อุปกรณ์ทำนายสถานะสุขภาพ (SOH) และวิธีการดำเนินการสิ่งนี้
Patent Information
- Authority / Receiving Office
- TH · TH
- Patent Type
- Applications
- Current Assignee / Owner
- LG ENERGY SOLUTION LTD
- Filing Date
- 2023-11-21
- Publication Date
- 2026-07-06
Smart Images
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Abstract
Description
SOH prediction device and its operation method
[0001] Cross-citation with related applications
[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2022-0160930, filed on November 25, 2022, and Korean Patent Application No. 10-2023-0160489, filed on November 20, 2023, the entire disclosure of which is 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] Recently, active research and development has been conducted on secondary batteries. 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 boast a significantly 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] To predict the life of a battery through EIS, data must be collected repeatedly under various conditions (e.g., temperature, state of charge (SOC), state of health (SOH)).
[0009] However, there is a problem that collecting EIS data in units of 1 degree is costly and time-consuming.
[0010] In addition, even if EIS data is fitted to an equivalent circuit and SOH is predicted based on the fitted data, prediction of the actual SOH is not guaranteed because the actual equivalent circuit is deformed according to SOH and SOC.
[0011] 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 from the descriptions below.
[0012] An SOH prediction device according to an embodiment disclosed in this document may include an EIS data acquisition unit that acquires EIS data of a battery, a feature point identification unit that identifies feature points in the EIS data, a resistance parameter calculation unit that calculates a resistance parameter of the battery based on the feature points, and an SOH determination unit that determines an SOH (state of health) of the battery according to the resistance parameter from relationship data among the temperature at which the EIS data was acquired, the resistance parameter, and the SOH.
[0013] According to one embodiment, the first feature point among the feature points may be determined based on the size of the real part of the EIS data.
[0014] According to one embodiment, the first feature point among the feature points may represent an impedance having the smallest real part in the EIS data or an impedance within a preset range from the impedance having the smallest real part.
[0015] According to one embodiment, the second feature point among the feature points may be determined based on the size of the imaginary part of the EIS data.
[0016] According to one embodiment, the second characteristic point among the characteristic points may represent an impedance at a point where the absolute value of the magnitude of the imaginary impedance is smallest among a frequency range smaller than the frequency at which the Warburg impedance appears in the EIS data and a frequency range at which the Warburg impedance appears, or an impedance within a preset range from the point where the absolute value is smallest.
[0017] According to one embodiment, the resistance parameter may be determined based on a value obtained by subtracting the real part value of the first feature point from the real part value of the second feature point among the feature points.
[0018] According to one embodiment, the relationship data is obtained through a comparison between first EIS data of a reference battery obtained at a specified time condition, a specified state of charge (SOC), a specified SOH, and a specified first temperature, and second EIS data of the reference battery obtained at the specified time condition, the specified SOC, the specified SOH, and a specified second temperature, wherein the first temperatures and the second temperatures may be different from each other.
[0019] According to one embodiment, the SOH prediction device may include a relationship data generation unit that generates formula candidates representing a tendency of the first EIS data, selects a formula with the smallest error among the formula candidates through the second EIS data, and generates relationship data between the resistance parameter and the SOH at third temperatures different from the first temperatures and the second temperatures through the formula with the smallest error.
[0020] In one embodiment, the temperature range of the first temperatures may include the temperature range of the second temperatures.
[0021] An operating method of an SOH prediction device according to an embodiment disclosed in the present document may include an operation of acquiring electrochemical impedance spectroscopy (EIS) data of a battery including at least one battery cell, an operation of identifying feature points in the EIS data, an operation of calculating a resistance parameter of the battery based on the feature points, and an operation of determining an SOH (state of health) of the battery according to the resistance parameter from relationship data among a temperature at which the EIS data was acquired, the resistance parameter, and the SOH.
[0022] According to one embodiment, the first feature point among the feature points may be determined based on the size of the real part of the EIS data.
[0023] According to one embodiment, the first feature point among the feature points may represent an impedance having the smallest real part in the EIS data or an impedance within a preset range from the impedance having the smallest real part.
[0024] According to one embodiment, the second feature point among the feature points may be determined based on the size of the imaginary part of the EIS data.
[0025] According to one embodiment, among the characteristic points, the second characteristic point may represent an impedance at a point where the absolute value of the magnitude of the imaginary impedance is smallest among a frequency range smaller than the frequency at which the Warburg impedance appears in the EIS data and a frequency at which the Warburg impedance appears, or an impedance within a preset range from the point where the absolute value is smallest.
