Server and battery analysis method thereof

Abstract

Provided is a battery analysis method performed on a server, the method comprising the steps of: acquiring electrochemical impedance spectroscopy (EIS) information for each of a plurality of different states of charge, the EIS information being confirmed by performing EIS on a battery cell in the plurality of states of charge; generating distribution of relaxation times (DRT) information for each of the plurality of states of charge by calculating a DRT for the EIS information for each of the plurality of states of charge; and generating, on the basis of the DRT information for each of the plurality of states of charge, a feature image corresponding to the battery cell, the feature image including magnitude information about impedance according to frequency, the magnitude information being calculated for each of the plurality of states of charge.

Description

Server and its battery analysis method

[0001] This application claims the benefit of priority to Republic of Korea Patent Application No. 2023-0174550, dated December 5, 2023, and Republic of Korea Patent Application No. 2023-0174544, dated December 5, 2023, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates to a method for analyzing a battery and a server performing the same.

[0003] One of the many conventional techniques used to analyze battery cells is electrochemical impedance spectroscopy (EIS). This analytical method applies alternating voltage or current at various frequencies to a battery cell, calculates impedance based on the resulting measured current or voltage, and expresses this as a Nyquist plot. It has been widely used for non-destructive analysis of battery cells.

[0004] One conventional technique used to facilitate the analysis of EIS results is the Distribution of Relaxation Times (DRT). This technique is widely used because it converts impedance data calculated from EIS results into a relaxation time distribution, enabling the identification of the battery's equivalent circuit without requiring prior knowledge of the battery's impedance.

[0005] Meanwhile, the analysis results of batteries obtained through EIS and DRT still only show somewhat limited characteristics of the battery, and therefore, an analysis method capable of analyzing more diverse characteristics of the battery is needed.

[0006] The disclosed embodiments provide a battery analysis method and a server for performing the same. Specifically, the present invention aims to provide a method for analyzing a wider range of battery characteristics based on EIS and DRT results. Furthermore, the present invention aims to provide a method for deriving a wider range of battery characteristics by analyzing characteristic images of battery cells obtained based on EIS and DRT results.

[0007] The technical tasks to be achieved by this embodiment are not limited to the technical tasks described above, and other technical tasks can be inferred from the following embodiments.

[0008] One aspect of the present disclosure provides a battery analysis method performed on a server, comprising: a step of obtaining electrochemical impedance spectroscopy (EIS) information for each of a plurality of different charge states for a battery cell, wherein the EIS information is confirmed by performing the EIS information at each of the plurality of charge states; a step of generating distribution of relaxation times (DRT) information for each of the plurality of charge states by calculating a DRT for the EIS information for each of the plurality of charge states; and a step of generating a feature image corresponding to the battery cell, the feature image including information on the magnitude of impedance according to frequency calculated for each of the plurality of charge states, based on the DRT information for each of the plurality of charge states.

[0009] In one embodiment of the present disclosure, the step of generating the feature image may include a battery analysis method including the step of generating the feature image in the form of a two-dimensional image in which a first axis is the frequency, a second axis is the charge state, and the degree of shading of each pixel is determined based on the magnitude information of the impedance calculated for each of the frequencies and the plurality of charge states, based on DRT information for each of the plurality of charge states.

[0010] In addition, in one embodiment of the present disclosure, the step of generating the feature image may include a step of: confirming first magnitude information of the impedance according to the frequency for each of the plurality of charging states for the charging process based on the first DRT information for each of the plurality of charging states during the charging process of the battery cell, and generating, based on the first magnitude information, a first feature image for the charging process, which is at least a part of the feature image; and a step of confirming, based on the second DRT information for each of the plurality of charging states during the discharging process of the battery cell, second magnitude information of the impedance according to the frequency for each of the plurality of charging states during the discharging process, and generating, based on the second magnitude information, a second feature image for the discharging process, which is at least a part of the feature image. The battery analysis method may include:

[0011] Additionally, in one embodiment of the present disclosure, the feature image may include a battery analysis method, wherein the first and second feature images are symmetrically arranged around a reference axis parallel to the first axis.

[0012] In addition, one embodiment of the present disclosure may include a battery analysis method further comprising a step of storing a set of feature images for a plurality of reference battery cells, including the feature images, in a database.

[0013] In addition, in one embodiment of the present disclosure, the feature image set may include a battery analysis method in which at least a portion of reference feature information including at least a portion of material information, deterioration information, and usage environment information of each reference battery cell corresponding to each image included in the feature image set is stored in association with the reference feature information.

[0014] In addition, in one embodiment of the present disclosure, a battery analysis method may further include a step of obtaining a plurality of target EIS information by performing the EIS at a plurality of different charging states of a target battery cell, obtaining target DRT information by calculating the DRT for the target EIS information, and generating a target image for the target battery cell based on the target DRT information; and a step of estimating feature information of the target battery cell based on the feature image set and the target image.

[0015] In addition, in one embodiment of the present disclosure, the step of estimating the feature information of the target battery cell may include a battery analysis method including the step of calculating the similarity between the target image and images included in the feature image set, and then estimating reference feature information associated with the image having the highest similarity as the feature information of the target battery cell.

