Battery diagnosis device and method therfor

Abstract

A battery diagnosis device according to an embodiment of the present document comprises: a memory on which one or more instructions are stored; and a processor for executing the one or more instructions, wherein the processor can acquire first impedance data related to a battery, identify a specified interval within the entire interval of the first impedance data on the basis of at least one feature point of the first impedance data, and repeatedly acquire second impedance data related to the battery in the specified interval.

Description

Battery diagnostic device and method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority based on Korean Patent Application No. 10-2024-0187512 filed on December 16, 2024, and includes all contents disclosed in the document of said patent application as part of this specification.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery diagnostic device and a method thereof.

[0005] Recently, active research and development on secondary batteries has been underway. Here, secondary batteries are rechargeable batteries that can be interpreted to encompass conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. With their scope of application expanding to include power sources for electric vehicles, they are garnering attention as a next-generation energy storage medium.

[0006] Batteries are electrochemical energy storage devices and are essential components of modern electronic devices, electric vehicles, and renewable energy storage systems. To efficiently manage battery performance and lifespan, technology capable of accurately evaluating and diagnosing the battery's condition is required. To this end, EIS is widely utilized as an effective tool for non-invasively evaluating the internal state of a battery. EIS applies an electrical signal (e.g., AC signal) within a specific frequency band to the battery, acquires impedance data through the resulting electrochemical reaction, and analyzes this data to determine the battery's internal resistance, State of Charge (SOC), and State of Health (SOH). Consequently, various studies are being conducted to accurately acquire impedance data.

[0007] According to the embodiments disclosed in this document, a battery diagnostic device and method are provided that identify feature points in impedance data and repeatedly perform diagnosis by setting a designated interval centered on the feature points.

[0008] According to the embodiments disclosed in this document, a battery diagnostic device and a method are provided that improve the reliability of data by performing filtering to remove noise from impedance data.

[0009] According to the embodiments disclosed in this document, the present invention aims to provide a battery diagnostic device and a method that improve the precision of impedance data for a specific frequency band and accurately diagnose the condition of a battery by performing repetitive diagnosis in a designated section.

[0010] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below.

[0011] A battery diagnostic device according to one embodiment of the present document includes a memory storing one or more instructions and a processor that executes said one or more instructions, wherein the processor acquires first impedance data related to a battery, identifies a designated section within the entire section of said first impedance data based on at least one feature point of said first impedance data, and can repeatedly acquire second impedance data related to the battery in said designated section.

[0012] In one embodiment, the feature point may correspond to at least one of a pole, a point where the Warbug characteristic starts, a point where the real component is at its maximum value, or any combination thereof, in a graph representing the first impedance data.

[0013] In one embodiment, the designated interval may be determined by at least one of the frequency of the electrical signal applied to the battery during the process of acquiring the first impedance data, the distribution of noise included in the first impedance data, or any combination thereof.

[0014] In one embodiment, the processor can obtain final impedance data based on filtering the first impedance data and the second impedance data.

[0015] In one embodiment, the processor may output the final impedance data based on the fact that the error rate between the first impedance data and the final impedance data is less than or equal to a specified value.

[0016] In one embodiment, the filtering may include at least one of a low-pass filter, a high-pass filter, a band-pass filter, a moving average filter, a Kalman filter, an adaptive filter, a Wiener filter, or any combination thereof.

[0017] In one embodiment, the processor may obtain at least one of the first impedance data, the second impedance data, or any combination thereof based on performing electrochemical impedance spectroscopy (EIS) on the battery.

[0018] In one embodiment, at least one of the first impedance data, the second impedance data, or any combination thereof may be represented by a Nyquist plot, a Bode plot, or at least one combination thereof.

[0019] In one embodiment, the specified interval may be related to the frequency of the electric signal when an electric signal is applied to the battery.

[0020] A battery diagnostic method according to one embodiment of the present document may include, by means of a processor, an operation of acquiring first impedance data related to a battery; by means of the processor, an operation of identifying a designated section among the entire section of the first impedance data based on at least one feature point of the first impedance data; and by means of the processor, an operation of repeatedly acquiring second impedance data related to the battery in the designated section.

[0021] In one embodiment, the feature point may correspond to at least one of a pole, a point where the Warbug characteristic starts, a point where the real component is at its maximum value, or any combination thereof, in a graph representing the first impedance data.

