t-1) loss factor (current (t) loss factor minus the previous (t-1) loss factor). For example, the first calculator 142 can use data from... Figure 3 The loss modulus and storage modulus are used to calculate the loss factor and the loss factor derivative. The loss factor is determined as the ratio of the storage modulus to the loss modulus, while the loss factor derivative is calculated as the difference between the current loss factor and the previous loss factor.

[0051] In this disclosure, if the loss factor is less than a reference value (e.g., 1), this can indicate that the loss modulus is greater than the storage modulus. This can indicate that the slurry exhibits a predominantly viscous response to external deformation, emphasizing energy dissipation. These slurries can be interpreted as having a gel-like state, and their flow properties can be highlighted if deformation is applied (e.g., when deformation is applied).

[0052] Furthermore, in this disclosure, if the loss factor is greater than a reference value (e.g., 1), this can indicate that the storage modulus is greater than the loss modulus. This can indicate that the slurry elastically stores deformation energy and has the property of low energy dissipation. The slurry can be interpreted as having harder and more elastic properties, like stone.

[0053] In this disclosure, the closer the loss factor of the slurry is to the reference value, the more the viscoelasticity can be interpreted as being in a more balanced state. If the loss factor of the slurry is less than the reference value, this can be interpreted as a viscous-dominant state. If the loss factor of the slurry exceeds the reference value, this can be interpreted as an elastic-dominant state.

[0054] Generator 143 can generate time series data based on the differential of the loss factor. Generator 143 can generate graphs to visually represent the time series data.

[0055] In one or more embodiments, generator 143 can generate a graph by plotting preset intervals for shear strain as points on the X-axis and plotting the differential of the loss factor corresponding to the X-axis on the Y-axis. Here, the points can include the result of converting each value of the shear strain from an initial value to a final value to a logarithmic scale and dividing the logarithmic scale by a specific interval. Figure 3 In the table, point 370 can refer to the logarithmic scale value of shear strain, 320.

[0056] Figure 4 The illustrations for 410, 420, 430, and 440 are plotted as points on the X-axis by drawing the preset intervals used for shear strain and plotting the differential of the loss factor corresponding to the X-axis as a curve on the Y-axis.

[0057] The second calculator 144 can... Figure 4 The graph in the middle figure performs a sliding window analysis to calculate the average of the derivatives of multiple loss factors included in each window.

[0058] Typically, sliding window analysis involves techniques for performing statistical analysis on continuous subsets (windows) of data. To perform sliding window analysis, the step size and window size can be set. Furthermore, the processing to be performed on the data included in the window can be specified.

[0059] In one or more embodiments of this disclosure, the second calculator 144 can set a plurality of points, determined by a preset ratio of points to total points, as the size of a window. For example, if from Figure 3The total number of points 370 is 21, and the preset ratio is a rounding value of 20%. Therefore, 4 points (ABS(21×20% / 100) = 4) can be set as the window size. The second calculator 144 can set the mechanism for moving one point at a time in the X-axis direction on the graph as the step size.

[0060] The second calculator 144 can move the window sequentially according to the step size, and calculate the average value of the derivatives of multiple loss factors included in the window at each position.

[0061] Figure 4 The diagram 410 includes a first window 411 containing points 1 to 4. The second calculator 144 can calculate the average of the multiple loss factor derivatives included in the first window 411. For example, if the multiple loss factor derivatives corresponding to points 1 to 4 included in the first window 411 are 0.005, 0.002, 0.001, and 0.001, the average value can be calculated as 0.00225.

[0062] Figure 4 The diagram 420 illustrates a second window 421 obtained by shifting the first window 411 by one point along the X-axis. The second window 421 may include points 2 to 5. The second calculator 144 can calculate the average of the multiple loss factor derivatives included in the second window 421. For example, if the multiple loss factor derivatives corresponding to points 2 to 5 included in the second window 421 are 0.002, 0.001, 0.001, and 0.001, the average value of 0.00125 can be calculated.