[0016] In addition, in one embodiment of the present disclosure, the step of estimating the feature information of the target battery cell may include a battery analysis method in which the feature image set and reference feature information corresponding to each image included therein are input as learning data and correct answer information therefor, the target image is input to a trained artificial neural network, and the artificial neural network processes the target image and estimates the output feature information as the feature information of the target battery cell.

[0017] In addition, in one embodiment of the present disclosure, the step of obtaining EIS information for each of the plurality of charging states may include a step of obtaining first EIS information, which is at least a part of the EIS information, which is a result of performing the EIS on the battery cell by the battery management device whenever the charging state of the battery cell increases at a constant interval during a charging process for changing the battery cell from the first charging state to the second charging state higher than the first charging state; and a step of obtaining second EIS information, which is at least a part of the EIS information, which is a result of performing the EIS on the battery cell by the battery management device whenever the charging state of the battery cell decreases at a constant interval during a discharging process for changing the battery cell from the second charging state to the first charging state.

[0018] In addition, in one embodiment of the present disclosure, the step of generating the DRT information may include a battery analysis method including a step of calculating the DRT for the plurality of first EIS information for each state of charge during a charging process of the battery cell, thereby generating the plurality of first DRT information for each state of charge for the charging process, which is at least a part of the DRT information; and a step of calculating the DRT for the plurality of second EIS information for each state of charge during a discharging process of the battery cell, thereby generating the plurality of second DRT information for each state of charge for the discharging process, which is at least a part of the DRT information.

[0019] In addition, in one embodiment of the present disclosure, a battery analysis method may be provided, further comprising a step of performing an analysis based on the feature image to estimate at least a portion of feature information including at least a portion of material information, deterioration information, and usage environment information of the battery cell.

[0020] In addition, in one embodiment of the present disclosure, the step of estimating at least a part of the characteristic information of the battery cell may include a battery analysis method including a step of determining symmetry between a first characteristic image corresponding to a charging process of the battery cell and a second characteristic image corresponding to a discharging process, which are included in the characteristic image; and a step of estimating at least a part of the material information of the battery cell based on the symmetry.

[0021] In addition, in one embodiment of the present disclosure, the step of determining the symmetry may include a battery analysis method including the step of calculating a similarity between the first feature image and the second feature image; and the step of determining that the symmetry does not exist between the first feature image and the second feature image based on the similarity being less than a threshold.

[0022] In addition, in one embodiment of the present disclosure, the step of estimating at least a part of the material information of the battery cell may include a battery analysis method including a step of determining that at least a part of the negative electrode of the battery cell includes silicon oxide based on the first feature image and the second feature image in which the symmetry does not exist.

[0023] In addition, in one embodiment of the present disclosure, the step of estimating at least a part of the material information of the battery cell may include a battery analysis method including a step of estimating the inclusion ratio of the silicon oxide so as to be inversely proportional to the similarity between the first feature image and the second feature image corresponding to the symmetry.

[0024] In addition, in one embodiment of the present disclosure, the step of estimating at least a part of the characteristic information of the battery cell may include a battery analysis method including the step of extracting a shape pattern according to a shade on the characteristic image by calculating the characteristic image; and the step of estimating at least a part of the usage environment information of the battery cell based on the shape pattern.

[0025] In addition, in one embodiment of the present disclosure, the step of estimating at least a part of the characteristic information of the battery cell may include a battery analysis method including the step of obtaining shade amount information on the characteristic image by calculating the characteristic image; and the step of estimating the deterioration information of the battery cell based on the shade amount information.

[0026] Another aspect of the present disclosure provides a server comprising: a memory for storing instructions; and a processor connected to the memory, wherein the processor is configured to acquire, from a battery management device, a plurality of EIS information for each state of charge, which is confirmed by performing electrochemical impedance spectroscopy (EIS) on a battery cell at a plurality of different states of charge, and to generate a plurality of DRT information for each state of charge by calculating a distribution of relaxation times (DRT) for the plurality of EIS information for each state of charge, and to generate a feature image corresponding to the battery cell, the feature image including information on the magnitude of impedance according to frequency calculated for each state of charge based on the plurality of DRT information for each state of charge.

[0027] Another aspect of the present disclosure may provide a computer-readable, non-transitory recording medium having recorded thereon a program for executing the battery analysis method described above on a server.

[0028] Specific details of other embodiments are included in the detailed description and drawings.

[0029] According to the proposed embodiment, one or more of the following effects can be expected.

[0030] According to an embodiment of the present specification, a feature image can be generated based on EIS and DRT results for each state of charge of a battery cell.

[0031] Additionally, according to the embodiment of the present specification, the characteristics of the target battery cell can be analyzed based on a feature image set including each feature image for each battery cell having each characteristic.

[0032] Additionally, according to an embodiment of the present specification, at least some of the material information, deterioration information, and usage environment information of the target battery cell may be analyzed based on a feature image set.

[0033] Additionally, according to the embodiments of the present specification, it is possible to non-destructively confirm whether silicon oxide is contained in the negative electrode of a battery cell by analyzing a feature image.

[0034] Additionally, according to an embodiment of the present specification, at least a portion of the degradation information and usage environment information of the battery cell may be analyzed based on the shape pattern or shading amount information of the feature image.

[0035] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0036] Figure 1 illustrates the interconnection relationship of a server that analyzes a battery according to one embodiment.

[0037] Figure 2 is a flowchart of a battery analysis method according to one embodiment.

