Apparatus for diagnosing battery and method thereof

Abstract

A battery diagnostic device according to one embodiment of the present document includes a memory for storing at least one instruction and at least one processor for executing said at least one instruction. The at least one processor can obtain a set of reference data for a reference battery cell corresponding to each of a plurality of designated temperatures, obtain cell data of a battery cell at a specific temperature, fit said reference data to said cell data such that the deviations between said reference data and said cell data included in said set of reference data are minimized, identify a first specific reference data included in said set of reference data that corresponds to the smallest deviation among said fitted reference data and said cell data, and diagnose the state of said battery cell based on the temperature corresponding to the first specific reference data that corresponds to the smallest deviation among said plurality of designated temperatures.

Description

Technology Field

[0001] The embodiments disclosed in this document relate to a battery diagnostic device and a method thereof. Background Technology

[0002] Recently, active research and development on secondary batteries has been underway. Here, secondary batteries are rechargeable batteries that can be interpreted to encompass conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. With their scope of application expanding to include power sources for electric vehicles, they are garnering attention as a next-generation energy storage medium.

[0003] With the proliferation of various electronic devices due to the Fourth Industrial Revolution, battery usage is rapidly increasing. Batteries are gaining prominence as an essential energy source in various fields, such as electric vehicles, portable electronic devices, and renewable energy storage systems. Consequently, the importance of battery condition diagnostic technology to improve battery performance and reliability is growing.

[0004] In particular, technology is being developed to identify the temperature of each battery cell included in a battery unit. By identifying the temperature of each battery cell, the battery condition diagnostic performance of the battery diagnostic device can be improved. This enables the optimization of battery performance and ensures the quality stability of the battery cells by inspecting for abnormalities. The problem to be solved

[0005] According to the embodiments disclosed in this document, a battery diagnostic device and a method for obtaining the temperature of each of the battery cells included in a battery unit through electrochemical cell data are provided.

[0006] According to the embodiments disclosed in this document, a battery diagnostic device and a method are provided that improve the condition diagnostic performance of battery cells by acquiring the temperature of each of the battery cells included in a battery unit.

[0007] According to the embodiments disclosed in this document, the present invention aims to provide a battery diagnostic device and a method that improve the accuracy of capacity measurement of a battery cell by acquiring the temperature of each of the battery cells included in a battery unit.

[0008] The technical problems of this document are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the descriptions below. means of solving the problem

[0009] A battery diagnostic device according to one embodiment of the present document may include a memory for storing at least one instruction and at least one processor for executing said at least one instruction.

[0010] According to one embodiment, the at least one processor can obtain a set of reference data for a reference battery cell corresponding to each of a plurality of designated temperatures, obtain cell data of a battery cell at a specific temperature, fit each of the reference data included in the set of reference data to the cell data such that the deviations between each of the reference data and the cell data are minimized, identify a first specific reference data included in the set of reference data that corresponds to the smallest deviation among the deviations between each of the fitted reference data and the cell data, and diagnose the state of the battery cell based on the temperature corresponding to the first specific reference data that corresponds to the smallest deviation among the plurality of designated temperatures.

[0011] According to one embodiment, the at least one processor divides the cell data into a plurality of intervals, identifies interval data included in at least a portion of the cell data and included in any one of the plurality of intervals, fits each reference data to the interval data such that the deviation between each reference data and the interval data is minimized, identifies a second specific reference data included in the set of reference data and corresponding to the smallest deviation among the deviations between each fitted reference data and the interval data, and identifies the temperature of the battery cell in any one of the intervals based on the temperature at which the second specific reference data is obtained among the plurality of designated temperatures.

[0012] According to one embodiment, cell data of the battery cell can be obtained during the activation process of the battery cell.

[0013] According to one embodiment, the at least one processor may, before acquiring the set of reference data, identify the first reference data of the reference battery cell acquired at the lowest first temperature among the plurality of designated temperatures and the second reference data of the reference battery cell acquired at the highest second temperature among the plurality of designated temperatures, and based on the first reference data and the second reference data, identify the set of reference data corresponding to each of the plurality of designated temperatures.

[0014] According to one embodiment, the at least one processor can identify a portion of a set of reference data corresponding to each of the temperatures that are higher than the first temperature and lower than the second temperature among a plurality of designated temperatures by interpolating the first reference data and the second reference data, and can identify the set of reference data based on the portion of the set of reference data, the first reference data, and the second reference data.

[0015] According to one embodiment, each of the reference data includes a capacity profile of the reference battery cell and a voltage profile of the reference battery cell, and the at least one processor can fit each of the reference data to the cell data by performing at least one of an operation of a specific first value, an addition of a specific second value, or any combination thereof with respect to the capacity profile or the voltage profile.

[0016] According to one embodiment, the cell data may include a capacity profile of the battery cell included in a specified capacity range, and a voltage profile of the battery cell according to the capacity profile.

[0017] According to one embodiment, each of the deviations of the respective reference data and the cell data may include the root mean squared error (RMSE) of the respective reference data and the cell data.

[0018] According to one embodiment, the at least one processor can identify the specific temperature based on the temperature corresponding to the first specific reference data among the plurality of specified temperatures.

[0019] A battery diagnostic method according to another embodiment of the present document may include: acquiring a set of reference data for a reference battery cell corresponding to each of a plurality of designated temperatures; acquiring cell data of a battery cell at a specific temperature; fitting each of the reference data included in the set of reference data to the cell data such that the deviations between each of the reference data and the cell data are minimized; identifying a first specific reference data included in the set of reference data and corresponding to the smallest deviation among the deviations between each of the fitted reference data and the cell data; and diagnosing the state of the battery cell based on the temperature corresponding to the first specific reference data corresponding to the smallest deviation among the plurality of designated temperatures.

[0020] According to one embodiment, the battery diagnostic method may further include: a step of dividing the cell data into a plurality of sections; a step of identifying section data that is included in at least a part of the cell data and is included in any one of the plurality of sections; a step of fitting each reference data to the section data such that the deviation between each reference data and the section data is minimized; a step of identifying a second specific reference data that is included in the set of reference data and corresponds to the smallest deviation among the deviations between each fitted reference data and the section data; and a step of identifying the temperature of the battery cell in any one of the sections based on the temperature at which the second specific reference data is obtained among the plurality of designated temperatures.