[0063] Figure 4 The diagram 430 illustrates a third window 431 obtained by shifting the second window 421 by one point in the X-axis direction. The third window 431 may include points 3 to 6. The second calculator 144 can calculate the average of the multiple loss factor derivatives included in the third window 431. For example, if the multiple loss factor derivatives corresponding to points 3 to 6 included in the third window 431 are 0.001, 0.001, 0.001, and 0.001, the average value of 0.001 can be calculated.

[0064] In this way, move one point at a time along the X-axis until the last point ( Figure 3 At the same time as 21), the average value of the derivatives of multiple loss factors included in the window can be calculated.

[0065] For example, the loss factor of a slurry indicates its viscoelastic properties. If the loss factor is less than a reference value (e.g., 1), the slurry exhibits a predominantly viscous response, dissipates energy, and behaves like a gel. Conversely, if the loss factor is greater than the reference value, the slurry elastically stores deformation energy and behaves like a solid. Generator 143 creates time-series data and graphs to visually represent these properties, while a second calculator 144 performs a sliding window analysis to calculate the average of the loss factor derivatives in each window, thereby enhancing the understanding of the viscoelastic behavior of the slurry.

[0066] The second calculator 144 sets the window size based on a preset ratio of points to total points and moves the window one point at a time along the X-axis. It calculates the average of the differential loss factor in each window, thus providing a detailed analysis of the viscoelastic properties of the slurry. This process is repeated for each location to ensure a comprehensive assessment of the slurry's behavior.

[0067] The derivation unit 145 can derive any average value from the results of calculating the average of multiple loss factor derivatives included in each window as the damping factor. In this disclosure, the derivation unit 145 can determine the window with the smallest average value from the results of calculating the average values ​​as a standard time period. The derivation unit 145 can determine the smallest average value in the standard time period as the damping factor. For example, the derivation unit 145 calculates the average of the loss factor derivatives in each window to determine the damping factor. It identifies the window with the smallest average value as the standard time period and uses the smallest average value of that time period as the damping factor.

[0068] Figure 4 Figure 440 shows the result of detecting the fourth window 441 with the smallest average value by comparing the average values ​​of the various windows. For example, the fourth window 441 with the smallest average value may include points 6 to 9. The second calculator 144 can calculate the average value of the multiple loss factor derivatives included in the fourth window 441. For example, if the multiple loss factor derivatives included in the fourth window 441 corresponding to points 6 to 9 are 0.001, 0.001, 0, and 0 respectively, the average value is calculated to be 0.0005, which may be the smallest average value among all the average values ​​of the windows. The derivation unit 145 can determine the fourth window 441 as a standard time period and determine the average value of 0.0005 as the damping factor.

[0069] In this embodiment, the minimum average value is determined as the damping factor because it best reflects the optimal or suitable viscoelastic properties of the slurry. When analyzing the differential of the loss factor, the window with the minimum average value represents the segment where the slurry exhibits the lowest viscoelastic response to external deformation, which can be an important indicator of the stability and consistency of the slurry.

[0070] In this way, by selecting the minimum average value as the damping factor from the loss factor derivatives obtained through sliding window analysis, it can be used as a method to accurately evaluate the improved or optimized properties of the slurry and further improve the applicability of the material.

[0071] Figure 5 This is a flowchart describing a viscoelasticity measurement method according to one or more embodiments of the present disclosure. For the sake of brevity, references to other methods will not be provided in the following description. Figures 1 to 4 The description repeats any details. The viscoelastic measurement method according to one or more embodiments will be described as the viscoelastic measurement device 100 performing the viscoelastic measurement method in the processor 140 with the aid of peripheral components (e.g., rheological amplitude module 110, sensing module 120 and / or memory 130, etc.).