[0038] Figures 3a and 3b illustrate examples of EIS information and DRT information obtained by calculating DRT for the EIS information.

[0039] FIG. 4 is an example drawing of a feature image generated by a battery analysis method according to one embodiment.

[0040] FIGS. 5A and 5B are diagrams illustrating the relationship between feature images and DRT information according to one embodiment.

[0041] FIG. 6 is an exemplary drawing of a feature image corresponding to a battery cell including silicon oxide in a cathode according to one embodiment.

[0042] Figure 7 illustrates a block diagram of a server according to one embodiment.

[0043] The terms used in the examples have been selected from widely used, current terms, taking into account the functions of the present disclosure. However, these terms may vary depending on the intentions of those skilled in the art, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, in which case their meanings will be described in detail in the relevant description. Therefore, the terms used in this disclosure should not be defined simply as names, but rather based on the meanings of the terms and the overall content of the present disclosure.

[0044] When a part of a specification is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise stated.

[0045] The expression "at least one of a, b, and c" described throughout the specification may encompass 'a alone', 'b alone', 'c alone', 'a and b', 'a and c', 'b and c', or 'all of a, b, and c'.

[0046] The "terminal" mentioned below may be implemented as a computer or portable terminal that can connect to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, laptop, etc. equipped with a web browser, and the portable terminal may include, for example, a wireless communication device that guarantees portability and mobility, and may include all types of handheld-based wireless communication devices such as communication-based terminals such as IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution), smartphones, tablet PCs, etc.

[0047] Below, embodiments of the present disclosure are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein.

[0048] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0049] Figure 1 illustrates the interconnection relationship of a server that analyzes a battery according to one embodiment.

[0050] Referring to FIG. 1, the server (100) can operate in conjunction with a battery management device (200) that manages battery cells (300). Meanwhile, FIG. 1 only illustrates components related to the present embodiment. Therefore, those skilled in the art will understand that, in addition to the components illustrated in FIG. 1, other general-purpose components may be included.

[0051] The server (100) is a device that configures and provides various information. The server (100) may provide the configured information as a web page or application screen, or may provide the information in a form that can be displayed as a web page or application screen on a receiving terminal.

[0052] The battery management device (200) may include one or more sensors for measuring parameters such as current, voltage, and temperature of the battery cell (300), and may include a memory and a processor (not shown) for various operations. That is, the battery management device (200) operates based on the memory and the processor similarly to the server (100), but additionally includes a sensor to measure and calculate the parameters of the battery cell (300). According to one embodiment, the battery management device (200) may perform electrochemical impedance spectroscopy (EIS) by applying an external AC power source, i.e., an AC voltage or an AC current, to the battery cell (300) at various frequencies and measuring the current or voltage flowing accordingly. In addition, such EIS may be performed at a constant charge state interval, at a plurality of designated charge state intervals, or at arbitrary charge state intervals while charging and discharging the battery. For example, when performing EIS at regular intervals of charge state, EIS may be performed whenever SoC increases by 1% until SoC reaches 99% from 1%, and EIS may be performed whenever SoC decreases by 1% until SoC reaches 1% from 99% while discharging again. In this way, the battery management device (200) may perform EIS in conjunction with the charging and discharging process.

[0053] Here, the server (100) and the battery management device (200) may be completely separate and independent entities, or they may exist only conceptually separated within a single device or system. That is, a single computing device equipped with a control function for battery cells may perform both the functions of the server (100) and the battery management device (200) described below, and therefore, such an embodiment is also considered to fall within the scope of the present disclosure.

[0054] The battery cell (300) may be one of various secondary batteries. For example, it may be any of various next-generation secondary batteries, such as lithium-ion batteries, as well as conventionally widely used secondary batteries, such as lithium-sulfur batteries, lithium-metal batteries, all-solid-state batteries, metal-air batteries, aluminum-ion batteries, potassium-ion batteries, or zinc-air batteries. According to one embodiment, as will be described later, the battery cell (300) may be a lithium-ion battery, which may or may not include silicon oxide in the negative electrode. Furthermore, according to one embodiment, the battery analysis method of the present disclosure may be performed to determine whether silicon oxide is included in the negative electrode of the lithium-ion battery. Of course, the configuration in which the battery cell (300) is a lithium-ion battery and whether silicon oxide is included in the negative electrode through the analysis method of the present disclosure is merely an example. That is, the battery cell (300) may be a secondary battery of various types, and through the analysis method of the present disclosure to be described later, it is possible to check the material information of the positive electrode or electrolyte, whether the negative electrode contains components other than silicon oxide, and the usage environment information or deterioration information of the battery.

[0055] Hereinafter, a battery analysis method according to one embodiment of the present disclosure will be described with reference to FIG. 2.

[0056] Figure 2 is a flowchart of a battery analysis method according to one embodiment.

[0057] Referring to FIG. 2, in step S210, the server (100) can acquire a plurality of EIS information for each state of charge, which is confirmed by performing EIS at multiple different states of charge for the battery cell (300). In step S220, the server (100) can generate a plurality of DRT information for each state of charge by calculating a distribution of relaxation times (DRT) for the EIS information for each state of charge. In step S230, the server (100) can generate a feature image corresponding to the battery cell (300), which includes information on the size of impedance according to frequency, calculated for each state of charge, based on the DRT information for each state of charge.

[0058] For easy understanding of the present disclosure, EIS and DRT information will be described with reference to FIG. 3.

