Diagnostic apparatus and method of operating diagnostic apparatus

Abstract

A diagnostic device according to one embodiment disclosed in the present document may include: a communication circuit configured to acquire information about a Raman spectrum of a mixed sample about different carbon materials including carbon nanotubes (CNTs) from a Raman spectrometer; and a processor configured to: acquire intensities of nodes divided by a reference wavenumber interval of the spectrum based on the information; generating a network of nodes based on the intensity; and calculating the CNT content of the sample based on the characteristics of the network.

Description

TECHNICAL FIELD

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2023-0093391, filed on July 18, 2023, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference.

[0004] Embodiments disclosed herein relate to a diagnostic apparatus and an operating method of a diagnostic apparatus. BACKGROUND

[0005] Carbon nanotubes (CNTs) are allotropes of carbon in which hexagonal honeycomb graphene sheets in which one carbon atom is bonded to three other carbon atoms are rolled into nanometer-sized diameters, and can be manufactured to have diameters of 1 nm to several tens of nm and different lengths of several μm to several tens of nm, thus having a structural feature of a very large aspect ratio.

[0006] CNTs are generally classified into single-walled nanotubes and multi-walled nanotubes, and are large carbon molecules having various physical and chemical properties according to size, shape, functional groups on the surface, coating, doping, etc. CNTs are light in weight, have excellent electrical and thermal conductivity, and have a tensile strength equivalent to that of steel.

[0007] For example, when CNTs are used in mixture with electrode materials of secondary batteries, battery performance can be significantly improved. Many methods for improving electrical conductivity, mechanical physical properties, and electrode life have been developed by using single-walled or multi-walled CNTs as components of cathodes or anodes in mixtures thereof.

[0008] When different types of carbon materials, such as graphite, carbon black (CB), and CNTs, are used as a mixture of two or more of the carbon materials in cathodes or anodes of lithium ion batteries, a method for quantitatively and accurately diagnosing whether the content of CNTs is uniformly distributed in the electrodes and how the content of CNTs in the electrodes and the distribution of CNTs change during use of the batteries is required. SUMMARY

[0009] [TECHNICAL PROBLEM]

[0010] Embodiments disclosed herein aim to quantitatively obtain the content of CNTs from Raman spectroscopic data measured for a mixed sample including a plurality of different carbon materials containing CNTs.

[0011] Technical problems of embodiments disclosed herein are not limited to the above-mentioned technical problems, and those of ordinary skill in the art will clearly understand other unmentioned technical problems from the following description.

[0012] [Technical Solution]

[0013] The diagnostic apparatus according to various embodiments disclosed herein includes a communication circuit configured to obtain information about a Raman spectrum of a mixed sample of different carbon materials including carbon nanotubes (CNTs) from a Raman spectrometer, and a processor configured to obtain intensities of nodes divided by reference wavenumber intervals of the spectrum based on the information, generate a network of the nodes based on the intensities, and calculate a CNT content of the sample based on a characteristic of the network.

[0014] The method of operating a diagnostic apparatus according to various embodiments disclosed herein includes obtaining information about a Raman spectrum of a sample from a Raman spectrometer, obtaining intensities of nodes divided by reference wavenumber intervals based on the information, generating a network of the nodes based on the intensities, and calculating a CNT content of the sample based on a characteristic of the network.

[0015] The battery management apparatus according to various embodiments disclosed herein includes a communication unit configured to obtain information about a Raman spectrum of a mixed sample of different carbon materials including CNTs from a Raman spectrometer, and a controller configured to obtain intensities of nodes divided by reference wavenumber intervals of the spectrum based on the information, generate a network of the nodes based on the intensities, and calculate a CNT content of the sample based on a characteristic of the network.

[0016] [Advantageous Effects]

[0017] The diagnostic apparatus and the method of operating a diagnostic apparatus according to various embodiments disclosed herein can obtain content information of CNTs from Raman spectra of a plurality of carbon mixed samples including CNTs.

[0018] Effects of the diagnostic apparatus and the method of operating a diagnostic apparatus according to the disclosure are not limited to what has been described above, and other effects not mentioned will become apparent to those skilled in the art from the disclosure herein. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 FIG. 1 is a block diagram illustrating a configuration of a diagnostic apparatus according to various embodiments disclosed herein.

[0020] Figure 2 FIG. 2 illustrates Raman spectra obtained from different standard samples according to various embodiments disclosed herein.

[0021] Figure 3 FIG. 3 is a graph of analyzing Raman spectra of random standard samples according to various embodiments disclosed herein.

[0022] Figure 4The adjacency matrix and distance matrix of a network architecture for converting Raman spectral data according to the embodiments disclosed herein are shown.

[0023] Figure 5 The variations in network entropy and network energy relative to CNT content are shown according to the embodiments disclosed herein.

[0024] Figure 6 A correlation graph is shown between the actual CNT content of a standard sample according to the embodiments disclosed herein and the CNT content calculated from network characteristic values ​​converted from Raman spectra.

[0025] Figure 7 This is a flowchart of the operation method of the diagnostic device according to the embodiments disclosed herein.

[0026] Figure 8 A battery pack according to an embodiment disclosed herein is shown.

[0027] Figure 9 This is a block diagram illustrating the configuration of a battery management device according to an embodiment disclosed herein.

[0028] Regarding the description of the accompanying drawings, similar reference numerals may be used to refer to similar or related parts. Detailed Implementation

[0029] In the following description, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, this description is not intended to limit the present disclosure to specific embodiments, and it should be construed as including various modifications, equivalents, and / or alternatives to embodiments of the present disclosure.

