Battery diagnostic apparatus and method, battery pack containing the apparatus and energy storage system

Abstract

This invention discloses a technique for diagnosing batteries with high accuracy using a Nyquist plot. According to one aspect of the invention, a battery diagnostic device includes: an impedance measurement module configured to measure impedance based on frequency changes while applying an AC voltage to a target battery; a memory module storing impedance reference values ​​at specific frequencies; and a processor configured to generate a Nyquist plot of the impedance measurements of the target battery measured by the impedance measurement module, extract inflection points from the generated Nyquist plot, and diagnose the target battery by comparing values ​​within a predetermined frequency range based on the extracted inflection points with the impedance reference values ​​at specific frequencies stored in the memory module.

Description

Technical Field

[0001] This application claims priority to Korean Patent Application No. 10-2020-0156002, filed in Korea on November 19, 2020, the disclosure of which is incorporated herein by reference.

[0002] This disclosure relates to battery diagnostic techniques, and more specifically, to battery diagnostic techniques capable of effectively diagnosing battery condition through impedance measurements. Background Technology

[0003] Currently available rechargeable batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, and lithium-ion batteries. Among them, lithium-ion batteries have attracted much attention because they have virtually no memory effect, low discharge rate, and high energy density compared to nickel-based rechargeable batteries.

[0004] Furthermore, in recent years, rechargeable batteries have been widely used for driving or energy storage in medium and large-sized devices such as electric vehicles or energy storage systems (ESS). For this reason, interest in rechargeable batteries has further increased, and related research and development are being conducted more actively.

[0005] Lithium-ion secondary batteries primarily use lithium-based oxides and carbon materials as positive and negative electrode active materials, respectively. Additionally, a lithium-ion secondary battery includes: an electrode assembly in which positive and negative electrode plates, respectively coated with positive and negative electrode active materials, are arranged and a separator is inserted between them; and an external battery casing for hermetically housing the electrode assembly and electrolyte together.

[0006] Batteries generate electrical energy through electrochemical oxidation and reduction reactions. However, when repeatedly charged / discharged, batteries do not maintain their initial manufacturing capacity, i.e., their performance at BOL (Start of Life), and may degrade over time. Without a proper understanding of the battery's degradation state, it can be difficult to accurately predict the battery's state of charge (SOC), usable time, lifespan, replacement timing, etc. Furthermore, failure to accurately predict these aspects could potentially cause unintended harm to battery users or managers.

[0007] Furthermore, batteries are increasingly being recycled. Specifically, when the performance of battery packs installed in electric vehicles deteriorates due to a period of use, research and plans are being actively undertaken to reuse these used battery packs by installing them in other applications such as energy storage systems (ESS).

[0008] If used batteries (waste batteries) are to be recycled in this way in another or the same field, a more accurate diagnosis of the battery's condition is needed. For example, in order to use battery packs for vehicles in energy storage systems, it should be determined whether the battery pack is recyclable. To date, various technologies have been proposed for diagnosing battery cells, battery modules, battery packs, etc., but it is difficult to say that they demonstrate sufficient performance in various aspects such as accuracy and speed.

[0009] Specifically, as one of the conventional representative techniques for diagnosing batteries, there is a method using electrochemical impedance spectroscopy (EIS). In this case, a Nyquist diagram is used for the EIS measurement data, and in this process, it is necessary to extract the constants of each element for the equivalent circuit model of the battery.

[0010] However, a limitation of existing techniques using this equivalent circuit model is that a perfect equivalent circuit model cannot be achieved. Specifically, during EIS measurements, it is difficult to distinguish the inductive and resistive components of the measurement probe, as well as the contact resistance at the measurement point, from the inductive and resistive components of the battery itself. Furthermore, since the inductive component of the measurement probe has a large deviation for each measurement, it can significantly affect the high-frequency band, especially the resistive component indicated by the SEI (solid electrolyte interface).

[0011] Therefore, according to the existing technology, the problem is that it is difficult to ensure sufficient accuracy when using Nyquist plots to diagnose batteries. If the battery's condition is not accurately diagnosed before reuse, it may have adverse effects on the device or system reusing the battery and on the user. Summary of the Invention

[0012] Technical issues

[0013] This disclosure is designed to address problems in related technologies; therefore, this disclosure relates to providing devices and methods for diagnosing batteries with high accuracy using Nyquist plots.

[0014] These and other objects and advantages of this disclosure will become clear from the following detailed description and will become even more apparent from exemplary embodiments thereof. Furthermore, it will be readily understood that the objects and advantages of this disclosure can be achieved by the means and combinations thereof shown in the appended claims.

[0015] Technical solution

[0016] In one aspect of this disclosure, a battery diagnostic device is provided, comprising: an impedance measurement module configured to measure impedance based on frequency changes while applying an AC voltage to a target battery; a memory module configured to store impedance reference values ​​for each frequency; and a processor configured to generate a Nyquist plot of the impedance measurements of the target battery measured by the impedance measurement module, extract inflection points from the generated Nyquist plot, and compare values ​​within a predetermined frequency range centered on the extracted inflection points with the impedance reference values ​​for each frequency stored in the memory module to diagnose the target battery.

[0017] Here, the processor can be configured to shift the generated Nyquist plot so that the extracted inflection point becomes the origin, and in the shifted state, compare it with the impedance reference value at each frequency.

[0018] Additionally, the processor can be configured to diagnose the target battery using the magnitude and angle of its impedance measurements.

[0019] Additionally, the memory module can be configured to store impedance reference values ​​for each frequency to classify a plurality of battery classes, and the processor is configured to classify the target battery class by matching the impedance measurement of the target battery with the battery class stored in the memory module.

[0020] Additionally, the processor can be configured to extract points where the anti-charge transfer region bends due to the anti-diffusion region as inflection points in the generated Nyquist plot.

[0021] Additionally, the processor can be configured to extract the inflection point by searching the generated Nyquist plot from the low-frequency region to the high-frequency region.

[0022] In addition, the memory module can be configured to pre-store the preliminary frequency information of the inflection point.

[0023] In another aspect of this disclosure, a battery pack is provided that includes a battery diagnostic device according to this disclosure.

[0024] In another aspect of this disclosure, an energy storage system is provided, which includes a battery diagnostic device according to this disclosure.