[0059] Figures 3a and 3b illustrate examples of EIS information and DRT information calculated based on the EIS information. Referring to Figure 3a, when performing EIS on a battery cell, the EIS information can be confirmed as a Nyquist plot in which the magnitude of the impedance for each frequency is expressed by dividing it into the real and imaginary axes. In addition, when calculating the DRT based on the Nyquist plot derived as in Figure 3a, the magnitude of the impedance for each frequency in a logarithmic scale can be derived as DRT information, as in Figure 3b.

[0060] The EIS analysis or EIS measurement disclosed in this document may refer to a process of performing electrochemical impedance spectroscopy on a battery cell, and the EIS information or EIS data may refer to information indicating an impedance value according to frequency for the battery cell. The EIS information may refer to impedance information that can be expressed as a Nyquist plot. The DRT analysis disclosed in this document may refer to an analysis technique for confirming an impedance value according to each frequency for EIS information, and the DRT information may be a distribution graph that converts the impedance information into a distribution for relaxation time. In other words, the DRT information may refer to a graph indicating a distribution of impedance values ​​according to frequency.

[0061] Below, we will explain each step in more detail.

[0062] First, the server (100) can obtain EIS information for each of a plurality of charging states for the battery cell (300). Here, such EIS information can be obtained from the battery management device (200). As described above, the battery management device (200) can measure EIS information by performing EIS at each of a plurality of different charging states while charging and discharging the battery cell (300). For example, the battery management device (200) can perform EIS at each charging state at regular intervals, or can perform EIS at a plurality of designated charging states. The server (100) can obtain such EIS information from the battery management device (200).

[0063] According to one embodiment, the EIS information may include first EIS information, which is a result of performing EIS on the battery cell (300) in a plurality of different charge states when the state of charge of the battery cell (300) increases during a charging process that brings the battery cell (300) from a first charge state to a second charge state that is higher than the first charge state, and second EIS information, which is a result of performing EIS on the battery cell (300) in a plurality of different charge states when the state of charge of the battery cell (300) decreases during a discharging process that brings the battery cell (300) from the second charge state to the first charge state. That is, the battery management device (200) may measure the first EIS information during a charging process and the second EIS information during a discharging process, and the server (100) may obtain EIS information including the first and second EIS information from the battery management device (200). As described above, during the charging and discharging process, EIS may be performed every time the state of charge increases and decreases at regular intervals, or EIS may be performed every predetermined number of charge states, or EIS may be performed every arbitrary state of charge.

[0064] Thereafter, the server (100) can generate a plurality of DRT information for each state of charge by calculating a DRT for the EIS information obtained in this manner. At this time, first DRT information and second DRT information can be generated for first and second EIS information corresponding to the charging process and the discharging process, respectively. Specifically, the server (100) can generate a plurality of first DRT information for each state of charge for the charging process, which is at least a part of the DRT information, by calculating a DRT for the plurality of first EIS information for each state of charge during the charging process of the battery cell (300). In addition, the server (100) can generate a plurality of second DRT information for each state of charge for the charging process, which is at least a part of the DRT information, by calculating a DRT for the plurality of second EIS information for each state of charge during the discharging process of the battery cell (300).

[0065] Thereafter, the server (100) can generate a feature image corresponding to the battery cell, which includes information on the magnitude of impedance according to frequency calculated for each of the plurality of charging states, based on the DRT information for each of the plurality of charging states. Here, the feature image can be in the form of a two-dimensional image in which the first axis represents frequency, the second axis represents the charging state, and the degree of shading of each pixel is determined based on the information on the magnitude of impedance calculated for each frequency and each of the plurality of charging states.

[0066] Here, the magnitude information of the impedance may be expressed as the degree of shading of each pixel as described above, but according to one embodiment, it may also be expressed as the height on a three-dimensional image. That is, in this case, the feature image will be in a three-dimensional form, and can be generated by expressing the value of the third axis, which is perpendicular to the plane of the two-dimensional image in which the first axis is frequency and the second axis is charge status, as the magnitude information of the impedance.

[0067] According to one embodiment, the server (100) may generate a first feature image and a second feature image by calculating first DRT information corresponding to a charging process and second DRT information corresponding to a discharging process, respectively. Specifically, the server (100) may confirm first magnitude information of impedance according to frequency for each of a plurality of charging states for the charging process based on the first DRT information for each of a plurality of charging states for the charging process of the battery cell (300), and may generate a first feature image for the charging process, which is at least a part of the feature image, based on the first magnitude information. In addition, the server (100) may confirm second magnitude information of impedance according to frequency for each of a plurality of charging states for the discharging process based on the second DRT information for each of a plurality of charging states for the discharging process, and may generate a second feature image for the discharging process, which is at least a part of the feature image, based on the second magnitude information.

[0068] According to one embodiment, the feature image may include first and second feature images symmetrically arranged around a reference axis parallel to the first axis. That is, the feature image may be generated so that the magnitude of the impedance expressed in shades can be easily confirmed for the charging process and the discharging process for the same frequency.

[0069] Refer to Fig. 4 to examine an example of a feature image generated as described above.

[0070] FIG. 4 is an example drawing of a feature image generated by a battery analysis method according to one embodiment.