[0030] It should be understood that the embodiments described in this document and the terminology used therein are not intended to limit the technical features set forth herein to specific embodiments, and include various modifications, equivalents, or alternatives for corresponding embodiments. Regarding the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that, unless the relevant context clearly indicates otherwise, the singular form of the noun corresponding to an item may include one or more things.

[0031] As used herein, 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 one or all possible combinations of the items listed together in the corresponding phrase within that phrase. For example, “first (1)” st ")", "Second (2)" ndThe terms "first", "second", "A", "B", "a", or "b" as used herein can only be used to distinguish one element from another, and do not otherwise connote important or ordinal significance unless otherwise indicated by the context.

[0032] In this document, it should be understood that when an element (for example, a first element) is referred to as being "connected", "coupled", or "linked" to another element (for example, a second element), or "coupled", or "linked" to the other element (for example, a second element), whether or not the term "operatively" or "communicatively" is used, it means that the element can be directly connected (for example, wired or wirelessly) or indirectly connected (for example, via a third element) to the other element.

[0033] The method according to various embodiments disclosed herein can be included and provided in a computer program product. The computer program product can be traded as a product between a seller and a buyer. The computer program product can be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or be distributed online via an application store (e.g., Google Play Store, Apple App Store, or Microsoft Store), or between two user devices (e.g., online download or upload of a file), directly or indirectly.

[0034] According to the embodiments disclosed herein, each of the above-described components (e.g., a module or a program) can include a single entity or multiple entities, and some of the multiple entities can be individually provided in different components. According to various embodiments disclosed herein, one or more of the above-described components can be omitted, or one or more other components can be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) can be integrated into a single component. In such a case, according to various embodiments, the integrated component can still perform one or more functions of each of the plurality of components in the same or similar manner as they are performed by a corresponding one of the plurality of components before the integration. According to the embodiments disclosed herein, operations performed by the module, the program, or other components can be executed sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations can be executed in a different order or omitted, or one or more other operations can be added.

[0035] Figure 1 is a block diagram illustrating a configuration of a diagnostic device according to an embodiment disclosed herein.

[0036] Referring to Figure 1The diagnostic device 101 can be connected to the Raman spectrometer 103 in wired and / or wireless manner.

[0037] According to an embodiment, the connection between the diagnostic device 101 and the Raman spectrometer 103 can be a communication connection through a wired network and / or a wireless network. According to an embodiment, the wired network can be based on a local area network (LAN) communication or a power line communication. According to an embodiment, the wireless network can be based on a short-range communication network (e.g., Bluetooth, wireless fidelity (WiFi), or infrared data association (IrDA)) or a long-range communication network (e.g., a cellular network, a fourth generation (4G) network, a fifth generation (5G) network).

[0038] According to another embodiment, the connection between the diagnostic device 101 and the Raman spectrometer 103 can be a connection using a device-to-device communication scheme (e.g., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0039] According to an embodiment, the Raman spectrometer 103 can be a device that performs Raman spectroscopy. According to an embodiment, the Raman spectrometer 103 can be a device for identifying a structure, a type, a content, etc. of a molecule included in the sample 115 by utilizing a Raman effect. According to an embodiment, the Raman spectrometer 103 can identify a structure, a type, a content, etc. of a molecule included in the sample 115 based on a principle that laser energy irradiated to the sample 115 is absorbed by a difference in electronic energy level of the molecule of the sample 115.

[0040] According to an embodiment, the diagnostic device 101 can be a mobile device (e.g., a mobile phone, a laptop computer, a smart phone, a smart pad) or a computer (e.g., a general purpose computer and a special purpose computer).

[0041] According to an embodiment, the diagnostic device 101 can include a communication circuit 120, a memory 130, and a processor 140. According to an embodiment, Figure 1 The diagnostic device 101 shown in FIG. 1 can further include at least one component (e.g., a display, an input device, or an output device) in addition to the components shown in FIG. 1. Figure 1

[0042] According to an embodiment, the communication circuit 120 can establish a wired communication channel and / or a wireless communication channel between the diagnostic device 101 and the Raman spectrometer 103, and transmit data to and receive data from the Raman spectrometer 103 through the established communication channel.

[0043] According to an embodiment, the memory 130 can include a volatile and / or a non-volatile memory. ​

[0044] According to an embodiment, the memory 130 can store data used by at least one component (e.g., the processor 140) of the diagnostic device 101. The data can include, for example, a program (or instructions related thereto), input data, or output data. According to an embodiment, the instructions, when executed by the processor 140, can cause the diagnostic device 101 to perform operations defined by the instructions.

[0045] According to an embodiment, the processor 140 can 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.

[0046] According to an embodiment, the processor 140 can execute a program (or instructions related thereto) to control at least one other component (e.g., a hardware or software component) in the diagnostic device 101 connected to the processor 140, and can process or compute various data.

[0047] In an embodiment, the processor 140 and the diagnostic device 101 connected to the processor 140 can be used as a component included in a battery management system (BMS).

[0048] In an embodiment, the "BMS" including the processor 140 and the diagnostic device 101 connected to the processor 140 can refer to any type of interface including a BMS.

[0049] In an embodiment of the disclosure, a mobile device including a BMS apparatus according to the present application can be provided.

[0050] Here, the "mobile device" can refer to a device capable of moving or a device that a user can easily carry, and examples thereof can include an electric vehicle, a mobile device, etc.

