Diagnostic device and method for operating diagnostic device

Abstract

The diagnostic device according to one embodiment disclosed in the present document may comprise: a communication circuit for acquiring information regarding a Raman spectrum regarding a mixed sample of multiple different carbon materials including carbon nanotubes from a Raman spectroscopy; and a processor for acquiring the strength of nodes divided at a reference wavenumber interval of a spectrum on the basis of the information, generating a network regarding the nodes on the basis of the strength, and calculating the carbon nanotube content of the sample on the basis of the characteristics of the network.

Description

Diagnostic device and method of operating the diagnostic device

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority to Republic of Korea Patent Application No. 10-2023-0093391, filed July 18, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] Embodiments disclosed in this document relate to a diagnostic device and a method of operating the diagnostic device.

[0005] Carbon nanotubes (CNTs) are carbon allotropes in the form of hexagonal honeycomb graphite sheets, each carbon atom bonded to three other carbon atoms, rolled up to a nano-sized diameter. They can be manufactured in diameters ranging from less than 1 nm to tens of nm and in various lengths ranging from several ㎛ to tens of mm, giving them a very large aspect ratio.

[0006] Carbon nanotubes are generally categorized as single-walled and multi-walled, and are giant carbon molecules with diverse physical and chemical properties depending on their size, shape, surface functional groups, coatings, and doping. They are lightweight, have excellent electrical and thermal conductivity, and possess tensile strength comparable to that of steel.

[0007] For example, it is known that mixing carbon nanotubes into the electrode materials of secondary batteries can significantly improve battery performance. Many methods are being developed to improve the electrical conductivity, mechanical properties, and lifespan of electrodes by mixing single-walled or multi-walled carbon nanotubes into the cathode or anode components.

[0008] When two or more different types of carbon materials, such as graphite, carbon black (CB), and carbon nanotubes (CNT), are mixed and used in the negative or positive electrode of a lithium-ion battery, a method is required to quantitatively and accurately diagnose whether the content of carbon nanotubes is evenly distributed within the electrode or how the content of carbon nanotubes and their distribution within the electrode change during use of the battery.

[0009] The embodiments disclosed in this document are intended to quantitatively obtain the content of carbon nanotubes from Raman spectrum data measured for a sample in which a plurality of different carbon materials including carbon nanotubes are mixed.

[0010] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0011] According to an embodiment disclosed in the present document, a diagnostic device may include a communication circuit for obtaining information on a Raman spectrum for a mixed sample of a plurality of different carbon materials including carbon nanotubes from a Raman spectroscopy, and a processor for obtaining intensities of nodes separated by a reference wavenumber interval of the spectrum based on the information, generating a network for the nodes based on the intensities, and calculating a carbon nanotube content of the sample based on characteristics of the network.

[0012] A method of operating a diagnostic device according to an embodiment disclosed in the present document may include the steps of obtaining information on a Raman spectrum for a sample from a Raman spectrometer, obtaining intensities of nodes separated by a reference wavenumber interval based on the information, creating a network for the nodes based on the intensities, and calculating a carbon nanotube content of the sample based on characteristics of the network.

[0013] A battery management device according to an embodiment disclosed in the present document may include a communication unit that obtains information on a Raman spectrum for a mixed sample of a plurality of different carbon materials including carbon nanotubes from a Raman spectrometer, and a controller that obtains intensities of nodes separated by a reference wavenumber interval of the spectrum based on the information, creates a network for the nodes based on the intensities, and calculates a carbon nanotube content of the sample based on characteristics of the network.

[0014] The diagnostic device and its operating method according to various embodiments disclosed in this document can obtain content information of carbon nanotubes from Raman spectra of a plurality of carbon mixed samples including carbon nanotubes.

[0015] The effects of the diagnostic device and its operating method according to the disclosure of this document are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art according to the disclosure of this document.

[0016] FIG. 1 is a block diagram showing the configuration of a diagnostic device according to one embodiment disclosed in this document.

[0017] FIG. 2 illustrates Raman spectra obtained from different standard samples according to one embodiment disclosed in the present document.

[0018] FIG. 3 illustrates a graph analyzing the Raman spectrum of an arbitrary standard sample according to one embodiment disclosed in the present document.

[0019] FIG. 4 illustrates an adjacency matrix and a distance matrix for a network structure that transforms Raman spectral data according to one embodiment disclosed in this document.

[0020] FIG. 5 illustrates changes in network entropy and network energy according to changes in CNT content according to one embodiment disclosed in this document.

[0021] FIG. 6 illustrates a correlation graph between the actual CNT content of standard samples according to one embodiment disclosed in the present document and the CNT content calculated from network characteristic values ​​converted from Raman spectra.

[0022] FIG. 7 is a flowchart showing an operation method of a diagnostic device according to one embodiment disclosed in this document.

[0023] FIG. 8 is a drawing showing a battery pack according to one embodiment disclosed in this document.

[0024] FIG. 9 is a block diagram showing the configuration of a battery management device according to one embodiment disclosed in this document.

[0025] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0026] Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0027] The embodiments and terminology used in this document are not intended to limit the technical features described in this document to a specific embodiment, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the item, unless the relevant context clearly indicates otherwise.

[0028] In this document, the phrases "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 each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding component from other corresponding components, and do not limit the corresponding components in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0029] In this document, when a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired or wirelessly), or indirectly (e.g., via a third component).