[0026] According to one embodiment, the resistance parameter may be determined based on a value obtained by subtracting the real part value of the first feature point from the real part value of the second feature point among the feature points.
[0027] According to one embodiment, the relationship data is obtained through a comparison between first EIS data of a reference battery obtained at a specified time condition, a specified state of charge (SOC), a specified SOH, and a specified first temperature, and second EIS data of the reference battery obtained at the specified time condition, the specified SOC, the specified SOH, and a specified second temperature, wherein the first temperatures and the second temperatures may be different from each other.
[0028] An operating method of an SOH prediction device according to one embodiment may further include an operation of generating formula candidates representing a tendency of the first EIS data, an operation of selecting a formula with the smallest error among the formula candidates through the second EIS data, and an operation of generating relationship data between the resistance parameter and the SOH at third temperatures different from the first temperatures and the second temperatures through the formula with the smallest error.
[0029] In one embodiment, the temperature range of the first temperatures may include the temperature range of the second temperatures.
[0030] According to various embodiments disclosed in this document, the SOH prediction device and its operating method can predict SOH based on resistance parameters without collecting EIS data at all temperatures. Accordingly, the SOH prediction device and its operating method based on resistance parameters according to various embodiments disclosed in this document can reduce the cost and time required to collect EIS data.
[0031] According to various embodiments disclosed in this document, the SOH prediction device and its operating method can prevent problems arising from fitting EIS data to an equivalent circuit by approximating resistance parameters through the shape and coordinates of EIS data.
[0032] The effects of the SOH prediction device and its operating method according to the disclosure of this document are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art according to the disclosure of this document.
[0033] FIG. 1 is a block diagram of an SOH prediction device according to an embodiment of the present disclosure.
[0034] FIG. 2 illustrates an EIS graph according to one embodiment of the present disclosure.
[0035] FIG. 3 illustrates a temperature-resistance parameter graph according to one embodiment of the present disclosure.
[0036] FIG. 4 illustrates graphs according to formulas approximating a temperature-resistance parameter graph according to one embodiment of the present disclosure.
[0037] FIG. 5 is a flowchart illustrating an operation method of an SOH prediction device according to an embodiment of the present disclosure.
[0038] FIG. 6 is a flowchart illustrating an operation method of an SOH prediction device according to an embodiment of the present disclosure.
[0039] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.
[0040] Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.
[0041] The embodiments and terminology used in this document are not intended to limit the technical features described in this document to a specific embodiment, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the item, unless the relevant context clearly indicates otherwise.
[0042] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding component from other corresponding components, and do not limit the corresponding components in any other respect (e.g., importance or order) unless specifically stated otherwise.
[0043] In this document, when a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired or wirelessly), or indirectly (e.g., via a third component).
[0044] The methods according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory, CD-ROM), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.
[0045] According to the embodiments disclosed in this document, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to the embodiments disclosed in this document, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments disclosed in this document, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.
[0046] FIG. 1 is a block diagram of a state of health (SOH) prediction device (100) according to a resistance parameter according to an embodiment of the present disclosure. FIG. 2 illustrates an EIS graph according to an embodiment of the present disclosure. FIG. 3 illustrates a temperature-resistance parameter graph (310) according to an embodiment of the present disclosure. FIG. 4 illustrates graphs according to formulas approximating a temperature-resistance parameter graph according to an embodiment of the present disclosure.
[0047] In one embodiment, the SOH prediction device (100) can calculate the resistance parameter of the battery pack (110). In one embodiment, the SOH prediction device (100) can determine the SOH of the battery pack (110) based on the resistance parameter of the battery pack (110). Here, the battery pack (110) can include a plurality of battery cells (111, 113, 115). In one embodiment, the battery pack (110) can be a battery for providing power to a two-wheeled electric vehicle (e.g., an electric bike). Here, the two-wheeled electric vehicle can have a replaceable battery pack (110). Hereinafter, for the convenience of explanation, it is assumed that the resistance parameter of the battery pack (110) is calculated, but the present invention is not limited to this example, and the SOH prediction device (100) can be configured to calculate the resistance parameter of a battery module, etc.
[0048] In one embodiment, the SOH prediction device (100) may be formed integrally with the battery pack (110). In this case, the SOH prediction device (100) may be included in the BMS (battery management system) of the battery pack (110).
