Control factor calculation device and operation method thereof

Abstract

The control factor calculation apparatus according to one embodiment disclosed herein includes: a data acquisition unit that periodically acquires data including a slurry loading amount and a slurry flow rate; and a processor that learns a correlation between a slurry loading amount and a slurry flow based on the data, where the processor may calculate a slurry flow control value to achieve a target slurry loading amount based on the correlation.

Description

Technical Field

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2022-0116615 filed in the Korean Intellectual Property Office on September 15, 2022, and Korean Patent Application No. 10-2023-0047518 filed in the Korean Intellectual Property Office on April 11, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0004] The invention relates to a control factor calculation device and an operation method thereof. Background Art

[0005] Recently, the research and development of secondary batteries have been actively carried out. In this article, the secondary battery as a rechargeable / dischargeable battery may include all conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, etc. and recent lithium-ion batteries. In secondary batteries, lithium-ion batteries have a much higher energy density than traditional Ni / Cd batteries, Ni / MH batteries, etc. Moreover, lithium-ion batteries can be made small and lightweight, so that lithium-ion batteries have been used as power sources for mobile devices, and the use range of lithium-ion batteries has been expanded to power sources for electric vehicles recently, attracting attention as the next generation of energy storage media.

[0006] With the expansion of the application field of lithium-ion batteries, the demand for higher capacity secondary batteries has increased rapidly. The electrode for the secondary battery is manufactured by coating the electrode slurry on the current collector and then performing a drying and rolling process. During the electrode manufacturing process, a high level of coating uniformity is required to improve the electrode slurry coating quality. For this coating uniformity, when an error occurs between the slurry loading amount and the target loading amount, the coating uniformity is improved by controlling certain factors (e.g., slurry discharge pump revolutions per minute (RPM), temperature, etc.). Summary of the invention

[0007] Technical issues

[0008] The embodiments disclosed herein are intended to provide a control factor calculation device and an operating method thereof, wherein a control factor for achieving a target slurry loading amount is recommended according to circumstances.

[0009] The embodiments disclosed herein are intended to provide a control factor calculation device and an operating method thereof, in which a slurry flow control value for reducing the time to reach a target slurry loading amount is recommended as a control factor for achieving a target slurry loading amount.

[0010] Technical problems of the embodiments disclosed herein are not limited to the above-mentioned technical problems, and other unmentioned technical problems will be clearly understood by those of ordinary skill in the art from the following description.

[0011] Technical Solution

[0012] According to the embodiment disclosed in this article, the control factor calculation device includes: a data acquisition unit, which is configured to periodically acquire data including slurry loading and slurry flow; and a processor, which is configured to learn the correlation between the slurry loading and the slurry flow based on the data, wherein the processor is also configured to calculate a slurry flow control value for achieving a target slurry loading based on the correlation.

[0013] In the control factor calculation device according to the embodiment disclosed in this article, the processor can also be configured to: learn a first correlation between first data and second data, the first data including the slurry loading amount of N cycles and the slurry flow rate of N+1 cycles, and the second data including the slurry loading amount of N+1 cycles; and calculate the slurry flow control value based on the first correlation.

[0014] In the control factor calculation device according to the embodiment disclosed in this article, the processor can also be configured to calculate the slurry flow control value based on the slurry loading amount of the N+1 cycle, the slurry loading amount of the N+1 cycle corresponding to the target slurry loading amount, and the first correlation.

[0015] In the control factor calculation device according to the embodiment disclosed in this article, the data may also include the revolutions per minute (RPM) of the slurry pump, and the processor may also be configured to: learn a second correlation between the slurry flow change data and the slurry pump RPM change data according to periodic changes; and calculate the slurry pump RPM control value for achieving the flow control value based on the second correlation.

[0016] In the control factor calculation device according to the embodiment disclosed herein, the data may further include a gap between the slurry discharge device and the substrate.