[0030] The methods according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory, CD-ROM), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0031] According to the embodiments disclosed in this document, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and arranged in other components. According to the embodiments disclosed in this document, one or more components or steps of the aforementioned components may be omitted, or one or more other components or steps may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments disclosed in this document, the steps performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the steps may be executed in a different order, omitted, or one or more other steps may be added.

[0032] FIG. 1 is a block diagram showing the configuration of a diagnostic device according to one embodiment disclosed in this document.

[0033] Referring to FIG. 1, the diagnostic device (101) can be connected to the Raman spectrometer (103) wired and / or wirelessly.

[0034] In one embodiment, the connection between the diagnostic device (101) and the Raman spectrometer (103) may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on a local area network (LAN) communication or a power line communication. In one embodiment, the wireless network may 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 4G network, a 5G network).

[0035] In another embodiment, the connection between the diagnostic device (101) and the Raman spectrometer (103) may be via a device-to-device communication method (e.g., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0036] According to one embodiment, the Raman spectrometer (103) may be a device that performs Raman spectroscopy. According to one embodiment, the Raman spectrometer (103) may be a device for confirming the structure, type, content, etc. of molecules included in the sample (115) by using the Raman effect. According to one embodiment, the Raman spectrometer (103) may confirm the structure, type, content, etc. of molecules included in the sample (115) based on the principle that the energy of a laser irradiated on the sample (115) is absorbed by the amount of the difference in the energy level of electrons of the molecules of the sample (115).

[0037] According to one embodiment, the diagnostic device (101) may be a mobile device (e.g., a mobile phone, a laptop computer, a smart phone, a smart pad), a computer (e.g., a general-purpose computer, a special-purpose computer).

[0038] According to one embodiment, the diagnostic device (101) may include a communication circuit (120), a memory (130), and a processor (140). According to an embodiment, the diagnostic device (101) illustrated in FIG. 1 may further include at least one component (e.g., a display, an input device, or an output device) other than the components illustrated in FIG. 1.

[0039] According to one embodiment, the communication circuit (120) can establish a wired communication channel and / or a wireless communication channel between the diagnostic device (101) and / or the Raman spectrometer (103), and transmit and receive data to and from the Raman spectrometer (103) through the established communication channel.

[0040] According to one embodiment, the memory (130) may include volatile memory and / or non-volatile memory.

[0041] According to one embodiment, the memory (130) may store data used by at least one component (e.g., the processor (140)) of the diagnostic device (101). For example, the data may include a program (or instructions related thereto), input data, or output data. According to one embodiment, the instructions, when executed by the processor (140), may cause the diagnostic device (101) to perform steps defined by the instructions.

[0042] According to one embodiment, the processor (140) 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.

[0043] According to one embodiment, the processor (140) may execute a program (or instructions related thereto) to control at least one other component (e.g., hardware or software component) of a diagnostic device (101) connected to the processor (140) and perform various data processing or operations.

[0044] In one embodiment, the processor (140) and the diagnostic device (101) connected to the processor (140) may be included and used as components of a battery management system (BMS).

[0045] In one embodiment, a “battery management system (BMS)” comprising a processor (140) and a diagnostic device (1010) connected to the processor (140) means any form of interface that includes a battery management system.

[0046] In one embodiment of the present invention, a mobile device including a battery management system device according to the present application can be provided.

[0047] In this specification, the term "mobile device" means a device that can move itself or can be easily carried by a user, and examples thereof include electric vehicles, mobile devices, etc.

[0048] Hereinafter, with reference to FIGS. 2 to 6, a method for quantitatively measuring the content of carbon nanotubes (CNTs) included in a sample (115) through spectrum data obtained from a Raman spectrometer (103) by a diagnostic device (101) is specifically described.

[0049] Obtaining the relational expression

[0050] FIG. 2 illustrates Raman spectra acquired from standard samples having different compositions according to one embodiment disclosed in the present document. FIG. 3 illustrates dividing Raman spectra (300) of standard samples into unit spectrum intensity and reference wavenumber intervals for converting peak intensity information of Raman spectra into a network structure according to one embodiment disclosed in the present document. FIG. 4 illustrates adjacency matrices and distance matrices characterizing a network structure according to one embodiment disclosed in the present document. FIG. 5 illustrates changes in network entropy and network energy according to CNT content according to one embodiment disclosed in the present document. FIG. 6 illustrates a correlation graph between actual CNT contents of standard samples according to one embodiment disclosed in the present document and CNT contents calculated from network characteristic values ​​converted from Raman spectra.

[0051] The Raman spectrometer (103) can acquire Raman spectra (210, 220, 230, 240, 250, 260) from different standard samples (115). According to one embodiment, the Raman spectrometer (103) can acquire Raman spectra from 1000 to 1800 cm -1Raman spectra (210, 220, 230, 240, 250, 260) can be acquired at a range of wave numbers (Raman Shift).

[0052] Different standard samples (115) may be samples in which carbon nanotubes are mixed at different weight percentages (wt%). For example, the carbon nanotube contents of different standard samples (115) may be 0, 25, 33.3, 50, 66.7, and 100 wt%. For example, Raman spectra (210, 220, 230, 240, 250, and 260) may be obtained when the carbon nanotube contents of the standard samples (115) are 100, 66.7, 50, 33.3, 25, and 0 wt%, respectively.