[0051] Hereinafter, a detailed description will be given of Figures 2 to 6 A method of quantitatively measuring a CNT content included in a sample 115 based on spectral data obtained from a Raman spectrometer 103, performed by the diagnostic device 101, will be described in detail.

[0052] Obtaining formula

[0053] Figure 2 Raman spectra obtained from standard samples having different compositions according to embodiments disclosed herein are shown. Figure 3 Raman spectra of standard samples are divided with respect to intensity and reference wavenumber sections of a unit spectrum for converting peak intensity information of the Raman spectra into a network architecture according to embodiments disclosed herein. Figure 4An adjacency matrix and a distance matrix characterizing a network architecture according to embodiments disclosed herein are shown. Figure 5 A network entropy and a network energy according to embodiments disclosed herein are shown with respect to a CNT content. Figure 6 A correlation plot between an actual CNT content of a standard sample and a CNT content calculated from a network eigenvalue converted from a Raman spectrum according to embodiments disclosed herein is shown.

[0054] The Raman spectrometer 103 can obtain Raman spectra 210, 220, 230, 240, 250, and 260 from different standard samples 115. According to embodiments, the Raman spectrometer 103 can obtain Raman spectra 210, 220, 230, 240, 250, and 260 with wave numbers (Raman shifts) from 1000 cm -1 to 1800 cm -1 .

[0055] The different standard samples 115 can be samples in which CNTs are mixed at different mass ratios (weight percent, wt%). For example, the CNT contents of the different standard samples 115 can be 0 wt%, 25 wt%, 33.3 wt%, 50 wt%, 66.7 wt%, and 100 wt%, respectively. For example, the Raman spectra 210, 220, 230, 240, 250, and 260 can be obtained at CNT contents of 100 wt%, 66.7 wt%, 50 wt%, 33.3 wt%, 25 wt%, and 0 wt% of the standard samples 115, respectively.

[0056] The Raman spectra 210, 220, 230, 240, 250, and 260 of the different standard samples 115 can have peaks in a D band 201 around a wavelength of 1350 cm -1 and a G band 205 around a wavelength of 1580 cm -1 .

[0057] The Raman spectrometer 103 can transmit information about the obtained Raman spectra 210, 220, 230, 240, 250, and 260 of the standard samples 115 with different compositions to the diagnostic device 101.

[0058] The diagnostic device 101 can obtain information about the Raman spectra 210, 220, 230, 240, 250, and 260 from the Raman spectrometer 103 through the communication circuit 120.

[0059] The processor 140 of the diagnostic apparatus 101 can analyze the Raman spectra 210, 220, 230, 240, 250, and 260. According to an embodiment, the processor 140 can divide the Raman spectra in reference wavenumber intervals. The Raman spectra divided by the wavenumber ranges 321 to 330 can be referred to as unit Raman spectra.

[0060] According to an embodiment, the processor 140 can set intensities of the unit Raman spectra. Herein, the intensity of the unit Raman spectrum can be set by the intensity of the starting point, the intensity of the midpoint, the intensity of the last point, or the average intensity of each section of the wavenumber range.

[0061] The processor 140 can classify the unit Raman spectra into nodes in the intensity ranges 311 to 319 for each reference wavenumber section based on the intensities of the unit Raman spectra.

[0062] Referring to Figure 3 the graph 300, it can be seen that the Raman spectra are subdivided into unit Raman spectra based on a grid of unit intensities and unit wavenumber intervals.

[0063] According to an embodiment, the processor 140 can generate an adjacency matrix 400 and a distance matrix 450 based on the unit Raman spectra. Referring to Figure 4 , the elements A 11 , A 12 , …, A 1n , A 21 , …, A nn of the adjacency matrix 400 can indicate adjacencies between the unit spectra C1, C2, …, C n , and the elements D 11 , D 12 , …, D 1n , D 21 , …, D nn of the distance matrix 450 can indicate distances between the unit spectra C1, C2, …, C n .

[0064] According to an embodiment, the unit Raman spectra divided by the wavenumber ranges 321 to 330 can be understood as the unit spectra C1, C2, …, C n of the adjacency matrix 400 and the distance matrix 450. For example, the unit Raman spectrum included in the wavenumber range 321 can be understood as a first node C1, the unit Raman spectrum included in the wavenumber range 322 can be understood as a second node C2, and the unit Raman spectrum included in the wavenumber range 323 can be understood as a third node C3.

[0065] According to an embodiment, the values of elements A 11 , A 12 , …, A 1n , A 21 , …, A nn of the adjacency matrix 400 can be assigned in such a way that the values of the elements A 12 and A 21 defined by the unit spectra C1 and C2 can be 1 when the node C1 corresponding to the wave number range 321 is included in the intensity range 319 and the node C2 corresponding to the wave number range 322 is included in the intensity range 318. In another example, the values of the elements A 12 and A 13 defined by the unit spectra C1 and C3 can be 0 when the node C1 corresponding to the wave number range 321 is included in the intensity range 319 and the node C3 corresponding to the wave number range 323 is included in the intensity range 316. The product of the assigned 0 or 1 and the weight factor can be used as the value of the element of the adjacency matrix 400.

[0066] According to an embodiment, the elements D 11 , D 12 , …, D 1n , D 21 , …, D nn of the distance matrix 450 can be set to the distance between the nodes C1, C2, …, C n defined based on the unit intensity section and the unit wave number section. For example, the distance between the unit spectra included in the same intensity range can be a value of at least 0 according to the difference between the unit wave number sections. In another example, the distance between the nodes included in different intensity ranges can be a value of at least 1 according to the difference between the unit wave number sections. For example, the distance between the node included in the intensity range 319 and the node included in the intensity range 317 can be 2 in the wave number range 321 and the wave number range 322, but can be 11 in the wave number range 321 and the wave number range 324. The product of the distance between two different nodes and the preset weight factor can be used as the value of the element of the distance matrix 450.