[0017] In the control factor calculation device according to the embodiment disclosed herein, the processor may also be configured to learn the correlation based on a machine learning model or a deep learning model.

[0018] The control factor calculation method according to the embodiment disclosed in this article includes: periodically acquiring data including slurry loading and slurry flow; learning the correlation between the slurry loading and the slurry flow based on the data; and calculating the slurry flow control value for achieving the target slurry loading based on the correlation.

[0019] In the control factor calculation method according to the embodiment disclosed in this article, the step of learning the correlation may include the following steps: learning a first correlation between first data and second data, the first data including the slurry loading amount of N cycles and the slurry flow rate of N+1 cycles, the second data including the slurry loading amount of N+1 cycles; and the step of calculating the slurry flow control value includes the step of calculating the slurry flow control value based on the first correlation.

[0020] In the control factor calculation method according to the embodiment disclosed in this article, the step of calculating the slurry flow control value may include the step of calculating the slurry flow control value based on the slurry loading amount of the N+1 cycle, the slurry loading amount of the N+1 cycle corresponding to the target slurry loading amount, and the first correlation.

[0021] In the control factor calculation method according to the embodiment disclosed in this article, the data may also include the revolutions per minute (RPM) of the slurry pump, and the step of learning the correlation includes the step of learning a second correlation between the slurry flow change data according to periodic changes and the slurry pump RPM change data, and the step of calculating the slurry pump RPM control value for achieving the flow control value based on the second correlation.

[0022] In the control factor calculation method according to the embodiment disclosed herein, the data may further include a gap between the slurry discharge device and the substrate.

[0023] In the control factor calculation method according to the embodiment disclosed in this article, the step of learning the correlation may include the step of learning the correlation based on a machine learning model or a deep learning model.

[0024] Beneficial Effects

[0025] According to embodiments disclosed herein, control factors for achieving a target slurry loading may be calculated based on correlations using slurry loading, slurry flow rate, gap between the slurry discharge device and the substrate, and / or slurry discharge pump revolutions per minute (RPM).

[0026] According to the embodiments disclosed herein, a slurry flow control value and / or a slurry pump RPM control value for achieving a target slurry loading amount may be calculated as a control factor to allow a user to use the control factor according to circumstances.

[0027] Furthermore, various effects directly or indirectly recognized from the present disclosure can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a block diagram of a control factor calculation device according to an embodiment.

[0029] Figure 2a is a view showing a first correlation according to an embodiment.

[0030] Figure 2b is a view showing a second correlation according to an embodiment.

[0031] Figure 3 is an operation flow chart of the control factor calculation device according to the embodiment. DETAILED DESCRIPTION

[0032] Hereinafter, various embodiments of the present disclosure will be described with reference to the accompanying drawings. However, the description is not intended to limit the present disclosure to a specific embodiment, and should be construed to include various modifications, equivalents and / or substitutions according to the embodiments of the present disclosure.

[0033] It should be understood that the various embodiments of this document and the terms used therein are not intended to limit the technical features set forth herein to specific embodiments, and include various changes, equivalents or substitutions of the corresponding embodiments. With respect to the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that the singular form of a noun corresponding to an item may include one or more items, unless the relevant context clearly indicates otherwise.

[0034] As used herein, each of 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” may include any or all possible combinations of items enumerated together in the corresponding one of the phrases. Unless otherwise specified, terms such as “1st,” “2nd,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used to simply distinguish corresponding components from one another and do not limit the components in other respects (e.g., importance or order).

[0035] Herein, it should be understood that when an element (e.g., a first element) is referred to as being “connected with,” “coupled with,” or “linked with,” or “coupled to,” or “connected to” another element (e.g., a second element) with or without the term “operably” or “communicatively,” this means that the element can be connected to the other element directly (e.g., by wire), wirelessly, or via a third element.