[0049] In one embodiment, the SOH prediction device (100) may be formed separately from the battery pack (110). In this case, the SOH prediction device (100) may be included in a battery swapping system (BSS). Here, the BSS may be a system having a slot into which a battery pack (110) can be inserted and capable of charging the battery pack (110) through the slot.
[0050] Additionally, when the SOH prediction device (100) is formed separately from the battery pack (110), the SOH prediction device (100) may be connected to the battery pack (110) via a network (e.g., wired or wireless). In this case, the SOH prediction device (100) may be implemented via a cloud server.
[0051] Referring to FIG. 1, the SOH prediction device (100) may include an electrochemical impedance spectroscopy (EIS) data acquisition unit (120), a feature point identification unit (130), a resistance parameter calculation unit (140), an SOH determination unit (150), relationship data (160), and a relationship data generation unit (170).
[0052] In one embodiment, the EIS data acquisition unit (120) can acquire EIS data of a battery. For example, the EIS data acquisition unit (120) can acquire EIS data (200) for each battery pack (110), but is not limited to this example. In one embodiment, the EIS data acquisition unit (120) can directly acquire EIS data by applying current and / or voltage to the battery pack (110). In this case, the EIS data acquisition unit (120) can include circuits for applying current and / or voltage and a processor for configuring EIS data according to the current and / or voltage. In one embodiment, the EIS data acquisition unit (120) can indirectly acquire EIS data from the battery pack (110). For example, the EIS data acquisition unit (120) can receive EIS data acquired by the battery pack (110). In this case, the EIS data acquisition unit (120) may include a communication circuit capable of wired and / or wireless network communication.
[0053] Referring to FIG. 2, the EIS graph may be a graph that coordinates the impedances of the battery pack (110) obtained according to frequency (i.e., EIS data (220)) into real and imaginary parts.
[0054] In one embodiment, the feature point identification unit (130) can identify feature points (210, 220) in the EIS data (200). In one embodiment, the feature point identification unit (130) can identify two feature points (210, 220) in the EIS data (200). A first feature point among the feature points can be determined based on the size of the real part of the impedance of the battery pack (110) in the EIS data (200).
[0055] According to an embodiment, the first characteristic point may represent an impedance (210) having the smallest real part in the EIS data (200) or an impedance within a preset range from the impedance (210) having the smallest real part. Here, the preset range may be variously set and changed in consideration of the allowable error of the EIS data, etc.
[0056] Among the feature points, the second feature point can be determined based on the size of the imaginary part of the impedance of the battery pack (110) in the EIS data (200).
[0057] According to an embodiment, the second characteristic point may be related to the impedance (220) at a minimum point in the EIS data (200). Alternatively, the second characteristic point among the characteristic points may be related to points where Warburg impedance exists in the EIS data (200). For example, the second characteristic point may correspond to the impedance of the point where the value of the imaginary impedance is the largest among the frequency range corresponding to the frequency of the point where the Warburg impedance appears and the region having a frequency lower than the frequency (220) where the Warburg impedance appears, or the impedance of the point located within a preset range from the largest point. In other words, the second characteristic point may correspond to the impedance at the point where the absolute value of the magnitude of the imaginary impedance is the smallest among the frequency ranges corresponding to the frequency (220) where the Warburg impedance appears and the frequency range smaller than the frequency (220) where the Warburg impedance appears, or the impedance at the point located within a preset range from the smallest point. Here, the preset range may be variously set and changed in consideration of the allowable error of EIS data, etc.
[0058] In one embodiment, the resistance parameter calculation unit (140) may calculate the resistance parameter of the battery pack (110) based on the feature points (210, 220). Here, the resistance parameter (230) may be determined based on a value obtained by subtracting the real part value of the first feature point (210) from the real part value of the second feature point (220). For example, the resistance parameter may be a value obtained by subtracting the real part value of the first feature point (210) from the real part value of the second feature point (220), or may be determined based on various methods, such as rounding off or approximating a value obtained by subtracting the real part value of the first feature point (210) from the real part value of the second feature point (220).
[0059] In one embodiment, the SOH determination unit (150) can determine the SOH of the battery pack (110) according to the resistance parameter (230) from the relationship data (160) between the temperature at which the EIS data (200) was acquired, the resistance parameter (230), and the SOH. Here, the temperature may be the temperature at the time of acquisition of the EIS data (200) of the battery pack (110). The temperature may be acquired by a temperature sensor (not shown) of the SOH prediction device (100) or a temperature sensor (not shown) of the battery pack (110).
[0060] In one embodiment, the relationship data (160) may be configured as shown in Table 1 below.