[0067] According to an embodiment, the processor 140 can obtain a network characteristic of the nodes C1, C2, …, C n based on the adjacency matrix 400 and the distance matrix 450. Herein, the network of the nodes C1, C2, …, C n may indicate the distance between the nodes C1, C2, …, C nconnection structure (distance and adjacency) between nodes. The network characteristics can include network energy E network , network entropy S network , and network temperature T network .

[0068] According to an embodiment, the network energy E network may be expressed as a product of the number of nodes included in the network and a function of the distance between the nodes. For example, the network energy E network may be defined by Equation 1 below.

[0069] [Equation 1]

[0070]

[0071] Herein, a can indicate the number information of nodes, and f(d i,j ) can indicate a function of the distance d between the i-th node and the j-th node. Herein, the function f can be a polynomial function set through a linear or non-linear regression technique. For example, the network energy E network may be the sum of the network energy E network,i,j obtained for all nodes.

[0072] According to an embodiment, the network entropy S network may be expressed as a product of a function value for the inverse of the topological distance between two nodes and a logarithm of the function value for the inverse of the topological distance between two nodes (herein, H can be g(1 / d ij ), and d ij may be defined as the topological distance between two nodes i and j). For example, the network entropy S network may be defined based on Equation 2.

[0073] [Equation 2]

[0074]

[0075] Herein, log can refer to a logarithmic function. Herein, H can be defined by Equation 3.

[0076] [Equation 3]

[0077]

[0078] g(1 / d i,j ) can indicate a function of the inverse of the distance d between the i-th node and the j-th node. Herein, the function g can be a polynomial function set through a linear or non-linear regression technique. For example, the network entropy S network may be the sum of the network entropy S network,i,j obtained for all nodes.

[0079] According to an embodiment, the network temperature T network may be preset (e.g., 45.87) according to the content of CNT included in the standard sample.

[0080] According to an embodiment, the processor 140 can obtain information about a relationship between the network energy E network , the network entropy S network and the content of CNT. According to an embodiment, the processor 140 can obtain information about a relationship between the network energy E network , the network entropy S network and the content of CNT based on a multiple linear regression (MLR). According to an embodiment, the information about the relationship can be obtained by a machine learning algorithm such as a support vector machine (SVM), a decision tree, a gradient boosting machine (GBM), an artificial neural network, etc., but the present disclosure is not limited to the algorithm. According to an embodiment, the processor 140 can obtain information about a relationship between the network energy E network , the network entropy S network and the content of CNT by using a machine learning model.

[0081] Figure 5 A change in the network entropy and the network energy with respect to the change in the content of CNT is shown. Figure 5 A contour map showing the content of CNT using the network energy E network and the network entropy S network as an index can be shown. Referring to Figure 5 , a relationship between the network energy E network , the network entropy S network and the content of CNT can be identified. It can be seen that the change in the content of CNT can cause a change in the network entropy S network to be greater than a change in the network energy E network .

[0082] According to an embodiment, the processor 140 can establish a relationship expression (or an operation formula) based on information about a relationship between the network energy E network , the network entropy S network , the network temperature T network and the content of CNT. Specifically, the processor 140 can establish a relationship expression (or an operation formula) between the network energy E network , the network entropy S network , the network temperature T network and the content of CNT as in Equation 4.

[0083] As described above, the network temperature T network may include a constant preset according to the content of CNT included in the standard sample, and the network energy Enetwork and network entropy S network may include variables.

[0084] That is, the processor 140 can derive the CNT content by inputting the network temperature T network and the network energy E network and the network entropy S network into Equation 4.

[0085] [Equation 4]

[0086]

[0087] In this context, NT Conc may refer to the CNT content. Referring to Figure 6 , it can be seen that the correlation (or Pearson correlation coefficient R) between the CNT content of the standard sample 115 and the value derived through Equation 4 is 0.997.

[0088] Measuring CNT content based on a relational expression

[0089] According to the embodiment, the diagnostic device 101 can calculate the content of CNTs included in a random carbon mixture sample having no CNT content information, based on a relationship expression between the network energy E network , the network entropy S network , the network temperature T network and the CNT content obtained from a standard sample through a series of operations.

[0090] Specifically, the diagnostic device 101 can obtain information about a Raman spectrum of a random sample from the Raman spectrometer 103. Thereafter, the diagnostic device 101 can analyze the Raman spectrum to obtain an adjacency matrix and a distance matrix. Thereafter, the diagnostic device 101 can obtain the network energy E network and the network entropy S network based on the obtained adjacency matrix and distance matrix. Finally, the diagnostic device 101 can obtain the CNT content by inputting the obtained network energy E network and the network entropy S network into a relationship expression (or operation formula).

[0091] As described above, the diagnostic device 101 can obtain the CNT content from the Raman spectrum. The diagnostic device 101 can also diagnose the state of a battery through the CNT content obtained from the Raman spectrum. Accordingly, the method for determining the CNT content from the Raman spectrum, which is performed by the diagnostic device 101, can be used to predict and diagnose the lifespan of a battery during the manufacturing and use (charging / discharging) of the battery, or to determine whether to reuse (or recycle) a discarded battery.

