Data processing apparatus and operating method thereof

Abstract

A battery diagnosis apparatus according to an embodiment disclosed herein may include: an acquisition unit for acquiring time-series voltage data of a plurality of battery cells; a calculation unit for calculating a voltage deviation between an average voltage of the plurality of battery cells and a voltage of each of the plurality of battery cells based on the time series voltage data; a generation unit for generating entropy voltage data representing a voltage deviation as an entropy value based on the voltage deviation of the plurality of battery cells; and a diagnosis unit for diagnosing anomalies of the plurality of battery cells based on the entropy voltage data.

Description

Technical Field

[0001] Cross-reference to related applications

[0002] This application claims priority to Korean Patent Application No. 10-2023-0117958, filed on September 5, 2023, the entire contents of which are incorporated herein by reference for all purposes. Technical Field

[0003] The embodiments disclosed herein relate to data processing apparatus and data processing methods. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively pursued. Here, rechargeable batteries refer to batteries that can be recharged, and include all conventional Ni / Cd batteries, Ni / MH batteries, and the more recent lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries offer a significantly higher energy density compared to conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured small and lightweight enough to be used as a power source for mobile devices, and their application has recently expanded to electric vehicles, attracting considerable attention as a next-generation energy storage medium.

[0005] In processing the data for these secondary batteries, outlier filtering techniques utilizing median characteristics were employed to remove outliers. For example, a technique was used to sort the process data by size and to remove outliers by considering any data falling outside the range between the lower and upper inner limits determined using quartiles in a box plot. Summary of the Invention

[0006] Technical issues

[0007] However, existing outlier filtering techniques have the following problems: the lower and upper inner limits of the box plot are fixed, which cannot reflect the process capability of the battery manufacturing equipment and poses a risk of removing normal data.

[0008] The embodiments disclosed herein are intended to provide data processing apparatus and methods capable of determining the scope of outlier removal by taking into account the process capabilities of battery manufacturing equipment.

[0009] The technical objectives of the embodiments disclosed herein are not limited to those described above, and other objectives not described herein will be clearly understood by those skilled in the art based on the following description.

[0010] Technical solution

[0011] A data processing apparatus according to an embodiment disclosed herein may include: a communication circuit configured to acquire process data related to a battery manufacturing process; and a processor configured to filter the process data, wherein the processor generates a reference distribution curve following a standard normal distribution based on the process data, calculates process capability parameters related to the process capability of process equipment involved in the battery manufacturing process based on the process data, generates a box plot for the battery manufacturing process based on the reference distribution curve and the process capability parameters, and removes data from the process data that falls outside the critical range corresponding to the box plot.

[0012] In the data processing apparatus disclosed herein, the box plot may include quartiles, interquartile ranges, minimum limits, and maximum limits, and the processor may set the quartiles and interquartile ranges based on the reference distribution curve, and set the minimum limits and maximum limits based on at least some of the quartiles, the interquartile ranges, and the process capability parameters.

[0013] In the data processing device disclosed herein, the critical range can be the interval between the minimum limit and the maximum limit.

[0014] In the data processing device disclosed herein, the processor can calculate a proportional value based on the process capability parameters, and set the minimum limit and the maximum limit based on Equations 1 and 2: [Equation 1] Minimum limit = Q1-k·IQR [Equation 2] Minimum limit = Q3+k·IQR (In Equations 1 and 2, Q1 represents the first quartile, Q3 represents the third quartile, k represents the ratio, and IQR represents the interquartile range.)

[0015] In the data processing device disclosed herein, the processor can calculate the ratio value based on Equation 3: [Equation 3] k= -0.5+（n / 1.35） (In Equation 3, k represents the ratio value, and n represents the process capability parameter).

[0016] In the data processing device disclosed herein, the processor can calculate the difference between the maximum and minimum values ​​of a predetermined process parameter based on the process data, calculate the standard deviation of the process parameter values ​​based on the process data, and calculate the process capability parameter based on the difference and the standard deviation.