[0025] In another aspect of this disclosure, a battery diagnostic method is provided, comprising the following steps: storing impedance reference values ​​for each frequency; measuring impedance based on frequency changes while applying an AC voltage to a target battery; generating a Nyquist plot of the impedance measurements of the target battery measured in the measurement step; extracting inflection points from the Nyquist plot generated in the generation step; comparing impedance measurements within a predetermined frequency range centered on the inflection points extracted in the extraction step with the impedance reference values ​​for each frequency stored in the storage step; and diagnosing the target battery based on the comparison results of the comparison step.

[0026] Beneficial effects

[0027] According to this disclosure, an effective battery diagnostic device is provided.

[0028] Specifically, according to embodiments of this disclosure, since the Nyquist plot is used to diagnose the battery without using an equivalent circuit model, it is not necessary to extract various component constant values ​​associated with the equivalent circuit model.

[0029] Therefore, according to this embodiment of the present disclosure, the accuracy and / or speed of battery diagnostics can be improved.

[0030] Furthermore, according to embodiments of this disclosure, when analyzing EIS measurement data, the influence of the inductive and resistive components of the measurement probe can be minimized by using a low-frequency region instead of a high-frequency region.

[0031] Therefore, according to this embodiment of the present disclosure, the accuracy of battery diagnostics can be further improved.

[0032] In addition, this disclosure can be readily applied to classify battery modules or battery packs when they are reused. Attached Figure Description

[0033] The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, serve to provide a further understanding of the technical features of the present disclosure. Therefore, the present disclosure is not to be construed as limited to the drawings.

[0034] Figure 1 This is a block diagram schematically illustrating the configuration of a battery diagnostic device according to an embodiment of the present disclosure.

[0035] Figure 2 This is a schematic diagram illustrating impedance reference values ​​stored in a memory module according to an embodiment of the present disclosure.

[0036] Figure 3 This is a diagram that schematically illustrates an example of a Nyquist plot generated by a processor according to an embodiment of the present disclosure.

[0037] Figure 4 It is an illustrative representation of... Figure 3 The Nyquist plot shift makes the extracted inflection points the configuration of the origin.

[0038] Figure 5 This is a schematic diagram illustrating the magnitude and angle of impedance measurements according to embodiments of the present disclosure.

[0039] Figure 6 This is a diagram showing a portion of impedance reference value data stored in a memory module according to an embodiment of the present disclosure.

[0040] Figure 7 This is a diagram showing a portion of impedance reference value data stored in a memory module according to another embodiment of the present disclosure.

[0041] Figure 8 This is a diagram showing a Nyquist plot generated according to an embodiment of the present disclosure, wherein each region is classified according to the type of factors affecting impedance.

[0042] Figure 9 This diagram schematically illustrates a configuration for extracting inflection points by a processor according to an embodiment of the present disclosure.

[0043] Figure 10 This is a flowchart illustrating a battery diagnostic method according to an embodiment of the present disclosure. Detailed Implementation

[0044] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Before the description, it should be understood that the terminology used in the specification and appended claims should not be construed as limited to its common or dictionary meanings, but rather as being interpreted based on the meanings and concepts corresponding to the technical aspects of the present disclosure, in a manner that enables the inventors to define terms suitable for best description.

[0045] Therefore, the description presented herein is merely a preferred example for illustrative purposes only and is not intended to limit the scope of this disclosure. It should be understood that other equivalents and modifications may be obtained without departing from the scope of this disclosure.

[0046] Figure 1 This is a block diagram schematically illustrating the configuration of a battery diagnostic device according to an embodiment of the present disclosure.

[0047] Reference Figure 1 The battery diagnostic device according to this disclosure includes an impedance measurement module 100, a memory module 200, and a processor 300.

[0048] Impedance measurement module 100 can be configured to measure the impedance of a target battery. Specifically, impedance measurement module 100 can measure the impedance of the target battery using electrochemical impedance spectroscopy (EIS). Here, target battery refers to the battery that is the target of diagnostics. For example, target battery can be a battery module or battery pack comprising multiple battery cells. Alternatively, target battery can refer to a battery cell, i.e., a secondary battery.

[0049] The impedance measurement module 100 can be configured to apply an AC voltage to a target battery in order to measure the impedance of the target battery. For example, the impedance measurement module 100 can be configured to charge the target battery while applying the AC voltage, and measure the internal impedance of the target battery during charging. Specifically, the impedance measurement module 100 can be configured to apply the AC voltage while changing the frequency.

[0050] The impedance measurement module 100 can employ various impedance measurement configurations and techniques known at the time of filing this application. For example, the impedance measurement module 100 can be configured to measure the internal impedance of a battery using a four-terminal pair method.

[0051] Additionally, the impedance measurement module 100 may include multiple components for measuring the internal impedance of the battery. For example, the impedance measurement module 100 may include contact probes for contacting battery terminals, a power supply for generating and supplying AC power, wires disposed between the power supply and the contact probes, and a voltage sensor, etc. Therefore, since the impedance measurement module 100 of this disclosure can employ known conventional impedance measurement configurations, it will not be described in detail.

[0052] The memory module 200 stores impedance reference values. Here, the impedance reference value is a value to be compared with the impedance measurement value of the target battery measured by the impedance measurement module 100, and can be pre-stored. Specifically, the impedance reference value can be a value obtained in advance for a reference battery whose specifications, type, characteristics, etc., are the same as or similar to those of the target battery.

[0053] Alternatively, impedance reference values ​​can be measured and stored in the same or similar manner as the impedance measurement method of impedance measurement module 100. For example, impedance reference values ​​stored in memory module 200 can be obtained by applying an AC voltage with the same or similar voltage magnitude and frequency as when the impedance measurement module 100 measures the impedance of the target battery.

[0054] Figure 2 This is a schematic diagram illustrating impedance reference values ​​stored in memory module 200 according to an embodiment of the present disclosure.

[0055] Reference Figure 2The memory module 200 can store impedance reference values ​​for each frequency. That is, the memory module 200 can be configured to classify frequencies into multiple frequencies (f1, f2, f3, f4, f5, f6, f7, ...) within a predetermined frequency range, such that impedance reference values ​​(Zre1, Zre2, Zre3, Zre4, Zre5, Zre6, Zre7, ...) are preset to correspond to each classified frequency. For example, the memory module 200 can pre-store impedance reference values ​​corresponding to each of the multiple frequencies included between 0.1 Hz and 10 Hz.

[0056] In addition to the above, the memory module 200 may also store data or programs required for operation or to perform the functions of other components of the battery diagnostic device according to this disclosure, such as the impedance measurement module 100 or the processor 300.

