Soh prediction apparatus and method of operation thereof

Abstract

An SOH prediction device according to one embodiment disclosed in the present document includes: a battery pack; an electrochemical impedance spectroscopy (EIS) data acquisition unit configured to acquire EIS data of the battery pack; a feature point identification unit configured to identify a plurality of feature points based on the EIS data; a resistance parameter calculation unit configured to calculate a resistance parameter of the battery pack based on impedances associated with the plurality of feature points; and an SOH prediction unit configured to predict an SOH of the battery pack corresponding to the resistance parameter based on resistance parameters corresponding to a plurality of temperatures, respectively, and a first function indicating a trend between the resistance parameters and the SOH of the battery pack.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0154429, filed with the Korean Intellectual Property Office on November 9, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to a health status (SOH) prediction device and its operation method. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively underway. Here, a rechargeable battery is a battery capable of being charged and discharged, and includes conventional Ni / Cd batteries, Ni / MH batteries, and all recent lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have the following advantages: they have a much higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be made smaller and lighter, and are therefore being used as power sources for mobile devices, and recently, due to their expanding applications in electric vehicles, they are attracting attention as a next-generation energy storage medium.

[0005] Battery swapping systems exist as a service associated with these secondary batteries. These systems provide users with the service of exchanging discharged batteries for rechargeable ones.

[0006] Such battery exchange systems can utilize methods for measuring the lifespan of the batteries used for servicing. For example, electrochemical impedance spectroscopy (EIS) can be used as a method for measuring battery lifespan. Summary of the Invention

[0007] Technical issues

[0008] One embodiment disclosed herein relates to an SOH prediction device and a method thereof for predicting the state of health (SOH) of a battery pack based on resistance parameters related to the impedance of the battery pack.

[0009] The embodiments disclosed herein are not limited to the purposes described above, and other purposes not described will be clearly understood by those skilled in the art from the following description.

[0010] Technical solution

[0011] A state of health (SOH) prediction device according to one embodiment disclosed herein may include: a battery pack; an electrochemical impedance spectroscopy (EIS) data acquisition unit configured to acquire EIS data of the battery pack; a feature point identification unit configured to identify a plurality of feature points based on the EIS data; a resistance parameter calculation unit configured to calculate a resistance parameter related to the impedance of the battery pack based on the impedance associated with the plurality of feature points; and an SOH prediction unit configured to predict the SOH of the battery pack corresponding to the resistance parameter based on the resistance parameter corresponding to each of a plurality of temperatures and a first function representing the trend between the resistance parameter and the SOH of the battery pack.

[0012] According to one embodiment, the EIS data may include data about an EIS diagram shown by dividing the impedance of the battery pack into real and imaginary parts, and a first feature point among a plurality of feature points may be determined based on the magnitude of the real part of the impedance among a plurality of points identified based on the EIS diagram.

[0013] According to one implementation, the first feature point may be associated with the point with the smallest real part of impedance among a plurality of points identified based on the EIS diagram, or with a point located within a preset range from the point with the smallest real part.

[0014] According to one implementation, a second feature point among a plurality of feature points may be associated with the maximum point of the EIS map among a plurality of points identified based on the EIS map, or with a point located within a preset range from the maximum point.

[0015] According to one implementation, the maximum point can be associated with the point with the largest absolute value among points with a negative imaginary impedance value among points with a higher frequency than the point where Warburg impedance exists, or a point located within a predetermined range from the point with the largest absolute value among points with a negative imaginary impedance value.

[0016] According to one embodiment, the resistance parameter calculation unit can calculate the resistance parameter based on the difference between the real part of the impedance associated with the second feature point and the real part of the impedance associated with the first feature point.

[0017] According to one implementation, the first function can be generated based on a plurality of SOHs included in a reference SOH under a preset SOC condition and a preset temperature condition of a reference battery pack in an idle state, as well as the resistance parameter corresponding to each of the plurality of SOHs.

[0018] According to one implementation, the first function may include at least one of an n-order (n is a natural number) polynomial function, an exponential function, and a logarithmic function.

