Battery diagnosis device and method therefor

Abstract

A battery diagnosis device according to one embodiment disclosed herein comprises: memory for storing at least one instruction; and at least one processor for executing the at least one instruction. The at least one processor can: identify changed capacities during a process in which target battery cells included in a target battery unit change from a first voltage to a second voltage, and the total capacities of the target battery cells; identify differences between predicted total capacities, which are total capacities of the respective target battery cells predicted through a learning model, and total capacities of the target battery cells corresponding to the predicted total capacities; and diagnose the state of the target battery unit on the basis of the differences.

Description

Battery diagnostic device and method

[0001] Cross-citation with related applications

[0002] The present application claims the benefit of priority based on Korean Patent Application No. 10-2024-0169886 filed on November 25, 2024, and Korean Patent Application No. 10-2025-0098390 filed on July 21, 2025, and includes all contents disclosed in the documents of said patent applications as part of this specification.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery diagnostic device and a method thereof.

[0005] 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.

[0006] 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.

[0007] In particular, as the importance of ensuring battery stability increases, technological development is being carried out to improve the accuracy of battery temperature measurement by diversifying the methods of measuring battery temperature.

[0008] According to the embodiments disclosed in this document, a battery diagnostic device and a method for identifying the temperature of a battery unit through the capacity of battery cells included in the battery unit are provided.

[0009] According to the embodiments disclosed in this document, a battery diagnostic device and a method for identifying the temperature of a battery unit without a temperature sensor through cell data of battery cells included in the battery unit are provided.

[0010] 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.

[0011] 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.

[0012] According to one embodiment, the at least one processor can diagnose the state of the target battery unit based on a predicted total capacity, which is the total capacity of each of the target battery cells included in the target battery unit and is predicted through a learning model, and the total capacity of the target battery cell corresponding to the predicted total capacity.

[0013] According to one embodiment, the at least one processor can identify the change capacities during the process in which the target battery cells change from a first voltage to a second voltage, and input the change capacities into the learning model to identify the predicted total capacity.

[0014] According to one embodiment, the at least one processor can diagnose the state of the target battery unit based on the predicted total capacity and the differences between the total capacity.

[0015] According to one embodiment, the at least one processor can identify the temperature of the target battery unit over time based on the differences.

[0016] According to one embodiment, the at least one processor can identify the deviation between the predicted total capacity and the total capacity based on the differences, and diagnose the state of the target battery unit based on the deviation.

[0017] According to one embodiment, the at least one processor can identify the temperature of the target battery unit having a negative correlation with the deviation based on the deviation.

[0018] According to one embodiment, the deviation may include RMSE (root mean squared error).

[0019] According to one embodiment, the at least one processor can identify the first voltage corresponding to the first reference change capacity of the reference battery cell not included in the target battery unit, and the second voltage corresponding to the second reference change capacity of the reference battery cell that differs from the first reference change capacity by a specified change capacity.

[0020] According to one embodiment, the at least one processor can calculate a first predicted total capacity value predicted as the total capacity based on the change capacities, calculate a first predicted correction value through a regression equation that calculates a correction value of the total capacity based on error factors that cause an error in the first predicted total capacity value, and calculate a second predicted correction value by applying the first predicted correction value to the first predicted total capacity value.

[0021] According to one embodiment, the regression coefficients included in the regression equation can be calculated based on a light gradient boosting machine (Light GBM).

[0022] A battery diagnostic method according to another embodiment of the present document may include: an operation of identifying differences between a predicted total capacity, which is the total capacity of each of the target battery cells included in the target battery unit and is predicted through a learning model, and the total capacity of the target battery cell corresponding to the predicted total capacity; and an operation of diagnosing the state of the target battery unit based on the differences.

[0023] According to one embodiment, the battery diagnostic method may further include the operation of identifying change capacities during the process in which the target battery cells change from a first voltage to a second voltage, and the operation of inputting the change capacities into the learning model to identify the predicted total capacity.