[0021] According to one embodiment, the operation of acquiring cell data of the battery cell at the specific temperature can be performed in the activation process of the battery cell.

[0022] According to one embodiment, the operation of acquiring a set of reference data for a reference battery cell corresponding to each of the plurality of designated temperatures may include, before acquiring the set of reference data, an operation of identifying a first reference data of the reference battery cell acquired at the lowest first temperature among the plurality of designated temperatures and a second reference data of the reference battery cell acquired at the highest second temperature among the plurality of designated temperatures, and an operation of identifying the set of reference data corresponding to each of the plurality of designated temperatures based on the first reference data and the second reference data.

[0023] According to one embodiment, the operation of identifying a set of reference data corresponding to each of the plurality of designated temperatures based on the first reference data and the second reference data may include the operation of identifying a part of a set of reference data corresponding to each of the temperatures among the plurality of designated temperatures that are higher than the first temperature and lower than the second temperature by interpolating the first reference data and the second reference data, and the operation of identifying the set of reference data based on the part of the set of reference data, the first reference data, and the second reference data.

[0024] According to one embodiment, each of the reference data includes a capacity profile of the reference battery cell and a voltage profile of the reference battery cell, and the operation of fitting each of the reference data to the cell data such that the deviations between each of the reference data and the cell data included in the set of reference data are minimized may include the operation of fitting each of the reference data to the cell data by performing at least one of an operation of a specific first value, an addition of a specific second value, or any combination thereof with respect to the capacity profile or the voltage profile.

[0025] According to one embodiment, the cell data may include a capacity profile of the battery cell included in a specified capacity range, and a voltage profile of the battery cell according to the capacity profile.

[0026] According to one embodiment, each of the deviations of the respective reference data and the cell data may include the root mean squared error (RMSE) of the respective reference data and the cell data.

[0027] According to one embodiment, the operation of diagnosing the state of the battery cell based on the temperature corresponding to the first specific reference data corresponding to the smallest deviation among the plurality of designated temperatures may include the operation of identifying the specific temperature based on the temperature corresponding to the first specific reference data among the plurality of designated temperatures. Effects of the invention

[0028] This technology can obtain the temperature of each battery cell included in a battery unit through electrochemical cell data.

[0029] In addition, the present technology can improve the condition diagnosis performance of battery cells by acquiring the temperature of each battery cell included in the battery unit.

[0030] In addition, the present technology can improve the accuracy of capacity measurement of battery cells by acquiring the temperature of each battery cell included in the battery unit.

[0031] In addition, various effects that can be identified directly or indirectly through this document may be provided. Brief explanation of the drawing

[0032] FIG. 1 is a block diagram showing a battery pack in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 2 is a block diagram showing the configuration of a battery diagnostic device in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 3 illustrates an example of a reference data fitted to cell data and a graph representing cell data in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 4 illustrates an example of a graph showing the deviation between reference data and cell data in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 5 illustrates an example of a graph showing a line in which the voltage included in reference data is differentiated by the capacity and a line in which the voltage included in cell data is differentiated by the capacity, in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 6 illustrates an example of a graph showing a line in which the capacity included in reference data is differentiated by voltage and a line in which the capacity included in cell data is differentiated by voltage in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 7 illustrates an example of a graph representing a set of reference data in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 8 illustrates a diagram and a table showing the temperature distribution of battery cells in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 9 illustrates an example of a learning model for identifying the capacity of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 10 illustrates an example of a diagram showing the influence of a plurality of factors of a learning model for identifying the capacity of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 11 illustrates a graph showing the temperature and parameters of a battery cell predicted in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 12 illustrates a graph showing the accuracy of the capacity prediction of a battery cell by a learning model in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 13 illustrates an example of the flow of operation of a battery diagnostic device for identifying the temperature of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 14 illustrates an example of the flow of operation of a battery diagnostic device for identifying the capacity of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. FIG. 15 is a block diagram showing the hardware configuration of a computing system performing a battery diagnostic method in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document. Specific details for implementing the invention

[0033] Some embodiments disclosed herein are described below with reference to the various embodiments of the accompanying drawings. However, this is not intended to limit the technology to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives to embodiments of the technology.

[0034] It should be noted that when assigning reference numerals to the components of each drawing, the same components are assigned the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the various embodiments disclosed in this document, if it is determined that a detailed description of related known configurations or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted. The singular form of a noun corresponding to an item may include one or more items unless the relevant context clearly indicates otherwise.

[0035] In describing the components of the embodiments of this document, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components and do not limit the essence, order, or sequence of the components. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0036] Additionally, in this disclosure, expressions of "greater than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled; however, this is merely for the purpose of expressing an example and does not exclude descriptions of "greater than" or "less than." Conditions described as "greater than" may be replaced with "greater than," conditions described as "less than" may be replaced with "less than," and conditions described as "greater than and less than" may be replaced with "greater than and less than." Furthermore, "A" to "B" below refer to at least one of the elements from A (including A) to B (including B).

[0037] In this document, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "at least one of A, B, or C" may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof.

[0038] In this document, where any component (e.g., 1) is referred to as being “connected,” “coupled,” or “joined” to another component (e.g., 2), with or without the terms “functionally” or “communicationally,” or where it is referred to as “coupled” or “connected,” it means that the component may be connected to the other component directly (e.g., via a wire), wirelessly, or through a third component.

[0039] According to one embodiment, the method according to the various embodiments disclosed herein may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0040] According to various embodiments, each component (e.g., module or program) of the described components may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, at least one of the aforementioned components or operations may be omitted, or at least one other component or operation may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform at least one function of each of the multiple components in the same or similar manner as it was performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or at least one other operation may be added.

[0041] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 15.