[0017] In the data processing device disclosed herein, the processor can calculate the process capability parameters based on Equation 4: [Equation 4]

[0018] n = T / (2·s)

[0019] (In Equation 4, n represents the process capability parameter, T represents the difference, and s represents the standard deviation value.)

[0020] A data processing method according to an embodiment disclosed herein may include: acquiring process data related to a battery manufacturing process; generating a reference distribution curve that follows a standard normal distribution based on the process data; calculating process capability parameters related to the process capability of process equipment involved in the battery manufacturing process based on the process data; generating a box plot for the battery manufacturing process based on the reference distribution curve and the process capability parameters; and removing data from the process data that falls outside the critical range corresponding to the box plot.

[0021] In the data processing method disclosed herein, the box plot may include quartiles, interquartile ranges, minimum limits, and maximum limits, and the step of generating the box plot may include setting the quartiles and interquartile ranges based on the reference distribution curve, and setting the minimum limits and maximum limits based on at least some of the quartiles, the interquartile ranges, and the process capability parameters.

[0022] In the data processing method disclosed herein, the critical range can be the interval between the minimum limit and the maximum limit.

[0023] The data processing method disclosed herein may further include calculating a ratio value based on the process capability parameters, and the minimum limit and the maximum limit are set based on Equations 1 and 2.

[0024] In the data processing method disclosed herein, the ratio value can be calculated based on Equation 3.

[0025] In the data processing method disclosed herein, calculating the process capability parameter may include: calculating the difference between the maximum and minimum values ​​of a predetermined process parameter based on the process data; calculating the standard deviation of the process parameter values ​​based on the process data; and calculating the process capability parameter based on the difference and the standard deviation.

[0026] In the data processing method disclosed herein, the process capability parameters can be calculated based on Equation 4.

[0027] Beneficial effects

[0028] According to the embodiments disclosed herein, existing conservative outlier filtering techniques can be improved by determining the outlier removal range based on the process capabilities of the battery manufacturing equipment.

[0029] In addition, various effects that can be identified directly or indirectly through this article can be provided. Attached Figure Description

[0030] Figure 1 It is a schematic diagram showing the reference distribution curve and typical box plot based on process data; Figure 2 This is a block diagram illustrating a data processing apparatus according to an embodiment; Figure 3 This is a diagram illustrating an exemplary box plot generated by a data processing device according to an embodiment; and Figure 4 This is a flowchart illustrating the operation of a data processing device according to an embodiment. Detailed Implementation

[0031] In the following description, various embodiments of the invention will be described with reference to the accompanying drawings. However, this description is not intended to limit the invention to the specific embodiments, and it should be construed as including various variations, equivalents, and / or alternatives to the embodiments described herein.

[0032] The various embodiments disclosed herein and the terminology used therein are not intended to limit the technical features described herein to specific embodiments, and this disclosure should be construed as including various variations, equivalents, and / or alternatives to the corresponding embodiments. In conjunction with the description of the accompanying drawings, the same reference numerals may be used for the same or related elements. The singular form of a noun corresponding to an item may include one or more items unless the relevant context clearly specifies otherwise.

[0033] In this document, each of the phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B or C” can include any item listed together in the corresponding phrase or any possible combination thereof. Terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used simply to distinguish these components from other components and do not otherwise limit the corresponding components (e.g., in terms of importance or order) unless otherwise specified.

[0034] In this document, when referring to (e.g., the first) component as “connected,” “linked,” or “accessed” to another (e.g., the second) component, whether or not the terms “functionally” or “communically” are used, it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0035] According to various embodiments, each of the above-described components (e.g., a module or program) may include a single object or multiple objects, and some of the multiple objects may be separately located in other components. According to various embodiments, one or more components or operations among the above-described components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of the corresponding components in the multiple components in the same or similar manner as the functions performed by each of the multiple components prior to integration. According to various embodiments, operations performed by modules, programs, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or executed in a different order, with one or more operations omitted, or with one or more other operations added.