[0053] Raman spectra (210, 220, 230, 240, 250, 260) of different standard samples (115) at a wavelength of 1350 cm -1 D band (201) located nearby and wavelength 1580 cm -1 It can have peak values ​​in the G band (205) located nearby.

[0054] The Raman spectrometer (103) can transmit information about Raman spectra (210, 220, 230, 240, 250, 260) of standard samples (115) with different compositions obtained to the diagnostic device (101).

[0055] The diagnostic device (101) can obtain information about Raman spectra (210, 220, 230, 240, 250, 260) from the Raman spectrometer (103) through a communication circuit (120).

[0056] The processor (140) of the diagnostic device (101) can analyze Raman spectra (210, 220, 230, 240, 250, 260). According to one embodiment, the processor (140) can classify the Raman spectra into reference wavenumber intervals. A Raman spectrum divided into wavenumber ranges (321 to 330) can be referred to as a unit Raman spectrum.

[0057] According to one embodiment, the processor (140) can set the intensity of a unit Raman spectrum. Here, the intensity of the unit Raman spectrum can be set by the intensity at the start point, the intensity at the middle point, the intensity at the end point, or the average intensity for each section of the wavenumber range.

[0058] The processor (140) can classify the unit Raman spectra into each node by reference wavenumber interval in intensity ranges (311 to 319) based on the intensity of the unit Raman spectra.

[0059] Referring to the graph (300) of FIG. 3, it can be seen that the Raman spectrum is segmented into unit Raman spectra based on a grid of unit intensity and unit wavenumber interval.

[0060] According to one embodiment, the processor (140) may generate an adjacency matrix (400) and a distance matrix (450) based on the unit Raman spectra. Referring to FIG. 4, elements (A) of the adjacency matrix (400) 11 , A 12 , ..., A 1n , A 21 , ..., A nn ) are unit spectra (C1, C2, ..., C n ) indicates whether they are adjacent or not, and the elements of the distance matrix (450) (D 11 , D 12 , ..., D 1n , D 21 , ..., D nn ) are unit spectra (C1, C2, ..., C n) can represent the distance between them.

[0061] According to one embodiment, each of the unit Raman spectra, which are divided into wavenumber ranges (321 to 330), is divided into unit spectra (C1, C2, ..., C) of the adjacency matrix (400) and the distance matrix (450). n ) can be understood as. For example, the unit Raman spectrum included in the wavenumber range (321) can be understood as the first node (C1), the unit Raman spectrum included in the wavenumber range (322) can be understood as the second node (C2), and the unit Raman spectrum included in the wavenumber range (323) can be understood as the third node (C3).

[0062] According to one embodiment, the elements (A) of the adjacency matrix (400) are set such that unit spectra between adjacent intensity ranges are set to be adjacent (i.e., the element value is 1) and unit spectra between non-adjacent intensity ranges are set to be non-adjacent (i.e., the element value is 0). 11 , A 12 , ..., A 1n , A 21 , ..., A nn ) can be assigned values. For example, if the node (C1) corresponding to the wavenumber range (321) is included in the intensity range (319) and the node (C2) corresponding to the wavenumber range (322) is included in the intensity range (318), the elements (A) defined by the unit spectra (C1, C2) 12 , A 21 ) may be 1. For another example, if the node (C1) corresponding to the wavenumber range (321) is included in the intensity range (319) and the node (C3) corresponding to the wavenumber range (323) is included in the intensity range (316), the elements (A) defined by the unit spectra (C1, C3) 12 , A 13) can be 0. The value obtained by multiplying the assigned 0 or 1 by the weight factor can be used as the value of the elements of the adjacency matrix (400).

[0063] According to one embodiment, the elements of the distance matrix (450) (D 11 , D 12 , ..., D 1n , D 21 , ..., D nn ) are nodes defined based on unit century intervals and unit wave intervals (C1, C2, ..., C n ) can be set as the distance between them. For example, the distance difference between unit spectra included in the same intensity range can be a value greater than or equal to 0 depending on the difference in the unit wavenumber interval. For another example, the distance difference between nodes included in different intensity ranges can be a value greater than or equal to 1 depending on the difference in the unit wavenumber interval. For example, the distance difference between a node included in the intensity range (319) and a node included in the intensity range (317) can be 2 in the wavenumber ranges (321) and (322), but can be 11 in the wavenumber ranges (321) and (324). The value obtained by multiplying the distance difference by a preset weight factor between two different nodes can be used as the value of the elements of the distance matrix (450).

[0064] According to one embodiment, the processor (140) determines nodes (C1, C2, ..., C) based on the adjacency matrix (400) and the distance matrix (450). n ) can obtain the network characteristics. Here, nodes (C1, C2, ..., C n ) is a network of nodes (C1, C2, ..., C n ) can represent the connection structure (distance difference, whether neighbors are present). And, the network characteristics are network energy (E network ), network entropy (S network ), and network temperature (Tnetwork ) may be included.

[0065] According to one embodiment, the network energy (E network ) can be expressed as a product of the number of nodes included in the network and the distance between nodes. For example, the network energy (E) is expressed by the following mathematical expression 1. network ) can be defined.