[0057] The memory module 200 may be implemented as at least one of the following types: flash memory, hard disk, SSD (solid-state drive), SDD (solid-state drive), micro multimedia card, RAM (random access memory), SRAM (static RAM), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), and PROM (programmable read-only memory), but this disclosure is not necessarily limited to this particular form of the memory module 200.

[0058] The processor 300 can be connected to the impedance measurement module 100 and configured to receive impedance measurement values ​​from the impedance measurement module 100. That is, when the impedance measurement module 100 measures the impedance of the target battery, the measurement result can be transmitted to the processor 300. Furthermore, the processor 300 can be configured to generate a Nyquist plot (Nyquist graph) of the impedance measurement values ​​of the target battery based on the impedance measurement results sent from the impedance measurement module 100 in this manner. Specifically, the impedance measurement module 100 can use an EIS analysis method to measure the impedance. Additionally, the EIS analysis data can be displayed as a Nyquist graph.

[0059] Figure 3 This is a diagram schematically illustrating an example of a Nyquist plot generated by processor 300 according to an embodiment of this disclosure.

[0060] Reference Figure 3A Nyquist plot can be generated using EIS measurements of the target battery. In the Nyquist plot, the horizontal axis can represent the real part of the impedance (Zreal), and the vertical axis can represent the imaginary part of the impedance (Zimag). The units for both the horizontal and vertical axes can be mΩ or Ω. Each point can be referred to as the impedance measurement at each frequency, i.e., the impedance point. Furthermore, it can be assumed that the frequency of each impedance point gradually increases in the direction indicated by arrow a1. In the Nyquist plot, the real and imaginary values ​​of the target battery's impedance change with frequency, and therefore their intersections can be displayed as points on the coordinate system, i.e., impedance points.

[0061] The processor 300 can generate a Nyquist plot based on impedance measurements sent from the impedance measurement module 100. In this case, the processor 300 can employ Nyquist plot generation techniques known at the time of filing of this application, and therefore will not be described in detail here.

[0062] If the Nyquist plot of the EIS measurement data is generated as described above, the processor 300 can be configured to extract inflection points from the generated Nyquist plot. Here, an inflection point can refer to a point in the EIS Nyquist plot where the direction of curvature changes. From a calculus perspective, for a function that is differentiable twice, an inflection point can be a point where the curve of the function changes from an upwardly convex state to a downwardly convex state or vice versa. That is, an inflection point can refer to a point where the curvature changes from negative (-) to positive (+) in a plane curve. The processor 300 can be configured to extract inflection points from the EIS Nyquist plot.

[0063] If an inflection point is extracted from the EIS Nyquist plot as described above, the processor 300 can select at least one value belonging to a predetermined frequency range centered on the extracted inflection point. Additionally, the processor 300 can be configured to compare the value selected in this manner near the inflection point with impedance reference values ​​for each frequency stored in the memory module 200.

[0064] For example, in Figure 3 In the configuration, when the inflection point is extracted as the impedance point indicated by f13 on the Nyquist plot, the processor 300 can select impedance points belonging to the portion indicated by b within a predetermined frequency range centered on the inflection point f13. At this time, the processor 300 can identify the frequency of the impedance point relative to the portion indicated by b and the impedance measurement value at each frequency.

[0065] Additionally, the processor 300 can identify the impedance measurement value of the selected impedance point and its corresponding impedance reference value from the memory module 200. That is, the processor 300 can read the impedance reference value corresponding to a frequency that is the same as or close to the frequency of the selected impedance point from the memory module 200. Furthermore, the processor 300 can compare the impedance reference value read in this manner with the impedance measurement value of the selected impedance point.

[0066] For example, when in Figure 3 In the implementation, the frequency of the impedance point within region b, which is set around the inflection point f13, corresponds to Figure 2 In the implementation of f2 to f6, the processor 300 can compare the impedance measurement value of the impedance point in region b with the impedance reference value (i.e., Zre2 to Zre6) corresponding to f2 to f6 in the memory module.

[0067] Additionally, the processor 300 can be configured to diagnose the target battery based on a comparison between the impedance measurement and the impedance reference value.

[0068] For example, when the impedance measurement exceeds the impedance reference value beyond the error range, the processor 300 can diagnose an abnormality in the target battery. In this case, the impedance reference value can be set as a reference value to determine whether the target battery is abnormal. As another example, the processor 300 can be configured to search for impedance reference values ​​that are the same as or within the error range of the impedance measurement value. In this case, the memory module 200 can store various information for diagnosing the state of the target battery to match against each impedance reference value. For example, the memory module 200 can store the battery's SOH (State of Health) information to match against each impedance reference value. Furthermore, the processor 300 can diagnose the state of the target battery using information matched against the searched impedance reference values.

[0069] According to the configuration disclosed herein, battery diagnosis can be performed simply and accurately. Specifically, according to the configuration disclosed herein, when diagnosing a battery using EIS data, the battery's equivalent circuit model is unnecessary. Therefore, it is not necessary to extract various constant values ​​for the equivalent circuit model of the battery-related EIS Nyquist plot. Therefore, according to this embodiment of the present disclosure, not only is the diagnostic process simple, but errors that may occur during constant value extraction can also be eliminated. Therefore, in this case, the battery can be diagnosed efficiently using the EIS Nyquist plot.

[0070] Furthermore, the processor 300 may optionally include a central processing unit (CPU), application-specific integrated circuit (ASIC), chipset, logic circuit, register, communication modem, data processing device, etc., known in the art, to execute the various control logics performed in this disclosure, or these terms may be used to represent them. Additionally, when the control logic is implemented in software, the processor 300 may be implemented as a collection of program modules. In this case, the program modules may be stored in internal memory or external memory module 200, etc., and executed by the processor 300. The memory module 200 may be located inside or outside the processor 300 and may be connected to the processor 300 via various well-known means.

[0071] In particular, if the diagnostic device according to this disclosure is implemented in the form of being included in a battery pack, the battery pack may include a control device referred to as a microcontroller unit (MCU) or a battery management system (BMS). In this case, the processor 300 may be implemented by a component such as an MCU or BMS disposed in a general battery pack.

[0072] Furthermore, in this specification, terms such as “for” or “configured to” used for the operation or function of the processor 300 may include the meaning of “programmed to”.

[0073] Furthermore, when extracting inflection points by generating a Nyquist plot based on EIS measurements, the processor 300 can shift the Nyquist plot so that the extracted inflection point becomes the origin. Additionally, relative to the shifted Nyquist plot, the processor 300 can be configured to compare and analyze the impedance reference value for each frequency. Figure 4 This will be described in more detail.