[0022] In one embodiment, the designated interval may be determined by at least one of the frequency of the electrical signal applied to the battery during the process of acquiring the first impedance data, the distribution of noise included in the first impedance data, or any combination thereof.

[0023] The battery diagnostic method according to one embodiment may include an operation of obtaining final impedance data based on filtering performed on the first impedance data and the second impedance data by the processor.

[0024] The battery diagnostic method according to one embodiment may include an operation of outputting the final impedance data by the processor based on the fact that the error rate between the first impedance data and the final impedance data is less than or equal to a specified value.

[0025] In one embodiment, the filtering may include at least one of a low-pass filter, a high-pass filter, a band-pass filter, a moving average filter, a Kalman filter, an adaptive filter, a Wiener filter, or any combination thereof.

[0026] This technology can identify feature points in impedance data and repeatedly perform diagnosis by setting a designated interval centered on the corresponding feature points.

[0027] In addition, this technology can improve the reliability of the data by performing filtering to remove noise from the impedance data.

[0028] In addition, by performing repetitive diagnosis in a designated section, this technology can improve the precision of impedance data for a specific frequency band and accurately diagnose the condition of the battery.

[0029] In addition, various effects that can be identified directly or indirectly through this document may be provided.

[0030] FIG. 1 is a block diagram showing a battery pack in a battery diagnostic device and battery diagnostic method according to one embodiment of the present document.

[0031] FIG. 2 illustrates an example of a block diagram showing the configuration of a battery diagnostic device according to one embodiment of the present document.

[0032] FIG. 3 illustrates an example of impedance data in one embodiment of the present document.

[0033] FIG. 4 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document.

[0034] FIG. 5 is a block diagram showing the hardware configuration of a computing system for performing a battery diagnosis method in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0035] Some embodiments disclosed herein are described below with reference to the various embodiments of the accompanying drawings. However, this is not intended to limit the technology to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives to embodiments of the technology.

[0036] It should be noted that when assigning reference numerals to the components of each drawing, the same components are assigned the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the various embodiments disclosed in this document, if it is determined that a detailed description of related known configurations or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted. The singular form of a noun corresponding to an item may include one or more items unless the relevant context clearly indicates otherwise.

[0037] In describing the components of the embodiments of this document, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components and do not limit the essence, order, or sequence of the components. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0038] Additionally, in this disclosure, expressions of "greater than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled; however, this is merely for the purpose of expressing an example and does not exclude descriptions of "greater than" or "less than." Conditions described as "greater than" may be replaced with "greater than," conditions described as "less than" may be replaced with "less than," and conditions described as "greater than and less than" may be replaced with "greater than and less than." Furthermore, "A" to "B" below refer to at least one of the elements from A (including A) to B (including B).

[0039] In this document, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "at least one of A, B, or C" may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof.

[0040] In this document, where any component (e.g., 1) is referred to as being “connected,” “coupled,” or “joined” to another component (e.g., 2), with or without the terms “functionally” or “communicationally,” or where it is referred to as “coupled” or “connected,” it means that the component may be connected to the other component directly (e.g., via a wire), wirelessly, or through a third component.

[0041] According to one embodiment, the method according to the various embodiments disclosed herein may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) 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 created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0042] According to various embodiments, each component (e.g., module or program) of the described components may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as they were performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically; one or more of the operations may be executed in a different order; may be omitted; or one or more other operations may be added.

[0043] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 5.

[0044] FIG. 1 is a block diagram showing a battery pack in a battery diagnostic device and battery diagnostic method according to one embodiment of the present document.

[0045] Referring to FIG. 1, the battery pack (1) may include a battery unit (12), a sensor unit (14), a switching unit (16), and a battery management system (BMS) (20). At this time, the battery pack (1) may be equipped with a plurality of battery units (12), sensor units (14), switching units (16), and battery management systems (20).

[0046] According to one embodiment, the battery unit (12) can supply power to a target device (not shown). To this end, the battery unit (12) may be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (1). For example, the target device may be an electric vehicle (EV), but is not limited thereto.

[0047] According to one embodiment, the battery unit (12) may include at least one rechargeable battery cell (10). Here, the battery cell (10) may be a basic unit of a battery cell capable of charging and discharging electrical energy. For example, the battery cell (10) may be a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion polymer) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-hydrogen (Ni-MH) battery, etc., but is not limited thereto.