[0042] FIG. 1 is a block diagram showing a battery pack in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0043] Referring to FIG. 1, the battery pack (1) may include a battery unit (12), a sensor unit (14), a switching unit (16), and a battery management system (BMS) (20). At this time, the battery pack (1) may be equipped with a plurality of battery units (12), sensor units (14), switching units (16), and battery management systems (20).

[0044] According to one embodiment, the battery unit (12) can supply power to a target device (not shown). To this end, the battery unit (12) may be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (1). For example, the target device may be an electric vehicle (EV) or an energy storage system (ESS), but is not limited thereto.

[0045] According to one embodiment, the battery unit (12) may include at least one battery cell (10) capable of charging and discharging. Here, the battery cell (10) may be a basic unit of a battery cell capable of charging and discharging electrical energy. For example, the battery cell (10) may be a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion polymer) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-hydrogen (Ni-MH) battery, etc., but is not limited thereto.

[0046] According to one embodiment, a plurality of battery units (12) may be connected in series or in parallel. For example, a battery unit (12) may be a battery module, a battery bank, or a set of battery cells (cell-to-pack structure).

[0047] According to one embodiment, the sensor unit (14) can obtain information related to the battery unit (12). According to one embodiment, the sensor unit (14) can obtain values ​​(or information) related to the state of each of the battery unit (12) or battery cells (10). In one embodiment, the values ​​related to the state may include at least one value for the voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature of the battery cell, or a combination thereof.

[0048] According to one embodiment, the sensor unit (14) can provide information of each of the plurality of battery units (12) to the battery management system (20).

[0049] According to one embodiment, the switching unit (16) may include an element for controlling the current flow for charging or discharging the battery unit (12). For example, the switching unit (16) may include at least one relay and / or magnetic contactor, etc., depending on the specifications of the battery pack (1).

[0050] According to one embodiment, a battery management system (BMS) (20) can monitor the voltage, current, temperature, etc. of a battery pack (1) and control or manage the battery pack (1) to prevent overcharging and over-discharging. For example, the battery management system (20) may include a plurality of terminals as an interface for receiving values ​​of the various parameters described above, and a circuit connected to these terminals to perform processing of the received values. Additionally, the battery management system (20) may control a sensor unit (14) and / or a switching unit (16). For example, the battery management system (20) may be connected to a plurality of battery units (12) to monitor the status of each of the plurality of battery units (12) and control the ON / OFF of relays or contactors.

[0051] According to one embodiment, the operation of the battery management system (20) can be performed by a battery management system (BMS) in the vehicle, as well as by various devices such as a server, cloud, charger, or discharger.

[0052] The upper controller (2) can transmit control signals for a plurality of battery units (12) to the battery management system (20). Accordingly, the operation of the battery management system (20) can be controlled based on the signals applied from the upper controller (2).

[0053] According to one embodiment, the battery management system (20) may include the battery diagnostic device (201) of FIG. 2. According to another embodiment, the battery management system (20) may be a different system from the battery diagnostic device (201) of FIG. 2. That is, the battery diagnostic device (201) of FIG. 2 may be included in the battery pack (1) or may be configured as another device outside the battery pack (1). For convenience of explanation, the following description assumes that the battery diagnostic device (201) is configured as another device outside the battery pack (1). Furthermore, the operation of the battery diagnostic device (201) below may be performed by a battery management system (BMS) within the vehicle, as well as by various devices such as a server, cloud, charger, or discharger.

[0054] FIG. 2 is a block diagram showing the configuration of a battery diagnostic device in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0055] FIG. 3 illustrates an example of a reference data fitted to cell data and a graph representing cell data in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0056] Referring to FIGS. 2 and FIGS. 3, a battery diagnostic device (201) may include a memory (203) and at least one processor (205). The memory (203) may store at least one instruction. The at least one processor (205) may execute at least one instruction.

[0057] The graph (301) may represent reference data fitted to the cell data and cell data. The first line (303) may represent reference data included in the set of reference data. In other words, the first line (303) may represent a voltage graph according to the capacity of the reference battery cell at one of a plurality of specified temperatures. The second line (305) may represent cell data. In other words, it may represent a voltage graph according to the capacity of the battery cell at a specific temperature. The region (307) may represent a portion of the cell data that falls outside the specified capacity range. The cell data referred to below may include data of the target battery cell. The reference data may include data of the reference battery cell.

[0058] In order to increase the accuracy of capacity measurement of a battery unit (e.g., battery pack, battery module), detect abnormalities in the battery unit, improve the quality of the battery unit, or monitor the battery unit, at least one processor (205) can measure the temperature of the battery unit.

[0059] This is because the performance of the battery unit is determined by its capacity and voltage, and the measurement of the battery unit's capacity can be affected by temperature. Additionally, if the temperature of the battery unit does not satisfy the specified range, an abnormality in the battery unit may be detected.

[0060] In particular, the temperature of the battery unit during the activation process can be used as an indicator to identify the uniformity and stability of chemical reactions occurring inside the battery unit, and can also be used as an indicator to detect abnormalities.

[0061] Here, the capacity, abnormality, and quality of the battery unit may be determined based on the capacity, abnormality, and quality of individual battery cells. The capacity, abnormality, and quality of individual battery cells included in the battery unit may vary depending on the battery cell. Therefore, in order to improve the accuracy of the capacity measurement and the accuracy of the abnormality determination of the battery unit, at least one processor (205) may need to measure the temperature of the individual battery cells.

[0062] In addition, the temperature of individual battery cells during the activation process can be used as an indicator to identify quality uniformity among battery cells, and can also be used to identify the cause of abnormalities in defective battery cells.

[0063] Temperature sensors may be attached per battery unit rather than per battery cell. This is because the cost of temperature measurement when a sensor is attached per battery cell is greater than the cost when a sensor is attached per battery unit. Therefore, battery diagnostic devices have faced difficulties in measuring the temperature of individual battery cells using temperature sensors.

[0064] According to one embodiment, the battery diagnostic device (201) can identify the temperature of an individual battery cell or identify the capacity of an individual battery cell based on an electrochemical signal of the battery cell.