[0036] Figure 1 It is a schematic diagram showing the reference distribution curve and typical box plot based on process data.

[0037] refer to Figure 1 The diagram shows a reference distribution curve generated based on process data related to the battery process and a typical box plot generated based on the reference distribution curve.

[0038] Here, process data can refer to data related to process parameters (e.g., temperature, output, etc.) obtained from battery manufacturing equipment and / or inspection data (e.g., dimensional data, thickness data, etc.) obtained from automated inspection devices installed in the battery manufacturing equipment. A reference distribution curve can represent a continuous probability distribution of process data with respect to predetermined process parameters. Furthermore, the reference distribution curve can follow a standard normal distribution.

[0039] A box plot can include quartiles (Q1, Q2, and Q3), interquartile range (IQR), and lower inner limit (Q1-1.5). IQR and upper inner limit (Q3+1.5) IQR).

[0040] Box plots can be generated based on the median of the process data. That is, the second quartile (Q2) can be set as the median of the process data.

[0041] The quartiles (Q1, Q2, and Q3) divide the reference distribution curve into four equal parts, representing the values ​​at the 25th, 50th, and 75th percentiles, respectively. Specifically, within the reference distribution curve, the range below the first quartile (Q1), the range from Q1 to the second quartile (Q2), the range from Q2 to the third quartile (Q3), and the range above Q3 each contain one-quarter of the total process data.

[0042] For example, when using the probability distribution function F(x) to represent the quartiles, F(Q1) = 0.25, F(Q2) = 0.5, and F(Q3) = 0.75. Therefore, in a standard normal distribution, the first quartile (Q1) can be -0.6745σ, the second quartile (Q2) can be 0, and the third quartile (Q3) can be 0.6745σ, where σ is the standard deviation of the process data.

[0043] The interquartile range (IQR) can be defined as the distance between the first quartile (Q1) and the third quartile (Q3). Therefore, the IQR can be 1.349σ.

[0044] According to the general definition of a box plot, the lower inner limit is defined as "Q1 - (1.5)". Therefore, the lower inner limit can be -2.698σ. Similarly, the upper inner limit is defined as "Q3 + (1.5σ)". Therefore, the upper inner limit can be 2.698σ.

[0045] Existing outlier removal methods, based on a reference distribution curve of process data, consider any data falling outside the range between the lower and upper inner limits of a typical box plot as outliers and remove them. However, this method does not reflect the process capabilities of battery manufacturing equipment, which may also lead to the removal of normal data.

[0046] Therefore, when removing outliers from process data, a technique is needed that takes into account the process capabilities of the manufacturing equipment and allows for flexible setting of the outlier removal range.

[0047] The following text will refer to Figure 2 and Figure 3 A data processing apparatus according to embodiments of the present disclosure is described.

[0048] Figure 2 This is a block diagram illustrating a data processing apparatus according to an embodiment. Figure 3 This is a diagram illustrating an exemplary box plot generated by a data processing device according to an embodiment.

[0049] refer to Figure 2The data processing device 100 may include a communication circuit 110, a memory 120, and a processor 130. According to an embodiment, Figure 2 The data processing device 100 shown may also include, in addition to Figure 2 At least one additional component (e.g., display, input device, or output device) other than the component shown.

[0050] According to one embodiment, the communication circuit 110 can establish a communication channel between the data processing device 100 and an external electronic device (e.g., a battery manufacturing device), and can send data to and receive data from the external electronic device via the established communication channel. According to the embodiment, the communication channel can be established based on a wired network and / or a wireless network. In the embodiment, the wired network can be based on a local area network (LAN) or power line communication. In the embodiment, the wireless network can be based on a short-range communication network (e.g., Bluetooth, Wi-Fi, Infrared Data Association (IrDA)) or a long-range communication network (e.g., a cellular network, a 4G network, and a 5G network).

[0051] According to an embodiment, the communication circuit 110 can acquire process data related to the battery manufacturing process. Here, process data may refer to data related to process parameters (e.g., temperature, output, etc.) obtained from the battery manufacturing equipment and / or inspection data (e.g., dimensional data, thickness data, etc.) obtained from an automated inspection device installed in the battery manufacturing equipment.