[0066]

[0067] Here, a represents the number of nodes, and f(d i,j ) can represent a function of the distance (d) between the i-th and j-th nodes. Here, the function f can be a polynomial function established by a linear or nonlinear regression technique. For example, the network energy (E network ) are the network energies (E) obtained for all nodes. network, i,j ) may be the sum of the values.

[0068] In one embodiment, the network entropy (S network ) can be expressed as the product of the function value for the inverse of the topological distance between two nodes and the logarithm of the function value for the inverse of the topological distance between two nodes. (Here, H is g(1 / d ij ) and d ij can be defined as the topological distance between two nodes i and j) For example, the network entropy (S) is given by the following mathematical expression 2 network ) can be defined.

[0069]

[0070] Here, log stands for the logarithmic function. Here, H can be defined by the mathematical expression 3 below.

[0071]

[0072] g(1 / d i,j ) can represent a function of the reciprocal of the distance (d) between the i-th and j-th nodes. Here, the function g can be a polynomial function established by a linear or nonlinear regression technique. For example, the network entropy (S network ) are the network entropies (S) obtained for all nodes. network, i, j ) may be the sum of the values.

[0073] According to one embodiment, the network temperature (T network ) can be preset (e.g., 45.87) depending on the content of carbon nanotubes included in the standard sample.

[0074] According to one embodiment, the processor (140) is configured to: network ), and network entropy (S network ) and CNT content. According to one embodiment, the processor (140) may obtain information on the relationship between the network energy (E) based on multiple linear regression (MLR). network ), and network entropy (S network ) and CNT content can be obtained. According to an embodiment, the information on the relationship can be obtained by a machine learning algorithm such as a support vector machine (SVM), a decision tree, a random forest, a gradient boosting machine (GBM), an artificial neural network, etc., but is not limited to the specified algorithm. According to an embodiment, the processor (140) uses a machine learning model to obtain network energy (E network ), and network entropy (S network ) and CNT content can be obtained.

[0075] Figure 5 illustrates the changes in network entropy and network energy according to changes in CNT content. Figure 5 shows the network energy (E network ), and network entropy (S network ) can be used as an indicator to represent a contour map indicating the CNT content. Referring to Fig. 5, the network energy (E network ), and network entropy (S network ) and the CNT content can be confirmed. The change in CNT content can be attributed to the network energy (E network ) than network entropy (S network ) can be seen to bring more changes.

[0076] According to one embodiment, the processor (140) is configured to: network ), network entropy (S network ) and network temperature (T network ) and CNT content, a relational expression (or an operational expression) can be established. Specifically, the processor (140) can establish a network energy (E as in the following mathematical expression 4 network ), network entropy (S network ) and network temperature (T network ) and the CNT content can be established.

[0077] As mentioned above, the network temperature (T network ) can be configured as a preset constant depending on the content of carbon nanotubes included in the standard sample, and the network energy (E network ) and network entropy (S network ) can be composed of variables.

[0078] That is, the processor (140) has a network temperature (T) corresponding to a constant network ) and the network energy (E) corresponding to the variable network ) and network entropy (S network ) can be input into mathematical equation 4 to derive the CNT content.

[0079]

[0080] Here, NT Conc may mean the CNT content. Referring to FIG. 6, a correlation of 0.997 (or Pearson correlation coefficient, R) can be confirmed between the CNT content of the standard samples (115) and the value derived by mathematical expression 4.

[0081] Measurement of CNT content based on relational equation

[0082] According to one embodiment, the diagnostic device (101) obtains network energy (E) from a standard sample through a series of steps. network ), network entropy (S network ) and network temperature (T network ) and CNT content, the CNT content contained in an arbitrary carbon mixture sample without CNT content information can be calculated.

[0083] Specifically, the diagnostic device (101) can obtain information on the Raman spectrum for an arbitrary 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) based on the obtained adjacency matrix and distance matrix. network ), and network entropy (S network ) can be obtained. Finally, the diagnostic device (101) can obtain the obtained network energy (E network ), and network entropy (S network ) can be obtained by entering the relational expression (or operational expression).

[0084] As described above, the diagnostic device (101) can obtain the CNT content from the Raman spectrum. In addition, the state of the battery can be diagnosed through the CNT content obtained from the Raman spectrum by the diagnostic device (101). Accordingly, the method of obtaining the CNT content from the Raman spectrum by the diagnostic device (101) can be utilized for predicting and diagnosing the life of the battery when manufacturing the battery, when using it (charging / discharging), or when determining whether to reuse (or recycle) a waste battery.

[0085] FIG. 7 is a flowchart showing an operation method of a diagnostic device according to one embodiment disclosed in this document.

[0086] Hereinafter, the operation method of the diagnostic device (101) will be specifically described with reference to FIGS. 1 to 6.

[0087] Since the diagnostic device (101) may be substantially the same as the diagnostic device (101) described with reference to FIGS. 1 to 6, it is briefly described below to avoid duplication of explanation.

[0088] Referring to FIG. 7, the operating method of the diagnostic device may include a step of obtaining information on a Raman spectrum for a standard sample from a Raman spectrometer (S101), a step of obtaining intensities of nodes separated by a reference wavenumber interval based on the information on the Raman spectrum (S102), a step of generating a network for nodes based on the intensities of the nodes (S103), a step of establishing a carbon nanotube content calculation formula based on the characteristics of the network (S104), and a step of calculating the carbon nanotube content of the sample based on the CNT content calculation formula (S105).