[0024] According to one embodiment, the operation of diagnosing the state of the target battery unit based on the predicted total capacity, which is the total capacity of each of the target battery cells predicted through the learning model, and the total capacity of the target battery cell corresponding to the predicted total capacity, may include the operation of diagnosing the state of the target battery unit based on the differences between the predicted total capacity and the total capacity.

[0025] According to one embodiment, the operation of diagnosing the state of the target battery unit based on the differences may include the operation of identifying the temperature of the target battery unit over time based on the differences.

[0026] According to one embodiment, the operation of diagnosing the state of the target battery unit based on the differences may include the operation of identifying the deviation between the predicted total capacity and the total capacity based on the differences, and the operation of diagnosing the state of the target battery unit based on the deviation.

[0027] According to one embodiment, the operation of diagnosing the state of the target battery unit based on the deviation may include the operation of identifying the temperature of the target battery unit having a negative correlation with the deviation based on the deviation.

[0028] According to one embodiment, the deviation may include RMSE (root mean squared error, RMSE).

[0029] According to one embodiment, the battery diagnostic method may further include the operation of identifying the first voltage corresponding to the first reference change capacity of a reference battery cell not included in the target battery unit, and the operation of identifying the second voltage corresponding to the second reference change capacity of the reference battery cell that differs from the first reference change capacity by a specified change capacity.

[0030] According to one embodiment, the battery diagnostic method may further include the operation of calculating a first predicted total capacity value predicted as the total capacity based on the changed capacities, the operation of calculating a first predicted correction value through a regression equation that calculates a correction value of the total capacity based on error factors that cause an error in the first predicted total capacity value, and the operation of calculating a second predicted correction value by applying the first predicted correction value to the first predicted total capacity value.

[0031] According to one embodiment, the battery diagnostic method may further include the operation of calculating regression coefficients included in the regression equation based on a light gradient boosting machine (Light GBM).

[0032] According to the embodiments disclosed in this document, the present technology can identify the temperature of a battery unit through the capacity of the battery cells included in the battery unit.

[0033] In addition, this technology can identify the temperature of a battery unit without a temperature sensor through cell data of the battery cells included in the battery unit.

[0034] In addition, various effects that can be identified directly or indirectly through this document may be provided.

[0035] 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.

[0036] 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.

[0037] FIG. 3 illustrates an example of a graph showing the voltage of a reference battery cell according to the discharge capacity of a reference battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0038] FIG. 4 illustrates an example of a graph showing the predicted total capacity and the deviation of the total capacity of each of the target battery cells when the discharge capacity is a first specified capacity, in a battery diagnostic device and battery diagnostic method according to one embodiment of the present document.

[0039] FIG. 5 illustrates an example of a graph showing the predicted total capacity and the deviation of the total capacity of each of the target battery cells when the discharge capacity is a second specified capacity in a battery diagnostic device and battery diagnostic method according to one embodiment of the present document.

[0040] FIG. 6 illustrates an example of a graph showing the correlation between the temperature and deviation of a target battery unit in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0041] FIG. 7 illustrates the flow of operation of a battery diagnostic device for estimating the temperature of a target battery unit in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0042] FIG. 8 illustrates the flow of operation of a battery diagnostic device for diagnosing the state of a target battery unit in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0043] FIG. 9 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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).

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 9.

[0053] 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.

[0054] 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).

[0055] 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.

[0056] 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.

[0057] 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).

[0058] 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.

[0059] 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).

[0060] 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).

[0061] 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.

[0062] 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.

[0063] 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).

[0064] 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.

[0065] 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.

[0066] FIG. 3 illustrates an example of a graph showing the voltage of a reference battery cell according to the discharge capacity of a reference battery cell in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0067] 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.