[0065] According to one embodiment, at least one processor (205) can acquire cell data of a battery cell at a specific temperature. The cell data may include a capacity profile of a battery cell included in a specified capacity range, and a voltage profile of a battery cell according to the capacity profile. According to one embodiment, the cell data of a battery cell may be acquired during an activation process of a battery cell, but the embodiments of this document are not limited thereto.

[0066] For example, cell data may include a voltage graph (e.g., second line (305)) according to the capacity of a battery cell having a capacity included in a specified capacity range at a specific temperature. The specified capacity range may not include the capacity in region (307). Referring to the specified capacity range may be intended to increase the accuracy of the fitting. The specified capacity range is described below with reference to FIG. 4.

[0067] According to one embodiment, at least one processor (205) can acquire a set of reference data (e.g., capacity profile of reference battery cell, voltage profile of reference battery cell) corresponding to each of a plurality of specified temperatures of a reference battery cell having a current profile value such as the value of the current profile (e.g., current, current density) of the battery cell from which cell data was acquired.

[0068] In other words, reference data can be acquired while the reference battery cell is being charged or discharged at a specified rate equal to the charging or discharging rate of the battery cell.

[0069] According to one embodiment, the set of reference data may include a set of capacity profiles of a reference battery cell corresponding to each of a plurality of specified temperatures, or a set of voltage profiles of a reference battery cell corresponding to each of a plurality of specified temperatures.

[0070] For example, the set of reference data may include a voltage graph according to the capacity of a reference battery cell at about 25°C, a voltage graph according to the capacity of a reference battery cell at about 26°C, a voltage graph according to the capacity of a reference battery cell at about 27°C, a voltage graph according to the capacity of a reference battery cell at about 28°C, or a voltage graph according to the capacity of a reference battery cell at about 29°C. The first line (303) may represent a voltage graph according to the capacity of a reference battery cell at about 33°C.

[0071] According to one embodiment, at least one processor (205) can fit each reference data to the cell data such that the deviations between each reference data and the cell data included in the set of reference data are minimized.

[0072] For example, at least one processor (205) can shrink or enlarge the voltage graph according to the capacity of the reference battery cell with respect to the capacity profile by calculating a specific first value in the capacity profile of the voltage graph according to the capacity of the reference battery cell at about 25°C included in the reference data.

[0073] For example, at least one processor (205) can move the voltage graph according to the capacity of the reference battery cell to the capacity profile axis or the voltage profile axis by adding a second value specific to the capacity profile or the voltage profile of the voltage graph according to the capacity of the reference battery cell at about 25°C.

[0074] For example, at least one processor (205) can fit the reference data to the cell data such that the deviation between the reference data and the cell data is minimized by shrinking, enlarging, or moving the reference data.

[0075] For example, at least one processor (205) can fit each reference data included in the set of reference data (e.g., a voltage graph according to the capacity of a reference battery cell at about 25°C, a voltage graph according to the capacity of a reference battery cell at about 33°C, a voltage graph according to the capacity of a reference battery cell at about 35°C) to the cell data.

[0076] According to one embodiment, at least one processor (205) can identify a first specific reference data (e.g., a first line (303)) corresponding to the smallest deviation among the deviations of each fitted reference data and cell data.

[0077] For example, the voltage graph and cell data according to the capacity of a reference battery cell at about 25°C may be about 0.29117 mV (milli-voltage), the minimized deviation at about 33°C may be about 0.15843 mV, and the minimized deviation at about 35°C may be about 0.21843 mV.

[0078] According to one embodiment, at least one processor (205) can identify a specific temperature of a battery cell based on a temperature (e.g., about 33°C) corresponding to a first specific reference data among a plurality of specified temperatures.

[0079] For example, at least one processor (205) can identify the temperature of the battery cell as approximately 33°C, since the temperature corresponding to the first specific reference data is approximately 33°C.

[0080] In FIGS. 2 and FIGS. 3, each reference data is described to be fitted with the entire cell data, but the embodiments of this document are not limited thereto.

[0081] According to one embodiment, at least one processor (205) divides cell data into a plurality of intervals, identifies interval data that is included in at least a portion of the cell data and is included in any one of the plurality of intervals, and fits each reference data to the interval data such that the deviation between each reference data and the interval data is minimized. This is because the temperature of the battery cell may change while the battery cell is being charged or discharged and cell data is being acquired.

[0082] According to one embodiment, at least one processor (205) is included in a set of reference data and can identify a second specific reference data corresponding to the smallest deviation among the deviations of each fitted reference data and the interval data. The at least one processor (205) can identify the temperature of a battery cell in one of a plurality of specified temperatures based on the temperature at which the second specific reference data is obtained.

[0083] FIG. 4 illustrates an example of a graph showing the deviation between reference data and cell data in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0084] Referring to FIG. 4, the graph (401) may represent the deviation between reference data and cell data. The line (405) may represent the deviation between either reference data and cell data. The area (403) may represent an area where the deviation is greater than or equal to a reference value (e.g., about 0.6 mV). The deviation between reference data and cell data may include the root mean squared error (RMSE) of the reference data and cell data.

[0085] According to one embodiment, the cell data may include a capacity profile of a battery cell included in a specified first capacity or greater and a specified second capacity or less, and a voltage profile of the battery cell according to the capacity profile. The specified second capacity may be greater than the specified first capacity.

[0086] According to one embodiment, when cell data is acquired, a deviation may occur above a reference value due to the difference between the starting potential of the reference battery cell and the starting potential of the battery cell. The closer the deviation is to zero, the higher the degree to which the reference data is fitted to the cell data. Referring to the graph (401), even though the degree of fitting of the part of the cell data corresponding to the area other than the area (403) is greater than the reference fitting degree, the degree of fitting of the part of the cell data corresponding to the area (403) may be less than the reference fitting degree.

[0087] Therefore, at least one processor (205) can fit reference data based on cell data of a battery cell not included in the region (307) to increase the accuracy of the fitting. In other words, at least one processor (205) can fit reference data based on a capacity profile of a battery cell with a specified first capacity or greater, and a voltage profile of the battery cell according to the capacity profile.