[0089] In step S101, the diagnostic device (101) can acquire a Raman spectrum for a standard sample. According to one embodiment, the diagnostic device (101) can acquire a Raman spectrum for the standard sample from a Raman spectrometer (103). Here, the content of carbon nanotubes in the standard sample can be 0, 25, 33.3, 50, 66.7, or 100 wt%.

[0090] In step S102, the diagnostic device (101) can set the intensity of the unit Raman spectrum. Here, the intensity of the unit Raman spectrum can be set by the intensity at the start point, the intensity at the middle point, the intensity at the end point, or the average intensity for each section of the wavenumber range.

[0091] In step S102, the diagnostic device (101) can classify the unit Raman spectra into each node by reference wavenumber interval in intensity ranges (311 to 319) based on the intensity of the unit Raman spectra.

[0092]

[0093] In step S103, the diagnostic device (101) may generate a network based on the Raman spectra. According to one embodiment, the diagnostic device (101) may generate an adjacency matrix and a distance matrix based on the unit Raman spectra. Elements of the adjacency matrix may indicate whether nodes are adjacent, and elements of the distance matrix may indicate distances between nodes.

[0094] In step S103, according to one embodiment, the diagnostic device (101) can generate a network of nodes based on an adjacency matrix and a distance matrix. Specifically, the diagnostic device (101) can generate a connection structure (distance difference, whether neighbors are present) between nodes constituting the network based on the values ​​of elements of the adjacency matrix and the distance matrix.

[0095] In step S103, according to one embodiment, the diagnostic device (101) determines network characteristics (e.g., network energy (E)) based on the network. network ), network entropy (S network ), and network temperature (T network )) can be obtained.

[0096] In step S104, the diagnostic device (101) can establish a CNT content calculation formula based on the characteristics of the network. According to one embodiment, the diagnostic device (101) can calculate the network energy (E network ), and network entropy (S network ) and CNT content, a relational expression (or an operational expression) can be established.

[0097] In step S105, the diagnostic device (101) can calculate the CNT content included in the new sample based on the established relational expression (or, calculation expression).

[0098] FIG. 8 is a drawing showing a battery pack according to one embodiment disclosed in this document.

[0099] Referring to FIG. 8, a battery pack (2000) according to one embodiment disclosed in the present document may include a battery module (2100), a battery management device (2200), and a relay (2300). According to various embodiments, the battery module (2100) may be a battery cell, in which case the battery pack (2000) may have a cell-to-pack structure.

[0100] In FIG. 8, a single battery module (2100) is illustrated, but depending on the embodiment, the battery module (2100) may be configured with a plurality of battery modules, and the battery pack (2000) may have a stacked structure of a plurality of battery modules. The battery module (2100) may include a plurality of battery cells (2110, 2120, 2130, 2140). In FIG. 8, the plurality of battery cells is illustrated as being four, but this is not limited thereto, and the battery module (2100) may be configured to include n (n is a natural number greater than or equal to 2) battery cells. In addition, each of the plurality of battery cells (2110, 2120, 2130, 2140) may be a cell group or battery bank in which at least two or more battery cells are connected in parallel.

[0101] The battery module (2100) can supply power to a target device (not shown). For this purpose, the battery module (2100) can be electrically connected to the target device. Here, the target device can include an electrical, electronic, or mechanical device that operates by receiving power from a battery pack (2000) including a plurality of battery cells (2110, 2120, 2130, 2140). For example, the target device can be, but is not limited to, an electric vehicle (EV) or an energy storage system (ESS).

[0102] A plurality of battery cells (2110, 2120, 2130, 2140) are basic units of a battery that can be used by charging and discharging electric energy, and may be, but are not limited to, a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion polymer) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-metal hydride (Ni-MH) battery, etc. Meanwhile, in FIG. 8, a single battery module (2100) is illustrated, but depending on the embodiment, the battery module (2100) may be configured in multiple units.

[0103] A battery management system (BMS) (2200) can manage and / or control the status and / or operation of a battery module (2100). For example, the battery management system (2200) can manage and / or control the status and / or operation of a plurality of battery cells (2110, 2120, 2130, 2140) included in the battery module (2100). The battery management system (2200) can manage charging and / or discharging of the battery module (2100).

[0104] The battery management device (2200) can control the 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. Additionally, the battery management device (2200) can short-circuit the relay (2300) when a charging device is connected to the battery pack (2000).

[0105] In addition, the battery management device (2200) can monitor the voltage, current, temperature, etc. of the battery module (2100) and / or each of the plurality of battery cells (2110, 2120, 2130, 2140) included in the battery module (2100). In addition, for monitoring by the battery management device (2200), sensors or various measurement modules not shown may be additionally installed in any location of the battery module (2100), the charging / discharging path, or the battery module (2100). The battery management device (2200) can calculate parameters indicating the state of the battery module (2100), for example, the State of Charge (SOC) or the State of Health (SOH), based on the measured values ​​of the monitored voltage, current, temperature, etc.

[0106] In addition, the operation of the battery management device (2200) below can be performed in various devices such as a server, cloud, charger, or charger / discharger connected to the battery management device (2200) or a vehicle equipped with the battery management device (2200).

[0107] Hereinafter, the battery management device (2200) will be specifically described with reference to FIGS. 1 to 7.