[0071] Referring to FIG. 4, the feature image may include a first feature image (110) and a second feature image (120) in a structure that is symmetrical about a reference axis (130). However, this structure is only an example, and the feature image may include the first feature image (110) and the second feature image (120) in another manner, for example, symmetrically about a reference axis that is parallel to the second axis. Alternatively, the feature image may include the first and second feature images (110 and 120) in a non-symmetrical structure, and it will be considered that all embodiments that generate feature images according to various such aspects are included in the present disclosure.

[0072] Meanwhile, with reference to FIGS. 5a and 5b, the relationship between the first part (111) corresponding to the third charging state and the second part (112) corresponding to the fourth charging state during the charging process shown in FIG. 4 and the DRT information will be described.

[0073] FIGS. 5A and 5B are diagrams illustrating the relationship between feature images and DRT information according to one embodiment.

[0074] First, Fig. 5a may be a part of DRT information corresponding to the third charging state during the charging process. Comparing Fig. 5a with the first part (111) of Fig. 3, it can be confirmed that in the part corresponding to the same frequency, the part where the impedance size is shown large in Fig. 5a is shown in bold in the first part (111) of Fig. 4. Similarly, Fig. 5b may be a part of DRT information corresponding to the fourth charging state during the charging process. Comparing Fig. 5b with the second part (112) of Fig. 3, it can be confirmed that in the part corresponding to the same frequency, the part where the impedance size is shown large in Fig. 5b is shown in bold in the second part (112) of Fig. 4.

[0075] According to one embodiment, as confirmed by comparing FIGS. 5a and 5b with the first and second parts (111 and 112) of FIG. 4, the server (100) may generate partial images for each charging state by determining the degree of shading using the magnitude information of impedance for each frequency indicated by the DRT information for each charging state, and may generate first and second feature images by sequentially stacking them according to the charging state values.

[0076] The feature images generated in this way may include unique patterns depending on the characteristics of each battery cell. That is, for example, battery cells with different positive or negative electrode materials may have distinguishable patterns in their generated feature images, and battery cells used in high-temperature and low-temperature environments may also have different patterns in their feature images. Furthermore, battery cells with identical characteristics may have somewhat similar patterns in their feature images. The characteristics of a battery cell can be estimated based on feature images having such characteristics. One related embodiment, a first embodiment, will be described below.

[0077] According to one embodiment, the server (100) may generate a feature image for a battery cell (300) as described above and store the same in a database as at least a portion of a feature image set. In addition, the server (100) may perform a similar operation on a plurality of different reference battery cells to generate a feature image for each of the reference battery cells and store the same in a database as at least a portion of a feature image set.

[0078] According to one embodiment, each image included in the feature image set may be stored in association with at least a portion of reference feature information including at least a portion of material information, degradation information, and usage environment information of each corresponding reference battery cell. That is, the server (100) may calculate a DRT based on the EIS result of each reference battery cell typed by reference feature information, generate feature images using the DRT, and store them in association with the reference feature information of each reference battery cell. For example, an administrator may prepare reference battery cells each having their own characteristics, generate feature images for these through the server (100), and construct a feature image set.

[0079] In the above description, examples have been given of the reference characteristic information including material information, degradation information, and usage environment information, but a person skilled in the art will understand that the reference characteristic information may include various characteristics related to a battery cell that are not limited thereto.

[0080] According to one embodiment, after such a feature image set is constructed, the server (100) can estimate feature information of the target battery cell based on the feature image set. That is, similar to the process described above, when a plurality of target EIS pieces of information are generated by performing EIS at multiple different charging states of the target battery cell during the charging and discharging processes by the battery management device, the server (100) can obtain the EIS pieces and calculate the DRT to calculate target DRT information. Thereafter, the server (100) can generate a target image for the target battery cell based on the target DRT information. Based on the target image and feature image set generated in this way, the server (100) can estimate feature information of the target battery cell.

[0081] Specifically, the server (100) can calculate the similarity between the target image and the images included in the feature image set. Thereafter, the reference feature information associated with the image with the highest similarity can be estimated as the feature information of the target battery cell. As described above, since each battery cell has its own unique feature image according to its characteristics, the target battery cell can be estimated to have similar features to the reference battery cell corresponding to the image with the highest similarity among the images included in the feature image set.

[0082] As a modified embodiment of the embodiment of estimating feature information of a target battery cell based on the similarity with images included in the feature image set as described above, an embodiment using an artificial neural network will be described. That is, according to one embodiment, the server (100) can estimate feature information of a target battery cell by loading an artificial neural network trained based on the feature image set. The artificial neural network can be trained using the feature image set and reference feature information associated with each image included therein as learning data and ground truth information therefor. The artificial neural network can be implemented through a widely known convolutional neural network (CNN) or various methods used for image processing. When the artificial neural network is implemented by a CNN, the artificial neural network can generate learning output information by calculating each image included in the feature image set as learning data, compare the learning output information with reference feature information as the ground truth information, generate a loss, and then backpropagate the loss to learn at least some of the parameters included in the artificial neural network. An artificial neural network trained in this way can, after calculating a target image, output feature information that the target battery cell is estimated to have.

[0083] Next, we will describe a second embodiment of estimating the features of a battery cell based on a feature image.

[0084] According to one embodiment, the server (100) may estimate at least a portion of the characteristic information of the battery cell (300) by performing an analysis based on a characteristic image. The characteristic information may include at least a portion of the material information, deterioration information, and usage environment information of the battery cell (300). Hereinafter, examples of estimating the material information, deterioration information, and usage environment information will be described.