[0048] According to one embodiment, a plurality of battery units (12) may be connected in series or in parallel. For example, a battery unit (12) may be a battery module, a battery bank, or a set of battery cells (cell-to-pack structure).

[0049] According to one embodiment, the sensor unit (14) can obtain information related to the battery unit (12). According to one embodiment, the sensor unit (14) can obtain values ​​(or information) related to the state of each of the battery unit (12) or battery cells (10). In one embodiment, the values ​​related to the state may include one or more values ​​for the voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature of the battery cell, or a combination thereof.

[0050] According to one embodiment, the sensor unit (14) can provide information of each of the plurality of battery units (12) to the battery management system (20).

[0051] According to one embodiment, the switching unit (16) may include an element for controlling the current flow for charging or discharging the battery unit (12). For example, the switching unit (16) may include at least one relay and / or magnetic contactor, etc., depending on the specifications of the battery pack (1).

[0052] According to one embodiment, a battery management system (BMS) (20) can monitor the voltage, current, temperature, etc. of a battery pack (1) and control or manage the battery pack (1) to prevent overcharging and over-discharging. For example, the battery management system (20) may include a plurality of terminals as an interface for receiving values ​​of the various parameters described above, and a circuit connected to these terminals to perform processing of the received values. Additionally, the battery management system (20) may control a sensor unit (14) and / or a switching unit (16). For example, the battery management system (20) may be connected to a plurality of battery units (12) to monitor the status of each of the plurality of battery units (12) and control the ON / OFF of relays or contactors.

[0053] According to one embodiment, the operation of the battery management system (20) can be performed by a battery management system (BMS) in the vehicle, as well as by various devices such as a server, cloud, charger, or charger / discharger.

[0054] The upper controller (2) can transmit control signals for a plurality of battery units (12) to the battery management system (20). Accordingly, the operation of the battery management system (20) can be controlled based on the signals applied from the upper controller (2).

[0055] According to one embodiment, the battery management system (20) may include the battery diagnostic device (200) of FIG. 2. According to another embodiment, the battery management system (20) may be a different system from the battery diagnostic device (200) of FIG. 2. That is, the battery diagnostic device (200) of FIG. 2 may be included in the battery pack (1) or may be configured as another device outside the battery pack (1). For convenience of explanation, the following description assumes that the battery diagnostic device (200) is configured as another device outside the battery pack (1). Furthermore, the operation of the battery diagnostic device (200) below may be performed by a battery management system (BMS) within the vehicle, as well as by various devices such as a server, cloud, charger, or charger / discharger.

[0056] FIG. 2 illustrates an example of a block diagram showing the configuration of a battery diagnostic device according to one embodiment of the present document.

[0057] Referring to FIG. 2, a battery diagnostic device (200) according to one embodiment may include a processor (210) and a memory (220). The processor (210) and the memory (220) may be electrically and / or operably coupled with each other by an electronic device including a communication bus.

[0058] In the following, the hardware being operatively coupled may include direct and / or indirect connections between the hardware being established via wired and / or wireless connections so that the second hardware is controlled by the first hardware among the hardware.

[0059] Although the hardware is illustrated in different blocks, the embodiment is not limited thereto. For example, some of the hardware in FIG. 2 may be included in a single integrated circuit including a system-on-a-chip (SoC). The type and / or number of hardware included in the battery diagnostic device (200) is not limited to that illustrated in FIG. 2. For example, the battery diagnostic device (200) may include only some of the hardware illustrated in FIG. 2.

[0060] A battery diagnostic device (200) according to one embodiment may include hardware for processing data based on one or more instructions. The hardware for processing data may include a processor (210).

[0061] For example, hardware for processing data may include an arithmetic and logic unit (ALU), a floating point unit (FPU), a field programmable gate array (FPGA), a central processing unit (CPU), and / or an application processor (AP). The processor (210) may have the structure of a single-core processor or the structure of a multi-core processor including a dual core, a quad core, a hexa core, or an octa core.

[0062] A memory (220) of a battery diagnostic device (200) according to one embodiment may include a hardware component for storing data and / or instructions that are input and / or output to a processor (210) of the battery diagnostic device (200).