[0092] Figure 7 is a flowchart of an operation method of a diagnostic apparatus according to the embodiments disclosed herein.

[0093] Hereinafter, the operation method of the diagnostic apparatus 101 will be described with reference to Figures 1 to 6

[0094] The diagnostic apparatus 101 can be substantially the same as the diagnostic apparatus 101 described with reference to Figures 1 to 6 and thus a brief description will be given to avoid redundant description.

[0095] With reference to Figure 7 , the operation method of the diagnostic apparatus can include an operation S101 of obtaining information about a Raman spectrum of a standard sample from a Raman spectrometer, an operation S102 of obtaining intensities of nodes divided by reference wavenumber intervals based on the information about the Raman spectrum, an operation S103 of generating a network of the nodes based on the intensities of the nodes, an operation S104 of establishing a CNT content calculation formula based on a characteristic of the network, and an operation S105 of calculating a CNT content of a sample based on the CNT content calculation formula.

[0096] In operation S101, the diagnostic apparatus 101 can obtain a Raman spectrum of a standard sample. According to an embodiment, the diagnostic apparatus 101 can obtain the Raman spectrum of the standard sample from the Raman spectrometer 103. Herein, the CNT contents of the standard samples can be 0 wt%, 25 wt%, 33.3 wt%, 50 wt%, 66.7 wt%, and 100 wt%, respectively.

[0097] In operation S102, the diagnostic apparatus 101 can set intensities of unit Raman spectra. Herein, the intensities of the unit Raman spectra can be set by intensities of starting points, intensities of midpoints, intensities of last points, or average intensities of each section of a wavenumber range.

[0098] In operation S102, the diagnostic apparatus 101 can classify the unit Raman spectra into nodes in the intensity range 311 to 319 for each reference wavenumber section based on the intensities of the unit Raman spectra.

[0099] In operation S103, the diagnostic apparatus 101 can generate a network based on the Raman spectrum. According to an embodiment, the diagnostic apparatus 101 can generate an adjacency matrix and a distance matrix based on the unit Raman spectrum. Elements of the adjacency matrix can indicate adjacencies between the nodes, and elements of the distance matrix can indicate distances between the nodes.

[0100] ​In operation S103, the diagnostic device 101 can generate a network of nodes based on the adjacency matrix and the distance matrix, according to an embodiment. Specifically, the diagnostic device 101 can generate a connection structure (distance and adjacency) between nodes constituting the network based on values of elements of the adjacency matrix and the distance matrix.

[0101] In operation S103, the diagnostic device 101 can obtain characteristics (for example, network energy E network , network entropy S network , and network temperature T network ) of the network based on the network, according to an embodiment.

[0102] In operation S104, the diagnostic device 101 can establish a CNT content calculation formula based on the characteristics of the network. According to an embodiment, the diagnostic device 101 can establish a relational expression (or a calculation formula) based on information about a relationship between the network energy E network , the network entropy S network , and the CNT content.

[0103] In operation S105, the diagnostic device 101 can calculate a content of CNTs included in a new sample based on the established relational expression (or calculation formula).

[0104] Figure 8 A battery pack according to an embodiment disclosed herein is illustrated.

[0105] Referring to Figure 8 , a battery pack 2000 according to an embodiment disclosed herein can include a battery module 2100, a battery management device 2200, and a relay 2300. According to various embodiments, the battery module 2100 can be a battery cell, and in this case, the battery pack 2000 can have a cell-to-pack structure.

[0106] Meanwhile, although Figure 8 one battery module 2100 is illustrated in Figure 8 , according to an embodiment, the battery module 2100 can be configured in plural, and the battery pack 2000 can have a stacked structure of a plurality of battery modules. The battery module 2100 can include a plurality of battery cells 2110, 2120, 2130, and 2140. Although the plurality of battery cells are illustrated as four in

[0107] The battery module 2100 can supply power to a target device (not shown). To this end, the battery module 2100 can be electrically connected to the target device. Herein, the target device can include an electrical device, an electronic device, or a mechanical device that operates by receiving power from the battery pack 2000 including a plurality of battery cells 2110, 2120, 2130, and 2140, and the target device can be, for example, an electric vehicle (EV) or an energy storage system (ESS), but is not limited thereto.

[0108] The plurality of battery cells 2110, 2120, 2130, and 2140 can be lithium-ion (Li-ion) batteries, lithium-ion polymer batteries, nickel-cadmium (Ni-Cd) batteries, nickel-metal hydride (Ni-MH) batteries, or the like, and are not limited thereto, each of the plurality of battery cells 2110, 2120, 2130, and 2140 being a basic unit of a battery that is available by charging and discharging electric energy. Meanwhile, although one battery module 2100 is shown in FIG. 2, the battery module 2100 can be configured in plural according to an embodiment. Figure 8

[0109] The battery management device (Battery Management System (BMS)) 2200 can manage and / or control a state and / or operation of the battery module 2100. For example, the battery management device 2200 can manage and / or control a state and / or operation of the plurality of battery cells 2110, 2120, 2130, and 2140 included in the battery module 2100. The battery management device 2200 can manage charging and / or discharging of the battery module 2100.

[0110] The battery management device 2200 can control an operation of the relay 2300. For example, the battery management device 2200 can short-circuit the relay 2300 to supply power to the target device. The battery management device 2200 can short-circuit the relay 2300 when the charging device is connected to the battery pack 2000.