[0036] According to an embodiment of the present disclosure, the method according to various embodiments of the present disclosure disclosed herein may be included and provided in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disk read-only memory (CD-ROM)), or distributed online (e.g., downloaded or uploaded) via an application store, or distributed directly between two user devices. If distributed online, at least a portion of the computer program product may be temporarily generated or at least temporarily stored in a machine-readable storage medium, such as a memory of a manufacturer server, a server of an application store, or a relay server.

[0037] According to various embodiments, each component (e.g., module or program) of the above-mentioned components may include a single entity or multiple entities, and some of the multiple entities may be individually arranged in different components. According to various embodiments, one or more of the above-mentioned components may be omitted, or one or more other components may be added. Alternatively or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, according to various embodiments, the integrated component may still perform one or more functions of each component in the multiple components in the same or similar manner as performed by the corresponding components in the multiple components before integration. According to various embodiments, the operations performed by a module, program or another component may be performed sequentially, in parallel, repeatedly or heuristically, or one or more operations may be performed or omitted in a different order, or one or more other operations may be added.

[0038] Figure 1 is a block diagram of a control factor calculation device according to an embodiment.

[0039] Reference Figure 1 The control factor calculation device 100 may include a data acquisition unit 110 and / or a processor 120 .

[0040] The data acquisition unit 110 can send data to an external device and / or an external server and receive data from an external device and / or an external server by wire or wirelessly. According to an embodiment, the data acquisition unit 110 can periodically acquire data including slurry loading, slurry flow, slurry discharge pump revolutions per minute (RPM) and / or the gap between the slurry discharge device and the substrate. In this article, slurry can refer to a mixture of solids and liquids or a composition in which fine solid powder particles are suspended in a liquid solvent. For example, slurry can refer to a composition for forming an electrode. Slurry loading can refer to the amount of slurry coated on a substrate per unit area. Slurry flow can refer to the amount of slurry supplied to the substrate by the slurry discharge device. Slurry pump RPM can refer to the output speed of a pump embedded in the slurry discharge device.

[0041] According to an embodiment, the data acquisition unit 110 may transmit the acquired data to the processor 120 .

[0042] The processor 120 may execute software to control at least one other component connected to the processor 120, and may process or calculate various data. According to one embodiment, the processor 120 may control at least one other component connected to the processor 120 to perform the overall operation of the control factor calculation device 110. The processor 120 may include at least one of a processing device such as an application specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a microcontroller, or a microprocessor.

[0043] According to an embodiment, the processor 120 may learn correlation between at least two types of factors among slurry loading, slurry flow rate, gap between the slurry discharge device and the substrate, and / or slurry pump RPM based on data acquired through the data acquisition unit 110 .

[0044] According to one embodiment, the processor 120 may learn a first correlation between first data and second data, the first data including the slurry loading amount of N cycles and the slurry flow rate of N+1 cycles, and the second data including the slurry loading amount of N+1 cycles. According to one embodiment, the first data may also include gap data of N+1 cycles between the slurry discharge device and the substrate. However, not limited to this, the first data may include various data related to electrode slurry coating to improve the accuracy of the learning model related to the first correlation.

[0045] According to an embodiment, the processor 120 may learn a second correlation between the slurry flow rate change data and the slurry pump RPM change data according to the periodic change. According to one embodiment, the slurry flow rate change (or slurry pump RPM change) may mean the difference between the slurry flow rates (or slurry pump RPM values) of two adjacent periods. For example, the slurry flow rate change (or slurry pump RPM change) may mean the difference between the slurry flow rate (or slurry pump RPM value) of the Nth period and the slurry flow rate (or slurry pump RPM value) of the N+1th period.

[0046] According to an embodiment, the processor 120 may learn the correlation (eg, the first correlation and / or the second correlation) based on a machine learning model or a deep learning model.

[0047] According to an embodiment, the processor 120 may calculate a slurry flow control value and / or a slurry pump RPM control value for achieving a target slurry loading.