[0052] According to an implementation, memory 120 may include volatile memory and / or non-volatile memory.

[0053] According to one embodiment, memory 120 may store data used by at least one component of data processing device 100 (e.g., processor 130). For example, the data may include software (or related instructions), input data, or output data. In one embodiment, instructions may be executed by processor 130 to cause data processing device 100 to perform operations defined by the instructions.

[0054] According to an implementation, the processor 130 may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0055] According to an implementation, the processor 130 can execute software stored in the memory 120 to control at least one other component (e.g., hardware or software component) connected to the processor 130 in the data processing device 100, and perform various data processing or calculations.

[0056] According to an implementation, the processor 130 can filter process data acquired by the communication circuit 110.

[0057] According to the implementation, the processor 130 can generate a reference distribution curve that follows a standard normal distribution based on the process data. Here, the reference distribution curve can represent a continuous probability distribution of the process data with respect to predetermined process parameters.

[0058] According to the implementation, the processor 130 can calculate process capability parameters related to the process capability of the process equipment involved in the battery manufacturing process based on process data.

[0059] According to the implementation, the processor 130 can calculate the difference between the maximum and minimum values ​​of a predetermined process parameter based on process data. For example, when the process data represents the temperature detected by a battery manufacturing equipment, the processor 130 can calculate the difference between the maximum and minimum temperature values.

[0060] According to the implementation, the processor 130 can calculate the standard deviation of a predetermined process parameter value based on process data. For example, when the process data represents the temperature detected by a battery manufacturing device, the processor 130 can calculate the standard deviation of the detected temperature value.

[0061] According to the implementation method, the processor 130 can calculate the process capability parameters based on Equation 1.

[0062] [Equation 1]

[0063] n = T / (2·s)

[0064] In Equation 1, n represents the process capability parameter, T represents the difference between the maximum and minimum values ​​of the predetermined process parameter, and s represents the standard deviation of the predetermined process parameter.

[0065] According to an implementation, processor 130 can generate a box plot of the battery process based on a reference distribution curve and process capability parameters. The box plot may include quartiles, interquartile ranges, minimum limits, and maximum limits.

[0066] According to the implementation, the processor 130 can set the quartiles and interquartile ranges based on a reference distribution curve. The processor 130 can set the values ​​of variables that divide the data of the reference distribution curve into four equal parts as quartiles. For example, the processor 130 can set the median of the reference distribution curve as the second quartile Q2, the value of the probability distribution function F(x) equal to 0.25 as the first quartile Q1, and the value of F(x) equal to 0.75 as the third quartile Q3. The processor 130 can set the range between the first quartile and the third quartile as the interquartile range.

[0067] According to the implementation, the processor 130 can set the minimum limit and the maximum limit based on at least some of the quartiles, interquartile ranges and process capability parameters.

[0068] According to the implementation method, the processor 130 can set the minimum limit and the maximum limit based on Equations 2 and 3.

[0069] [Equation 2]

[0070] Minimum limit = Q1-k·IQR

[0071] [Equation 3]

[0072] Minimum limit = Q3+k·IQR

[0073] In Equations 2 and 3, Q1 represents the first quartile, Q3 represents the third quartile, k represents the proportional value, and IQR represents the interquartile range. Here, the proportional value is related to the process capability parameter, and the method used to calculate the proportional value will be described below.

[0074] According to the implementation method, the processor 130 can calculate the scaling factor based on process capability parameters. For example, the processor 130 can calculate the scaling factor based on Equation 4.

[0075] [Equation 4]

[0076] k = -0.5 + (n / 1.35)

[0077] In Equation 4, k represents the proportional value, and n represents the process capability parameter.

[0078] According to one implementation, processor 130 can remove data from process data that falls outside the critical range corresponding to the box plot. The critical range may, for example, refer to the interval between the minimum limit and the maximum limit.