[0068] Graph (301) may represent a graph showing the voltage of a reference battery cell according to the depth of discharge (DD) of the reference battery cell. Line (303) may represent the voltage of a battery cell according to the DOD of the reference battery cell. A first point (305) is located on line (303) and may represent the voltage when the discharge capacity of the reference battery cell is approximately 0% of the total capacity of the battery cell. A second point (307) is located on line (303) and may represent the voltage when the discharge capacity of the reference battery cell is approximately 10% of the total reference capacity of the battery cell. A third point (309) is located on line (303) and may represent the voltage when the discharge capacity of the reference battery cell is approximately 20% of the battery cell. DOD represents the ratio of the discharged capacity to the full charge capacity of the battery cell and may be expressed as a value between 0 and 1, or a value between 0% and 100%.

[0069] According to one embodiment, at least one processor (205) may measure the temperature of a target battery unit to increase the accuracy of capacity measurement of a target battery unit (e.g., battery pack, battery module), detect abnormalities in the target battery unit, improve the quality of the target battery unit, or monitor the target battery unit. The target battery unit may represent a battery unit subject to condition diagnosis, and the target battery cell may represent a battery cell included in the target battery unit and subject to condition diagnosis.

[0070] According to one embodiment, at least one processor (205) can identify the temperature of the target battery unit based on the change capacity of the target battery unit. The change capacity may indicate the degree to which the charge capacity is changed when the voltage of the battery cell (e.g., reference battery cell, target battery cell) is changed. For example, if the charge capacity is changed as the battery cell is discharged, the change capacity may indicate the discharge capacity, and if the charge capacity is changed as the battery cell is charged, the change capacity may indicate the charge capacity.

[0071] In the following description, discharge capacity is described, but discharge capacity is merely an example of change capacity, and the embodiments of this document are not limited thereto. According to one embodiment, the change capacity may also include charge capacity.

[0072] According to one embodiment, at least one processor (205) can identify a voltage according to the change capacity of a reference battery cell to identify a voltage that serves as a reference for identifying change capacities.

[0073] According to one embodiment, at least one processor (205) divides the change capacity of a reference battery cell into a plurality of sections having a specified range (e.g., a section where the depth of discharge (DOD) is about 0% or more and less than about 10%, a section where the DOD is about 10% or more and less than about 20%, a section where the DOD is about 20% or more and less than 30%, a section where the DOD is about 30% or more and less than 40%, a section where the DOD is about 40% or more and less than 50%, a section where the DOD is about 50% or more and less than 60%, a section where the DOD is about 60% or more and less than 70%, a section where the DOD is about 70% or more and less than 80%, a section where the DOD is about 80% or more and less than 90%, a section where the DOD is about 90% or more and less than 100%), and a reference battery corresponding to each of the reference change capacitys distinguishing the plurality of sections. The voltage of the cell can be measured.

[0074] According to one embodiment, at least one processor (205) can identify a first voltage of a reference battery cell corresponding to a first reference change capacity. The first reference change capacity may represent a capacity that serves as a reference for distinguishing multiple intervals. For example, the first reference change capacity may represent the capacity of a reference battery cell where the DOD is a specific value (e.g., about 0%, about 10%, about 20%, about 30%, ...).

[0075] According to one embodiment, at least one processor (205) can identify a second voltage of a reference battery cell corresponding to a second reference change capacity. The second reference change capacity may differ from the first reference change capacity by a specified change capacity. For example, the second reference change capacity may represent the capacity of a reference battery cell where the DOD differs from a specific value by a specified DOD value (e.g., about 10%) (e.g., about 10%, about 20%, about 30%, ...).

