Health status prediction (SOH) device and how to operate it.

Abstract

The invention relates to a State of Health (SOH) predictor that may include a battery, an Electrochemical Impedance Spectroscopy (EIS) data acquisition device constructed to acquire EIS data of the battery, a feature point identification device constructed to identify multiple feature points based on EIS data, a resistance parameter calculation device constructed to calculate resistance parameters related to the impedance of the battery based on impedances related to multiple feature points, and an SOH predictor constructed to predict the SOH of the battery in relation to the resistance parameter based on the resistance parameter corresponding to each of several temperatures and a first function representing the trend between the resistance parameter and the SOH of the battery.

Description

SOH prediction device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2023-0154429, filed on November 9, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to an SOH prediction device and an operating method thereof.

[0005] Research and development on secondary batteries has been actively conducted recently. Here, the term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of a much higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them a popular power source for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] Battery exchange systems exist as a service related to these secondary batteries. These systems can provide users with a service that exchanges discharged batteries for charged ones.

[0007] Such battery replacement systems may utilize methods for measuring the lifespan of batteries used in service. For example, electrochemical impedance spectroscopy (EIS) may be used to measure battery lifespan.

[0008] According to one embodiment disclosed in this document, an SOH prediction device and an operating method thereof for predicting the SOH of a battery pack based on a resistance parameter related to the impedance of the battery pack are provided.

[0009] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention pertains from the description below.

[0010] An SOH prediction device according to an embodiment disclosed in the present document may include a battery pack, an EIS data acquisition unit that acquires EIS (Electrochemical Impedance Spectroscopy) data of the battery pack, a feature identification unit that identifies a plurality of feature points based on the EIS data, a resistance parameter calculation unit that calculates a resistance parameter related to an impedance of the battery pack based on an impedance related to the plurality of feature points, and an SOH prediction unit that predicts an SOH of the battery pack corresponding to the resistance parameter based on a first function representing a tendency between the resistance parameter and the SOH of the battery pack and a resistance parameter corresponding to each of a plurality of temperatures.

[0011] According to one embodiment, the EIS data includes data regarding an EIS graph that depicts the impedance of the battery pack by dividing it into a real part and an imaginary part, and a first characteristic point among the plurality of characteristic points may be determined based on the size of the real part of the impedance among the plurality of points identified based on the EIS graph.

[0012] According to one embodiment, the first characteristic point may be related to a point having the smallest real part value of impedance among a plurality of points identified based on the EIS graph or a point located within a preset range from the point having the smallest real part value.

[0013] According to one embodiment, the second feature point among the plurality of feature points may be related to a local maximum point of the EIS graph or a point located within a preset range from the local maximum point among the plurality of points identified based on the EIS graph.

[0014] According to one embodiment, the local maximum may be associated with a point having the largest absolute value among points having negative imaginary impedance values ​​among points having frequencies greater than the frequencies of points where Warburg impedance exists among a plurality of points identified based on the EIS graph, or a point located within a preset range from a point having the largest absolute value among points having negative imaginary impedance values.

[0015] According to one embodiment, the resistance parameter calculation unit may calculate the resistance parameter based on the difference between the real part value of the impedance associated with the second feature point and the real part value of the impedance associated with the first feature point.

[0016] According to one embodiment, the first function may be generated based on a plurality of SOHs included in the reference SOH and resistance parameters corresponding to each of the plurality of SOHs under preset SOC conditions and preset temperature conditions of the reference battery pack in the resting state.

[0017] According to one embodiment, the first function may include at least one of an n-th order polynomial function (where n is a natural number), an exponential function, and a logarithmic function.

[0018] An operating method of an SOH prediction device according to an embodiment disclosed in the present document may include the steps of acquiring EIS (Electrochemical Impedance Spectroscopy) data of a battery pack, identifying a plurality of feature points based on the EIS data, calculating a resistance parameter related to an impedance of the battery pack based on an impedance associated with the plurality of feature points, and predicting an SOH corresponding to the calculated resistance parameter based on a first function representing a tendency between the resistance parameter and the SOH of the battery pack and a resistance parameter corresponding to each of a plurality of temperatures.