[0048] According to an embodiment, the processor 120 may calculate a slurry flow control value based on a first correlation. According to one embodiment, the processor 120 may calculate a slurry flow control value for achieving a target slurry loading amount based on the first correlation. In this article, the slurry flow control value may mean a slurry flow increase value or a slurry flow decrease value to achieve a target slurry loading amount. According to one embodiment, the processor 120 may calculate a slurry flow control value for achieving a target slurry loading amount based on a slurry loading amount of an N+1 cycle, a target slurry loading amount (e.g., a slurry loading amount of an N+2 cycle), and a first correlation. For example, the processor 120 may calculate the slurry flow of an N+2 cycle required to achieve the target slurry loading amount. The processor 120 may calculate the slurry flow control value based on the difference between the slurry flow of an N+2 cycle and the slurry flow of an N+1 cycle.

[0049] According to an embodiment, the processor 120 can calculate a slurry flow control value for achieving a target slurry loading amount based on the slurry loading amount of the N+1 cycle, the target slurry loading amount (e.g., the slurry loading amount of the N+2 cycle), the gap between the slurry discharge device and the substrate of the N+2 cycle, and the first correlation.

[0050] According to an embodiment, the processor 120 may calculate a slurry pump RPM control value based on the first correlation. According to an embodiment, the processor 120 may calculate a slurry pump RPM control value for achieving a slurry flow control value based on the first correlation. Herein, the slurry pump RPM control value may mean a slurry pump RPM increase value or a slurry pump RPM decrease value for achieving a calculated slurry flow control value.

[0051] According to an embodiment, the processor 120 may calculate the slurry pump RPM control value based on the slurry flow control value and the second correlation. For example, the processor 120 may calculate the slurry pump RPM control value between period N+1 and period N+2 based on the slurry flow control value between period N+1 and period N+2 and the second correlation.

[0052] Figure 2a A first correlation according to an embodiment is shown. Figure 1 to describe the components Figure 2a .

[0053] refer to Figure 2a The first correlation 210 may represent the correlation between the slurry loading amount of the N cycle, the slurry flow rate of the N+1 cycle, the gap between the slurry discharge device and the substrate of the N+1 cycle, and the slurry loading amount of the N+1 cycle.

[0054] According to an embodiment, the gap between the slurry discharge device and the substrate of the N+1 cycle may be excluded from the factors of the first correlation 210. In this case, the processor 120 may calculate another factor based on two factors of the slurry loading amount of the N cycle, the slurry flow rate of the N+1 cycle, or the slurry loading amount of the N+1 cycle. For example, the processor 120 may calculate the slurry flow rate of the N+1 cycle based on the slurry loading amount of the N cycle, the slurry loading amount of the N+1 cycle, and the first correlation 210.

[0055] According to an embodiment, the first correlation 210 may include the slurry loading of the N cycle, the slurry flow rate of the N+1 cycle, the gap of the N+1 cycle, and the slurry loading of the N+1 cycle as factors. In this case, the processor 120 may calculate another factor based on three factors among the slurry loading of the N cycle, the slurry flow rate of the N+1 cycle, the gap of the N+1 cycle, or the slurry loading of the N+1 cycle. For example, the processor 120 may calculate the slurry flow rate of the N+1 cycle based on the slurry loading of the N cycle, the gap of the N+1 cycle, the slurry loading of the N+1 cycle, and the first correlation 210.

[0056] Figure 2b is a diagram showing a second correlation according to an embodiment. Figure 1 to describe the components Figure 2b .

[0057] refer to Figure 2b , the second correlation 220 may refer to the correlation between the slurry pump RPM change and the slurry flow rate change.

[0058] According to an embodiment, the processor 120 may calculate another factor based on a factor between the slurry pump RPM change or the slurry flow change. For example, the processor 120 may calculate the slurry pump RPM change based on the slurry flow change and the second correlation 220.

[0059] Figure 3 is an operation flow chart of the control factor calculation device according to the embodiment. Figure 1 to describe the components Figure 3 .