[0019] A method for predicting the state of operational health (SOH) according to one embodiment disclosed herein may include: acquiring electrochemical impedance spectroscopy (EIS) data of a battery pack; identifying multiple feature points based on the EIS data; calculating impedance-related resistance parameters of the battery pack based on impedances associated with the multiple feature points; and predicting the SOH of the battery pack corresponding to the calculated resistance parameters based on resistance parameters corresponding to each of a plurality of temperatures and a first function representing the trend between the resistance parameters and the SOH of the battery pack.

[0020] According to one embodiment, the EIS data may include data about an EIS diagram shown by dividing the impedance of the battery pack into real and imaginary parts, and a first feature point among a plurality of feature points may be determined based on the magnitude of the real part of the impedance among a plurality of points identified based on the EIS diagram.

[0021] According to one implementation, the first feature point may be associated with the point with the smallest real part of impedance among a plurality of points identified based on the EIS diagram, or with a point located within a preset range from the point with the smallest real part, and the second feature point may be associated with the point with the largest value in the EIS diagram among a plurality of points identified based on the EIS diagram.

[0022] According to one implementation, the first function can be generated based on a plurality of SOHs included in a reference SOH under a preset SOC condition and a preset temperature condition of a reference battery pack in an idle state, as well as the resistance parameter corresponding to each of the plurality of SOHs.

[0023] According to one implementation, the first function may include at least one of an n-order (n is a natural number) polynomial function, an exponential function, and a logarithmic function.

[0024] Beneficial effects

[0025] A SOH prediction device according to one embodiment disclosed herein can predict SOH based on resistance parameters without collecting resistance parameters corresponding to each SOH in all SOHs.

[0026] According to one embodiment disclosed herein, the SOH prediction device can approximate the resistance parameters by using patterns and coordinates of EIS data to obtain approximate resistance parameters.

[0027] The effects of one embodiment disclosed herein are not limited to those described above, and those skilled in the art will be able to clearly understand other effects not described based on the disclosure of this document. Attached Figure Description

[0028] Figure 1 This is a view used to describe a health status (SOH) prediction device according to one embodiment disclosed herein.

[0029] Figure 2 This is a view used to describe electrochemical impedance spectroscopy (EIS) data according to one embodiment disclosed herein.

[0030] Figure 3 It is a graph used to describe the trend between the resistance parameter and SOH according to one embodiment disclosed herein.

[0031] Figure 4 It is a view used to describe relational data generated according to one embodiment disclosed herein.

[0032] Figure 5 This is a flowchart describing the operation of an SOH prediction device according to one embodiment disclosed herein. Detailed Implementation

[0033] In the following, the embodiments disclosed herein will be described in detail with reference to the exemplary accompanying drawings. When adding reference numerals to components in each drawing, it should be noted that they will, as far as possible, have the same reference numerals even when the same components are shown in different drawings. Furthermore, in describing the embodiments disclosed herein, detailed descriptions will be omitted when it is determined that a detailed description of the associated known configuration or function obscures the understanding of the embodiments disclosed herein.

[0034] Terms such as first, second, A, B, (a), and (b) may be used to describe components of the embodiments disclosed in this document. These terms are used only for the purpose of distinguishing one component from another, and the nature, order, sequence, etc., of the corresponding components are not limited by these terms. Furthermore, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Unless expressly defined in this application, terms defined in commonly used dictionaries should be interpreted as meanings matching the meanings of terms from the context of the relevant art, and should not be interpreted as ideal or overly formal meanings.

[0035] Figure 1 This is a view used to describe a health status (SOH) prediction device according to one embodiment disclosed herein.

[0036] Battery pack 100 may include multiple battery modules 110, 120, and 130. (See reference...) Figure 1The diagram shows a battery pack 100 comprising three battery modules 110, 120, and 130, but is not limited thereto, and the battery pack 100 may include n (n is a natural number) battery modules. Each of the plurality of battery modules 110, 120, and 130 may include a plurality of battery cells (not shown). The plurality of battery cells (not shown) may be lithium-ion (Li-ion) batteries, nickel-metal hydride (Ni-H) batteries, etc., but is not limited thereto.