[0019] According to one embodiment, the EIS data includes data regarding an EIS graph that depicts the impedance of the battery pack by dividing it into a real part and an imaginary part, and a first characteristic point among the plurality of characteristic points may be determined based on the size of the real part of the impedance among the plurality of points identified based on the EIS graph.

[0020] According to one embodiment, the first characteristic point may be related to a point having a smallest real part value of impedance among a plurality of points identified based on the EIS graph or a point located within a preset range from the point having the smallest real part value, and the second characteristic point may be related to a maximum point of the EIS graph among a plurality of points identified based on the EIS graph.

[0021] According to one embodiment, the first function may be generated based on a plurality of SOHs included in the reference SOH and resistance parameters corresponding to each of the plurality of SOHs under preset SOC conditions and preset temperature conditions of the reference battery pack in the resting state.

[0022] According to one embodiment, the first function may include at least one of an n-th order polynomial function (where n is a natural number), an exponential function, and a logarithmic function.

[0023] An SOH prediction device according to an embodiment disclosed in this document can predict an SOH according to a resistance parameter without collecting resistance parameters corresponding to each SOH.

[0024] An SOH prediction device according to an embodiment disclosed in this document can obtain a resistance parameter by approximating it through the shape and coordinates of EIS data.

[0025] The effects according to the embodiments disclosed in this document are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art according to the disclosure of this document.

[0026] FIG. 1 is a drawing for explaining an SOH prediction device according to an embodiment disclosed in this document.

[0027] FIG. 2 is a diagram illustrating EIS data according to one embodiment disclosed in this document.

[0028] FIG. 3 is a graph illustrating the trend between resistance parameters and SOH according to one embodiment disclosed in this document.

[0029] FIG. 4 is a diagram for explaining relational data generated according to one embodiment disclosed in this document.

[0030] FIG. 5 is a flowchart for explaining the operation of an SOH prediction device according to an embodiment disclosed in this document.

[0031] Hereinafter, embodiments disclosed in this document will be described in detail with reference to exemplary drawings. When designating components in each drawing, it should be noted that, where possible, identical components are given identical reference numerals, even if they appear in different drawings. Furthermore, when describing embodiments disclosed in this document, detailed descriptions of related known structures or functions will be omitted if they are deemed to hinder understanding of the embodiments disclosed in this document.

[0032] In describing the components of the embodiments disclosed in this document, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only intended to distinguish the components from other components and do not limit the nature, order, or sequence of the components. In addition, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this application.

[0033] FIG. 1 is a drawing for explaining an SOH prediction device according to an embodiment disclosed in this document.

[0034] The battery pack (100) may include a plurality of battery modules (110, 120, 130). Referring to FIG. 1, the battery pack (100) is illustrated as including three battery modules (110, 120, 130), but is not limited thereto, and the battery pack (100) may be configured to include n (n is a natural number) battery modules. Each of the plurality of battery modules (110, 120, 130) may include a plurality of battery cells (not shown). The plurality of battery cells (not shown) may be, but are not limited to, lithium ion (Li-ion) batteries, nickel-hydrogen (Ni-H) batteries, etc.

[0035] The battery pack (100) may be configured to supply power to a target device (not shown), and for this purpose, the battery pack (100) may be electrically connected to the target device (not shown). Here, the target device (not shown) may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (100). For example, the target device (not shown) may be, but is not limited to, a two-wheeled electric vehicle such as an electric vehicle (EV) or an electric scooter. In addition, when the target device is a two-wheeled electric vehicle such as an electric scooter, the battery pack (100) mounted on the two-wheeled electric vehicle may be replaceable through a battery swapping station (BSS).

[0036] The SOH prediction device (200) can predict the SOH (State of Health) of the battery pack (100) based on the resistance parameter of the battery pack (100). Referring to FIG. 1, the SOH prediction device (200) can include an EIS data acquisition unit (210), a feature point identification unit (220), a resistance parameter calculation unit (230), an SOH prediction unit (240), and a memory (250).

[0037] The EIS data acquisition unit (210) can acquire EIS data obtained as a result of EIS (Electrochemical Impedance Spectroscopy) measurement of the battery pack (100). According to an embodiment, the EIS measurement may include an operation of measuring the impedance of the battery pack (100) by applying an AC voltage to the battery pack (100).