[0111] In addition, the battery management device 2200 can monitor a voltage, a current, a temperature, and the like of the battery module 2100 and / or each of the plurality of battery cells 2110, 2120, 2130, and 2140 included in the battery module 2100. A sensor or various measurement modules (not shown) for performing the monitoring by the battery management device 2200 can be additionally installed at the battery module 2100, a charging / discharging path, any position of the battery module 2100, and the like. The battery management device 2200 can calculate a parameter indicating a state of the battery module 2100, such as a state of charge (SOC), a state of health (SOH), and the like, based on a measured value, such as a monitored voltage, a current, a temperature, and the like.

[0112] ​The following operations of the battery management device 2200 can also be performed in the battery management device 2200 or various devices such as a server, a cloud, a charger, a charger / discharger, etc., which are connected to a vehicle on which the battery management device 2200 is installed.

[0113] Hereinafter, the battery management device 2200 will be described with reference to Figures 1 to 7 The battery management device 2200.

[0114] The battery management device 2200 can be substantially the same as the diagnosis device 101 described with reference to Figures 1 to 7 and thus will be briefly described to avoid redundant description.

[0115] The battery management device 2200 can be connected to the Raman spectrometer 103 via a wired and / or wireless communication link.

[0116] According to an embodiment, the connection between the battery management device 2200 and the Raman spectrometer 103 can be a communication connection through a wired and / or wireless network. According to an embodiment, the wired network can be based on LAN communication or power line communication. According to an embodiment, the wireless network can be based on a short-range communication network (e.g., Bluetooth, WiFi, or IrDA) or a long-range communication network (e.g., a cellular network, a 4G network, a 5G network).

[0117] According to another embodiment, the connection between the battery management device 2200 and the Raman spectrometer 103 can be a connection using a device-to-device communication scheme (e.g., bus, GPIO, SPI, or MIPI).

[0118] According to an embodiment, the Raman spectrometer 103 can be a device that performs Raman spectroscopy. According to an embodiment, the Raman spectrometer 103 can be a device for identifying the structure, type, content, etc. of molecules included in the sample 115 by utilizing the Raman effect. According to an embodiment, the Raman spectrometer 103 can identify the structure, type, content, etc. of molecules included in the sample 115 based on the principle that the energy of laser light irradiated to the sample 115 is absorbed by the difference in the energy level of the electrons of the molecules of the sample 115.

[0119] Figure 9 is a block diagram illustrating a configuration of a battery management device according to an embodiment disclosed herein.

[0120] Referring to Figure 9 , the battery management device 2200 can include a communication unit 2210 and a controller 2220. According to an embodiment, Figure 9 The battery management device 2200 illustrated in Figure 9 may further include at least one component (e.g., a display, an input device, or an output device) in addition to the components illustrated in

[0121] According to an embodiment, the communication unit 2210 can establish a wired communication channel and / or a wireless communication channel between the battery management device 2200 and the Raman spectrometer 103, and transmit and receive data to and from the Raman spectrometer 103 through the established communication channel.

[0122] The memory (not shown) included in the battery management device 2200 can include volatile and / or non-volatile memory. According to an embodiment, the memory can store data used by at least one component (e.g., the controller 2220) of the battery management device 2200. For example, the data can include a program (or instructions related thereto), input data, or output data. According to an embodiment, the instructions, when executed by the controller 2220, can cause the battery management device 2200 to perform operations defined by the instructions.

[0123] According to an embodiment, the controller 2220 can 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.

[0124] According to an embodiment, the controller 2220 can execute software to control at least one other component (e.g., a hardware component or a software component) of the battery management device 2200 connected to the controller 2220, and can process or compute various data.

[0125] Hereinafter, a detailed description will be made of a method of quantitatively measuring a CNT content included in the sample 115 based on the spectral data obtained from the Raman spectrometer 103, which is performed by the battery management device 2200.

[0126] The Raman spectrometer 103 can obtain Raman spectra 210, 220, 230, 240, 250, and 260 from different standard samples 115. According to an embodiment, the Raman spectrometer 103 can obtain Raman spectra 210, 220, 230, 240, 250, and 260 having a wave number (Raman shift) of from 1000 cm -1 to 1800 cm -1 .

[0127] The different standard samples 115 can be samples in which CNTs are mixed at different mass ratios (weight percentage, wt%). For example, the CNT content of the different standard samples 115 can be 0 wt%, 25 wt%, 33.3 wt%, 50 wt%, 66.7 wt%, and 100 wt%, respectively. For example, the Raman spectra 210, 220, 230, 240, 250, and 260 can be obtained when the CNT content of the standard samples 115 is 100 wt%, 66.7 wt%, 50 wt%, 33.3 wt%, 25 wt%, and 0 wt%, respectively.

[0128] The Raman spectra 210, 220, 230, 240, 250, and 260 of the different standard samples 115 can have peaks in a D band 201 around a wavelength of 1350 cm -1 and a G band 205 around a wavelength of 1580 cm -1 .

[0129] The Raman spectrometer 103 can transmit information about the obtained Raman spectra 210, 220, 230, 240, 250, and 260 of the standard samples 115 having different compositions to the battery management device 2200.

[0130] The battery management device 2200 can obtain information about the Raman spectra 210, 220, 230, 240, 250, and 260 from the Raman spectrometer 103 through the communication unit 2210.

[0131] The controller 2220 of the battery management device 2200 can analyze the Raman spectra 210, 220, 230, 240, 250, and 260. According to an embodiment, the controller 2220 can divide the Raman spectra in reference wavenumber intervals. The Raman spectra divided by the wavenumber ranges 321 to 330 can be referred to as unit Raman spectra.