[0037] The battery pack 100 can be configured to supply power to a target device (not shown), and for this purpose, the battery pack 100 can 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, and for example, the target device (not shown) may be an electric vehicle (EV) or a two-wheeled electric vehicle such as an electric scooter, but is not limited thereto. Furthermore, in the case where the target device is a two-wheeled electric vehicle such as an electric scooter, the battery pack 100 installed on the two-wheeled electric vehicle can be replaced via a battery swapping station (BSS).

[0038] The SOH prediction device 200 can predict the SOH of the battery pack 100 based on the resistance parameters of the battery pack 100. (Refer to...) Figure 1 The SOH prediction device 200 may 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.

[0039] The EIS data acquisition unit 210 can acquire EIS data as a result of EIS measurements of the battery pack 100. According to an embodiment, the EIS measurement may include measuring the impedance of the battery pack 100 by applying an AC voltage to the battery pack 100.

[0040] According to the implementation, the EIS data may include data about the EIS plot. For example, the EIS plot may be a Nyquist plot as shown below, which divides the impedance of the battery pack 100, measured by changing the frequency of the AC voltage, into real and imaginary parts.

[0041] According to one embodiment, the EIS data acquisition unit 210 can 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.

[0042] According to one embodiment, the EIS data acquisition unit 210 can 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 in a wired and / or wireless manner.

[0043] The feature point recognition unit 220 can identify multiple feature points based on the acquired EIS data. According to one embodiment, the feature point recognition unit 220 can identify at least two feature points among multiple feature points identified based on the EIS map included in the EIS data.

[0044] According to the implementation method, the first feature point among the plurality of feature points may correspond to the point in the EIS diagram where the real part of the impedance of the battery pack 100 is the smallest or the point where the imaginary part of the impedance of the battery pack 100 is zero, or correspond to any point within a preset range from the point where the real part of the impedance is the smallest or the point where the imaginary part of the impedance is zero.

[0045] According to the implementation method, the second feature point among the plurality of feature points may correspond to the maximum point in the EIS diagram or one of the points located within a preset range from the maximum point. Here, the maximum point may correspond to the point with the smallest imaginary impedance value among the points identified based on the EIS diagram that have a higher frequency than the point where Warburg impedance exists.

[0046] The resistance parameter calculation unit 230 can calculate resistance parameters 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 parameters can be determined based on the difference between the real part of the impedance corresponding to the second feature point and the real part of the impedance corresponding to the first feature point.

[0047] The SOH prediction unit 240 can predict the SOH of the battery pack 100 based on the temperature at which EIS data is acquired and the resistance parameters 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 representing the trend between the resistance parameters and the SOH of the battery pack 100.

[0048] Memory 250 may store data related to the first function. Additionally, memory 250 may store relational data (not shown) based on the first function, indicating the relationship between the temperature, resistance parameters, and SOH of the battery pack 100. According to embodiments, memory 260 may include a volatile memory device such as static random access memory (SRAM) or dynamic random access memory (DRAM), or a non-volatile memory device such as read-only memory (ROM), programmable ROM (PROM), or flash memory.

[0049] Reference Figure 1 The diagram shows that the memory 250 is 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.

[0050] According to the implementation, the feature point recognition unit 220, the resistance parameter calculation unit 230, and the SOH prediction unit 240 can be implemented as a single processor or a separate processor. Here, the processor can execute software to control one or more other components (e.g., hardware or software) of the SOH prediction device 200 or perform operations to process and / or calculate various data.

[0051] According to one embodiment, the SOH prediction device 200 can be integrally formed with the battery pack 100. In this case, the SOH prediction device 200 can be included in the battery management system (BMS) of the battery pack 100.

[0052] According to one embodiment, the SOH prediction device 200 can be formed separately from the battery pack 100. In this case, the SOH prediction device 200 can be connected to the battery pack 100 via a wired and / or wireless network. In this case, the SOH prediction device 200 can be implemented via a cloud server.