[0061]
[0062] Temperature 1 SOH...N SOH10R ct10,1 ...R ct10,N 11R ct11,1 ...R ct11,N 12R ct12,1 ...R ct12,N ............44R ct44,1 ...R ct44,N 45R ct45,1 ...R ct45,N
[0063] Entries distinguished by temperature and SOH in Table 1 may represent resistance parameters (230). For example, referring to Table 1, when the temperature of the battery pack (110) is 10 and it has the first SOH, the resistance parameter is R ct10,1 It can be understood that it has . Here, the first SOH can be 100.5865%, and the Nth SOH can be 97.338%. And, N can be an integer greater than or equal to 2.
[0064] For example, if the temperature at which EIS data (200) was acquired is 12 degrees and the resistance parameter (230) is R ct12,1In this case, the SOH determination unit (150) can determine the header value indicating the SOH (i.e., the first SOH) as the SOH of the battery pack (110) by referring to the relationship data (160) as shown in Table 1.
[0065] Meanwhile, the above-described Table 1 is an exemplary embodiment, and the temperature range of the relationship data (160) can be set to include various ranges other than 10 degrees to 45 degrees, and the interval between temperatures can also be set to have various intervals other than 1 degree.
[0066] As described above, the SOH prediction device (100) can predict the SOH of a battery pack (110) according to resistance parameters without collecting EIS data (200) at all temperatures.
[0067] Below, the method by which the relational data generation unit (170) generates relational data (160) is described.
[0068] In one embodiment, the relational data generation unit (170) can obtain first EIS data of a reference battery pack at specified SOC, specified SOH, and specified first temperatures under specified time conditions. Here, the reference battery pack may be a battery pack of the same type as the battery pack (110). According to one embodiment, the specified time condition may be a rest time condition, such as a condition in which the reference battery pack is in a rest state or in an open state, but is not limited to these examples.
[0069] In one embodiment, the relationship data generation unit (170) can calculate resistance parameters at different temperatures based on the first EIS data. Referring to FIG. 3, the graph (310) can represent a temperature-resistance parameter graph based on the first EIS data.
[0070] In one embodiment, the relationship data generation unit (170) may generate formula candidates representing a tendency of the first EIS data. In one embodiment, the relationship data generation unit (170) may generate formula candidates representing a temperature-resistance parameter graph based on the first EIS data. For example, the formula candidates may include a first formula representing a relationship between the reciprocal of temperature and the resistance parameter, a second formula representing a relationship between a second-order polynomial for temperature and the resistance parameter, a third formula representing a relationship between a third-order polynomial for temperature and the resistance parameter, and a fourth formula representing a relationship between the reciprocal of temperature and the logarithm of the reciprocal of the square root of the resistance parameter. For example, referring to FIG. 4, graph (410) may be a graph according to the first formula, graph (430) may be a graph according to the second formula, graph (450) may be a graph according to the third formula, and graph (470) may be a graph according to the fourth formula.
[0071] However, the formulas disclosed in this document are not limited to these examples. For example, the formula candidates may include various formulas, such as a formula representing the relationship between an n-th order polynomial (where n is an integer greater than or equal to 2) with respect to temperature and a resistance parameter, or a formula representing the relationship between an n-th order polynomial (where n is an integer less than or equal to -1) with respect to temperature and a resistance parameter. In addition, according to an embodiment, n may have a value of a rational number or an irrational number as well as a positive integer or a negative integer. In addition, the formula candidates according to an embodiment disclosed in this document may include a formula representing the relationship between the reciprocal of the temperature and the logarithm of the reciprocal of the nth order square root of the resistance parameter, a relational formula expressed as an exponential function or a logarithmic function, etc. As above, the formula candidates according to an embodiment disclosed in this document may be expressed in various ways as long as they are based on the relationship between the temperature and the resistance parameter.
[0072] In one embodiment, the relational data generation unit (170) can predict the resistance parameter of the reference battery pack at second temperatures using the formula candidates. In one embodiment, the first temperatures and the second temperatures may be different from each other. The temperature range of the first temperatures may include the temperature range of the second temperatures. For example, the first temperatures may be 10, 15, 25, 35, and 45 degrees, and the second temperatures may be 20, 30, and 40 degrees. However, this is exemplary and the embodiments disclosed in this document are not limited to these examples.