[0132] According to an embodiment, the controller 2220 can set the intensity of the unit Raman spectra. Herein, the intensity of the unit Raman spectra can be set by the intensity of the starting point, the intensity of the midpoint, the intensity of the last point, or the average intensity of each section of the wavenumber range.

[0133] The controller 2220 can classify the unit Raman spectra into nodes in the intensity range 311 to 319 for each reference wavenumber section based on the intensity of the unit Raman spectra.

[0134] It can be seen that the Raman spectra are subdivided into unit Raman spectra based on the grid of unit intensity and unit wavenumber interval.

[0135] According to the implementation, the controller 2220 can generate an adjacency matrix 400 and a distance matrix 450 based on a unit Raman spectrum. The elements A of the adjacency matrix 400... 11 A 12 A 1n A 21 A nn It can indicate the unit spectrum C1, C2, ..., C n The elements D that are adjacent to each other and have a distance of 450 from each other in the distance matrix. 11 D 12 ... D 1n D 21 ... D nn It can indicate the unit spectrum C1, C2, ..., C n The distance between them.

[0136] According to the implementation method, the unit Raman spectrum divided by the wavenumber range of 321 to 330 can be understood as the unit spectra C1, C2, ..., C3 of the adjacency matrix 400 and the distance matrix 450. n For example, the unit Raman spectrum included in the wavenumber range of 321 can be understood as the first node C1, the unit Raman spectrum included in the wavenumber range of 322 can be understood as the second node C2, and the unit Raman spectrum included in the wavenumber range of 323 can be understood as the third node C3.

[0137] According to the implementation method, the unit spectrum is set to be adjacent to each other between adjacent intensity ranges (i.e., its element value is 1), and the unit spectrum is set to be non-adjacent to each other between non-adjacent intensity ranges (i.e., its element value is 0). In this way, the elements A of the adjacency matrix 400 can be... 11 A 12 A 1n A 21 A nn The values ​​are assigned. For example, when node C1 corresponding to wavenumber range 321 is included in intensity range 319 and node C2 corresponding to wavenumber range 322 is included in intensity range 318, element A, defined by unit spectra C1 and C2, is assigned. 12 and A 21 The value can be 1. In another example, when node C1 corresponding to wavenumber range 321 is included in intensity range 319 and node C3 corresponding to wavenumber range 323 is included in intensity range 316, the element A defined by unit spectra C1 and C3... 12 and A 13 The value can be 0. The product of the assigned 0 or 1 with the weight factor can be used as the value of an element in the adjacency matrix 400.

[0138] According to the implementation method, the element D of distance matrix 450 11 D 12 ... D 1n D 21 ... D nn Nodes C1, C2, ..., C can be set based on unit intensity and unit wavenumber segments. n The distance between them. For example, the distance between unit spectra within the same intensity range, based on the difference between unit wavenumber intervals, can have a value equal to or greater than 0. In another example, the distance between nodes in different intensity ranges, based on the difference between unit wavenumber intervals, can have a value equal to or greater than 1. For example, the distance between a node included in intensity range 319 and a node included in intensity range 317 can be 2 in wavenumber ranges 321 and 322, but can be 11 in wavenumber ranges 321 and 324. The product of the distance between two different nodes and a preset weighting factor can be used as the value of an element of distance matrix 450.

[0139] According to the implementation method, the processor 2220 can obtain nodes C1, C2, ..., C based on the adjacency matrix 400 and the distance matrix 450. n The network characteristics. In this paper, nodes C1, C2, ..., C n The network can indicate nodes C1, C2, ..., C n The connection structure between them (distance and adjacency). Network characteristics can include network energy E. network Network entropy S network and network temperature T network .

[0140] According to the implementation method, network energy E network It can be expressed as the product of the number of nodes in the network and the distance between the nodes. For example, the network energy E network It can be defined by Equation 5 below.

[0141] [Formula 5]

[0142]

[0143] In this paper, 'a' can indicate the number of nodes, and f(d) can indicate the number of nodes. i,j The function f can indicate the distance d between the i-th node and the j-th node. In this paper, the function f can be a polynomial function set by linear or nonlinear regression techniques. For example, the network energy E network It can be the network energy E obtained for all nodes. network,i,j The sum of .

[0144] According to the implementation method, network entropy Snetwork may be expressed as a product of a number of nodes included in the network and a function of distances between the nodes. For example, the network entropy S network may be defined by Equation 6 below.

[0145] [Equation 6]

[0146]

[0147] Here, log can refer to a logarithmic function. Here, H can be defined by Equation 7.

[0148] [Equation 7]

[0149]

[0150] g(1 / d i,j ) can indicate a function of the inverse of the distance d between the i-th node and the j-th node. In this context, the function g can be a polynomial function set through a linear or non-linear regression technique. For example, the network entropy S network may be a sum of the network entropy S network,i,j obtained for all nodes.

[0151] According to an embodiment, the network temperature T network may be set in advance (e.g., 45.87) according to the content of CNT included in the standard sample.

[0152] According to an embodiment, the controller 2220 can obtain information about a relationship between the network energy E network , the network entropy S network , and the content of CNT. According to an embodiment, the controller 2220 can obtain information about a relationship between the network energy E network , the network entropy S network , and the content of CNT based on the MLR. According to an embodiment, the information about the relationship can be obtained through a machine learning algorithm such as SVM, decision tree, random forest, GBM, artificial neural network, etc., but the present disclosure is not limited to the algorithm. According to an embodiment, the controller 2220 can obtain information about a relationship between the network energy E network , the network entropy S network , and the content of CNT by using a machine learning model.