[0088] The area (307) of Fig. 3, the area (507) of Fig. 5, and the area (607) of Fig. 6 can also represent areas to be excluded from cell data to increase the accuracy of the fitting.

[0089] In FIG. 4, only the area (307) representing a designated first capacity, which is a reference for reducing deviations above a reference value due to the starting potential of the battery cell, is shown, but deviations above a reference value may occur due to overvoltage even when the battery cell is charged above a designated second capacity.

[0090] Therefore, at least one processor (205) can fit reference data based on the capacity profile of a battery cell with a capacity less than the second capacity and the voltage profile of the battery cell according to the capacity profile in order to increase the accuracy of the fitting.

[0091] FIG. 5 illustrates an example of a graph showing a line in which the voltage included in reference data is differentiated by the capacity and a line in which the voltage included in cell data is differentiated by the capacity, in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0092] FIG. 6 illustrates an example of a graph showing a line in which the capacity included in reference data is differentiated by voltage and a line in which the capacity included in cell data is differentiated by voltage in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0093] Referring to FIGS. 5 and 6, the first graph (501) is a graph in which voltage is differentiated by capacity in reference data and cell data, and may include a first line (503) and a second line (505). The first line (503) may represent a graph in which the voltage of a reference battery cell is differentiated by the capacity of a reference battery cell based on reference data. The second line (505) may represent a graph in which the voltage of a battery cell is differentiated by the capacity of a battery cell based on cell data. The first region (507) may represent a region in which the absolute value of the difference between the first line (503) and the second line (505) is greater than or equal to a reference value.

[0094] The second graph (601) is a graph in which the capacity is differentiated by the voltage in reference data and cell data, and may include a third line (603) and a fourth line (605). The third line (603) may represent a graph in which the capacity of a reference battery cell is differentiated by the voltage of a reference battery cell based on reference data. The fourth line (605) may represent a graph in which the capacity of a reference battery cell is differentiated by the voltage of a reference battery cell based on reference data. The second region (507) may represent a region in which the absolute value of the difference between the first line (503) and the second line (505) is greater than or equal to a reference value.

[0095] According to one embodiment, the smaller the difference between the first line (503) and the second line (505), the higher the degree of fitting may be. This is because the smaller the difference between the first line (503) and the second line (505), the smaller the difference between the slope of the cell data and the slope of the reference data. Referring to the graph (501), even though the degree of fitting of the part of the cell data corresponding to the area other than the area (507) is greater than or equal to the reference fitting degree, the degree of fitting of the part of the cell data corresponding to the area (507) may be less than the reference fitting degree.

[0096] Therefore, at least one processor (205) can fit reference data based on cell data of a battery cell not included in the region (507) to increase the degree of fitting. In other words, at least one processor (205) can fit reference data based on the capacity profile of a battery cell included in a specified capacity range, and the voltage profile of the battery cell according to the capacity profile.

[0097] According to one embodiment, the smaller the difference between the third line (603) and the fourth line (605), the smaller the difference between the slope of the cell data and the slope of the reference data, so the degree of fitting may be high. Referring to graph (601), similar to graph (501), even though the degree of fitting of the part of the cell data corresponding to the area other than the area (607) is greater than or equal to the reference fitting degree, the degree of fitting of the part of the cell data corresponding to the area (607) may be less than the reference fitting degree.

[0098] Therefore, at least one processor (205) can fit reference data based on cell data of a battery cell not included in the region (607) to increase the degree of fitting. In other words, at least one processor (205) can fit reference data based on the capacity profile of a battery cell included in a specified capacity range, and the voltage profile of the battery cell according to the capacity profile.

[0099] FIG. 7 illustrates an example of a graph representing a set of reference data in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0100] Referring to FIG. 7, the graph (701) can represent reference data as a voltage graph according to capacity. The first line (703) can represent first reference data of a reference battery cell obtained at the lowest first temperature (e.g., about 25°C) among a plurality of designated temperatures. The second line (705) can represent second reference data of a reference battery cell obtained at the highest second temperature (e.g., about 45°C) among a plurality of designated temperatures.

[0101] The third line (711) may represent reference data at a third temperature (e.g., about 26°C) between the first temperature and the second temperature. The fourth line (713) may represent reference data at a fourth temperature (e.g., about 27°C) between the third temperature and the second temperature. The fifth line (715) may represent reference data at a fifth temperature (e.g., about 28°C) between the fourth temperature and the second temperature. The sixth line (717) may represent reference data at a sixth temperature (e.g., about 44°C) between the fifth temperature and the second temperature. The third temperature may be higher than the first temperature, the fourth temperature may be higher than the third temperature, the fifth temperature may be higher than the fourth temperature, the sixth temperature may be higher than the fifth temperature, and the second temperature may be higher than the sixth temperature.

[0102] According to one embodiment, at least one processor (205) can identify a part of a set of reference data (e.g., a third line (711), a fourth line (713), a fifth line (715), a sixth line (717)) corresponding to each of the temperatures among a plurality of designated temperatures that are higher than the first temperature and lower than the second temperature by interpolating a first line (703) representing first reference data and a second line (705) representing second reference data.

[0103] For example, at least one processor (205) can obtain voltage values ​​of reference data at a plurality of specified temperatures corresponding to a specific capacity by dividing the interval between a first voltage value of a first reference data corresponding to a specific capacity and a second voltage value of a second reference data corresponding to a specific capacity into a specified number of intervals (e.g., about 20 intervals). The size of each interval may be the same.

[0104] According to one embodiment, at least one processor (205) can identify a set of reference data based on a part of a set of reference data, a first reference data, and a second reference data.

[0105] FIG. 8 illustrates a diagram and a table showing the temperature distribution of battery cells in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0106] Referring to FIG. 8, the diagram (801) may show the temperature distribution of battery cells included in the battery unit. The table (803) may show the temperature distribution of the battery cells.

[0107] A battery diagnostic device according to one embodiment can identify the temperature distribution of each battery cell even in a process step where it is difficult to identify the temperature of each battery cell.