[0060] Figure 3 The illustrated embodiments may be examples, and the order of operations according to various embodiments of the present disclosure may be different from Figure 3 The order shown and can be omitted Figure 3 For some of the operations shown, the order of operations may be changed, or the operations may be combined.

[0061] refer to Figure 3 In operation 305 , the control factor calculation device 100 may periodically acquire data.

[0062] According to an embodiment, the control factor calculation device 100 may periodically acquire data including slurry loading amount, slurry flow rate, slurry discharge pump RPM, and / or a gap between the slurry discharge device and the substrate.

[0063] In operation 310 , the control factor calculation apparatus 100 may learn the correlation.

[0064] According to an embodiment, the control factor calculation device 100 may learn correlations between at least two types of factors among slurry loading, slurry flow rate, a gap between a slurry discharge device and a substrate, and / or a slurry pump RPM based on the data acquired in operation 305 .

[0065] According to an embodiment, the control factor calculation device 100 may learn a first correlation between first data including the slurry loading amount of the N cycle and the slurry flow rate of the N+1 cycle and second data including the slurry loading amount of the N+1 cycle. According to one embodiment, the first data may also include gap data between the slurry discharge device and the substrate of the N+1 cycle. However, not limited to this, the first data may include various data related to the electrode slurry coating to improve the accuracy of the learning model related to the first correlation.

[0066] According to an embodiment, the control factor calculation device 100 may learn a second correlation between the slurry flow rate change data and the slurry pump RPM change data according to the periodic change. According to an embodiment, the slurry flow rate change (or slurry pump RPM change) may mean the difference between the slurry flow rate (or slurry pump RPM value) of two adjacent periods. For example, the slurry flow rate change (or slurry pump RPM change) may mean the difference between the slurry flow rate (or slurry pump RPM value) of the Nth period and the slurry flow rate (or slurry pump RPM value) of the N+1th period.

[0067] According to an embodiment, the control factor calculation device 100 may learn the correlation (eg, the first correlation and / or the second correlation) based on a machine learning model or a deep learning model.

[0068] In operation 315 , the control factor calculation apparatus 100 may calculate a control factor.

[0069] According to an embodiment, the control factor calculation device 100 may calculate a slurry flow control value and / or a slurry pump RPM control value to achieve a target slurry loading amount.

[0070] According to an embodiment, the control factor calculation device 100 may calculate the slurry flow control value based on the first correlation learned in operation 310. According to an embodiment, the control factor calculation device 100 may calculate the slurry flow control value for achieving the target slurry loading amount based on the first correlation. In this article, the slurry flow control value may mean a slurry flow increase value or a slurry flow decrease value to achieve the target slurry loading amount. According to an embodiment, the control factor calculation device 100 may calculate the slurry flow control value for achieving the target slurry loading amount based on the slurry loading amount of the N+1 cycle, the target slurry loading amount (e.g., the slurry loading amount of the N+2 cycle) and the first correlation. For example, the control factor calculation device 100 may calculate the slurry flow of the N+2 cycle required to achieve the target slurry loading amount. The control factor calculation device 100 may calculate the slurry flow control value based on the difference between the slurry flow of the N+2 cycle and the slurry flow of the N+1 cycle.

[0071] According to an embodiment, the control factor calculation device 100 can calculate the slurry flow control value for achieving the target slurry loading amount based on the slurry loading amount of the N+1 cycle, the target slurry loading amount (for example, the slurry loading amount of the N+2 cycle), the gap between the slurry discharge device and the substrate of the N+2 cycle, and the first correlation.

[0072] According to an embodiment, the control factor calculation device 100 may calculate the slurry pump RPM control value based on the second correlation learned in operation 310. According to an embodiment, the control factor calculation device 100 may calculate the slurry pump RPM control value for achieving the slurry flow control value based on the first correlation. Herein, the slurry pump RPM control value may mean a slurry pump RPM increase value or a slurry pump RPM decrease value for achieving the calculated slurry flow control value.