[0063] For example, the memory (220) may include volatile memory including random-access memory (RAM) and / or non-volatile memory including read-only memory (ROM).

[0064] For example, volatile memory may include at least one of DRAM (dynamic RAM), SRAM (static RAM), Cache RAM, PSRAM (pseudo SRAM), or any combination thereof.

[0065] For example, non-volatile memory may include at least one of PROM (programmable ROM), EPROM (erasable PROM), EEPROM (electrically erasable PROM), flash memory, hard disk, compact disk, SSD (solid state drive), eMMC (embedded multi-media card), or any combination thereof.

[0066] For example, within the memory (220) of the battery diagnostic device (200), one or more instructions (or commands) representing operations and / or actions to be performed on data by the processor (210) of the battery diagnostic device (200) may be stored. A set of one or more instructions may be referred to as a program, firmware, operating system, process, routine, sub-routine, and / or application. Hereinafter, the statement that an application is installed within the battery diagnostic device (200) may mean that one or more instructions provided in the form of an application are stored within the memory (220), and that one or more applications are stored in an executable format (e.g., a file having an extension specified by the operating system of the battery diagnostic device (200)) by the processor (210) of the battery diagnostic device (200).

[0067] A processor (210) of a battery diagnostic device (200) according to one embodiment can acquire first impedance data related to a battery. For example, the processor (210) can acquire the first impedance data based on performing a diagnosis on the battery. For example, the diagnosis on the battery may include electrochemical impedance spectroscopy (EIS).

[0068] For example, the processor (210) can obtain first impedance data based on performing EIS on the battery.

[0069] For example, the first impedance data can be represented by at least one of a Nyquist plot, a Bode plot, or any combination thereof.

[0070] In one embodiment, the processor (210) can identify at least one feature point of the first impedance data. For example, the processor (210) can identify a designated section within the entire section of the first impedance data based on at least one feature point of the first impedance data.

[0071] For example, the characteristic point may correspond to at least one of a pole, a point where the Warburg characteristic begins, a point where the real component is at its maximum value, or any combination thereof, in a graph representing the first impedance data. For example, the Warburg characteristic is one of the phenomena that appear in an electrochemical system and may refer to an impedance element related to a diffusion-controlled reaction.

[0072] For example, the designated interval may be determined by at least one of the frequency of the electrical signal applied to the battery during the process of acquiring the first impedance data, the distribution of noise included in the first impedance data, or any combination thereof.

[0073] For example, the specified interval may be related to the frequency of the electric signal when an electric signal is applied to the battery.

[0074] In one embodiment, the processor (210) can repeatedly acquire second impedance data related to the battery in a designated interval.

[0075] For example, the processor (210) can obtain second impedance data based on repeatedly performing EIS on the battery. For example, the processor (210) can repeatedly obtain second impedance data based on repeatedly performing EIS on the battery.

[0076] For example, the processor (210) can perform filtering on the first impedance data and the second impedance data. For example, the processor (210) can merge the first impedance data and the second impedance data. For example, the processor (210) can perform filtering based on the merger of the first impedance data and the second impedance data.

[0077] For example, the processor (210) can repeatedly perform a diagnosis of the battery within a designated interval to obtain second impedance data that corrects at least a portion of the first impedance data.

[0078] In one embodiment, the processor (210) may merge the first impedance data and the second impedance data. For example, the processor (210) may obtain merged impedance data based on merging the first impedance data and the second impedance data. For example, the processor (210) may perform filtering on the merged impedance data.

[0079] For example, the filtering may include at least one of a low-pass filter, a high-pass filter, a band-pass filter, a moving average filter, a Kalman filter, an adaptive filter, a Wiener filter, or any combination thereof.

[0080] For example, the processor (210) may apply a moving average filter to the first impedance data and the second impedance data. For example, the processor (210) may remove high-frequency noise components by applying a moving average filter to the first impedance data and the second impedance data. For example, the processor (210) may reduce data variability and more clearly represent the shape of the curve in the Nyquist plot or Bode plot by applying a moving average filter to the first impedance data and the second impedance data. The processor (210) may reduce noise and obtain a result of high reliability (e.g., final impedance data) by applying a moving average filter to the first impedance data and the second impedance data.

[0081] For example, the processor (210) can obtain final impedance data based on filtering the first impedance data and the second impedance data.