[0074] Figure 4 It is an illustrative representation of... Figure 3 The Nyquist plot shift makes the extracted inflection points the configuration of the origin. However, in Figure 4 For ease of illustration, the following have been excluded. Figure 3 The high-frequency region in the Nyquist plot.

[0075] Reference Figure 4 The Nyquist plot is shifted, so that in Figure 3 In the implementation method, the inflection point f13 extracted becomes the origin. That is, Figure 4 The Nyquist plot can be considered as a plot that preserves the coordinate axes while... Figure 3 The Nyquist plot is shifted to the left and downward so that the inflection point f13 is located at the origin.

[0076] Additionally, the processor 300 can be configured to compare impedance measurements within a predetermined frequency range around the origin of the Nyquist plot (i.e., the inflection point f13) with impedance reference values ​​corresponding to the respective frequencies.

[0077] According to the configuration disclosed herein, by positioning the inflection point at the origin, the impedance measurement value and the impedance reference value can be compared more clearly. Furthermore, according to this embodiment, when the impedance reference value stored in the memory module 200 is stored in the form of a Nyquist plot, the impedance measurement value and the impedance reference value can be compared more easily around the inflection point. Additionally, according to this embodiment, comparisons can be performed more easily and clearly when comparing with Nyquist plots measured and generated from the same battery at different times, or when comparing with Nyquist plots measured and generated from other batteries. Moreover, according to this embodiment, the form around the inflection point can be compared and analyzed more clearly relative to the Nyquist plot.

[0078] Additionally, the processor 300 can be configured to compare impedance measurements in a frequency region centered on the extracted inflection point, ranging from a first predetermined frequency to a second predetermined frequency, with impedance reference values. Specifically, in the Nyquist plot, the impedance measurement value for each frequency is indicated by an impedance point. Therefore, the processor 300 can be configured to search for a predetermined number of impedance points in the high-frequency and / or low-frequency directions starting from the inflection point, and compare the impedance measurements of the found impedance points with the impedance reference values.

[0079] Here, the first predetermined frequency and the second predetermined frequency can be configured to be the same as each other. That is, the processor 300 can be configured to search for the same number of impedance points in both the high-frequency and low-frequency directions, centered on the extracted inflection point.

[0080] For example, in Figure 4 In this implementation, the processor 300 can be configured to search for two impedance points in the low-frequency and high-frequency directions around the inflection point f13, respectively, and compare the impedance measurements of the searched impedance points with impedance reference values. That is, the processor 300 can compare the impedance measurements of two points f14 and f15 in the low-frequency direction (right direction) starting from the inflection point f13, and two points f11 and f12 in the high-frequency direction (left direction) starting from the inflection point f13, with impedance reference values ​​corresponding to the same frequency as each impedance point.

[0081] According to the configuration disclosed herein, by analyzing the frequency information and impedance measurements at different measurement points far from the origin together with the frequency information at the origin, the impedance characteristics of the target battery around the inflection point can be more clearly understood.

[0082] Additionally, the processor 300 can be configured to diagnose the target battery by using the magnitude and angle of the target battery's impedance measurements. (Refer to...) Figure 5 This will be described in more detail.

[0083] Figure 5 This is a schematic diagram illustrating the magnitude and angle of impedance measurements according to embodiments of the present disclosure.

[0084] More specifically, Figure 5 Can be regarded as Figure 4 The Nyquist plot is based on a plot where the horizontal axis (real part Z) is vertically reversed, causing the positive imaginary part of the impedance (+Zimag) to lie in the upper portion. That is, in Figure 4 In the diagram, the negative imaginary part of the impedance (-Zimag) is shown as being located in the first and second quadrants, but... Figure 5 In the diagram, the negative imaginary part of the impedance (-Zimag) is shown as being located in the third and fourth quadrants. Additionally, Figure 5 This is a magnified view showing the low-frequency region (i.e., only a portion of the first quadrant) within a predetermined frequency range around the origin. Therefore, in Figure 5 In the diagram, only points f14 and f15 are shown among the multiple impedance points.

[0085] exist Figure 5 In the diagram, referencing the first point f14 in the low-frequency direction (to the right) starting from the origin f13, its magnitude is represented by r14 and its angle by θ14. In this case, the magnitude r14 and the angle θ14 can be calculated as follows.

[0086] r14 =(x14 2 +y14 2 ) 1 / 2

[0087] θ14 = tan -1 (y14 / x14)

[0088] Here, x14 and y14 can be referred to as the x-axis component (real part of impedance) and y-axis component (imaginary part of impedance) at point f14, respectively. Furthermore, in this way, the processor 300 can calculate the magnitude (r15) and angle (θ15) of point f15, which is the second point in the low-frequency direction from the origin f13.

[0089] Additionally, although not shown in the figure, the processor 300 can calculate the size and angle of the first and second points in the high-frequency direction (left direction) starting from the origin f13 in a similar manner.

[0090] In this embodiment, the memory module 200 can store the magnitude and angle of the impedance as impedance reference values ​​corresponding to multiple frequencies. That is, it can also store the magnitude and angle of each impedance reference value so that the processor 300 can compare it with the magnitude and angle of the impedance measurement value.

[0091] Figure 6 This is a diagram showing a portion of the impedance reference value data stored in the memory module 200 according to an embodiment of the present disclosure.

[0092] Reference Figure 6 The memory module 200 stores impedance reference values ​​corresponding to each of a plurality of frequencies (2.154 Hz, 1.468 Hz, 1 Hz, 0.681 Hz, 0.464 Hz). Specifically, the impedance reference values ​​stored in the memory module 200 have the magnitude and angle of the impedance for each frequency. For example, in Figure 6 In this context, the impedance reference value corresponding to a frequency of 1.468 Hz can be 0.39 mΩ and -141.7°. Furthermore, the impedance reference value corresponding to a frequency of 0.681 Hz can be 0.29 mΩ and -38.3°.

[0093] Specifically, the memory module 200 can store the magnitude and angle of impedance reference values ​​at predetermined frequencies around the origin, based on a specific frequency. For example, as Figure 6 As shown, when the 1 Hz frequency point is the origin, the memory module 200 can store the magnitude and angle of the impedance reference value for each of the surrounding frequencies (0.681 Hz, 0.464 Hz, 1.468 Hz, 2.154 Hz).