[0072] In operation S510, processor 140 may collect viscoelastic property data, including loss modulus and storage modulus measured by applying shear strain to an active material slurry for a secondary battery at preset intervals. In one or more embodiments, when collecting viscoelastic property data, processor 140 may collect shear stress measured by applying shear strain to an active material slurry for a secondary battery at preset intervals, and may collect loss modulus and storage modulus measured based on the phase angle between shear strain and shear stress.

[0073] In operation S520, processor 140 can calculate the loss factor, which is defined as the ratio between energy storage modulus and loss modulus, and calculate the difference between the current loss factor and the previous loss factor as the loss factor derivative.

[0074] In operation S530, processor 140 can generate time-series data based on the loss factor derivative. In one or more embodiments, processor 140 can generate a graph to visually represent the time-series data. In one or more embodiments, if time-series data is being generated (e.g., when generating time-series data), processor 140 can generate the graph by plotting a preset interval for shear strain as points on the X-axis and plotting the loss factor derivative corresponding to the X-axis on the Y-axis. Here, processor 140 can convert each value of the shear strain from the initial value to the final value into a logarithmic scale, and determine the point by dividing the logarithmic scale by a specific interval.

[0075] In operation S540, processor 140 may perform a sliding window analysis on time series data to calculate the average of multiple loss factor derivatives included in each window. In one or more embodiments, when calculating the average of the loss factors, processor 140 may load a graph generated by plotting a preset interval for shear strain as points on the X-axis and plotting the loss factor derivatives corresponding to the X-axis on the Y-axis. Processor 140 may set the size of the window including a preset number of points on the graph and the step size of the window. Processor 140 may move the window sequentially according to the step size and calculate the average of the multiple loss factor derivatives included in the window at each position. In one or more embodiments, processor 140 may set the size of the window as a plurality of points determined by a preset ratio of points to the total number of points, and set the step size as a mechanism for moving the window one point at a time in the direction of the X-axis.

[0076] In operation S550, processor 140 can derive any average value from the results of calculating the average values ​​of multiple loss factor derivatives included in each window as a damping factor. In one or more embodiments, processor 140 can determine the window with the minimum average value from the results of calculating the average values ​​as a standard time period, and determine the minimum average value in the standard time period as the damping factor.

[0077] According to this disclosure, the viscoelastic properties of active material slurries for secondary batteries can be accurately determined, and the evaluation results can be standardized so that all evaluators can evaluate the viscoelastic properties under the same conditions and standards.

[0078] In addition, data processing can be simplified by automating the evaluation logic, thereby reducing data processing time.

[0079] In one or more embodiments of this disclosure, the viscoelastic measurement method is performed by a viscoelastic measuring device 100, which includes components such as a rheological amplitude module 110, a sensing module 120, a memory 130, and a processor 140. The method begins with the processor 140 collecting viscoelastic property data, including loss modulus and storage modulus, by applying shear strain to an active material slurry for a secondary battery at preset intervals. This data collection also involves measuring shear stress and determining the phase angle between shear strain and shear stress.

[0080] Next, processor 140 calculates the loss factor, defined as the ratio between the storage modulus and the loss modulus, and determines the differential of the loss factor by comparing the current loss factor with previous loss factors. Then, processor 140 generates time-series data based on these differentials and creates graphs to visually represent the data. A sliding window analysis is performed on the time-series data to calculate the average value of the loss factor differential in each window. The window size and step size can be set based on a preset ratio of points to the total number of points. Finally, processor 140 derives the damping factor by identifying the window with the smallest average value and using that value as the standard time period. This method ensures accurate determination and standardization of the viscoelastic properties of the slurry, thereby simplifying the data processing and reducing evaluation time.

[0081] In the context of a viscoelastic measuring device, and unless otherwise specified, the processor and other components can be implemented as electronic circuits. The processor can be an electronic circuit designed to perform data analysis, processing, and result generation. It can execute algorithms such as machine learning or neural network models to analyze viscoelastic property data and perform calculations such as determining the loss factor and its derivative.