[0108] According to one embodiment, with reference to diagram (801) and table (803), the number of battery cells with a temperature of about 30°C in the battery unit may be about 4, the number of battery cells with a temperature of about 31°C may be about 7, the number of battery cells with a temperature of about 32°C may be about 10, the number of battery cells with a temperature of about 33°C may be about 5, the number of battery cells with a temperature of about 34°C may be about 9, and the number of battery cells with a temperature of about 35°C may be about 23.

[0109] According to one embodiment, the average temperature of the battery cells included in the battery unit may be about 33.3°C. While charging the battery unit, the average temperature of the charger / discharger may be about 32.7°C, and the temperature at the completion of charging of the charger / discharger may be about 33.3°C.

[0110] FIG. 9 illustrates an example of a learning model for identifying the capacity of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0111] Referring to FIG. 9, the learning model (903) may include a learning model that learns the correlation between the capacity of the battery cell and the parameters related to the fitting of the first specific reference data. The first specific reference data may correspond to the smallest deviation among the deviations of each fitted reference data and the cell data.

[0112] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) inputs the value of a parameter of a first specific reference data corresponding to a battery cell as input data (901), and outputs the capacity of the battery cell from the learning model (903) as output data (905).

[0113] According to one embodiment, if at least one processor (205) of the battery diagnostic device (201) includes an artificial intelligence dedicated processor (e.g., a neural processing unit (NPU)) for training an artificial neural network (ANN) model, the artificial intelligence dedicated processor can train the artificial neural network by utilizing weight data stored in memory as training data for the machine learning model.

[0114] According to one embodiment, examples of learning algorithms may include supervised learning, unsupervised learning, self-supervised learning, semi-supervised learning, or reinforcement learning.

[0115] According to one embodiment, the artificial neural network included in the learning model may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values ​​and can perform neural network operations through operations between the operation result of the previous layer and the plurality of weights. The plurality of weights possessed by the plurality of neural network layers may be optimized by the learning results of the artificial intelligence model. For example, the plurality of weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized.

[0116] According to one embodiment, the artificial neural network may include a deep neural network, such as a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks, but is not limited to the examples described above.

[0117] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can output the capacity of a battery cell, which is output data (905) correlated with the value of a parameter of a first specific reference data, which is input data (901), based on a selected artificial intelligence model.

[0118] According to one embodiment, as the temperature of the battery cell increases, the slope of the voltage increase with respect to capacity increase can become gentler.

[0119] Therefore, as the temperature of the battery cell increases, the first value calculated in the capacity profile of the reference battery cell to fit the reference battery cell can increase. As the first value increases, the value of the parameter can increase. As the temperature of the battery cell increases, the resistance of the battery cell can decrease. As the resistance of the battery cell decreases, the capacity of the battery cell can be measured as higher.

[0120] Therefore, the first value of the first specific reference data and the capacity of the battery cell may have a positive correlation. However, the first value and the capacity of the battery cell may not be limited to having a linear positive correlation.

[0121] In addition, the value of the parameter of the first specific reference data and the capacity of the battery cell may have a positive correlation. As with the first value, the value of the parameter and the capacity of the battery cell may not be limited to having a linear positive correlation.

[0122] The charging capacity of the battery cell identified through the learning model (903) may include the total capacity of the battery.

[0123] FIG. 10 illustrates an example of a diagram showing the influence of a plurality of factors of a learning model for identifying the capacity of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0124] Referring to FIG. 10, the diagram (1001) may indicate the influence of multiple factors that are input as input data to a learning model (e.g., the learning model (903) of FIG. 9) for identifying the capacity of a battery cell. The influence of each of the multiple factors may indicate the degree to which each factor is considered when the learning model derives output data from the input data.

[0125] According to one embodiment, the influence of a parameter (e.g., input data (901) of FIG. 9) may be the highest. The first temperature factor may represent the average temperature of a tray containing multiple battery cells when charging for shipment during the activation process. The second temperature factor may represent the average temperature of the tray during discharge within the activation process. The capacity factor may represent the capacity at the completion of the first charge within the activation process. The categorized temperature may represent the temperature of a battery cell corresponding to one of the temperatures categorized at specified intervals (e.g., approximately 1°C). The Y-axis shift parameter may represent the degree of shift along the Y-axis when fitting the first specific reference data to the cell data. The third temperature factor may represent the average temperature during the first charge process within the activation process. The error may represent the degree of symmetry in the overall profile. The absolute value of the battery cell's Y-axis shift parameter may represent the absolute value of the degree of shift along the Y-axis when fitting the first specific reference data to the cell data. The X-axis shift parameter can indicate the degree of shift along the X-axis when fitting the first specific reference data to the cell data.

[0126] FIG. 11 illustrates a graph showing the temperature and parameters of a battery cell predicted in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0127] Referring to FIG. 11, the predicted temperature may represent a temperature predicted to be the temperature of the battery cell among the categorized temperatures. The parameter may represent a value determined based on a first value calculated on the capacity profile of the reference data to fit the reference data to the cell data.

[0128] According to one embodiment, at least one processor (205) can predict the temperature of a battery cell through a plurality of factors described in FIG. 10 among categorized temperatures. The predicted temperature may represent the temperature of a battery cell corresponding to one of the temperatures categorized at specified intervals (e.g., in units of about 1°C).

[0129] According to one embodiment, at least one processor (205) can improve the precision of the temperature of the battery cell predicted through parameters.

[0130] For example, referring to graph (1101), the parameters of reference data corresponding to a battery cell having a temperature of about 30°C can be distributed from about 100.5 to about 104. At least one processor (205) can subdivide the categorized predicted temperature by referring to the parameters through the learning model of FIG. 9.

[0131] For example, referring to graph (1101), if the parameter of reference data corresponding to a first battery cell having a temperature of about 30°C is about 100.5 and the parameter of reference data corresponding to a second battery cell having a temperature of about 30°C is about 104, the temperature of the first battery cell may be lower than the temperature of the second battery cell. At least one processor (205) may identify the temperature of the battery cell by considering the parameter of reference data corresponding to each battery cell. As an example, the temperature of the first battery cell may be about 30°C and the temperature of the second battery cell may be about 30.8°C.