[0073] In one embodiment, the relationship data generation unit (170) may select a formula with the smallest error among formula candidates through the second EIS data. For example, the relationship data generation unit (170) may obtain second EIS data of a reference battery pack at specified time conditions, specified SOC, specified SOH, and specified second temperatures. Thereafter, the relationship data generation unit (170) may calculate resistance parameters at different second temperatures based on the second EIS data. Thereafter, the relationship data generation unit (170) may compare the resistance parameters calculated at the second temperatures with the resistance parameters predicted using the formula candidates. The relationship data generation unit (170) may select a formula candidate with the smallest error based on the comparison result.
[0074] In one embodiment, the relationship data generation unit (170) can generate relationship data (160) between the resistance parameter and SOH at third temperatures different from the first and second temperatures through a formula with the smallest error. Here, the third temperatures may be temperatures not included in the first and second temperatures (e.g., temperatures such as 11, 12, 13, 14, 41, 42, 43, 44, and 45 degrees).
[0075] Thereafter, the relationship data generation unit (170) can obtain the first EIS data and the second EIS data in a situation where at least one of the designated SOC or the designated SOH is different, and supplement the relationship data (160) based on the obtained first EIS data and the second EIS data.
[0076] In one embodiment, the relationship data generation unit (170) may be obtained through a comparison between first EIS data of the battery pack obtained at a specified SOC, a specified SOH, and a specified first temperature under a specified time condition, and second EIS data of the battery pack obtained at the specified SOC, the specified SOH, and a specified second temperature under the specified rest time condition.
[0077] The above-described feature point identification unit (130), resistance parameter calculation unit (140), SOH determination unit (150), and relationship data generation unit (170) may be implemented as one processor or separate processors. Here, the processor may execute software to control at least one other component (e.g., hardware or software component) of the SOH prediction device (100) and perform various data processing or calculations. In addition, the relationship data (160) may be stored in a memory (not shown) (e.g., volatile memory and / or non-volatile memory) of the SOH prediction device (100). In one embodiment, the memory may store data used by at least one component of the SOH prediction device (100). For example, the data may include software (or instructions related thereto), input data, or output data. In one embodiment, when the instructions are executed by the processor, the SOH prediction device (100) may perform operations defined by the instructions.
[0078] According to an embodiment, the SOH prediction device (100) may transmit the generated relationship data (160) and / or the predicted SOH of the battery pack (110) to an external device (e.g., a cloud server or a user terminal). Here, the cloud server may provide a service for providing the predicted SOH of the battery pack (110) to each of a plurality of users. In addition, the user terminal may include a terminal such as a personal computer (PC) or a smartphone.
[0079] FIG. 5 is a flowchart showing an operation method of an SOH prediction device (100) according to a resistance parameter (230) according to one embodiment of the present disclosure.
[0080] Referring to FIG. 5, in operation 510, the SOH prediction device (100) can obtain EIS data (510) of a battery pack (110). The EIS graph can be a graph that coordinates the impedances (i.e., EIS data (220)) of the battery pack (110) obtained according to frequency into real and imaginary parts.
[0081] In operation 520, the SOH prediction device (100) can identify feature points (210, 220) in the EIS data (200). Among the feature points, a first feature point can be determined based on the size of the real part of the impedance of the battery pack (110) in the EIS data (200). According to an embodiment, the first feature point can represent an impedance within a preset range from the impedance (210) having the smallest real part of the impedance of the battery pack (110) in the EIS data (200) or the impedance (210) having the smallest real part. Here, the preset range can be variously set and changed in consideration of the allowable error of the EIS data, etc.
[0082] Among the feature points, the second feature point can be determined based on the size of the imaginary part of the impedance of the battery pack (110) in the EIS data (200).
[0083] According to an embodiment, the second characteristic point may be related to the impedance (220) at a minimum point in the EIS data (200). Alternatively, the second characteristic point may be related to points where Warburg impedance exists in the EIS data (200). For example, the second characteristic point may correspond to the impedance of the point where the value of the imaginary impedance is the largest among the frequency ranges corresponding to the frequency of the point where the Warburg impedance appears and the region where the frequency is smaller than the frequency (220) where the Warburg impedance appears, or the impedance of the point located within a preset range from the largest point. In other words, the second characteristic point may correspond to the impedance at the point where the absolute value of the magnitude of the imaginary impedance is smallest among the frequency ranges corresponding to the frequency (220) at which the Warburg impedance appears and the frequency range smaller than the frequency (220) at which the Warburg impedance appears, or the impedance at the point located within a preset range from the smallest point.