[0038] According to one embodiment, the EIS data may include data regarding an EIS graph. For example, the EIS graph may be a Nyquist plot that divides the impedance of a battery pack (100) measured by varying the frequency of an AC voltage into a real part and an imaginary part.

[0039] According to one embodiment, the EIS data acquisition unit (210) may acquire EIS data by directly applying voltage and / or current to the battery pack (100). In this case, the EIS data acquisition unit (210) may include various circuits for applying voltage and / or current to the battery pack (100) and a processor for processing the acquired EIS data.

[0040] According to one embodiment, the EIS data acquisition unit (210) may indirectly acquire EIS data from the battery pack (100). In this case, the EIS data acquisition unit (210) may include a communication module for communicating with the battery pack (100) via wires and / or wirelessly.

[0041] The feature point identification unit (220) can identify a plurality of feature points based on the acquired EIS data. According to an embodiment, the feature point identification unit (220) can identify at least two feature points among the plurality of feature points identified based on the EIS graph included in the EIS data.

[0042] According to an embodiment, among the plurality of characteristic points, the first characteristic point may correspond to a point in the EIS graph where the real part value of the impedance of the battery pack (100) is the smallest or a point where the imaginary part value of the impedance of the battery pack (100) is 0, or may correspond to any one of the points located within a preset range from the point where the real part value of the impedance is the smallest or the imaginary part value of the impedance is 0.

[0043] According to an embodiment, the second characteristic point among the plurality of characteristic points may correspond to any one of the local maximum points in the EIS graph or points located within a preset range from the local maximum points. Here, the local maximum point may correspond to a point having the smallest imaginary impedance value among points having a frequency greater than the frequency of points where Warburg impedance exists among the plurality of points identified based on the EIS graph.

[0044] The resistance parameter calculation unit (230) can calculate a resistance parameter related to the impedance of a first feature point and a second feature point among a plurality of feature points. According to an embodiment, the resistance parameter can be determined based on the difference between the real part value of the impedance corresponding to the second feature point and the real part value of the impedance corresponding to the first feature point.

[0045] The SOH prediction unit (240) can predict the SOH (State of Health) of the battery pack (100) based on the temperature at which the EIS data was acquired and the resistance parameter calculated based on the EIS data. According to an embodiment, the SOH prediction unit (240) can predict the SOH of the battery pack (100) based on a first function indicating a tendency between the resistance parameter and the SOH of the battery pack (100).

[0046] The memory (250) can store data related to the first function. In addition, the memory (250) can store relationship data (not shown) indicating the relationship between the temperature, the resistance parameter, and the SOH of the battery pack (100) based on the first function. According to an embodiment, the memory (260) can include a volatile memory device such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), or a non-volatile memory device such as a read only memory (ROM), a programmable ROM (PROM), or a flash memory.

[0047] Referring to FIG. 1, the memory (250) is illustrated as being included in the SOH prediction device (200), but is not limited thereto, and the memory (250) may be located outside the SOH prediction device (200).

[0048] According to an embodiment, the feature point identification unit (220), the resistance parameter calculation unit (230), and the SOH prediction unit (240) may be implemented as one processor or as separate processors. Here, the processor may execute software to control at least one other component (e.g., hardware or software) of the SOH prediction device (200), or perform operations such as processing and / or calculating various data.

[0049] According to an embodiment, the SOH prediction device (200) may be formed integrally with the battery pack (100). In this case, the SOH prediction device (200) may be included in the BMS (Battery Management System) of the battery pack (100).

[0050] According to an embodiment, the SOH prediction device (200) may be formed separately from the battery pack (100). In this case, the SOH prediction device (200) may be connected to the battery pack (100) via a wired and / or wireless network, etc. In this case, the SOH prediction device (200) may be implemented via a cloud server.

[0051] According to an embodiment, the SOH prediction device (200) can transmit the relationship data and / or the SOH of the battery pack (100) predicted by the SOH prediction unit (240) to an external device (e.g., a cloud server or a user terminal). The cloud server can be configured to provide the predicted SOH of the battery pack (100) to each of a plurality of users, and the user terminal can include a terminal such as a personal computer (PC) or a smartphone.