[0079] In this way, the data processing device 100 can flexibly set the outlier removal range by establishing new minimum and maximum limits based on the process capability parameters of the battery manufacturing equipment, thereby reflecting the process capability of the equipment.

[0080] Figure 4 This is a flowchart illustrating the operation of a data processing device according to an embodiment. Figure 4 It can be shown Figure 2 The operation of the data processing device 100 in the middle, and can be based on Figure 2 The configuration shown is described below.

[0081] Figure 4 The embodiments shown are merely one embodiment, and the order of operation according to various embodiments of the present invention may vary. Figure 4 The differences shown can be omitted or combined. Figure 4 Some of the operations shown or their order can be changed.

[0082] refer to Figure 4 In operation 405, the data processing device 100 can acquire process data related to the battery manufacturing process. Here, process data may refer to data related to process parameters (e.g., temperature, output, etc.) obtained from the battery manufacturing equipment and / or inspection data (e.g., dimensional data, thickness data, etc.) obtained from automated inspection devices installed in the battery manufacturing equipment.

[0083] In operation 410, the data processing device 100 can generate a reference distribution curve that follows a standard normal distribution based on the process data acquired in operation 405. Here, the reference distribution curve can represent a continuous probability distribution of the process data with respect to predetermined process parameters.

[0084] In operation 415, data processing device 100 can calculate process capability parameters related to the process capability of the process equipment involved in the battery manufacturing process based on the process data acquired in operation 405.

[0085] According to the implementation, the data processing device 100 can calculate the difference between the maximum and minimum values ​​of a predetermined process parameter based on process data. For example, when the process data represents the temperature detected by a battery manufacturing device, the data processing device 100 can calculate the difference between the maximum and minimum temperature values ​​detected.

[0086] According to the implementation, the data processing device 100 can calculate the standard deviation of a predetermined process parameter value based on process data. For example, when the process data represents the temperature detected by a battery manufacturing device, the data processing device 100 can calculate the standard deviation of the detected temperature value.

[0087] According to the implementation method, the data processing device 100 can calculate the process capability parameters based on Equation 1.

[0088] In operation 420, data processing device 100 can generate a box plot for the battery manufacturing process based on the reference distribution curve created in operation 410 and the process capability parameters calculated in operation 415. The box plot may include quartiles, interquartile ranges, minimum limits, and maximum limits.

[0089] According to the implementation, the data processing device 100 can set the quartiles and interquartile ranges based on a reference distribution curve. The data processing device 100 can set the values ​​of variables that divide the data of the reference distribution curve into four equal parts as quartiles. For example, the data processing device 100 can set the median of the reference distribution curve as the second quartile Q2, the value of the probability distribution function F(x) equal to 0.25 as the first quartile Q1, and the value of F(x) equal to 0.75 as the third quartile Q3. The data processing device 100 can set the range between the first quartile and the third quartile as the interquartile range.

[0090] According to the implementation, the data processing device 100 can set the minimum limit and the maximum limit based on at least some of the quartiles, the interquartile range, and the process capability parameters.

[0091] According to the implementation method, the data processing device 100 can set the minimum limit and the maximum limit based on Equations 2 and 3.

[0092] According to the implementation method, the data processing device 100 can calculate the scale value based on process capability parameters. For example, the data processing device 100 can calculate the scale value based on Equation 4.

[0093] In operation 425, data processing device 100 can remove data from the process data that falls outside the critical range corresponding to the box plot. The critical range can, for example, refer to the interval between the minimum limit and the maximum limit.