[0053] According to an implementation, the SOH prediction device 200 can send the SOH and / or relationship data 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 may include a terminal such as a personal computer (PC) or a smartphone.

[0054] According to one embodiment, the SOH prediction device 200 may be included in a BSS. The BSS may be a system that has a slot into which a battery pack 100 can be inserted and charges the inserted battery pack 100.

[0055] Figure 2 This is a view used to describe electrochemical impedance spectroscopy (EIS) data according to one embodiment disclosed herein.

[0056] Reference Figure 2 The diagram illustrates the EIS plot included in the EIS data.

[0057] As above Figure 1 As described in the detailed description, the EIS data may include data about the EIS diagram shown below: by changing the direction of the battery pack 100 (see... Figure 1The impedance of the battery pack 100, measured based on the frequency of the applied AC power, is divided into a real part Re(Z) and an imaginary part Im(Z). According to an embodiment, the horizontal axis of the EIS graph can be a coordinate axis corresponding to the real part Re(Z), and the vertical axis can be a coordinate axis corresponding to the imaginary part Im(Z).

[0058] Feature point recognition unit 220 (see Figure 1 The system can identify a first feature point 210 and a second feature point 220 among multiple feature points identified based on the EIS diagram. According to an embodiment, the first feature point 210 may correspond to a point in the EIS diagram where the real part of the impedance of the battery pack 100 has the minimum impedance, or a point where the imaginary part of the impedance of the battery pack 100 is zero, or any point within a preset range from the point where the real part of the impedance is the minimum or the point where the imaginary part of the impedance is zero. Furthermore, the second feature point 220 may correspond to a maximum point in the EIS diagram or one of the points within a preset range from the maximum point. Here, the maximum point may correspond to the point with the smallest imaginary impedance value among the multiple points identified based on the EIS diagram that has a higher frequency than the point where Warburg impedance exists. That is, the second feature point 220 may correspond to the point with the largest absolute value among the points with negative imaginary impedance values ​​among the points with higher frequencies than the point where Warburg impedance exists, or a point within a preset range from the point with the largest absolute value among the points with negative impedance values.

[0059] According to the implementation method, the preset range can be set and changed in various ways according to the allowable error range of EIS analysis. In the following description, for ease of description, it will be described by the following assumptions: the first feature point 210 is the point where the real part of the impedance of the battery pack 100 is the minimum, and the second feature point 220 is the maximum point, but this document is not limited to such an implementation.

[0060] Resistance parameter calculation unit 230 (see) Figure 1 The resistance parameter 230 can be calculated 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 resistance value of the second feature point 220 and the real resistance value of the first feature point 210.

[0061] According to the embodiment, the difference between the real resistance value of the impedance of the second feature point 220 and the real resistance value of the impedance of the first feature point 210 can be approximated by the difference between the real resistance value of the impedance of the battery pack 100 (see embodiment 220). Figure 1 The resistance parameter calculation unit 230 can compare the charge transfer resistance Rct of the battery pack 100 obtained based on EIS data with the resistance parameter and determine the value of k.

[0062] According to the implementation, the value of k may include 0.5. In this case, the resistance parameter calculated by the resistance parameter calculation unit 230 can be approximated as half of the charge transfer resistance Rct of the battery pack 100 (0.5*Rct). However, this is exemplary, and the value of k can be set in various ways according to the characteristics of the battery pack 100.

[0063] Figure 3 It is a graph used to describe the trend between the resistance parameters and SOH according to one embodiment disclosed herein, and Figure 4 It is a view used to describe relational data generated according to one embodiment disclosed herein.

[0064] Figure 3 A graph showing the trend between the resistance parameter R and the state of equilibrium (SOH) is presented, 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 can be... Figure 1 The battery pack shown is of the same type as the battery pack 100.

[0065] There may be a trend between the temperature T and the resistance parameter R of the reference battery pack when acquiring EIS data of the reference battery pack entering the idle zone.

[0066] There may be a trend between the resistance parameter R of the reference battery pack entering the idle section and the SOH of the reference battery pack.