[0052] According to an embodiment, the SOH prediction device (200) may be included in a BSS (Battery Swapping System). The BSS may be a system having a slot into which a battery pack (100) can be inserted and capable of charging the inserted battery pack (100).

[0053] FIG. 2 is a diagram illustrating EIS data according to one embodiment disclosed in this document.

[0054] Referring to Figure 2, an EIS graph included in the EIS data is schematically illustrated.

[0055] As described above in the detailed description of FIG. 1, EIS data may include data regarding an EIS graph that divides the impedance of a battery pack (100, see FIG. 1) into a real part (Re(Z)) and an imaginary part (Im(Z)) and shows the impedance of the battery pack (100) measured while changing the frequency of the AC power applied to the battery pack. According to an embodiment, the horizontal axis of the EIS graph may be a coordinate axis corresponding to the real part (Re(Z)), and the vertical axis may be a coordinate axis corresponding to the imaginary part (Im(Z)).

[0056] The feature point identification unit (220, see FIG. 1) can identify a first feature point (210) and a second feature point (220) among a plurality of feature points identified based on the EIS graph. According to an embodiment, the first feature point (210) may correspond to a point having the smallest real impedance value among the impedances of the battery pack (100) in the EIS graph or a point having 0 as the imaginary part of the impedance of the battery pack (100), or may correspond to any one of the points located within a preset range from the point having the smallest real part of the impedance or the point having 0 as the imaginary part of the impedance. In addition, the second feature point (220) may correspond to any one of the points located within a preset range from the maximum point in the EIS graph or the maximum point. Here, the maximum point may correspond to a point with the smallest imaginary impedance value among points having a frequency greater than the frequency of points where Warburg impedance exists among a plurality of points identified based on the EIS graph. In other words, the second characteristic point (220) may correspond to a point with the largest absolute value among points where the imaginary impedance value is negative among points having a frequency greater than the frequency of points where Warburg impedance exists, or a point located within a preset range from a point with the largest absolute value among points where the impedance value is negative.

[0057] According to an embodiment, the preset range can be variously set and changed depending on the allowable error range of EIS analysis, etc. In the following, for convenience of explanation, the first characteristic point (210) is assumed to be the point where the real part value of the impedance of the battery pack (100) is the smallest, and the second characteristic point (220) is assumed to be the maximum point. However, the present invention is not limited to these embodiments.

[0058] The resistance parameter calculation unit (230, see FIG. 1) can calculate the resistance parameter (230) based on the first feature point (210) and the second feature point (220). According to an embodiment, the resistance parameter (230) can be determined based on the difference between the real part resistance value of the second feature point (220) and the real part resistance value of the first feature point (210).

[0059] According to an embodiment, the difference between the real resistance value of the impedance of the second characteristic point (220) and the real resistance value of the impedance of the first characteristic point (210) can be approximated to a real multiple of the charge transfer resistance (Rct) exhibited by the electrode reaction of the battery pack (100, see FIG. 1), for example, k*Rct (where k is a real number). For example, the resistance parameter calculation unit (230) can compare the transfer resistance (Rct) of the battery pack (100) obtained based on EIS data with the resistance parameter to determine the value of k.

[0060] According to an embodiment, the value of k may include 0.5. In this case, the resistance parameter calculated by the resistance parameter calculation unit (230) may be approximated to (0.5*Rct), which corresponds to half of the transfer resistance (Rct) of the battery pack (100). However, this is exemplary, and the value of k may be set in various ways depending on the characteristics of the battery pack (100).

[0061] FIG. 3 is a graph for explaining a trend between a resistance parameter and SOH according to an embodiment disclosed in the present document, and FIG. 4 is a diagram for explaining relationship data generated according to an embodiment disclosed in the present document.

[0062] Referring to FIG. 3, a graph is shown showing a trend between a resistance parameter and SOH, where the horizontal axis corresponds to the resistance parameter (R) and the vertical axis corresponds to the SOH of a reference battery pack. Here, the reference battery pack may be a battery pack of the same type as the battery pack (100) illustrated in FIG. 1.

[0063] There may be a trend between the temperature (T) at which the EIS data of the reference battery pack entered the resting period and the resistance parameter (R) of the reference battery pack.