[0082] For example, the processor (210) can output final impedance data based on the fact that the error rate between the first impedance data and the final impedance data is less than or equal to a specified value.

[0083] In one embodiment, the processor (210) can diagnose the state of the battery by outputting final impedance data. For example, the processor (210) can diagnose at least one of the battery's SOH (state of health), battery's SOC (state of charge), battery degradation, battery internal resistance, or any combination thereof by using the final impedance data. For example, when diagnosing battery degradation, the processor (210) can diagnose battery degradation by identifying at least one of the battery's SEI (solid electrolyte interface) layer growth, loss of electrode active material, electrolyte degradation, or any combination thereof.

[0084] For convenience of explanation, the embodiments of this document describe an example in which a battery diagnostic device (200) identifies a single feature point in the first impedance data and repeatedly acquires the second impedance data, but the embodiments are not limited thereto. For example, the processor (210) of the battery diagnostic device (200) may identify a plurality of feature points and, based on setting a plurality of designated sections using the plurality of feature points, acquire the second impedance data related to the battery in each of the plurality of designated sections.

[0085] As described above, the battery diagnostic device (200) according to one embodiment can provide the effect of reducing noise and improving the reliability of impedance data by performing repetitive diagnosis on at least a portion of the impedance data.

[0086] FIG. 3 illustrates an example of impedance data in one embodiment of the present document.

[0087] Referring to FIG. 3, a processor (210) of a battery diagnostic device (200) according to one embodiment can acquire impedance data (300) related to the battery. For example, the impedance data (300) of FIG. 3 may include the first impedance data described in FIG. 2.

[0088] For example, the impedance data (300) may include real and imaginary components. For example, the impedance data (300) may be obtained by performing a diagnosis on the battery. For example, the processor (210) may obtain the impedance data (300) based on performing EIS on the battery.

[0089] In one embodiment, the processor (210) can identify a feature point (301) in the impedance data (300). For example, the feature point (301) may include a pole. For example, the pole may include at least one of a maximum point, a minimum point, or any combination thereof. For example, the feature point (301) may correspond to a point located within a preset range from the point with the largest absolute value among points where the imaginary component of the impedance data (300) is negative, or from the point where the value corresponding to the impedance data (300) is negative, among points where the impedance data (300) has a frequency greater than the frequency of points where the Warburg impedance exists.

[0090] In FIG. 3, a pole is shown as an example of a feature point (301), but the embodiment is not limited thereto. For example, the feature point (301) may include at least one of a pole, a point where the Warburg characteristic starts, a point where the real component is at its maximum value, or any combination thereof.

[0091] For example, the processor (210) may set a specified interval (310) centered on the feature point (301). For example, when the processor (210) sets a specified interval (310) centered on the feature point (301), it may set a specified interval (310) between a first value (303) and a second value (305). For example, the first value (303) may have a minimum value of the real component in the specified interval (310), and the second value (305) may have a maximum value of the real component in the specified interval (310). For example, the specified interval (310) may be determined by at least one of the frequency of the electrical signal applied to the battery during the process of acquiring impedance data (300), the distribution of noise included in the impedance data (300), or any combination thereof.

[0092] In one embodiment, the processor (210) can repeatedly diagnose the battery using an electrical signal having a frequency corresponding to the designated section (310), based on setting a designated section (310). For example, the processor (210) can repeatedly acquire second impedance data using an electrical signal having a frequency corresponding to the designated section (310). For example, the second impedance data may refer to data with a higher resolution than the first impedance data. Because the second impedance data has a higher resolution than the first impedance data, the precision of the data is increased, and the data may refer to data that allows for more specific and accurate interpretation.

[0093] In one embodiment, the processor (210) may acquire a second impedance data obtained using a frequency corresponding to a designated interval (310) and a first impedance data including impedance data (300). For example, the processor (210) may merge the first impedance data and the second impedance data. Since the second impedance data is data with increased resolution in at least a portion of the first impedance data, at least a portion of the first impedance data may be replaced with the second impedance data.

[0094] In the embodiments of this document, for convenience of explanation, the feature point (301) and the designated section (310) are set to one, but the embodiments are not limited thereto.