[0108] The battery management device (2200) may be substantially the same as the diagnostic device (101) described with reference to FIGS. 1 to 7, and thus is briefly described below to avoid duplication of explanation.

[0109] The battery management device (2200) can be connected to the Raman spectrometer (103) wired and / or wirelessly.

[0110] In one embodiment, the connection between the battery management device (2200) and the Raman spectrometer (103) may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on a local area network (LAN) communication or a power line communication. In one embodiment, the wireless network may 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 4G network, or a 5G network).

[0111] In another embodiment, the connection between the battery management device (2200) and the Raman spectrometer (103) may be via a device-to-device communication method (e.g., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0112] According to one embodiment, the Raman spectrometer (103) may be a device that performs Raman spectroscopy. According to one embodiment, the Raman spectrometer (103) may be a device for confirming the structure, type, content, etc. of molecules included in the sample (115) by using the Raman effect. According to one embodiment, the Raman spectrometer (103) may confirm the structure, type, content, etc. of molecules included in the sample (115) based on the principle that the energy of a laser irradiated on the sample (115) is absorbed by the amount of the difference in the energy level of electrons of the molecules of the sample (115).

[0113] FIG. 9 is a block diagram showing the configuration of a battery management device according to one embodiment disclosed in this document.

[0114] Referring to FIG. 9, the battery management device (2200) may include a communication unit (2210) and a controller (2220). According to an embodiment, the battery management device (2200) illustrated in FIG. 9 may further include at least one component (e.g., a display, an input device, or an output device) other than the components illustrated in FIG. 9.

[0115] According to one embodiment, the communication unit (2210) can establish a wired communication channel and / or a wireless communication channel between the battery management device (2200) and / or the Raman spectrometer (103), and transmit and receive data with the Raman spectrometer (103) through the established communication channel.

[0116] The memory (not shown) included in the battery management device (2200) may include volatile memory and / or non-volatile memory. According to one embodiment, the memory may store data used by at least one component (e.g., the controller (2220)) of the battery management device (2200). For example, the data may include a program (or instructions related thereto), input data, or output data. According to one embodiment, the instructions, when executed by the controller (2220), may cause the battery management device (2200) to perform steps defined by the instructions.

[0117] According to one embodiment, the controller (2220) 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.

[0118] According to one embodiment, the controller (2220) may execute a program (or instructions related thereto) to control at least one other component (e.g., hardware or software component) of the battery management device (2200) connected to the controller (2220) and perform various data processing or operations.

[0119] Hereinafter, a method for quantitatively measuring the content of carbon nanotubes (CNTs) included in a sample (115) through spectrum data obtained from a Raman spectrometer (103) by a battery management device (2200) is specifically described.

[0120] The Raman spectrometer (103) can acquire Raman spectra (210, 220, 230, 240, 250, 260) from different standard samples (115). According to one embodiment, the Raman spectrometer (103) can acquire Raman spectra from 1000 to 1800 cm -1Raman spectra (210, 220, 230, 240, 250, 260) can be acquired at a range of wave numbers (Raman Shift).

[0121] Different standard samples (115) may be samples in which carbon nanotubes are mixed at different weight percentages (wt%). For example, the carbon nanotube contents of different standard samples (115) may be 0, 25, 33.3, 50, 66.7, and 100 wt%. For example, Raman spectra (210, 220, 230, 240, 250, and 260) may be obtained when the carbon nanotube contents of the standard samples (115) are 100, 66.7, 50, 33.3, 25, and 0 wt%, respectively.

[0122] Raman spectra (210, 220, 230, 240, 250, 260) of different standard samples (115) at a wavelength of 1350 cm -1 D band (201) located nearby and wavelength 1580 cm -1 It can have peak values ​​in the G band (205) located nearby.

[0123] The Raman spectrometer (103) can transmit information about Raman spectra (210, 220, 230, 240, 250, 260) of standard samples (115) with different compositions obtained to the battery management device (2200).

[0124] The battery management device (2200) can obtain information about Raman spectra (210, 220, 230, 240, 250, 260) from the Raman spectrometer (103) through the communication unit (2210).

[0125] The controller (2220) of the battery management device (2200) can analyze Raman spectra (210, 220, 230, 240, 250, 260). In one embodiment, the controller (2220) can classify the Raman spectra into reference wavenumber intervals. A Raman spectrum divided into wavenumber ranges (321 to 330) can be referred to as a unit Raman spectrum.

[0126] According to one embodiment, the controller (2220) can set the intensity of a unit Raman spectrum. Here, the intensity of the unit Raman spectrum can be set by the intensity at the start point, the intensity at the middle point, the intensity at the end point, or the average intensity for each section of the wavenumber range.

[0127] The controller (2220) can classify the unit Raman spectra into each node by reference wavenumber interval in intensity ranges (311 to 319) based on the intensity of the unit Raman spectra.

[0128] It can be seen that the Raman spectrum is segmented into unit Raman spectra based on the grid of unit intensity and unit wavenumber interval.

[0129] According to one embodiment, the controller (2220) can generate an adjacency matrix (400) and a distance matrix (450) based on the unit Raman spectra. Elements (A) of the adjacency matrix (400) 11 , A 12 , ..., A 1n , A 21 , ..., A nn ) are unit spectra (C1, C2, ..., C n ) indicates whether they are adjacent or not, and the elements of the distance matrix (450) (D 11 , D 12 , ..., D 1n , D 21 , ..., D nn ) are unit spectra (C1, C2, ..., C n ) can represent the distance between them.