[0067] According to an implementation, the first function may be a function representing the trend between the resistance parameter R of the reference battery pack and the state of charge (SOH) of the reference battery pack. For example, the first function may be a function based on the relationship between the resistance parameter R of the reference battery pack and the state of charge (SOH) of the reference battery pack under preset temperature conditions T1 and preset SOC conditions. Figure 3 and Figure 4 The SOH (first SOH to Nth SOH) of the reference battery pack corresponding to the resistance parameters R11 to R1N shown are generated.

[0068] According to the implementation, the first function may include an nth-order (n is a natural number) polynomial function. For example, when n is 1, the trend between the resistance parameters R11 to R1N of the reference battery pack and the state of equilibrium (SOH) of the reference battery pack can be represented by a linear function (ax + b, where a and b are real numbers). Here, the coefficients of the linear function and the coefficients of the constant term can be set and changed in various ways. Furthermore, the implementation disclosed herein is not limited to linear functions, and for example, the first function may be a function that can represent the trend between the resistance parameters of the reference battery pack and the SOH of the reference battery pack, such as a logarithmic function or an exponential function (here, the exponent is an integer, an irrational number, or a rational number).

[0069] According to the implementation, when the first function is an nth-order polynomial function, at least (n+1) data points between the resistance parameters of the reference battery pack and the state of equilibrium (SOH) of the reference battery pack may be required to derive the first function. For example, when the first function is represented as a linear function, at least two data points between the resistance parameters and the SOH may be required. However, this is the minimum standard for deriving the first function, and the more data used to derive the first function, the higher the accuracy of the first function can be. When the first function is represented by an nth-order polynomial function, the SOH prediction unit 240 can derive the first function based on at least (n+1) data points, and use more data points between the resistance parameters and the SOH to derive a more complex first function.

[0070] According to the implementation method, the first function can vary according to predetermined temperature conditions and predetermined SOC conditions.

[0071] According to the implementation method, the amount of data between the resistance parameters of the reference battery pack and the SOH of the reference battery pack required to derive the first function can be applied in various ways according to the design.

[0072] Reference Figure 4 This illustrates relational data that can be generated based on the first function described above. The relational data can be stored in memory 250 in the form of a lookup table.

[0073] The relational data may include data indicating the relationship between the temperature at which the EIS data of the reference battery pack was acquired, the state of equilibrium (SOH) of the reference battery pack, and the resistance parameters of the reference battery pack. According to an embodiment, the relational data may include: data 261b regarding the resistance parameters R_11 to R_MN at a specific temperature T1 and the SOH (first SOH to Nth SOH) of the reference battery pack corresponding to the resistance parameters R_11 to R_MN; and data 261a regarding the resistance parameters R_11 to R_M1 at a specific SOH (first SOH) and the temperatures T1 to TM corresponding to the resistance parameters R_11 to R_M1. Figure 4 The diagram shows data relating M temperatures T1 to T5 to N SOHs (first SOH to Nth SOH), which is an example.

[0074] According to an implementation, the relational data may include multiple temperatures T1 to TM and resistance parameters corresponding to the state of resistance (SOH) of a reference battery pack at each of the multiple temperatures. For example, the relational data may include data on resistance parameters R_11 to R_1N corresponding to multiple SOHs (first SOH to Nth SOH) at a first temperature T1. Here, the number of multiple temperatures T1 to TM is shown as M, but the multiple temperatures included in the relational data, the number of resistance parameters at each of the multiple temperatures, and / or the intervals between temperatures can be applied in various ways according to the design. For example, the relational data may include data on M temperatures in the range of 10°C to 45°C and resistance parameters corresponding to multiple SOHs at the M temperatures, and data on multiple temperatures in various temperature ranges other than the range of 10°C to 45°C and resistance parameters corresponding to multiple SOHs at the multiple temperatures, and the intervals between the M temperatures may be set in various ways, such as 1°C, 0.5°C, or 2°C.