[0064] There may be a trend between the resistance parameter (R) of the reference battery pack that has entered the resting period and the SOH of the reference battery pack.

[0065] According to an embodiment, the first function may be a function representing a tendency between a resistance parameter (R) of a reference battery pack and an SOH of the reference battery pack. For example, the first function may be a function that can be generated based on the SOH (first SOH to Nth SOH) of the reference battery pack corresponding to each of the resistance parameters (R11 to R1N) illustrated in FIGS. 3 and 4, in a reference battery pack under a preset temperature condition (T1) and a preset SOC (State of Charge) condition.

[0066] According to an embodiment, the first function may include an n-th order polynomial function (where n is a natural number). For example, when n is 1, the tendency between the resistance parameters (R11 to R1N) of the reference battery pack and the SOH of the reference battery pack may be expressed in the form of a linear function (ax+b, a, b are real numbers). Here, the coefficients of the linear function and the coefficients of the constant terms may be variously set and changed. In addition, the embodiment disclosed in the present document is not limited to the linear function, and for example, the first function may be a function that can represent the tendency between the resistance parameters of the reference battery pack and the SOH of the reference battery pack, such as a logarithmic function, an exponential function (where the exponent is an integer, an irrational number, a rational number, etc.).

[0067] According to an embodiment, when the first function is an n-th order polynomial function, at least (n+1) data between the resistance parameters of the reference battery pack and the SOH of the reference battery pack may be required to derive the first function. For example, when the first function is expressed in the form of a linear function, at least two data between the resistance parameters and the SOH may be required. However, this is a minimum standard for deriving the first function, and the accuracy of the first function may increase as more data exist to derive the first function. When the first function is expressed as an n-th order polynomial function, the SOH prediction unit (240) can derive the first function based on at least (n+1) data, and can utilize more data between the resistance parameters and the SOH to derive a more precise first function.

[0068] According to an embodiment, the first function may be different depending on the preset temperature condition and the preset SOC condition.

[0069] According to an embodiment, the number of data between the resistance parameter of the reference battery pack and the SOH of the reference battery pack required to derive the first function can be variously changed and applied depending on the design.

[0070] Referring to FIG. 4, relational data that can be generated based on the first function described above is illustrated. The relational data can be stored in memory (250) in the form of a look-up table.

[0071] The relationship data may include data indicating a relationship between the temperature at which the EIS data of the reference battery pack was acquired, the SOH of the reference battery pack, and the resistance parameters of the reference battery pack. According to an embodiment, the relationship data may include data (261b) related to the resistance parameters (R_11 to R_MN) at a specific temperature (T1) and the SOH (the first SOH to the Nth SOH) of the reference battery pack corresponding to each of the resistance parameters (R_11 to R_MN), and data (261a) related to the resistance parameters (R_11 to R_M1) at a specific SOH (the first SOH) and the temperatures (T1 to TM) corresponding to each of the resistance parameters (R_11 to R_M1). Although relationship data between M temperatures (T1 to T5) and N SOHs (the first SOH to the Nth SOH) is illustrated in FIG. 4 , this is exemplary.

[0072] According to one embodiment, the relationship data may include resistance parameters corresponding to a plurality of temperatures (T1 to TM) and an SOH of a reference battery pack at each of the plurality of temperatures. For example, the relationship data may include data related to resistance parameters (R_11 to R_1N) corresponding to each of a plurality of SOHs (a first SOH to an Nth SOH) at a first temperature (T1). Here, the plurality of temperatures (T1 to TM) are illustrated as M, but the number of the plurality of temperatures included in the relationship data and the number of resistance parameters at each of the plurality of temperatures and / or the intervals between the temperatures may be variously changed and applied depending on the design. For example, the relationship data may include data about resistance parameters corresponding to each of a plurality of SOHs at each of the M temperatures and at each of the M temperatures in the range of 10 degrees Celsius to 45 degrees Celsius, and in addition to this example, the relationship data may include data about resistance parameters corresponding to each of a plurality of temperatures and at each of the plurality of SOHs in a range of temperatures other than the range of 10 degrees Celsius to 45 degrees Celsius, and for example, the intervals between the M temperatures may be variously set, such as 1 degree, 0.5 degrees, 2 degrees, etc.