[0094] In this embodiment, the processor 300 can compare the magnitude and angle of the impedance measurement value of the target battery with the magnitude and angle of the impedance reference value stored in the memory module 200 for the same frequency. Furthermore, the processor 300 can diagnose the battery based on the comparison results of the magnitude and angle.

[0095] Specifically, the processor 300 can be configured to compare the impedance measurement with an impedance reference value that has the same frequency at its origin. For example, Figure 6 The impedance reference value shown is set such that the origin has a frequency of 1 Hz. At this time, in Figure 4 In the implementation, when the point indicated by f13, which serves as the inflection point, corresponds to a 1 Hz frequency, the processor 300 can... Figure 4 The impedance measurement value of the implementation method and Figure 6 The impedance reference values ​​of the implementation methods are compared with each other to diagnose the target battery.

[0096] More specifically, in Figure 4 In this implementation, the points indicated by f11, f12, f14, and f15 can correspond to frequencies of 2.154 Hz, 1.468 Hz, 0.681 Hz, and 0.464 Hz, respectively. In this case, the processor 300 can obtain the magnitude and angle of the impedance measurement value for each of f11, f12, f14, and f15, and compare the obtained magnitude and angle of each point with the magnitude and angle of the impedance reference value, such as... Figure 6 As shown in the image. Here, it can be as follows: Figure 5 The implementation method describes obtaining the magnitude and angle of the impedance measurement at each point.

[0097] Furthermore, at each frequency corresponding to the various frequencies of the AC voltage applied by the impedance measurement module 100, the memory module 200 can store the impedance magnitude and impedance angle as impedance reference values ​​corresponding to the frequency. Specifically, for all frequencies available when the impedance measurement module 100 measures the impedance of the target battery, the memory module 200 can pre-store the impedance reference value corresponding to each frequency. For example, when the impedance measurement module 100 is set to measure impedance by applying an AC voltage while changing frequencies such as 2.154 Hz, 1.468 Hz, 1 Hz, 0.681 Hz, 0.464 Hz, ..., the memory module 200 can pre-store the impedance reference value corresponding to each of the frequencies (2.154 Hz, 1.468 Hz, 1 Hz, 0.681 Hz, 0.464 Hz, ...) that are the same frequencies as the set impedance measurement module 100.

[0098] Alternatively, the impedance measurement module 100 can be configured to change its frequency according to a frequency pre-stored in the memory module 200 when an AC voltage is applied. For example, if the impedance reference value is... Figure 6 If the form shown is pre-stored in the memory module 200, the impedance measurement module 100 can be configured to apply an AC voltage while changing frequencies such as 2.154 Hz, 1.468 Hz, 1 Hz, 0.681 Hz, and 0.464 Hz, and obtain the impedance measurement value at each frequency.

[0099] In addition, Figure 6 In the embodiment, an impedance reference value centered at an origin of 1 Hz is illustrated, but the origin may not be 1 Hz. Therefore, the memory module 200 can store the impedance reference value in... Figure 6The data can be stored in the form shown, and can also include data for various cases other than the case where the origin is 1 Hz. For example, for the cases where the origin is 0.681 Hz or 1.468 Hz, the memory module 200 can store data related to the magnitude and angle of the impedance reference value at surrounding frequency points. In this case, based on the frequency of the extracted inflection point, the processor 300 can obtain suitable impedance reference value data from the memory module 200 and compare the obtained impedance reference value data with the impedance measurement value.

[0100] The memory module 200 can store impedance reference values ​​for each frequency, categorized for each of the multiple battery grades. The reference... Figure 7 This will be described in more detail.

[0101] Figure 7 This is a diagram illustrating a portion of impedance reference value data stored in memory module 200 according to another embodiment of this disclosure. Regarding Figure 7 , will describe in detail with Figure 6 The different features of the implementation methods.

[0102] Reference Figure 7 The memory module 200 can store the impedance reference values ​​for each frequency in tabular form, for example, in multiple tables. In this case, each table can be referred to as the impedance reference value for each frequency group corresponding to each of the different battery grades.

[0103] More specifically, the memory module 200 can classify the battery into three levels: Level 1, Level 2, and Level 3, and store impedance reference values ​​for each frequency group for each battery level. Here, the frequency can be set to the same for each battery level, and the impedance reference values ​​for each battery level can have different magnitudes and angles.

[0104] In this configuration, the processor 300 can be configured to match the impedance measurement value of the target battery with the battery rating stored in the memory module 200. Additionally, the processor 300 can be configured to classify the target battery rating based on the matching result.

[0105] Specifically, the processor 300 can search among multiple sets of impedance reference values ​​for battery grades stored in the memory module 200 for the set of impedance reference values ​​that is the same as or closest to the impedance measurement value of the target battery, and identify the corresponding battery grade. Furthermore, the target battery can be classified using the battery grades identified in this way. For example, when determining the magnitude and angle of the impedance measurement value of the target battery and setting it to... Figure 7When the magnitude and angle of the impedance reference value group of Class 1 are most similar, the processor 300 can classify the target battery as Class 1. Furthermore, when the magnitude and angle of the impedance measurement value of the target battery are determined to be similar to those set to Class 1, the processor 300 can classify the target battery as Class 1. Figure 7 When the magnitude and angle of the impedance reference value group of Class 2 or Class 3 are most similar, the processor 300 can classify the target battery as Class 2 or Class 3.

[0106] According to the configuration disclosed herein, the target battery can be effectively classified by grade. Specifically, according to the configuration disclosed herein, when a battery is about to be reused, it can be classified by grade in a relatively simple and clear manner, which is useful in determining the battery's reusability, use, and selling price. For example, according to this embodiment of the disclosure, for lithium-ion battery packs that have reached the end of their lifespan for electric vehicles, the above configuration can be used to determine the battery pack's use, remaining lifespan, performance, etc.

[0107] In this embodiment, various data matching techniques known at the time of filing of this application can be used for the configuration of determining whether the impedance measurement value is the same as or similar to the impedance reference value. Furthermore, various methods can be used in this disclosure as the configuration for determining whether the impedance measurement value matches the impedance reference value, and this disclosure is not limited to any specific determination method.