[0095] For example, the processor (210) can identify a plurality of feature points including a feature point (301). The processor (210) can set designated intervals centered on each of the plurality of feature points. For example, the processor (210) can use impedance data obtained based on a diagnosis of the battery using a frequency interval corresponding to each of the designated intervals.

[0096] FIG. 4 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document.

[0097] In the following, it is assumed that the battery diagnostic device (200) of FIG. 2 performs the process of FIG. 4. Also, in the description of FIG. 4, the operation described as being performed by the device can be understood as being controlled by the processor (210) of the battery diagnostic device (200).

[0098] At least one of the operations of FIG. 4 can be performed by the battery diagnostic device (200) of FIG. 2. At least one of the operations of FIG. 4 can be controlled by the processor (210) of FIG. 2. Each of the operations of FIG. 4 can be performed sequentially, but is not necessarily performed sequentially. For example, the order of each of the operations can be changed, and at least two operations can be performed in parallel.

[0099] Referring to FIG. 4, a battery diagnostic method according to one embodiment may include, in operation S401, an operation of obtaining first impedance data related to the battery.

[0100] In operation S403, the battery diagnostic method according to one embodiment may include an operation of identifying a designated section among the entire section of the first impedance data based on at least one feature point of the first impedance data.

[0101] For example, the feature point (301) may correspond to at least one of a pole, a point where the Warburg characteristic starts, a point where the real component is at its maximum value, or any combination thereof, in a graph representing the first impedance data.

[0102] For example, the designated interval (310) may be determined by at least one of the frequency of the electrical signal applied to the battery during the process of acquiring the first impedance data, the distribution of noise included in the first impedance data, or any combination thereof.

[0103] For example, the designated interval (310) may be related to the frequency of the electric signal when applying an electric signal to the battery.

[0104] In operation S405, the battery diagnostic method according to one embodiment may include the operation of repeatedly acquiring second impedance data related to the battery in a designated interval.

[0105] For example, the battery diagnostic method may include an operation of performing filtering on the first impedance data and the second impedance data. For example, the battery diagnostic method may include an operation of obtaining final impedance data based on the filtering performed on the first impedance data and the second impedance data.

[0106] For example, filtering may include performing a low-pass filter, a high-pass filter, a band-pass filter, a moving average filter, a Kalman filter, an adaptive filter, a Wiener filter, or any combination thereof.

[0107] For example, the battery diagnostic method may include an operation of identifying an error rate between first impedance data and final impedance data. For example, the battery diagnostic method may include an operation of outputting final impedance data based on the fact that the error rate between first impedance data and final impedance data is less than or equal to a specified value.

[0108] For example, the battery diagnostic method may include an operation of obtaining at least one of first impedance data, second impedance data, or any combination thereof, based on performing EIS on the battery.

[0109] For example, at least one of the first impedance data, the second impedance data, or any combination thereof may be represented by a Nyquist plot, a Bode plot, or at least one of any combination thereof.

[0110] FIG. 5 is a block diagram showing the hardware configuration of a computing system for performing a battery diagnostic method in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0111] Referring to FIG. 5, a computing system (1100) according to one embodiment disclosed in this document may include an MCU (1110), memory (1120), an input / output I / F (1130), and a communication I / F (1140).

[0112] The MCU (1110) may be a processor that executes various programs stored in memory (1120) (e.g., battery cell data collection program, graph generation program, data analysis program, data decomposition algorithm, normalization program, battery cell diagnosis program, etc.), processes various information including characteristic data and potential variables of the battery cell through these programs, and performs the functions of the battery diagnosis device (200) shown in FIGS. 1 to 4.

[0113] The memory (1120) can store various programs such as a battery cell data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, and a battery cell diagnosis program.

[0114] These memories (1120) may be provided in multiple quantities as needed. The memories (1120) may be volatile memories or non-volatile memories. As volatile memories, the memory (1120) may use RAM, DRAM, SRAM, etc. As non-volatile memories, the memory (1120) may use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the listed memories (1120) are merely examples and are not limited to these examples.

[0115] The input / output I / F (1130) can provide an interface that enables data transmission and reception between an input device (not shown), such as a keyboard, mouse, or touch panel, an output device (not shown), and an MCU (1110).

[0116] The communication I / F (1140) is configured to transmit and receive various data with a server and may be various devices capable of supporting wired or wireless communication. For example, the battery diagnostic device (200) can transmit and receive various information, including the shape model of a battery cell, from a separately provided external server via the communication I / F (1140).