[0073] According to an embodiment, the SOH prediction unit (240, see FIG. 1) can supplement the relationship data based on the first function described above in the description of FIG. 3. For example, the SOH prediction unit (240) can obtain an SOH corresponding to an acquired resistance parameter not shown in FIG. 4 based on the first function.

[0074] Predicting battery pack lifespan through EIS analysis requires repeatedly collecting EIS data under varying conditions (e.g., temperature, SOC, SOH). However, this process of repeatedly collecting EIS data can be time-consuming.

[0075] An SOH prediction device according to an embodiment disclosed in this document can predict the SOH of a battery pack based on a function obtained by changing conditions in which a trend exists, without having to collect EIS data under various conditions (e.g., all SOH). In addition, an SOH prediction device according to an embodiment disclosed in this document can utilize a resistance parameter calculated based on the impedance between a first feature point and a second feature point as an approximation of a real number multiple of the transfer resistance, and thus can predict the SOH of a battery pack without having to measure the transfer resistance of the battery pack.

[0076] FIG. 5 is a flowchart for explaining the operation of an SOH prediction device according to an embodiment disclosed in this document.

[0077] In step S501, the SOH prediction device (200, see FIG. 1) can obtain EIS data of the battery pack (100, see FIG. 1). According to an embodiment, the EIS data can include data regarding an EIS graph that divides the impedance of the battery pack (100) into real and imaginary parts.

[0078] In step S503, the SOH prediction device (200) can identify a plurality of characteristic points based on EIS data. According to an embodiment, a first characteristic point among the plurality of characteristic points may correspond to a point in the EIS graph where the real part value of the impedance of the battery pack (100) is the smallest, or a point where the imaginary part value of the impedance of the battery pack (100) is 0, or any one of points located within a preset range from the point where the real part value of the impedance is the smallest, or the point where the imaginary part value of the impedance is 0. In addition, according to an embodiment, a second characteristic point among the plurality of characteristic points may correspond to any one of points located within a preset range from a local maximum point in the EIS graph. Here, the local maximum point may correspond to a point where the imaginary impedance value is the smallest among points having a frequency greater than a frequency of points where Warburg impedance exists among the plurality of points identified based on the EIS graph. In other words, the second characteristic point (220) may correspond to a point with the largest absolute value among points with a negative imaginary impedance value among points having a frequency greater than the frequency of points where Warburg impedance exists.

[0079] In step S505, the SOH prediction device (200) can calculate a resistance parameter of the battery pack (100) based on impedances associated with a plurality of feature points. According to an embodiment, the resistance parameter can be determined based on a difference between a real part value of the impedance corresponding to the second feature point and a real part value of the impedance corresponding to the first feature point.

[0080] At step S507, the SOH prediction device (200) can predict the SOH of the battery pack (100) based on the first function.

[0081] According to an embodiment, the first function may be generated based on a plurality of SOHs included in the reference SOH and resistance parameters corresponding to each of the plurality of SOHs under preset SOC conditions and preset temperature conditions of the reference battery pack in the resting state.

[0082] According to an embodiment, the first function may be a function representing a trend between a resistance parameter (R) of a reference battery pack and an SOH of the reference battery pack.

[0083] According to an embodiment, the first function may include an n-th order polynomial function (where n is a natural number).

[0084] In the above, all components constituting the embodiments have been described as being combined or operating in combination as one. However, this is not necessarily limited to such embodiments, and within the scope of the purpose, all components may be selectively combined and operated in one or more combinations. Furthermore, terms such as "include," "comprise," or "have" described above, unless specifically stated to the contrary, imply that the corresponding component may be inherent, and therefore should be interpreted to include other components rather than excluding other components.

[0085] The above description is merely an example of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document.

[0086] Accordingly, the embodiments disclosed in this document are intended to illustrate, rather than limit, the technical concepts disclosed in this document, and the scope of the technical concepts disclosed in this document is not limited by these embodiments. The scope of protection of the technical concepts disclosed in this document should be interpreted by the claims below, and all technical concepts within the equivalent scope should be interpreted as being included within the scope of the rights of this document.