[0132] FIG. 12 illustrates a graph showing the accuracy of the capacity prediction of a battery cell by a learning model in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0133] Referring to FIG. 12, the graph (1201) can represent the predicted capacity of a battery cell relative to the actual capacity of the battery cell. The predicted capacity of the battery cell can be predicted through the learning model of FIG. 9.

[0134] According to one embodiment, the smaller the root mean square error (RMSE) of the graph (1201), the better the prediction accuracy of the learning model (e.g., the learning model (903) of FIG. 9). The RMSE of the graph (1201) may be approximately 0.191.

[0135] According to one embodiment, the learning model (903) can predict the capacity of the battery cell based on a plurality of factors (e.g., a plurality of factors of FIG. 10).

[0136] FIG. 13 illustrates an example of the flow of operation of a battery diagnostic device for identifying the temperature of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0137] In the following, it is assumed that at least one processor (205) included in the battery diagnostic device (201) of FIG. 2 performs the process of FIG. 13. Additionally, in the description of FIG. 13, the operation described as being performed by the battery diagnostic device (201) can be understood as being controlled by at least one processor included in the battery diagnostic device (201).

[0138] In the following, the data of the battery cell can be understood as the cell data of the target battery cell. The reference data can be understood as the data of the reference battery cell.

[0139] Referring to FIG. 13, in the first operation (1301), at least one processor (205) according to one embodiment can obtain a set of reference data of a reference battery cell corresponding to each of a plurality of designated temperatures.

[0140] In the second operation (1303), at least one processor (205) according to one embodiment can obtain cell data of a battery cell at a specific temperature.

[0141] In the third operation (1305), at least one processor (205) according to one embodiment can fit each reference data to the cell data such that the deviations between each reference data and the cell data included in the set of reference data are minimized.

[0142] In the fourth operation (1307), at least one processor (205) according to one embodiment can identify a first specific reference data that is included in a set of reference data and corresponds to the smallest deviation among the deviations of each fitted reference data and cell data.

[0143] In the fifth operation (1309), at least one processor (205) according to one embodiment can identify a specific temperature based on a temperature corresponding to a first specific reference data corresponding to the smallest deviation among a plurality of specified temperatures.

[0144] FIG. 14 illustrates an example of the flow of operation of a battery diagnostic device for identifying the capacity of a battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0145] In the following, it is assumed that at least one processor (205) included in the battery diagnostic device (201) of FIG. 2 performs the process of FIG. 14. Additionally, in the description of FIG. 14, the operation described as being performed by the battery diagnostic device (201) can be understood as being controlled by at least one processor included in the battery diagnostic device (201).

[0146] In the following, the data of the battery cell can be understood as the cell data of the target battery cell. The reference data can be understood as the data of the reference battery cell.

[0147] Referring to FIG. 14, in the first operation (1401), at least one processor (205) according to one embodiment can obtain a set of reference data of a reference battery cell corresponding to each of a plurality of designated temperatures.

[0148] In the second operation (1403), at least one processor (205) according to one embodiment can obtain cell data of a battery cell at a specific temperature.

[0149] In the third operation (1405), at least one processor (205) according to one embodiment can fit each reference data to the cell data such that the deviations between each reference data and the cell data included in the set of reference data are minimized.

[0150] In the fourth operation (1407), at least one processor (205) according to one embodiment can identify a first specific reference data that is included in a set of reference data and corresponds to the smallest deviation among the deviations of each fitted reference data and cell data.

[0151] In the fifth operation (1409), at least one processor (205) according to one embodiment can obtain the capacity of the battery cell based on the value of a parameter related to the fitting of the first specific reference data.

[0152] FIG. 15 is a block diagram showing the hardware configuration of a computing system performing a battery diagnostic method in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0153] Referring to FIG. 15, a computing system (1500) according to one embodiment disclosed in this document may include an MCU (1510), memory (1520), an input / output I / F (1530), and a communication I / F (1540).

[0154] The MCU (1510) may be one or more processors that execute various programs stored in memory (1520) (e.g., battery cell data collection program, graph generation program, data analysis program, data decomposition algorithm, normalization program, battery cell diagnosis program, etc.), process various information including characteristic data of the battery cell, potential variables, etc. through these programs, and perform the functions of the battery diagnosis device (201) shown in FIGS. 2 to 14.

[0155] The memory (1520) can store various programs such as a battery cell data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, and a battery cell diagnosis program.

[0156] These memories (1520) may be provided in multiple quantities as needed. The memories (1520) may be volatile memories or non-volatile memories. As volatile memories, the memory (1520) may use RAM, DRAM, SRAM, etc. As non-volatile memories, the memory (1520) may use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the listed memories (1520) are merely examples and are not limited to these examples.

[0157] The input / output I / F (1530) can provide an interface that enables data transmission and reception between an input device (not shown), such as a keyboard, mouse, or touch panel, an output device (not shown), and an MCU (1510).

[0158] The communication I / F (1540) is configured to transmit and receive various data with a server and may be various devices capable of supporting wired or wireless communication. For example, the battery diagnostic device (201) can transmit and receive various information, including the shape model of a battery cell, from a separately provided external server via the communication I / F (1540).

[0159] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a module that performs, for example, the functions illustrated in FIG. 2, by being recorded in memory (1520) and processed by an MCU (1510).

[0160] As described above, even though all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purposes of the embodiments disclosed in this document, all components may be selectively combined in one or more ways to operate.

[0161] Furthermore, terms such as "include," "compose," or "have" as described above, unless specifically stated otherwise, mean that the relevant component may be inherent; thus, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their contextual meanings in the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0162] The foregoing disclosure outlines the features of several embodiments to enable those skilled in the art to better understand the aspects of the present disclosure. Those skilled in the art will understand that the present disclosure can be readily used as a basis for designing or modifying other structures to perform the same purpose or achieve the same advantages as the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent configurations do not depart from the scope of the present disclosure and that various changes, substitutions, and modifications may be made in the present specification without departing from the scope of the present disclosure.