[0085] First, the server (100) can determine the symmetry based on the maximum SoC between the first feature image corresponding to the charging process of the battery cell (300) and the second feature image corresponding to the discharging process included in the feature image. Here, the determination of the symmetry can be made based on the similarity when the first feature image and the second feature image are compared at the same scale in terms of the state of charge and frequency. That is, the first axis of the first and second feature images is a frequency of a logarithmic scale, the second axis is a state of charge, and the first point of the second axis is a state where SoC is 0% and the second point is a state where SoC is 100%, and then the similarity of the first and second feature images can be calculated after the same scale. When the similarity is greater than a threshold, the server (100) can determine that there is symmetry between the first and second feature images constituting the feature image. If the similarity is less than the threshold, the server (100) may determine that there is no symmetry in the feature image. Here, the similarity may correspond to the similarity between the first and second feature images for the charge / discharge process of a lithium ion battery that does not contain any silicon oxide in the negative electrode, and the similarity between such images may be calculated by applying various algorithms known in the art. For example, the shade values ​​of each coordinate of the first and second feature images may be compared to calculate the difference value for each coordinate, and the similarity may be calculated inversely proportional to the sum of the difference values ​​for all coordinates.

[0086] At this time, the server (100) can determine that the battery cell (300) does not include silicon oxide in the negative electrode if symmetry exists. If silicon oxide is included in the negative electrode of a lithium ion battery, hysteresis occurs in the electrode state during the charge and discharge process due to the characteristic reaction of silicon oxide in the negative electrode. Therefore, if silicon oxide is included in the negative electrode, the first and second characteristic images for the charging process and the discharging process may be different, and if silicon oxide is not included in the negative electrode, the first and second characteristic images may be the same because the hysteresis does not occur. Using such characteristics, it is possible to determine whether the negative electrode includes silicon oxide based on the characteristic image. In order to examine such an embodiment, reference will be made again to FIG. 4 and FIG. 6 described above.

[0087] Referring again to the aforementioned FIG. 4, it can be confirmed that the first feature image (110) and the second feature image (120) are symmetrical with respect to the reference axis (130). The calculation of the similarity described above can be performed after flipping either the first feature image (110) or the second feature image (120) upside down with respect to the reference axis (130). When flipping either one upside down with respect to the reference axis (130) in this way, the shades of the corresponding coordinates of the first feature image (110) and the second feature image (120) represent the impedance size for the same charge state and frequency, so it can be seen that the first feature image (110) and the second feature image (120) are adjusted to the same scale and the similarity is calculated as described above. As shown in FIG. 4, when the first characteristic image (110) and the second characteristic image (120) are symmetrical, the server (100) can determine that the battery cell (300) does not include silicon oxide in the negative electrode. In addition, referring to FIG. 4, the symmetry mentioned in the present disclosure can also be understood as the symmetry of the first characteristic image (110) and the second characteristic image (120) centered on the reference axis (130).

[0088] FIG. 6 is an exemplary drawing of a feature image corresponding to a battery cell including silicon oxide in a cathode according to one embodiment.

[0089] FIG. 6 is an example drawing of a feature image corresponding to a battery cell including silicon oxide in the negative electrode, and it can be confirmed that the first feature image (110) and the second feature image (120) are asymmetrical with respect to the reference axis (130). As described above, when silicon oxide is included in the negative electrode, the charge / discharge process is not symmetrical due to the hysteresis phenomenon, and thus, the feature images may also be derived asymmetrically with respect to the reference axis (130), with the first feature image (110) and the second feature image (120). When the first and second feature images (110 and 120) are determined to be asymmetrical as shown in FIG. 6, the server (100) may determine that the battery cell (300) includes silicon oxide in the negative electrode.

[0090] In one embodiment, the server (100) can estimate the proportion of silicon oxide contained in the cathode based on the similarity. The more silicon oxide contained in the cathode, the greater the hysteresis phenomenon, which can lead to more asymmetrical charging and discharging processes. Since the more asymmetrical the charging and discharging processes, the lower the calculated similarity between the first and second feature images, the server (100) can estimate the proportion of silicon oxide contained in the cathode inversely proportional to the similarity.

[0091] The embodiments described above may be applicable to cases where the cathode includes not only silicon oxide but also other silicon-containing compounds, such as silicon carbide, silicon nitride, or silicon alloys. Alternatively, the embodiments may be applicable to cases where any component that induces a hysteresis phenomenon is included in at least a portion of the anode, cathode, or electrolyte.

[0092] Meanwhile, according to one embodiment, the server (100) may extract a shape pattern according to a shade on the feature image by calculating a feature image. Thereafter, the server (100) may estimate at least a portion of the usage environment information of the battery cell (300) based on the shape pattern. For example, a battery cell that has been continuously used in a low or high temperature location may exhibit a characteristic shape pattern on the feature image, such as, for example, a straight line at a specific frequency or a curved shape that slopes upward or downward. Alternatively, a characteristic shape pattern may also exist when the battery cell has been frequently rapid-charged or when it is installed in a vehicle, etc., and charging and discharging are alternated at short time intervals due to regenerative braking, etc. Based on such a shape pattern, the server (100) may be able to estimate at least a portion of the usage environment information of the battery cell (300). In addition, according to one embodiment, the server (100) may also estimate at least a portion of the material information of the battery cell (300) based on the shape pattern. For example, the server (100) may compare the shape pattern appearing on the feature image with a shape pattern characteristic of a specific material included in the anode or another specific material included in the electrolyte, and if the shape pattern is similar, it may be estimated that the specific materials are included in the anode or the electrolyte.