[0087] [Explanation of symbols]

[0088] 100: Battery pack

[0089] 110, 120, 130, 140: Battery modules

[0090] 200: SOH prediction device

[0091] 210: EIS data acquisition unit

[0092] 220: Feature point identification section

[0093] 230: Resistance parameter calculation unit

[0094] 240: SOH Prediction Department

[0095] 250: Memory

Claims

1. Battery pack; An EIS data acquisition unit for acquiring EIS (Electrochemical Impedance Spectroscopy) data of the above battery pack; A feature point identification unit that identifies a plurality of feature points based on the above EIS data; A resistance parameter calculation unit that calculates a resistance parameter related to the impedance of the battery pack based on the impedance related to the plurality of characteristic points; and An SOH prediction device comprising: an SOH prediction unit that predicts the SOH of the battery pack corresponding to the resistance parameter based on a resistance parameter corresponding to each of a plurality of temperatures and a first function indicating a tendency between the resistance parameter and the SOH of the battery pack; 2. In paragraph 1, The above EIS data includes data regarding an EIS graph that depicts the impedance of the battery pack divided into real and imaginary parts, Among the above multiple feature points, the first feature point is: An SOH prediction device determined based on the size of the real part of impedance among a plurality of points identified based on the above EIS graph.

3. In the second paragraph, the first characteristic point is, An SOH prediction device associated with a point having the smallest real part value of impedance among a plurality of points identified based on the above EIS graph or a point located within a preset range from the point having the smallest real part value.

4. In paragraph 3, The second feature point among the above multiple feature points is, An SOH prediction device associated with a point located at a maximum point of the EIS graph or within a preset range from the maximum point among a plurality of points identified based on the EIS graph.

5. In the fourth paragraph, the maximum point is An SOH prediction device associated with a point located within a preset range from a point having the largest absolute value among points having negative imaginary impedance values ​​among points having frequencies greater than the frequencies of points having Warburg impedance among a plurality of points identified based on the EIS graph, or a point having the largest absolute value among points having negative imaginary impedance values.

6. In the fourth paragraph, the resistance parameter calculation unit, An SOH prediction device that calculates the resistance parameter based on the difference between the real part value of the impedance associated with the second feature point and the real part value of the impedance associated with the first feature point.

7. In the 6th paragraph, the first function is, An SOH prediction device generated based on a plurality of SOHs included in the reference SOH and resistance parameters corresponding to each of the plurality of SOHs under preset SOC conditions and preset temperature conditions of the reference battery pack in the above-mentioned rest state.

8. In the 7th paragraph, the first function is, An SOH prediction device including at least one of an n-th order polynomial function (where n is a natural number), an exponential function, and a logarithmic function.

9. Step of acquiring EIS (Electrochemical Impedance Spectroscopy) data of the battery pack; A step of identifying a plurality of feature points based on the above EIS data; A step of calculating a resistance parameter related to the impedance of the battery pack based on the impedance associated with the plurality of feature points; and An operating method of an SOH prediction device, comprising: a step of predicting an SOH corresponding to the calculated resistance parameter based on a resistance parameter corresponding to each of a plurality of temperatures and a first function indicating a tendency between the resistance parameter and the SOH of the battery pack; 10. In paragraph 9, The above EIS data includes data regarding an EIS graph that depicts the impedance of the battery pack divided into real and imaginary parts, Among the above multiple feature points, the first feature point is: An operating method of an SOH prediction device determined based on the size of the real part of impedance among a plurality of points identified based on the above EIS graph.

11. In paragraph 10, The above first characteristic point is, Among the multiple points identified based on the EIS graph, the point having the smallest real part value of the impedance or the point located within a preset range from the point having the smallest real part value, The second characteristic point is, An operating method of an SOH prediction device related to a point located within a preset range from a maximum point of the EIS graph or a point among a plurality of points identified based on the EIS graph.

12. In paragraph 9, The above first function is an operating method of an SOH prediction device generated based on a plurality of SOHs included in the reference SOH and resistance parameters corresponding to each of the plurality of SOHs under the preset SOC conditions and preset temperature conditions of the reference battery pack in the resting state.

13. In the 12th paragraph, the first function is, An operating method of an SOH prediction device including at least one of an n-th order polynomial function (where n is a natural number), an exponential function, and a logarithmic function.

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