[0130] According to one embodiment, each of the unit Raman spectra, which are divided into wavenumber ranges (321 to 330), is divided into unit spectra (C1, C2, ..., C) of the adjacency matrix (400) and the distance matrix (450). n ) can be understood as. For example, the unit Raman spectrum included in the wavenumber range (321) can be understood as the first node (C1), the unit Raman spectrum included in the wavenumber range (322) can be understood as the second node (C2), and the unit Raman spectrum included in the wavenumber range (323) can be understood as the third node (C3).

[0131] According to one embodiment, the elements (A) of the adjacency matrix (400) are set such that unit spectra between adjacent intensity ranges are set to be adjacent (i.e., the element value is 1) and unit spectra between non-adjacent intensity ranges are set to be non-adjacent (i.e., the element value is 0). 11 , A 12 , ..., A 1n , A 21 , ..., A nn ) can be assigned values. For example, if the node (C1) corresponding to the wavenumber range (321) is included in the intensity range (319) and the node (C2) corresponding to the wavenumber range (322) is included in the intensity range (318), the elements (A) defined by the unit spectra (C1, C2) 12 , A 21 ) may be 1. For another example, if the node (C1) corresponding to the wavenumber range (321) is included in the intensity range (319) and the node (C3) corresponding to the wavenumber range (323) is included in the intensity range (316), the elements (A) defined by the unit spectra (C1, C3) 12 , A 13 ) can be 0. The value obtained by multiplying the assigned 0 or 1 by the weight factor can be used as the value of the elements of the adjacency matrix (400).

[0132] According to one embodiment, the elements of the distance matrix (450) (D 11 , D 12 , ..., D 1n , D 21 , ..., D nn ) are nodes defined based on unit century intervals and unit wave intervals (C1, C2, ..., C n ) can be set as the distance between them. For example, the distance difference between unit spectra included in the same intensity range can be a value greater than or equal to 0 depending on the difference in the unit wavenumber interval. For another example, the distance difference between nodes included in different intensity ranges can be a value greater than or equal to 1 depending on the difference in the unit wavenumber interval. For example, the distance difference between a node included in the intensity range (319) and a node included in the intensity range (317) can be 2 in the wavenumber ranges (321) and (322), but can be 11 in the wavenumber ranges (321) and (324). The value obtained by multiplying the distance difference by a preset weight factor between two different nodes can be used as the value of the elements of the distance matrix (450).

[0133] According to one embodiment, the controller (2220) determines nodes (C1, C2, ..., C) based on the adjacency matrix (400) and the distance matrix (450). n ) can obtain the network characteristics. Here, nodes (C1, C2, ..., C n ) is a network of nodes (C1, C2, ..., C n ) can represent the connection structure (distance difference, whether neighbors are present). And, the network characteristics are network energy (E network ), network entropy (S network ), and network temperature (T network ) may be included.

[0134] According to one embodiment, the network energy (E network) can be expressed as a product of the number of nodes included in the network and the distance between nodes. For example, the network energy (E) is expressed by the following mathematical expression 5. network ) can be defined.

[0135]

[0136] Here, a represents the number of nodes, and f(d i,j ) can represent a function of the distance (d) between the i-th and j-th nodes. Here, the function f can be a polynomial function established by a linear or nonlinear regression technique. For example, the network energy (E network ) are the network energies (E) obtained for all nodes. network, i, j ) may be the sum of the values.

[0137] In one embodiment, the network entropy (S network ) can be expressed as the product of the number of nodes included in the network and the distance between nodes. For example, the network entropy (S) is expressed by the following mathematical expression 6. network ) can be defined.

[0138]

[0139] Here, log stands for the logarithmic function. Here, H can be defined by the mathematical expression 7 below.

[0140]

[0141] g(1 / d i,j ) can represent a function of the reciprocal of the distance (d) between the i-th and j-th nodes. Here, the function g can be a polynomial function established by a linear or nonlinear regression technique. For example, the network entropy (S network ) are the network entropies (S) obtained for all nodes. network, i, j ) may be the sum of the values.

[0142] According to one embodiment, the network temperature (T network ) can be preset (e.g., 45.87) depending on the content of carbon nanotubes included in the standard sample.

[0143] According to one embodiment, the controller (2220) controls network energy (E network ), and network entropy (S network ) and CNT content. According to one embodiment, the controller (2220) may obtain information on the relationship between the network energy (E) based on multiple linear regression (MLR). network ), and network entropy (S network ) and CNT content can be obtained. According to an embodiment, the information on the relationship can be obtained by a machine learning algorithm such as a support vector machine (SVM), a decision tree, a random forest, a gradient boosting machine (GBM), an artificial neural network, etc., but is not limited to the specified algorithm. According to an embodiment, the controller (2220) uses a machine learning model to obtain network energy (E network ), and network entropy (S network ) and CNT content can be obtained.

[0144] Network Energy (E network ), and network entropy (S network ) and the CNT content can be confirmed. The change in CNT content can be attributed to the network energy (E network ) than network entropy (S network ) can be seen to bring more changes.