[0075] According to the implementation method, the SOH prediction unit 240 (see...) Figure 1 ) can be based on Figure 3 The first function mentioned above is used to supplement the relational data. For example, the SOH prediction unit 240 can obtain the resistance parameters obtained based on the first function ( Figure 4 The corresponding SOH (not shown in the image)

[0076] To predict battery pack lifespan through EIS analysis, EIS data needs to be collected repeatedly by varying various conditions (e.g., temperature, SOC, and SOH). However, as mentioned above, the process of repeatedly collecting EIS data can be time-consuming.

[0077] The SOH prediction device according to one embodiment disclosed herein can predict the SOH of the battery pack based on a function obtained by changing the conditions under which a trend exists, without having to collect EIS data under any conditions (e.g., all SOH). Furthermore, since the SOH prediction device according to one embodiment disclosed herein can approximate the resistance parameter calculated based on the impedance between the first and second feature points as a real multiple of the charge transfer resistance, the SOH of the battery pack can be predicted without measuring the charge transfer resistance of the battery pack.

[0078] Figure 5 This is a flowchart describing the operation of an SOH prediction device according to one embodiment disclosed herein.

[0079] In operation S501, the SOH prediction device 200 (see...) Figure 1 You can get a battery pack of 100 (see) Figure 1The EIS data may include data about an EIS diagram showing the impedance of the battery pack 100 by dividing it into real and imaginary parts, according to an embodiment.

[0080] In operation S503, the SOH prediction device 200 can identify multiple feature points based on EIS data. According to an embodiment, a first feature point among the multiple feature points may correspond to the point in the EIS diagram where the real part of the impedance of the battery pack 100 is the smallest, or the point where the imaginary part of the impedance of the battery pack 100 is zero, or any point within a preset range from the point where the real part of the impedance is the smallest or the point where the imaginary part of the impedance is zero. Furthermore, according to an embodiment, a second feature point among the multiple feature points may correspond to the maximum point in the EIS diagram or one of the points within a preset range from the maximum point. Here, the maximum point may correspond to the point with the smallest imaginary impedance value among the multiple points identified based on the EIS diagram that has a higher frequency than the point where Warburg impedance exists. That is, the second feature point 220 may correspond to the point with the largest absolute value among the points with a negative imaginary impedance value among the points that have a higher frequency than the point where Warburg impedance exists.

[0081] In operation S505, the SOH prediction device 200 can calculate the resistance parameters of the battery pack 100 based on the impedance associated with multiple feature points. According to an embodiment, the resistance parameters can be determined based on the difference between the real part of the impedance corresponding to the second feature point and the real part of the impedance corresponding to the first feature point.

[0082] In operation S507, the SOH prediction device 200 can calculate the SOH of the battery pack 100 based on the first function.

[0083] According to the implementation method, the first function can be generated based on a plurality of SOHs included in a reference SOH under a preset SOC condition and a preset temperature condition of a reference battery pack in an idle state, as well as the resistance parameter corresponding to each of the plurality of SOHs.

[0084] According to the implementation method, the first function may be a function representing the trend between the resistance parameter R of the reference battery pack and the SOH of the reference battery pack.

[0085] According to the implementation method, the first function may include an nth-order (n is a natural number) polynomial function.

[0086] All components constituting the embodiments have been described above as operating as one or by means of coupling, but are not necessarily limited to these embodiments, and within the scope of this purpose, one or more of all components may operate by selective coupling. Furthermore, unless otherwise stated, the foregoing terms such as “comprising,” “constituting,” or “having” mean that the corresponding component may be inherent and should therefore be interpreted as including other components rather than excluding other components.

[0087] The above description is merely an exemplary description of the technical spirit disclosed herein, and those skilled in the art to which the embodiments disclosed herein pertain will be able to make various modifications and alterations to the embodiments without departing from the essential characteristics of the embodiments disclosed herein.

[0088] Therefore, the embodiments disclosed herein are not intended to limit the technical spirit disclosed herein, but rather to describe the technical spirit disclosed herein, and the scope of the technical spirit disclosed herein is not limited by these embodiments. The scope of the technical spirit disclosed herein should be interpreted by the appended claims, and all technical spirit within the equivalent scope should be interpreted as including within the scope of this document.