[0073] According to an embodiment, the control factor calculation device 100 can calculate the slurry pump RPM control value based on the slurry flow control value and the second correlation. For example, the control factor calculation device 100 can calculate the slurry pump RPM control value between the N+1 cycle and the N+2 cycle based on the slurry flow control value between the N+1 cycle and the N+2 cycle and the second correlation.

[0074] Unless otherwise specified, the above terms such as "including", "consisting of" or "having" may mean that the corresponding components may be inherent, and therefore should be interpreted as further including other components rather than excluding other components. Unless otherwise defined, all terms including technical or scientific terms have the same meanings as those generally understood by ordinary technicians in the field to which the embodiments disclosed herein belong. Terms generally used with terms defined in dictionaries should be interpreted as having the same meanings as the contextual meanings of the relevant technology, and should not be interpreted as having ideal or overly formal meanings unless they are explicitly defined in this document.

Claims

1. A control factor calculation device, the control factor calculation device comprising: a data acquisition unit configured to periodically acquire data including slurry loading and slurry flow rate; as well as a processor configured to learn a correlation between the slurry loading and the slurry flow rate based on the data, Wherein, the processor is further configured to calculate a slurry flow control value for achieving a target slurry loading amount based on the correlation.

2. The control factor calculation device according to claim 1, wherein: The processor is further configured to: learning a first correlation between first data and second data, the first data comprising a slurry loading amount of an N-cycle and a slurry flow rate of an N+1-cycle, and the second data comprising a slurry loading amount of an N+1-cycle; as well as The slurry flow control value is calculated based on the first correlation.

3. The control factor calculation device according to claim 2, wherein: The processor is further configured to calculate the slurry flow control value based on the slurry loading amount of the N+1 cycle, the slurry loading amount of the N+1 cycle corresponding to the target slurry loading amount, and the first correlation.

4. The control factor calculation device according to claim 2, wherein: The data also includes the slurry pump revolutions per minute (RPM), and The processor is further configured to: learning a second correlation between the slurry flow rate variation data and the slurry pump RPM variation data according to the periodic variation; and A slurry pump RPM control value for achieving the flow control value is calculated based on the second correlation.

5. The control factor calculation device according to claim 1, wherein: The data also includes the gap between the slurry discharge device and the substrate.

6. The control factor calculation device according to claim 1, wherein: The processor is also configured to learn the correlation based on a machine learning model or a deep learning model.

7. A control factor calculation method, the control factor calculation method comprising the following steps: Periodically obtain data including slurry loading and slurry flow rate; learning a correlation between the slurry loading and the slurry flow rate based on the data; as well as A slurry flow control value for achieving a target slurry loading is calculated based on the correlation.

8. The control factor calculation method according to claim 7, wherein: The step of learning the correlation comprises the following steps: learning a first correlation between first data and second data, the first data comprising a slurry loading amount of the N cycle and a slurry flow rate of the N+1 cycle, the second data comprising a slurry loading amount of the N+1 cycle; and The step of calculating the slurry flow rate control value includes the step of calculating the slurry flow rate control value based on the first correlation.

9. The control factor calculation method according to claim 8, wherein: The step of calculating the slurry flow control value includes the step of calculating the slurry flow control value based on the slurry loading amount of the N+1 cycle, the slurry loading amount of the N+1 cycle corresponding to the target slurry loading amount, and the first correlation.

10. The control factor calculation method according to claim 8, wherein: The data also includes the slurry pump revolutions per minute (RPM), The step of learning the correlation includes the step of learning a second correlation between the slurry flow rate variation data and the slurry pump RPM variation data according to the periodic variation, and The step of calculating a slurry pump RPM control value for achieving the flow control value based on the second correlation.

11. The control factor calculation method according to claim 7, wherein: The data also includes the gap between the slurry discharge device and the substrate.

12. The control factor calculation method according to claim 7, wherein: The step of learning the correlation includes the step of learning the correlation based on a machine learning model or a deep learning model.

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