[0145] According to one embodiment, the controller (2220) controls network energy (Enetwork ), network entropy (S network ) and network temperature (T network ) and CNT content, a relational expression (or an operational expression) can be established. Specifically, the controller (2220) can establish a network energy (E as in mathematical expression 8 below. network ), network entropy (S network ) and network temperature (T network ) and the CNT content can be established.

[0146] As mentioned above, the network temperature (T network ) can be configured as a preset constant depending on the content of carbon nanotubes included in the standard sample, and the network energy (E network ) and network entropy (S network ) can be composed of variables.

[0147] That is, the controller (2220) determines the network temperature (T) corresponding to the constant network ) and the network energy (E) corresponding to the variable network ) and network entropy (S network ) can be input into mathematical expression 8 to derive the CNT content.

[0148]

[0149] Here, NT Conc may mean the CNT content. A correlation of 0.997 (or Pearson correlation coefficient, R) can be confirmed between the CNT content of the standard samples (115) and the value derived by mathematical expression 8.

[0150] Measurement of CNT content based on relational equation

[0151] According to one embodiment, the battery management device (2200) obtains network energy (E) from a standard sample through a series of steps. network ), network entropy (S network ) and network temperature (T network) and CNT content, the CNT content contained in an arbitrary carbon mixture sample without CNT content information can be calculated.

[0152] Specifically, the battery management device (2200) can obtain information on the Raman spectrum for an arbitrary sample from the Raman spectrometer (103). Thereafter, the battery management device (2200) can analyze the Raman spectrum to obtain an adjacency matrix and a distance matrix. Thereafter, 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 network ) can be obtained. Finally, the battery management device (2200) can obtain the obtained network energy (E network ), and network entropy (S network ) can be obtained by entering the relational expression (or operational expression).

[0153] As described above, the battery management device (2200) can obtain the CNT content from the Raman spectrum. In addition, the battery management device (2200) can diagnose the state of the battery through the CNT content obtained from the Raman spectrum. Accordingly, the method of obtaining the CNT content from the Raman spectrum by the battery management device (2200) can be utilized for predicting and diagnosing the lifespan of a battery when manufacturing the battery, when using it (charging / discharging), or when determining whether to reuse (or recycle) a waste battery.

Claims

1. A communication circuit for obtaining information on Raman spectra for a mixed sample of different carbon materials including carbon nanotubes (CNTs) from a Raman spectrometer; and A diagnostic device comprising a processor that obtains the intensities of nodes separated by a reference wavenumber interval of a spectrum based on the above information, creates a network for the nodes based on the intensities, and calculates the carbon nanotube content of the sample based on the characteristics of the network.

2. In claim 1, A diagnostic device characterized in that the processor calculates the carbon nanotube content of the sample based on the network energy and network entropy and the network characteristics.

3. In claim 2, A diagnostic device characterized in that the processor calculates the network energy and the network entropy, and inputs the network energy and the network entropy into a relational expression to calculate the carbon nanotube content.

4. In claim 3, A diagnostic device characterized in that the processor calculates the carbon nanotube content based on the relational expression expressed by the mathematical expression 1 below. [Mathematical Formula 1] NT Conc =E network -T network* S network In mathematical expression 1, NT Conc is the carbon nanotube content, and E network is the network energy, and T network is the network temperature, and S network is the above network entropy.

5. In claim 1, A diagnostic device characterized in that the processor generates the network defined by an adjacency matrix and a distance matrix of the nodes.

6. In claim 5, A diagnostic device characterized in that the processor sets elements of the adjacency matrix indicating whether the nodes are neighbors in the intensity range between the nodes and elements of the distance matrix indicating the distance between the nodes divided by a reference frequency interval.

7. A step of obtaining information about the Raman spectrum of a sample from a Raman spectrometer; A step of obtaining the strength of nodes separated by a reference frequency interval based on the above information; a step of creating a network for the nodes based on the above century; and A method of operating a diagnostic device, comprising a step of calculating a carbon nanotube (CNT) content of the sample based on the characteristics of the network.

8. In claim 7, The step of calculating the carbon nanotube content of the sample based on the characteristics of the above network is A method of operating a diagnostic device characterized by generating the above network including network energy and network entropy.

9. In claim 8, The step of calculating the carbon nanotube content of the sample based on the characteristics of the above network is Comprising a step of calculating the network energy and the network entropy, An operating method of a diagnostic device characterized in that the carbon nanotube content is calculated by inputting the network energy and the network entropy into a relational expression.

10. In claim 9, The step of calculating the carbon nanotube content of the sample based on the characteristics of the above network is An operating method of a diagnostic device characterized in that the carbon nanotube content is calculated based on the above relationship expressed by the mathematical expression 1 below. [Mathematical Formula 1] NT Conc =E network -T network* S network In mathematical expression 1, NT Conc is the carbon nanotube content, and E network is the network energy, and T network is the network temperature, and S network is the above network entropy.

11. In claim 7, The step of creating a network for the nodes based on the above century is A method of operating a diagnostic device characterized in that it generates the network defined by the adjacency matrix and distance matrix of the nodes.

12. In claim 11, The step of creating a network for the nodes based on the above century is An operating method of a diagnostic device, characterized in that elements of the adjacency matrix indicating whether the nodes are neighbors in the intensity range and elements of the distance matrix indicating the distance between the nodes divided by a reference wave number interval are set.

Images