[0084] In operation 530, the SOH prediction device (100) can calculate a resistance parameter (230) of the battery pack based on the feature points (210, 220). Here, the resistance parameter (230) can be determined based on a value obtained by subtracting the real part value of the first feature point (210) from the real part value of the second feature point (220). For example, the resistance parameter can be a value obtained by subtracting the real part value of the first feature point (210) from the real part value of the second feature point (220), or can be determined based on various methods, such as rounding or approximating a value obtained by subtracting the real part value of the first feature point (210) from the real part value of the second feature point (220).
[0085] In operation 540, the SOH prediction device (100) can determine the SOH of the battery pack (110) according to the temperature and resistance parameter (230). In one embodiment, the SOH prediction device (100) can determine the SOH of the battery pack (110) according to the resistance parameter (230) from the relationship data (160) between the temperature at which the EIS data (200) was acquired, the resistance parameter (230), and the SOH. Here, the temperature may be the temperature at the time of acquiring the EIS data (200) of the battery pack (110).
[0086] FIG. 6 is a flowchart showing an operation method of an SOH prediction device (100) according to a resistance parameter (230) according to one embodiment of the present disclosure.
[0087] Referring to FIG. 6, in operation 610, the SOH prediction device (100) can obtain first EIS data and second EIS data. For example, the SOH prediction device (100) can obtain first EIS data of a reference battery pack under specified time conditions, at specified SOCs, specified SOHs, and specified first temperatures. According to an embodiment, the specified time conditions may be idle time conditions in which the reference battery pack is in an idle state or in an open state, but are not limited to these examples. The relationship data generation unit (170) can obtain second EIS data of the reference battery pack under specified time conditions, at specified SOCs, specified SOHs, and specified second temperatures. Here, the reference battery pack may be a battery pack of the same type as the battery pack (110). The temperature range of the first temperatures may include the temperature range of the second temperatures. For example, the first temperatures may be 10, 15, 25, 35, and 45 degrees, and the second temperatures may be 20, 30, and 40 degrees.
[0088] In operation 620, the SOH prediction device (100) may generate candidate equations representing a tendency of the first EIS data. For example, the candidate equations may include a first equation representing a relationship between a reciprocal of temperature and a resistance parameter, a second equation representing a relationship between a second-order polynomial for temperature and a resistance parameter, a third equation representing a relationship between a third-order polynomial for temperature and a resistance parameter, and a fourth equation representing a relationship between a reciprocal of temperature and a logarithm of a reciprocal of a square root of the resistance parameter.
[0089] However, the formulas disclosed in this document are not limited to these examples. For example, the formula candidates may include various formulas, such as a formula representing the relationship between an n-th order polynomial (where n is an integer greater than or equal to 2) with respect to temperature and a resistance parameter, or a formula representing the relationship between an n-th order polynomial (where n is an integer less than or equal to -1) with respect to temperature and a resistance parameter. In addition, according to an embodiment, n may have a value of a rational number or an irrational number as well as a positive integer or a negative integer. In addition, the formula candidates according to an embodiment disclosed in this document may include a formula representing the relationship between the reciprocal of the temperature and the logarithm of the reciprocal of the nth order square root of the resistance parameter, a relational formula expressed as an exponential function or a logarithmic function, etc. As above, the formula candidates according to an embodiment disclosed in this document may be expressed in various ways as long as they are based on the relationship between the temperature and the resistance parameter.
[0090] In operation 630, the SOH prediction device (100) can select a formula with the smallest error among formula candidates through the second EIS data. For example, the SOH prediction device (100) can predict the resistance parameter of the reference battery pack at second temperatures using the formula candidates. In addition, for example, the SOH prediction device (100) can calculate the resistance parameter at different second temperatures based on the second EIS data. Thereafter, the SOH prediction device (100) can compare the resistance parameter calculated at the second temperatures with the resistance parameter predicted using the formula candidates. The SOH prediction device (100) can select the formula candidate with the smallest error based on the comparison result.
[0091] In operation 640, the SOH prediction device (100) can generate relationship data between the resistance parameter and the SOH through a formula with the smallest error. For example, the SOH prediction device (100) can generate relationship data (160) between the resistance parameter and the SOH at third temperatures different from the first temperatures and the second temperatures through a formula with the smallest error. Here, the third temperatures may be temperatures not included in the first temperatures and the second temperatures (e.g., temperatures such as 11, 12, 13, 14, 41, 42, 43, 44, and 45 degrees).
[0092] Thereafter, the SOH prediction device (100) can obtain the first EIS data and the second EIS data in a situation where at least one of the specified SOC or the specified SOH is different, and supplement the relationship data (160) based on the obtained first EIS data and the second EIS data.