[0082] The sensing module may include electronic circuitry such as pressure sensors, strain gauges, and torque sensors. These sensors measure the shear strain and shear stress of the slurry and convert these measurements into digital signals for further processing. The memory may be an electronic storage device that holds the code executed by the processor. It stores the data, algorithms, and intermediate results required to perform the viscoelastic measurement method. The rheological amplitude module may be electronic circuitry that applies shear strain to the slurry and measures its viscoelastic properties, such as loss modulus and storage modulus.

[0083] These components / circuits work together as part of a viscoelasticity measurement device to accurately determine and standardize the viscoelastic properties of active material slurries used in secondary batteries.

[0084] In the context of this disclosure, unless otherwise specified, the terms “use,” “in use,” and “being used” may be considered synonymous with “exploitation,” “being exploited,” and “being exploited,” respectively.

[0085] In view of the full contents of this disclosure, those skilled in the art will recognize that each suitable feature of the various embodiments of this disclosure may be combined in whole or in part or with one another, and may be technically interlocked and operated in a variety of suitable ways, and unless otherwise stated or implied, each embodiment may be implemented independently of one another or in combination with one another in any suitable way.

[0086] Although this disclosure has been described with reference to exemplary embodiments and accompanying drawings, it is not limited thereto, and it will be apparent to those skilled in the art to which this disclosure pertains that one or more suitable modifications and variations may be made within the scope of this disclosure and its technical ideas, the scope of the claims, and the equivalents of the claims.

Claims

1. A viscoelasticity measurement method executed by a processor of a viscoelasticity measuring device, the viscoelasticity measurement method comprising: Collect viscoelastic property data, including loss modulus and storage modulus measured by applying shear strain to an active material slurry for a secondary battery at preset intervals. The loss factor, defined as the ratio between the energy storage modulus and the loss modulus, is calculated, and the difference between the current loss factor and the previous loss factor is calculated as the differential of the loss factor. Time series data are generated based on the differential of the loss factor; A sliding window analysis is performed on the time series data to calculate the average of the derivatives of multiple loss factors in each window; as well as The average value of the average value of the derivatives of the plurality of loss factors in each window is derived as the damping factor.

2. The viscoelasticity measurement method according to claim 1, wherein, The collection of the viscoelastic property data includes: The shear stress is collected by applying the shear strain to the active material slurry used in the secondary battery at the predetermined intervals; and The loss modulus and the storage modulus are collected based on the phase angle between the shear strain and the shear stress.

3. The viscoelasticity measurement method according to claim 1, wherein, The generation of the time series data includes generating a curve by plotting the preset intervals for the shear strain as points on the X-axis and plotting the differential of the loss factor corresponding to the X-axis on the Y-axis.

4. The viscoelasticity measurement method according to claim 3, wherein, The generation of the curve includes: converting each value from the initial value to the final value of the shear strain into a logarithmic scale, dividing the logarithmic scale by a specific interval, and determining the result of the division as the point.

5. The viscoelasticity measurement method according to claim 1, wherein, The calculation of the average value of the derivatives of the plurality of loss factors includes: A loading curve is generated by plotting the preset interval for the shear strain as points on the X-axis and plotting the differential of the loss factor corresponding to the X-axis on the Y-axis. Set the size of the window on the graph, which includes a preset number of points, and the step size of the window; and While moving the window sequentially according to the step size, the average value of the derivatives of the plurality of loss factors in the window is calculated at each position.

6. The viscoelasticity measurement method according to claim 5, wherein, The settings include: setting a plurality of points determined by a preset ratio of the points to the total number of points as the size of the window, and setting a mechanism for moving the window by one point in the direction of the X-axis as the step size.

7. The viscoelasticity measurement method according to claim 1, wherein, The derivation of the damping factor by taking any average of the results of calculating the average of the derivatives of the plurality of loss factors in each window includes: The window with the minimum average value is determined from the results of calculating the average value as the standard time period; and The minimum average value within the standard time period is determined as the damping factor.

8. A computer-readable recording medium on which a computer program is recorded for causing a computer to perform the viscoelastic measurement method according to any one of claims 1 to 7.