[0094] Furthermore, unless otherwise specifically stated, terms such as “comprising,” “including,” or “having” as used above imply the presence of corresponding components and should therefore be interpreted as capable of further including rather than excluding other components. Unless otherwise defined herein, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It will also be understood that the meanings of terms such as those defined in common dictionaries should be interpreted as consistent with their meanings in the context of the relevant field and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

Claims

1. A data processing apparatus comprising: communication circuitry configured to acquire process data related to a battery manufacturing process; and a processor configured to filter the process data, wherein the processor generates a reference distribution curve following a standard normal distribution based on the process data, calculates a process capability parameter related to a process capability of a process equipment involved in the battery manufacturing process based on the process data, generates a box plot for the battery manufacturing process based on the reference distribution curve and the process capability parameter, and removes data falling outside a critical range corresponding to the box plot from the process data. The box plot includes quartiles, a quartile range, a minimum limit, and a maximum limit, and the processor sets the quartiles and the quartile range based on the reference distribution curve, and sets the minimum limit and the maximum limit based on at least some of the quartiles, the quartile range, and the process capability parameter.

2. The data processing device of claim 1, wherein, The critical range is an interval between the minimum limit and the maximum limit.

3. The data processing device of claim 2, wherein, The processor calculates a proportion value based on the process capability parameter, and sets the minimum limit and the maximum limit based on Equation 1 and Equation 2:

4. The data processing device according to claim 2, wherein, [Equation 1] Q1 - k · IQR min limit [Equation 2] Q3 + k · IQR min limit (In Equation 1 and Equation 2, Q1 denotes a first quartile, Q3 denotes a third quartile, k denotes the proportion value, and IQR denotes the quartile range. The processor calculates the proportion value based on Equation 3:

5. The data processing device of claim 4, wherein, [Equation 3] (In Equation 3, k denotes the proportion value, and n denotes the process capability parameter). k= -0.5+（n / 1.35） The processor calculates a difference between a maximum value and a minimum value among numerical values of a predetermined process parameter based on the process data, calculates a standard deviation value of the numerical values of the process parameter based on the process data, and calculates the process capability parameter based on the difference and the standard deviation value.

6. The data processing device of claim 1, wherein, The processor calculates the process capability parameter based on Equation 4:

7. The data processing device of claim 6, wherein, [Equation 4] n = T / (2 · s) (In Equation 4, n denotes the process capability parameter, T denotes the difference, and s denotes the standard deviation value). 8.A data processing method comprising the steps of: acquiring process data related to a battery manufacturing process; generating a reference distribution curve following a standard normal distribution based on the process data; calculating a process capability parameter related to a process capability of a process equipment involved in the battery manufacturing process based on the process data; generating a box plot for the battery manufacturing process based on the reference distribution curve and the process capability parameter; and removing data falling outside a critical range corresponding to the box plot from the process data. ​ ​ 9. The data processing method of claim 8, wherein, The box plot includes quartiles, interquartile range, minimum limit, and maximum limit, and the step of generating the box plot includes setting the quartiles and the interquartile range based on the reference distribution curve and setting the minimum limit and the maximum limit based on at least some of the quartiles, the interquartile range, and the process capability parameter.

10. The data processing method of claim 9, wherein, The critical range is an interval between the minimum limit and the maximum limit.

11. The data processing method according to claim 9, further comprising calculating a scale value based on the process capability parameter, and setting the minimum limit and the maximum limit based on Equation 1 and Equation 2: [Equation 1] Q1 - k - IQR [Equation 2] Q3 + k - IQR (in Equations 1 and 2, Q1 represents the first quartile, Q3 represents the third quartile, k represents the scale value, and IQR represents the interquartile range. min limit The scale value is calculated based on Equation 3: [Equation 3] k = 2.5 (in Equation 3, k represents the scale value, and n represents the process capability parameter). min limit The step of calculating the process capability parameter includes: calculating a difference between a maximum value and a minimum value among values of a predetermined process parameter based on the process data; 12. The data processing method of claim 11, wherein, calculating a standard deviation value of the values of the process parameter based on the process data; and calculating the process capability parameter based on the difference and the standard deviation value. k= -0.5+（n / 1.35） The process capability parameter is calculated based on Equation 4:

13. The data processing method of claim 8, wherein, [Equation 4] n = T / (2 - s) (in Equation 4, n represents the process capability parameter, T represents the difference, and s represents the standard deviation value). ​ ​ ​ 14. The data processing method of claim 13, wherein, ​ ​ ​ ​

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