[0108] Specifically, the processor 300 can be configured to determine the state of health (SOH) of the target battery based on the target battery's rating. For example, in Figure 7 In this embodiment, the SOH corresponding to level 1 can be 80%, the SOH corresponding to level 2 can be 75%, and the SOH corresponding to level 3 can be 70%. In this case, the processor 300 can estimate the SOH of the target battery by determining the level of the impedance reference value that is closest to the impedance measurement value. If it is determined that the impedance measurement value of the target battery is closest to the impedance reference value group of level 2, the processor 300 can estimate the SOH of the target battery as 75% corresponding to level 2. According to this embodiment, the processor 300 can easily determine the SOH of the target battery.

[0109] In addition, Figure 7 In this implementation, only three battery levels are illustrated, but this is merely for ease of description, and the memory module 200 can store impedance reference values ​​for each frequency for each of four or more battery levels. Specifically, because the battery levels are subdivided into a very large number, the target battery can be diagnosed and classified into various levels more accurately. For example, the memory module 200 can classify SOH from 100% to 0% in 2.5% intervals and store impedance reference values ​​for each frequency group for each category of SOH.

[0110] In addition, Figure 6 and Figure 7 In the implementation, an example has been given of searching and comparing two impedance points in both the high-frequency and low-frequency directions based on an impedance point at a frequency of 1 Hz. However, the number of impedance point comparisons based on the origin is merely an example, and this disclosure does not limit the specific examples of such a number. For example, based on the origin, it is possible to configure the comparison of three or four impedance points in both the high-frequency and low-frequency directions.

[0111] If a Nyquist plot of the impedance measurements of the target cell is generated, the processor 300 can be configured to extract the points in the generated Nyquist plot where the anti-charge transfer region bends due to the anti-diffusion region as inflection points. (Refer to...) Figure 8 This will be described in more detail.

[0112] Figure 8 This is a diagram illustrating a Nyquist plot generated according to an embodiment of the present disclosure, wherein each region is classified according to the type of factors affecting impedance. Figure 8 In China, basic characteristics and Figure 3 They are the same, therefore the different features will be described in detail.

[0113] Reference Figure 8 For each factor affecting battery impedance, the EIS Nyquist plot can be divided into four regions: E1, E2, E3, and E4. First, region E1 is the highest frequency region and can be primarily determined by the electrolyte resistance within the target battery. Next, region E2 is a region with a lower frequency than region E1 but a higher frequency than region E3, and can be primarily affected by factors such as the SEI (solid electrolyte interface) formed on the surface of the electrode particles in the target battery. Furthermore, region E3 is a region with a lower frequency than region E2 and can be primarily affected by charge transfer in the target battery. Specifically, region E3 can be determined by the Li ion oxidation and reduction reactions at the electrode material interface of the target battery. Region E3 can be considered an anti-charge transfer region. Finally, region E4 is the lowest frequency band and can be primarily affected by diffusion. Specifically, region E4 can be determined by chemical diffusion intercalating into the crystal structure of the grains in the target battery. Region E4 can be considered an anti-diffusion region. The memory module 200 can pre-store information about these four regions, such as frequency ranges.

[0114] The processor 300 can be configured to search for inflection points in the anti-charge transfer region E3 among four regions. Specifically, in the EIS Nyquist plot, the anti-charge transfer region E3 may bend due to the anti-diffusion region E4. The processor 300 can extract the points where the anti-charge transfer region E3 bends due to the anti-diffusion region E4 as described above as inflection points. Furthermore, there may be two or more inflection points in the Nyquist plot. In this case, the processor 300 can extract the points where the anti-charge transfer region E3 bends due to the anti-diffusion region E4 among two or more inflection points, and use the inflection points extracted in this way to diagnose the battery or perform battery rating classification.

[0115] For example, in Figure 4 In the Nyquist plot, processor 300 can extract point f13 as the point where the anti-charge transfer region E3 bends due to the anti-diffusion region E4. Additionally, processor 300 can diagnose the target battery by using point f13 as the final inflection point.

[0116] According to the configuration disclosed herein, when diagnosing a target battery using EIS data, the accuracy of battery diagnosis can be improved by using a low-frequency region instead of a high-frequency region. Specifically, such as Figure 8 High-frequency regions like region E1 can be heavily influenced by the inductive or resistive components of the measurement probe. Therefore, when using portions severely affected by region E1 or inflection points within region E1, significant deviations and reduced accuracy may occur. However, according to the above embodiment, data from regions E3 and E4, which are low-frequency regions not significantly affected by region E1, are analyzed, and the battery can be diagnosed using this data. Therefore, in this case, the influence of the inductive and resistive components of the measurement probe can be minimized, thus further improving the accuracy of battery diagnosis.

[0117] Additionally, the processor 300 can be configured to extract inflection points by searching in a direction from the low-frequency region to the high-frequency region relative to the generated Nyquist plot. For example, in Figure 8 In one implementation, as indicated by arrow a2, the processor 300 can be configured to search for inflection points while moving to the left from a predetermined point on the right side of the Nyquist plot. That is, the processor 300 can be configured to extract inflection points while moving from a low-frequency region to a high-frequency region in the EIS Nyquist plot.

[0118] Specifically, the processor 300 can search for inflection points while moving from a low-frequency region to a high-frequency region, and extract the first inflection point found among them. Furthermore, the processor 300 can perform the aforementioned battery diagnostic process using the first inflection point found in this way. In the Nyquist plot, Figure 8The anti-charge transfer and anti-diffusion regions indicated by E3 and E4 can exist in the low-frequency region. Therefore, the first inflection point found in the low-frequency region can be regarded as the point where the anti-charge transfer region bends due to the anti-diffusion region.

[0119] Therefore, according to this embodiment, it is easy to identify the point where the anti-charge transfer region bends due to the anti-diffusion region.

[0120] Reference Figure 9 A more detailed description of the inflection point extraction configuration.

[0121] Figure 9 This diagram schematically illustrates the configuration for extracting inflection points by the processor 300 according to an embodiment of the present disclosure. Specifically, Figure 9 Can be regarded as Figure 8 A magnified graph of region E3.

[0122] Reference Figure 9 The processor 300 can search for inflection points by comparing and analyzing the tilt in the direction of gradually increasing frequency, as indicated by the arrows in the EIS Nyquist plot.

[0123] More specifically, the slope of the impedance at each frequency measurement point can be calculated as follows.

[0124] Inclination=(EISi[i+1]-EISi[i]) / (EISr[i+1]-EISr[i])

[0125] Here, it can be seen that EISi[i] refers to the imaginary part of impedance point i, and EISr[i] refers to the real part of impedance point i.

[0126] For example, in Figure 9 In this implementation, the processor 300 can calculate the slope C between impedance point f24 and impedance point f25 as follows.