9. A viscoelasticity measuring device, comprising: One or more processors; as well as A memory operatively connected to the one or more processors and storing at least a piece of code to be executed by the one or more processors. Wherein, when the at least one piece of code is executed by the one or more processors, the one or more processors are able to: Collect viscoelastic property data, including loss modulus and storage modulus measured by applying shear strain to an active material slurry for a secondary battery at preset intervals. The loss factor, defined as the ratio between the energy storage modulus and the loss modulus, is calculated, and the difference between the current loss factor and the previous loss factor is calculated as the differential of the loss factor. Time series data are generated based on the differential of the loss factor; Perform a sliding window analysis on the time series data to calculate the average of the derivatives of multiple loss factors in each window; and The average value of the average value of the derivatives of the plurality of loss factors in each window is derived as the damping factor.

10. The viscoelasticity measuring device according to claim 9, wherein, When the at least one piece of code is executed by the one or more processors, the one or more processors are further enabled to: collect, in the collection of the viscoelastic property data, shear stress measured by applying the shear strain to the active material slurry for the secondary battery at the preset intervals, and collect the loss modulus and the storage modulus measured based on the phase angle between the shear strain and the shear stress.

11. The viscoelasticity measuring device according to claim 9, wherein, When the at least one piece of code is executed by the one or more processors, the one or more processors are further enabled to: generate a graph in the generation of the time series data by plotting the preset intervals for the shear strain as points on the X-axis and plotting the differential of the loss factor corresponding to the X-axis on the Y-axis.

12. The viscoelasticity measuring device according to claim 11, wherein, When the at least one piece of code is executed by the one or more processors, the one or more processors are further enabled to: in the generation of the graph, convert each value from the initial value to the final value of the shear strain into a logarithmic scale, divide the logarithmic scale by a specific interval, and determine the result of the division as the point.

13. The viscoelasticity measuring device according to claim 9, wherein, When the at least one piece of code is executed by the one or more processors, it further enables the one or more processors to perform the calculation of the average value of the derivatives of the plurality of loss factors: A loading curve is generated by plotting the preset interval for the shear strain as points on the X-axis and plotting the differential of the loss factor corresponding to the X-axis on the Y-axis. Define a window on the graph that includes a preset number of points; and While moving the window by one point in the direction of the X-axis, the average value of the derivatives of the plurality of loss factors in the window is calculated at each position.

14. The viscoelasticity measuring device according to claim 13, wherein, When the at least one piece of code is executed by the one or more processors, it further enables the one or more processors to: set the window in the window settings to include a number of points determined according to a preset ratio of the points to the total number of points.

15. The viscoelasticity measuring device according to claim 9, wherein, When the at least one piece of code is executed by the one or more processors, it further enables the one or more processors to: In the derivation of the damping factor by taking any average of the results of calculating the average of the derivatives of the plurality of loss factors in each window, the window with the minimum average value is determined from the results of calculating the average values ​​as the standard time period. The minimum average value within the standard time period is determined as the damping factor.

16. A viscoelasticity measuring device, comprising: One or more processors; A memory operatively connected to the one or more processors and storing at least a piece of code to be executed by the one or more processors; as well as The sensing module is configured to measure the shear strain and shear stress of the active material slurry used in secondary batteries. Wherein, when the at least one piece of code is executed by the one or more processors, the one or more processors are able to: Collect viscoelastic property data, including loss modulus and storage modulus measured by applying the shear strain to the active material slurry for the secondary battery at preset intervals; The loss factor, defined as the ratio between the energy storage modulus and the loss modulus, is calculated, and the difference between the current loss factor and the previous loss factor is calculated as the differential of the loss factor. Time series data are generated based on the differential of the loss factor; Perform a sliding window analysis on the time series data to calculate the average of the derivatives of multiple loss factors in each window; and The average value of the average value of the derivatives of the plurality of loss factors in each window is derived as the damping factor.

Images