[0093] In addition, according to one embodiment, the server (100) may obtain information on the amount of shading on the feature image by calculating the feature image. Thereafter, the server (100) may estimate the degradation information of the battery cell (300) based on the shading amount information. If the battery cell (300) is used for a long time and has a high degree of degradation, the internal resistance increases. As the internal resistance increases, the magnitude of the impedance increases, and accordingly, the shading of each coordinate of the feature image described above is expressed darker. Therefore, in the case of a battery cell (300) that has been used for a long time, the overall amount of shading on the feature image may be seen to be greater than that of a battery cell that has been used less. Accordingly, the server (100) may estimate the degradation information based on the shading amount information such that the amount of shading is proportional to the degree of degradation. Through the above method, the feature information of the target battery cell can be non-destructively and accurately estimated.

[0094] Figure 7 illustrates a block diagram of a server according to one embodiment.

[0095] According to one embodiment, the server (100) may include a memory (101) and a processor (102). The server (100) illustrated in FIG. 7 only illustrates components related to the present embodiment. Therefore, those skilled in the art will understand that, in addition to the components illustrated in FIG. 7, other general-purpose components may be included. In one embodiment, the processor (102) may be included in a controller.

[0096] The processor (102) can control the overall operation of the server (100) and process data and signals. The processor (102) can be composed of at least one hardware unit. In addition, the processor (102) can operate by one or more software modules generated by executing program codes stored in the memory (101). The processor (102) can include a memory, and the processor (102) can control the overall operation of the server (100) and process data and signals by executing the program codes stored in the memory.

[0097] The processor (102) may be configured to acquire, from a battery management device, EIS information for each of a plurality of different charging states, which is confirmed by performing electrochemical impedance spectroscopy (EIS) on the battery cell, and to generate DRT information for each of the plurality of charging states by calculating a distribution of relaxation times (DRT) for the EIS information for each of the plurality of charging states, and to generate a feature image corresponding to the battery cell, which includes information on the magnitude of impedance according to frequency calculated for each of the plurality of charging states based on the DRT information for each of the plurality of charging states.

[0098] Depending on the embodiment, the server (100) may additionally include a transceiver for performing wired / wireless communication. The server (100) may communicate with an external electronic device (e.g., a battery management device (200)) using the transceiver. The external electronic device may be a terminal or a server. In addition, the communication technologies used by the transceiver may include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth (Bluetooth), RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), etc.

[0099] The server according to the above-described embodiments may include a processor, a memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with an external device, and a user interface device such as a touch panel, a key, a button, etc. Methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable codes or program instructions executable on the processor. Here, the computer-readable recording medium includes a magnetic storage medium (e.g., read-only memory (ROM), random-access memory (RAM), floppy disk, hard disk, etc.) and an optical reading medium (e.g., CD-ROM, DVD: Digital Versatile Disc)). The computer-readable recording medium may be distributed to computer systems connected to a network, so that the computer-readable code may be stored and executed in a distributed manner. The medium may be readable by a computer, stored in a memory, and executed by a processor.

[0100] The present embodiment may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various hardware and / or software configurations that perform specific functions. For example, the embodiment may employ direct circuit configurations such as memory, processing, logic, look-up tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. Similarly, the present embodiment may be implemented in a programming or scripting language such as C, C++, Java, assembler, etc., including various algorithms implemented as a combination of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms that execute on one or more processors. Furthermore, the present embodiment may employ conventional techniques for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism," "element," "means," and "composition" can be used broadly and are not limited to mechanical or physical structures. These terms can also encompass a series of software routines, such as those associated with a processor.

[0101] The above-described embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

1. In a battery analysis method performed on a server, A step of obtaining electrochemical impedance spectroscopy (EIS) information for each of a plurality of different states of charge for a battery cell, the EIS information being confirmed by performing the EIS at each of the plurality of different states of charge; A step of generating DRT information for each of the plurality of charging states by calculating a distribution of relaxation times (DRT) for the EIS information for each of the plurality of charging states; and A battery analysis method, comprising the step of generating a feature image corresponding to the battery cell, the feature image including information on the size of impedance according to frequency calculated for each of the plurality of charging states, based on the DRT information for each of the plurality of charging states.

2. In paragraph 1, The steps for generating the above feature image are: A battery analysis method, comprising the step of generating a feature image in the form of a two-dimensional image, wherein a first axis is the frequency, a second axis is the charge state, and the shading degree of each pixel is determined based on the magnitude information of the impedance calculated for each of the frequencies and the plurality of charge states, based on the DRT information for each of the plurality of charge states.

3. In paragraph 2, The steps for generating the above feature image are: A step of checking first size information of impedance according to the frequency for each of the plurality of charging states for the charging process based on the first DRT information for each of the plurality of charging states during the charging process of the battery cell, and generating a first feature image for the charging process, which is at least a part of the feature images, based on the first size information; and A battery analysis method, comprising: a step of confirming second size information of impedance according to the frequency for each of the plurality of charge states for the discharge process based on the second DRT information for each of the plurality of charge states during the discharge process of the battery cell; and generating a second feature image for the discharge process, which is at least a part of the feature images, based on the second size information.