[0153] A relationship between the network energy E network , the network entropy S network , and the content of CNT can be identified. It can be seen that a change in the content of CNT can cause a change in the network entropy S network to be greater than a change in the network energy E network .

[0154] According to the implementation method, the controller 2220 can be based on information about the network energy E. network Network entropy S network Network temperature T network Information about the relationship between CNT content and the network energy E is used to establish an equation (or calculation formula). Specifically, controller 2220 can establish the network energy E. network Network entropy S network The relationship between CNT content and the formula (or operation formula) is as shown in Equation 8.

[0155] As mentioned above, network temperature T network This can include a preset constant based on the content of CNTs included in the standard sample, and the network energy E network and network entropy S network It can include variables.

[0156] In other words, the controller 2220 can use the network temperature T as a constant. network And the network energy E as a variable network and network entropy S network Input the CNT content into Equation 8.

[0157] [Formula 8]

[0158]

[0159] In this article, NT Conc This can refer to the CNT content. It can be seen that the correlation (or Pearson correlation coefficient R) between the CNT content of standard sample 115 and the value obtained through Equation 8 is 0.997.

[0160] Measuring CNT content based on a relational expression

[0161] According to an implementation, the battery management device 2200 can be based on the network energy E obtained from a standard sample through a series of operations. network Network entropy S network Network temperature T network The relationship between CNT content and CNT content is expressed to calculate the CNT content in a random carbon mixture sample that does not have CNT content information.

[0162] Specifically, the battery management device 2200 can obtain information about the Raman spectrum of a random sample from the Raman spectrometer 103. Subsequently, the battery management device 2200 can analyze the Raman spectrum to obtain an adjacency matrix and a distance matrix. Then, the battery management device 2200 can obtain the network energy E based on the obtained adjacency matrix and distance matrix. network and network entropy S networkFinally, the battery management device 2200 can obtain the CNT content by inputting the obtained network energy E network and the network entropy S network into a relational expression (or an operation formula).

[0163] As described above, the battery management device 2200 can obtain the CNT content from the Raman spectrum. The battery management device 2200 can also diagnose the state of the battery through the CNT content obtained from the Raman spectrum. Thus, the method performed by the battery management device 2200 for determining the CNT content from the Raman spectrum can be used to predict and diagnose the life of the battery during the manufacturing and use (charging / discharging) of the battery, or to determine whether to reuse (or recycle) a discarded battery.

Claims

1. A diagnostic apparatus comprising: a communication circuit configured to obtain, from a Raman spectrometer, information about a Raman spectrum of a mixed sample of different carbon materials including carbon nanotubes (CNTs); and a processor configured to: obtain, based on the information, intensities of nodes partitioned by reference wavenumber intervals of the spectrum; generate, based on the intensities, a network of the nodes; and calculate, based on a property of the network, a CNT content of the sample.

2. The diagnostic device of claim 1, wherein, The processor is further configured to calculate, based on the property of the network including a network energy and a network entropy, the CNT content of the sample.

3. The diagnostic device of claim 2, wherein, The processor is further configured to calculate the network energy and the network entropy, and input the network energy and the network entropy to a relational expression to calculate the CNT content.

4. The diagnostic device of claim 3, wherein, The processor is further configured to calculate the CNT content based on the relational expression represented by Equation 1, [Equation 1] , where NT Conc indicates the CNT content, E network indicates the network energy, T network indicates the network temperature, and S network indicates the network entropy.

5. The diagnostic device of claim 1, wherein, The processor is further configured to generate the network defined by an adjacency matrix and a distance matrix of the nodes.

6. The diagnostic device of claim 5, wherein, The processor is further configured to: set elements of the adjacency matrix indicating adjacencies between the nodes in an intensity range, and elements of the distance matrix indicating distances between the nodes partitioned by the reference wavenumber intervals. 7.An operating method of a diagnostic apparatus, the operating method comprising: obtaining, from a Raman spectrometer, information about a Raman spectrum of a sample; obtaining, based on the information, intensities of nodes partitioned by reference wavenumber intervals; generating, based on the intensities, a network of the nodes; and calculating, based on a property of the network, a carbon nanotube (CNT) content of the sample. The calculating, based on the property of the network, of the CNT content of the sample includes generating the network including a network energy and a network entropy.

8. The method of operating a diagnostic device according to claim 7, wherein, The calculating, based on the property of the network, of the CNT content of the sample includes:

9. The method of operating a diagnostic device according to claim 8, wherein, calculating the network energy and the network entropy; and calculating the CNT content by inputting the network energy and the network entropy to a relational expression. The calculating, based on the property of the network, of the CNT content of the sample includes calculating the CNT content based on the relational expression represented by Equation 1, 10. The method of operating a diagnostic device according to claim 9, wherein, [Equation 1] The generating, based on the intensities, of the network of the nodes includes generating the network defined by an adjacency matrix and a distance matrix of the nodes. , where NT Conc indicates the CNT content, E network indicates the network energy, T network indicates the network temperature, and S network indicates the network entropy.

11. The method of operating a diagnostic device according to claim 7, wherein, The generating, based on the intensities, of the network of the nodes includes: setting elements of the adjacency matrix indicating adjacencies between the nodes in an intensity range, and elements of the distance matrix indicating distances between the nodes partitioned by the reference wavenumber intervals.

12. The method of operating a diagnostic device according to claim 11, wherein, ​

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