[0127] Inclination C = (EISi[f25] - EISi[f24]) / (EISr[f25] - EISr[f24])

[0128] In this way, the processor 300 can obtain the tilt between points (e.g., between f23 and f24, between f22 and f23, between f21 and f22, etc.). Furthermore, the processor 300 can extract inflection points where the magnitude (absolute value) of the tilt between each impedance point gradually increases and then decreases in the direction of the arrow (high frequency direction).

[0129] For example, in Figure 9In this implementation, when the magnitude (absolute value) of the Nyquist plot's tilt gradually increases from point f28 to point f23 and then decreases from point f23, the processor 300 can extract point f23 as an inflection point. That is, the processor 300 can extract the point where the absolute value of the tilt increases and then decreases as an inflection point. Alternatively, the processor 300 can compare the tilt change as it moves along the Nyquist plot in the high-frequency direction and extract the first point where the tilt changes from positive (+) to negative (-) as an inflection point. Specifically, an inflection point can be considered as a point in the Nyquist plot where the anti-charge transfer region bends due to the anti-diffusion region. In this case, an inflection point for battery diagnosis or classification can be easily extracted.

[0130] In this embodiment, the processor 300 can obtain information in advance related to the point where the search for the inflection point begins in the Nyquist plot. For example, the memory module 200 can pre-store impedance point information corresponding to the inflection point search start point, and the processor 300 can identify the impedance point information by accessing the memory module 200 before extracting the inflection point. Furthermore, based on the impedance point information identified by the memory module 200 as described above, the processor 300 can be configured to search for the inflection point from the corresponding point.

[0131] For example, in Figure 9 In this implementation, the memory module 200 may pre-store point f27 as an impedance point for inflection point search. Then, the processor 300 can identify this information from the memory module 200 and search for inflection points in the direction from point f27 to points f26, f25, f24, ...

[0132] Here, the impedance information at the starting point of the inflection point search can be frequency information. For example, the memory module 200 can pre-store impedance information related to... Figure 9 The frequency information corresponding to point f27 in the implementation scheme. In this case, the processor 300 can determine the slope of the impedance map from the frequency corresponding to point f27 to higher frequencies and extract the inflection point.

[0133] Alternatively, the impedance information at the starting point of the inflection point search can be information corresponding to the real part of the impedance. For example, memory module 200 can pre-store the real part information of the impedance at point f27.

[0134] Alternatively, processor 300 may determine, rather than obtain from memory module 200, information related to the starting point of the inflection point search. Specifically, processor 300 may identify minimum and maximum points in the Nyquist plot while moving from the low-frequency portion to the high-frequency direction. Furthermore, processor 300 may be configured to extract the inflection point between the identified minimum and maximum points as described above.

[0135] For example, see Figure 8In the illustrated implementation, processor 300 can identify a portion d1 as a minimum point and a portion d2 as a maximum point in the Nyquist plot. Furthermore, processor 300 can be configured to extract inflection points in the region between the minimum point d1 and the maximum point d2 identified as described above.

[0136] In this case, the points where the anti-charge transfer region bends due to the anti-diffusion region can be easily identified in the Nyquist plot.

[0137] Furthermore, in an EIS Nyquist plot, there may be two or more maxima and / or minima. In this case, the processor 300 can extract the inflection points that exist between the maxima and / or minima with the lowest frequency among the multiple maxima and / or minima. That is, in Figure 8 In one implementation, the processor 300 moves in the direction indicated by arrow a2 while checking the minimum and maximum points of the Nyquist plot, but can extract the inflection points that exist between the initially identified minimum and maximum points, and use the extracted inflection points to diagnose the battery.

[0138] Additionally, the memory module 200 can pre-store preliminary frequency information related to the inflection point. Here, the preliminary frequency information related to the inflection point can be information related to the frequency at which the inflection point is estimated. Specifically, the memory module 200 can pre-store information related to the frequency range at which the inflection point is estimated as preliminary frequency information.

[0139] For example, memory module 200 may pre-store an initial frequency range of 0.4 Hz to 2.2 Hz as frequency information for estimating the presence of inflection points. In this case, processor 300 may be configured to first search for inflection points within the initial frequency range of 0.4 Hz to 2.2 Hz.

[0140] According to the configuration disclosed herein, the search range when the processor 300 extracts the inflection point can be reduced. Therefore, in this case, the inflection point extraction rate of the processor 300 can be improved, and the computational load during the extraction process can be reduced.

[0141] Furthermore, the memory module 200 can store the preliminary frequency information in multiple levels. In this case, the processor 300 can use the preliminary frequency information stored in multiple levels sequentially. In this case, the order of the preliminary frequency information stored in multiple levels can be predetermined. In addition, the processor 300 can first search for frequency information of priority order from the preliminary frequency information stored in multiple levels, and then, if no inflection point is extracted from the preliminary frequency information of priority order found, the processor 300 can be configured to search for preliminary frequency information of the next priority order.

[0142] For example, memory module 200 can store primary preliminary frequency information, second preliminary frequency information, and third preliminary frequency information. In this case, the primary preliminary frequency information can have the highest priority, and the third preliminary frequency information can have the lowest priority. In this case, processor 300 can first extract the inflection point within the corresponding range by referring to the primary preliminary frequency information. Alternatively, if no inflection point is extracted from the primary preliminary frequency information, the inflection point can be extracted within the corresponding range by referring to the second preliminary frequency information. Furthermore, if no inflection point is extracted even at this point, the inflection point can be extracted by referring to the third preliminary frequency information.

[0143] According to the configuration disclosed herein, the inflection point extraction rate and efficiency can be further improved.

[0144] The memory module 200 can store preliminary frequency information related to the inflection point for classification based on the magnitude of the AC voltage applied to the battery. That is, when the impedance measurement module 100 measures the impedance based on frequency changes while applying an AC voltage to the target battery, the preliminary frequency information can be configured to vary according to the magnitude of the applied AC voltage.

[0145] For example, when the impedance measurement module 100 measures the impedance of the target battery while applying an AC voltage of 0.5V, the memory module 200 can store preliminary frequency information as fp1. Additionally, when the impedance measurement module 100 measures the impedance of the target battery while applying an AC voltage of 0.7V, the memory module 200 can store preliminary frequency information as fp2. In this case, fp1 and fp2 can be set to different frequency values ​​or different frequency ranges.