4. In paragraph 3, A battery analysis method, wherein the above feature image includes the first and second feature images symmetrically centered on a reference axis parallel to the first axis.

5. In paragraph 1, A battery analysis method further comprising the step of storing a set of feature images for a plurality of reference battery cells, including the above feature images, in a database.

6. In paragraph 5, A battery analysis method, wherein the above feature image set is stored in association with at least a portion of reference feature information including at least a portion of material information, deterioration information, and usage environment information of each reference battery cell corresponding to each image included in the feature image set.

7. In paragraph 5, A step of obtaining a plurality of target EIS information by performing the EIS at different multiple charging states of the target battery cell, obtaining target DRT information by calculating the DRT for the target EIS information, and generating a target image for the target battery cell based on the target DRT information; and A battery analysis method further comprising a step of estimating feature information of the target battery cell based on the feature image set and the target image.

8. In paragraph 7, The step of estimating the characteristic information of the target battery cell is: A battery analysis method, comprising the step of calculating the similarity between the target image and images included in the feature image set, and then estimating reference feature information associated with the image having the highest similarity as the feature information of the target battery cell.

9. In paragraph 7, The step of estimating the characteristic information of the target battery cell is: A battery analysis method, wherein the feature image set and reference feature information corresponding to each image included therein are input as learning data and correct answer information therefor, the target image is input to a learned artificial neural network, and the artificial neural network processes the target image and estimates the output feature information as the feature information of the target battery cell.

10. In paragraph 1, The step of obtaining EIS information for each of the above multiple charging states is: In a charging process for changing the battery cell from a first state of charge to a second state of charge higher than the first state of charge, a step of obtaining first EIS information, which is at least a part of the EIS information, which is a result of performing EIS on the battery cell by a battery management device whenever the state of charge of the battery cell increases at a constant interval; and A battery analysis method, comprising the step of obtaining second EIS information, which is at least a part of the EIS information, which is a result of performing EIS on the battery cell by the battery management device whenever the state of charge of the battery cell decreases at a regular interval during a discharge process for changing the state of charge of the battery cell from the second state of charge to the first state of charge.

11. In paragraph 1, The steps for generating the above DRT information are: A step of calculating the DRT for the plurality of state-of-charge first EIS information during the charging process of the battery cell, and generating the plurality of state-of-charge first DRT information for the charging process, which is at least a part of the DRT information; and A battery analysis method, comprising the step of calculating the DRT for the plurality of state-of-charge second EIS information during the discharge process of the battery cell, and generating the plurality of state-of-charge second DRT information for the discharge process, which is at least a part of the DRT information.

12. In paragraph 1, A battery analysis method further comprising a step of estimating at least a portion of feature information including at least a portion of material information, deterioration information, and usage environment information of the battery cell by performing an analysis based on the feature image.

13. In paragraph 12, The step of estimating at least some of the characteristic information of the battery cell comprises: A step of determining symmetry between a first feature image corresponding to a charging process of the battery cell and a second feature image corresponding to a discharging process, included in the feature image; A battery analysis method, comprising a step of estimating at least a part of the material information of the battery cell based on the symmetry.

14. In paragraph 12, The step of judging the above symmetry is: A step of calculating the similarity between the first feature image and the second feature image; A battery analysis method, comprising a step of determining that there is no symmetry between the first feature image and the second feature image based on the similarity being less than a threshold value.

15. In paragraph 12, The step of estimating at least a part of the material information of the battery cell is: A battery analysis method, comprising a step of determining that at least a portion of the negative electrode of the battery cell includes silicon oxide based on the first feature image and the second feature image in which the symmetry does not exist.

16. In paragraph 12, The step of estimating at least a part of the material information of the battery cell is: A battery analysis method, comprising a step of estimating an inclusion ratio of the silicon oxide so as to be inversely proportional to the similarity between the first feature image and the second feature image corresponding to the symmetry.

17. In paragraph 12, The step of estimating at least some of the characteristic information of the above battery cell is: A step of extracting a shape pattern according to a shade on the feature image by calculating the feature image; and A battery analysis method, comprising a step of estimating at least a part of the usage environment information of the battery cell based on the shape pattern.

18. In paragraph 12, The step of estimating at least some of the characteristic information of the above battery cell is: A step of calculating the above feature image to obtain information on the amount of shading on the feature image; and A battery analysis method, comprising a step of estimating the degradation information of the battery cell based on the shade amount information.

19. A non-transitory computer-readable storage medium having recorded thereon a program for executing the method of any one of clauses 1 to 18 on a server.

20. On the server, memory for storing instructions; and comprising a processor connected to said memory, The above processor, For a battery cell, electrochemical impedance spectroscopy (EIS) information for each of the plurality of different charging states is obtained from the battery management device by performing EIS at each of the plurality of different charging states. By calculating the distribution of relaxation times (DRT) for the EIS information for each of the plurality of charge states, the DRT information for each of the plurality of charge states is generated, A server configured to generate a feature image corresponding to the battery cell, the feature image including information on the size of impedance according to frequency, calculated for each of the plurality of charging states, based on the DRT information for each of the plurality of charging states.

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