Claims

Claim 1 A battery diagnostic device comprising: a memory for storing at least one instruction; and at least one processor for executing said at least one instruction, wherein the at least one processor is configured to acquire a set of reference data of a reference battery cell corresponding to each of a plurality of designated temperatures, acquire cell data of a battery cell at a specific temperature, fit said cell data by reducing, enlarging, or moving said reference data such that the deviations between said reference data and said cell data included in the set of reference data are minimized, identify a first specific reference data included in the set of reference data and corresponding to the smallest deviation among said fitted reference data and said cell data, and diagnose said specific temperature, which is the temperature of the battery cell, or the capacity of said battery cell based on the temperature corresponding to the first specific reference data corresponding to the smallest deviation among the plurality of designated temperatures. Claim 2 A battery diagnostic device according to claim 1, wherein the at least one processor divides the cell data into a plurality of intervals, identifies interval data included in at least a part of the cell data and included in any one of the plurality of intervals, fits each reference data to the interval data such that the deviation between each reference data and the interval data is minimized, identifies a second specific reference data included in the set of reference data and corresponding to the smallest deviation among the deviations between each fitted reference data and the interval data, and identifies the temperature of the battery cell in any one of the plurality of specified temperatures based on the temperature at which the second specific reference data is obtained. Claim 3 A battery diagnostic device according to claim 1, configured to acquire cell data of the battery cell during the activation process of the battery cell. Claim 4 A battery diagnostic device according to claim 1, wherein the at least one processor is configured to identify, before acquiring the set of reference data, a first reference data of the reference battery cell acquired at the lowest first temperature among the plurality of designated temperatures and a second reference data of the reference battery cell acquired at the highest second temperature among the plurality of designated temperatures, and to identify the set of reference data corresponding to each of the plurality of designated temperatures based on the first reference data and the second reference data. Claim 5 A battery diagnostic device according to claim 4, wherein the at least one processor is configured to identify a portion of a set of reference data corresponding to each of the temperatures higher than the first temperature and lower than the second temperature among a plurality of designated temperatures by interpolating the first reference data and the second reference data, and to identify the set of reference data based on the portion of the set of reference data, the first reference data, and the second reference data. Claim 6 A battery diagnostic device according to claim 1, wherein each reference data comprises a capacity profile of the reference battery cell and a voltage profile of the reference battery cell, and the at least one processor is configured to fit each reference data to the cell data by performing at least one of an operation of a specific first value, an addition of a specific second value, or any combination thereof with respect to the capacity profile or the voltage profile. Claim 7 A battery diagnostic device according to claim 1, wherein the cell data comprises: a capacity profile of the battery cell included in a specified capacity range; and a voltage profile of the battery cell according to the capacity profile. Claim 8 A battery diagnostic device according to claim 1, wherein each of the deviations of each of the reference data and the cell data comprises the root mean squared error (RMSE) of each of the reference data and the cell data. Claim 9 delete Claim 10 A battery diagnosis method comprising: acquiring a set of reference data for a reference battery cell corresponding to each of a plurality of designated temperatures; acquiring cell data of a battery cell at a specific temperature; fitting the cell data by reducing, enlarging, or moving each of the reference data included in the set of reference data so that the deviations between each of the reference data and the cell data are minimized; identifying a first specific reference data included in the set of reference data and corresponding to the smallest deviation among the deviations between each of the fitted reference data and the cell data; and diagnosing the specific temperature, which is the temperature of the battery cell, or the capacity of the battery cell based on the temperature corresponding to the first specific reference data corresponding to the smallest deviation among the plurality of designated temperatures. Claim 11 A battery diagnostic method according to claim 10, further comprising: a step of dividing the cell data into a plurality of intervals; a step of identifying interval data included in at least a portion of the cell data and included in any one of the plurality of intervals; a step of fitting each reference data to the interval data such that the deviation between each reference data and the interval data is minimized; a step of identifying a second specific reference data included in the set of reference data and corresponding to the smallest deviation among the deviations between each fitted reference data and the interval data; and a step of identifying the temperature of the battery cell in any one of the intervals based on the temperature at which the second specific reference data is obtained among the plurality of designated temperatures. Claim 12 A battery diagnostic method according to claim 10, wherein the operation of acquiring cell data of a battery cell at the specific temperature is performed in the activation process of the battery cell. Claim 13 A battery diagnostic method according to claim 10, wherein the operation of acquiring a set of reference data of the reference battery cell corresponding to each of the plurality of designated temperatures comprises: an operation of identifying, before acquiring the set of reference data, a first reference data of the reference battery cell acquired at the lowest first temperature among the plurality of designated temperatures, and a second reference data of the reference battery cell acquired at the highest second temperature among the plurality of designated temperatures; and an operation of identifying a set of reference data corresponding to each of the plurality of designated temperatures based on the first reference data and the second reference data. Claim 14 A battery diagnostic method according to claim 13, wherein the operation of identifying a set of reference data corresponding to each of the plurality of designated temperatures based on the first reference data and the second reference data comprises: an operation of identifying a part of a set of reference data corresponding to each of the temperatures among the plurality of designated temperatures that are higher than the first temperature and lower than the second temperature by interpolating the first reference data and the second reference data; and an operation of identifying a set of reference data based on the part of the set of reference data, the first reference data, and the second reference data. Claim 15 A battery diagnostic method according to claim 10, wherein each reference data includes a capacity profile of the reference battery cell and a voltage profile of the reference battery cell, and the operation of fitting each reference data to the cell data such that the deviations between each reference data and the cell data included in the set of reference data are minimized comprises, with respect to the capacity profile or the voltage profile, performing at least one of the operation of a specific first value, the addition of a specific second value, or any combination thereof, thereby fitting each reference data to the cell data. Claim 16 A battery diagnostic method according to claim 10, wherein the cell data comprises: a capacity profile of the battery cell included in a specified capacity range; and a voltage profile of the battery cell according to the capacity profile. Claim 17 A battery diagnostic method according to claim 10, wherein each of the deviations of each of the reference data and the cell data comprises the root mean squared error (RMSE) of each of the reference data and the cell data. Claim 18 delete

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