[0146] According to this configuration of the present disclosure, when the processor 300 extracts the inflection point using the preliminary frequency information stored in the memory module 200, the inflection point can be extracted more efficiently. Specifically, the form of the EIS Nyquist plot can vary depending on the magnitude of the applied voltage. Therefore, according to the above embodiment, the inflection point can be extracted efficiently by providing appropriate preliminary frequency information that takes into account the variation in form according to the magnitude of the applied voltage.

[0147] The battery diagnostic device according to this disclosure can be applied to battery packs. That is, a battery pack according to this disclosure may include the battery diagnostic device described above. In addition to the battery diagnostic device according to this disclosure, the battery pack according to this disclosure may also include components commonly found in battery packs, such as one or more secondary batteries, a BMS (Battery Management System), a current sensor, a relay, a fuse, and a battery pack casing. In this case, the secondary batteries included in the battery pack can be the target batteries to be diagnosed by the battery diagnostic device according to this disclosure. Furthermore, at least some components of the battery diagnostic device according to this disclosure can be implemented as conventional components included in the battery pack. For example, at least some functions or operations of the processor 300 of the battery diagnostic device according to this disclosure can be implemented by the BMS included in the battery pack.

[0148] Furthermore, the battery diagnostic equipment according to this disclosure can be applied to energy storage systems (ESS). That is, the energy storage system according to this disclosure may include the battery diagnostic equipment described above. Specifically, since energy storage systems do not require the high output of electric vehicles, they can be a representative application for recycling battery packs (waste batteries) that have already been used in electric vehicles until the end of their service life. The energy storage system can determine whether to install or utilize the battery by using the battery diagnostic technology according to this disclosure before installing the waste battery, after diagnosing the battery or classifying it by battery grade.

[0149] Figure 10 This is a schematic flowchart illustrating a battery diagnostic method according to an embodiment of the present disclosure. Figure 10 In this process, each step can be performed by the respective components of the aforementioned battery diagnostic device.

[0150] Reference Figure 10 The battery diagnostic method according to this disclosure may include an impedance reference value storage step (S110), an impedance measurement step (S120), a Nyquist plot generation step (S130), an inflection point extraction step (S140), a comparison step (S150), and a diagnostic step (S160).

[0151] Step S110 is the step of storing the impedance reference value for each frequency. For example, step S110 can store the impedance reference value through pre-testing. Figure 6 or Figure 7 The impedance reference value information is shown.

[0152] Step S120 is a step of measuring impedance based on frequency changes while applying an AC voltage to the target battery. Step S130 is a step of generating a Nyquist plot of the impedance measurements of the target battery measured in step S120. For example, step S130 can generate a plot such as... Figure 3The Nyquist diagram shown.

[0153] Step S140 is the step of extracting inflection points from the Nyquist plot generated in step S130. For example, in step S140, as in... Figure 9 The implementation described herein allows for the extraction of inflection points from the Nyquist plot.

[0154] Step S150 is a step of comparing the value within a predetermined frequency range centered on the inflection point extracted in step S140 with the impedance reference value for each frequency stored in step S110.

[0155] Furthermore, step S160 is a step of diagnosing the target battery based on the comparison results in step S150. For example, step S160 can be used to classify the target battery's grade.

[0156] For the battery diagnostic methods according to this disclosure, the features described relative to the battery diagnostic equipment can be applied in the same or similar manner, and will not be described in detail.

[0157] This disclosure has been described in detail. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of this disclosure, are given by way of illustration only, because those skilled in the art will understand from the detailed description the various changes and modifications within the scope of this disclosure.

[0158] [Figure Labels]

[0159] 100: Impedance Measurement Module

[0160] 200: Memory Module

[0161] 300: Processor

Claims

1. A battery diagnostic device, the battery diagnostic device comprising: An impedance measurement module is configured to measure impedance based on frequency changes while an AC voltage is applied to a target battery. A memory module configured to store impedance reference values ​​for each frequency; as well as A processor configured to generate a Nyquist plot of impedance measurements of the target battery as measured by the impedance measurement module, extract inflection points from the generated Nyquist plot, and compare impedance measurements within a predetermined frequency range centered on the extracted inflection points with impedance reference values ​​at each frequency stored in the memory module to diagnose the target battery.

2. The battery diagnostic device according to claim 1, in, The processor is configured to search for a predetermined number of impedance points in the high-frequency and / or low-frequency directions starting from the inflection point, and to compare the impedance measurement values ​​of the found impedance points with the impedance reference value.

3. The battery diagnostic device according to claim 1, wherein The processor is configured to shift the generated Nyquist plot such that the extracted inflection point becomes the origin, and compare it with the impedance reference value at each frequency in the shifted state.

4. The battery diagnostic device according to claim 1, wherein, The processor is configured to diagnose the target battery using the magnitude and angle of the impedance measurements.

5. The battery diagnostic device according to claim 1, wherein The memory module is configured to store impedance reference values ​​for each frequency for each of the multiple battery class categories, and The processor is configured to classify the target battery by matching the impedance measurement of the target battery with the battery rating stored in the memory module.

6. The battery diagnostic device according to claim 5, wherein The processor is configured to determine the health status of the target battery based on the target battery's rating.

7. The battery diagnostic device according to claim 1, wherein The processor is configured to extract the points where the anti-charge transfer region bends due to the anti-diffusion region as inflection points in the generated Nyquist plot.

8. The battery diagnostic device according to claim 7, wherein The processor is configured to extract the inflection point by searching the generated Nyquist plot from the low-frequency region to the high-frequency region.

9. The battery diagnostic device according to claim 1, wherein, The memory module is configured to pre-store preliminary frequency information of the inflection point, and The preliminary frequency information is the frequency information that estimates the existence of the inflection point.

10. A battery pack comprising a battery diagnostic device according to any one of claims 1 to 9.

11. An energy storage system comprising a battery diagnostic device according to any one of claims 1 to 9.

12. A battery diagnostic method, the battery diagnostic method comprising the following steps: A storage step, wherein the storage step stores the impedance reference value for each frequency; The measurement step involves measuring impedance based on frequency changes while applying an AC voltage to the target battery. A generation step, wherein the generation step generates a Nyquist plot of the impedance measurements of the target battery measured in the measurement step; The extraction step extracts inflection points from the Nyquist plot generated in the generation step; The comparison step compares impedance measurements within a predetermined frequency range centered on the inflection point extracted in the extraction step with impedance reference values ​​for each frequency stored in the storage step. as well as A diagnostic step, which diagnoses the target battery based on the comparison result of the comparison step.

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