[0117] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a module that performs, for example, the functions illustrated in FIG. 2, by being recorded in memory (1120) and processed by an MCU (1110).

[0118] As described above, even though all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purposes of the embodiments disclosed in this document, all components may be selectively combined in one or more ways to operate.

[0119] Furthermore, terms such as "include," "compose," or "have" as described above, unless specifically stated otherwise, mean that the relevant component may be inherent; thus, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their contextual meanings in the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0120] The foregoing disclosure outlines the features of several embodiments to enable those skilled in the art to better understand the aspects of the present disclosure. Those skilled in the art will understand that the present disclosure can be readily used as a basis for designing or modifying other structures to perform the same purpose or achieve the same advantages as the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent configurations do not depart from the scope of the present disclosure and that various changes, substitutions, and modifications may be made in the present specification without departing from the scope of the present disclosure.

Claims

1. Memory in which one or more instructions are stored; and It includes a processor that executes one or more of the above instructions, The above processor is, Acquire first impedance data related to the battery, and Based on at least one feature point of the first impedance data, a designated section is identified within the entire section of the first impedance data, and A battery diagnostic device configured to repeatedly acquire second impedance data related to the battery in the above-mentioned designated section.

2. In Paragraph 1, The above feature points are, A battery diagnostic device corresponding to at least one of a pole, a point where the Warbug characteristic begins, a point where the real component is at its maximum value, or any combination thereof, in a graph representing the first impedance data above.

3. In Paragraph 1, The above-mentioned section is, A battery diagnostic device determined by at least one of the frequency of an electrical signal applied to the battery in the process of acquiring the first impedance data, the distribution of noise included in the first impedance data, or any combination thereof.

4. In Paragraph 1, The above processor is, A battery diagnostic device configured to obtain final impedance data based on filtering the first impedance data and the second impedance data.

5. In Paragraph 4, The above processor is, A battery diagnostic device configured to output the final impedance data based on the fact that the error rate between the first impedance data and the final impedance data is less than or equal to a specified value.

6. In Paragraph 4, The above filtering is, A battery diagnostic device comprising at least one of a low-pass filter, a high-pass filter, a band-pass filter, a moving average filter, a Kalman filter, an adaptive filter, a Wiener filter, or any combination thereof.

7. In Paragraph 1, The above processor is, A battery diagnostic device configured to obtain at least one of the first impedance data, the second impedance data, or any combination thereof, based on performing electrochemical impedance spectroscopy (EIS) on the battery.

8. In Paragraph 1, At least one of the first impedance data, the second impedance data, or any combination thereof, is A battery diagnostic device represented by at least one of a Nyquist plot, a Bode plot, or any combination thereof.

9. In Paragraph 1, The above-mentioned section is, A battery diagnostic device related to the frequency of the electrical signal when an electrical signal is applied to the battery.

10. An operation of acquiring first impedance data related to a battery by a processor; An operation of identifying a designated section among the entire section of the first impedance data based on at least one feature point of the first impedance data by the processor; and A battery diagnostic method comprising the operation of repeatedly acquiring second impedance data related to the battery in the specified interval by the above processor.

11. In Paragraph 10, The above feature points are, A battery diagnostic method corresponding to at least one of a pole, a point where the Warbug characteristic begins, a point where the real component is at its maximum value, or any combination thereof, in a graph representing the first impedance data above.

12. In Paragraph 10, The above-mentioned section is, A battery diagnostic method determined by at least one of the frequency of an electrical signal applied to the battery in the process of acquiring the first impedance data, the distribution of noise included in the first impedance data, or any combination thereof.

13. In Paragraph 10, The above battery diagnostic method is, A battery diagnostic method comprising the operation of obtaining final impedance data based on filtering the first impedance data and the second impedance data by the above processor.

14. In Paragraph 13, The above battery diagnostic method is, A battery diagnostic method comprising the operation of outputting the final impedance data based on the error rate between the first impedance data and the final impedance data being less than or equal to a specified value by the processor.

15. In Paragraph 13, The above filtering is, A battery diagnostic method comprising at least one of a low-pass filter, a high-pass filter, a band-pass filter, a moving average filter, a Kalman filter, an adaptive filter, a Wiener filter, or any combination thereof.

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