[0089] [Description of reference numerals in the attached figures]

[0090] 100: Battery pack

[0091] 110, 120, 130, 140: Battery modules

[0092] 200: SOH Prediction Device

[0093] 210: EIS Data Acquisition Unit

[0094] 220: Feature Point Recognition Unit

[0095] 230: Resistance Parameter Calculation Unit

[0096] 240: SOH Prediction Unit

[0097] 250: Memory

Claims

1. A health status (SOH) prediction device, comprising: Battery pack; An electrochemical impedance spectroscopy (EIS) data acquisition unit is configured to acquire EIS data of the battery pack; A feature point recognition unit is configured to recognize multiple feature points based on the EIS data; A resistance parameter calculation unit is configured to calculate resistance parameters related to the impedance of the battery pack based on the impedance associated with the plurality of feature points; as well as The SOH prediction unit is configured to predict 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 representing the trend between the resistance parameter and the SOH of the battery pack.

2. The SOH prediction device according to claim 1, wherein, The EIS data includes data about an EIS diagram shown by dividing the impedance of the battery pack into real and imaginary parts, and The first feature point among the plurality of feature points is determined based on the magnitude of the real part of the impedance among the plurality of points identified based on the EIS diagram.

3. The SOH prediction device according to claim 2, wherein, The first feature point is associated with the point with the smallest real part of impedance among multiple points identified based on the EIS diagram, or a point located within a preset range from the point with the smallest real part.

4. The SOH prediction device according to claim 3, wherein, The second feature point among the plurality of feature points is related to the largest point in the EIS map among the plurality of points identified based on the EIS map, or a point located within a preset range from the largest point.

5. The SOH prediction device according to claim 4, wherein, The maximum point is related to the point with the largest absolute value among the points with a negative imaginary impedance value among the points with a higher frequency than the points with Warburg impedance identified based on the EIS diagram, or to a point located within a preset range from the point with the largest absolute value among the points with a negative imaginary impedance value.

6. The SOH prediction device according to claim 4, wherein, The resistance parameter calculation unit calculates the resistance parameter based on the difference between the real part of the impedance associated with the second feature point and the real part of the impedance associated with the first feature point.

7. The SOH prediction device according to claim 6, wherein, The first function is generated based on a plurality of SOHs included in the reference SOH under the preset SOC conditions and preset temperature conditions of the reference battery pack in the idle state, as well as the resistance parameters corresponding to each of the plurality of SOHs.

8. The SOH prediction device according to claim 7, wherein, The first function includes at least one of an n-order (n is a natural number) polynomial function, an exponential function, and a logarithmic function.

9. A method for predicting operating health status (SOH) of a device, the method comprising: Acquire electrochemical impedance spectroscopy (EIS) data of the battery pack; Multiple feature points are identified based on the EIS data; The resistance parameters related to the impedance of the battery pack are calculated based on the impedance associated with the plurality of feature points. as well as The SOH of the battery pack corresponding to the calculated resistance parameters is predicted based on the resistance parameters corresponding to each of a plurality of temperatures and a first function representing the trend between the resistance parameters and the SOH of the battery pack.

10. The method according to claim 9, wherein, The EIS data includes data about an EIS diagram shown by dividing the impedance of the battery pack into real and imaginary parts, and The first feature point among the plurality of feature points is determined based on the magnitude of the real part of the impedance among the plurality of points identified based on the EIS diagram.

11. The method according to claim 10, wherein, The first feature point is associated with the point with the smallest real part of impedance among multiple points identified based on the EIS diagram, or with a point located within a preset range from the point with the smallest real part. The second feature point is associated with the largest point in the EIS map among multiple points identified based on the EIS map, or with a point located within a preset range from the largest point.

12. The method according to claim 9, wherein, The first function is generated based on a plurality of SOHs included in the reference SOH under the preset SOC conditions and preset temperature conditions of the reference battery pack in the idle state, as well as the resistance parameters corresponding to each of the plurality of SOHs.

13. The method according to claim 12, wherein, The first function includes at least one of an n-order (n is a natural number) polynomial function, an exponential function, and a logarithmic function.

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