[0076] According to one embodiment, in graph (301), when the DOD of the reference battery cell is about 0%, the voltage of the reference battery cell may be about 4.2223V; when the DOD of the reference battery cell is about 10%, the voltage of the reference battery cell may be about 4.0599V; when the DOD of the reference battery cell is about 20%, the voltage of the reference battery cell may be about 3.9491V; when the DOD of the reference battery cell is about 30%, the voltage of the reference battery cell may be about 3.8367V; when the DOD of the reference battery cell is about 40%, the voltage of the reference battery cell may be about 3.7282V; when the DOD of the reference battery cell is about 50%, the voltage of the reference battery cell may be about 3.6353V; and when the DOD of the reference battery cell is about 60%, the voltage of the reference battery cell may be about It may be 3.5773V, and when the DOD of the reference battery cell is about 70%, the voltage of the reference battery cell may be about 3.5293V, and when the DOD of the reference battery cell is about 80%, the voltage of the reference battery cell may be about 3.4692V, and when the DOD of the reference battery cell is about 90%, the voltage of the reference battery cell may be about 3.37V, and when the DOD of the reference battery cell is about 100%, the voltage of the reference battery cell may be about 2.5V.

[0077] According to one embodiment, at least one processor (205) can identify changing capacities in a target battery cell based on the voltage identified in a reference battery cell. The type and model of the reference battery cell and the type and model of the target battery cell may match.

[0078] For example, at least one processor (205) can identify the change capacity of the target battery cell when the voltage of the target battery cell changes from a first voltage of the reference battery cell corresponding to the first reference change capacity (e.g., about 4.223 V) to a second voltage of the reference battery cell corresponding to the second reference change capacity (e.g., about 4.0599 V). Additionally, at least one processor (205) can identify the total capacity of the target battery cell.

[0079] According to one embodiment, at least one processor (205) inputs the change capacities during the process of changing the target battery cells from a first voltage to a second voltage into a learning model and can identify the predicted total capacity predicted as the total capacity of each target battery cell.

[0080] The learning model may include a light gradient boosting machine (Light GBM).

[0081] According to one embodiment, at least one processor (205) can calculate a first predicted total capacity value predicted as the total capacity of a target battery cell based on the change capacity of the target battery cell through a learning model, calculate a first predicted correction value through a regression equation that calculates a correction value of the total capacity based on error factors that cause an error in the first predicted total capacity value, and calculate a second predicted correction value by applying the first predicted correction value to the first predicted total capacity value.

[0082] According to one embodiment, the regression coefficients included in the regression equation can be calculated based on a light gradient boosting machine (Light GBM).

[0083] According to one embodiment, at least one processor (205) can identify deviations between the predicted total capacity and the total capacity based on the differences between the predicted total capacity and the total capacity. A method for identifying deviations between the predicted total capacity and the total capacity is described below with reference to FIGS. 3 to 5.

[0084] According to one embodiment, at least one processor (205) can identify the temperature of a target battery unit based on the predicted total capacity and the deviation of the total capacity. A method for identifying the temperature of a target battery unit is described below with reference to FIG. 6.

[0085] FIG. 4 illustrates an example of a graph showing the predicted total capacity and the deviation of the total capacity of each of the target battery cells when the discharge capacity is a first specified capacity, in a battery diagnostic device and battery diagnostic method according to one embodiment of the present document.

[0086] Referring to FIG. 4, the graph (401) may represent the distribution of the predicted total capacity of each of the target battery cells based on the total capacity of each of the target battery cells and the change capacity of the target battery cell in a first interval (e.g., an interval in which the voltage of the target battery cell changes from a first voltage to a second voltage). The first voltage in the first interval may be the voltage of a reference battery cell with a DOD of approximately 0%, and the second voltage in the first interval may be the voltage of a reference battery cell with a DOD of approximately 10%.

[0087] According to one embodiment, the total capacity may represent the measured total capacity of each of the target battery cells. The predicted total capacity may represent the total capacity of each of the target battery cells predicted through a learning model.

[0088] At least one processor (205) can identify differences between the predicted total capacity and the total capacity, identify deviations between the predicted total capacity and the total capacity based on the differences, and diagnose the temperature of the target battery unit based on the deviations.

[0089] The deviation may include the root mean square error (RMSE). The RMSE can be calculated by Equation 1.

[0090]

[0091] n may represent the number of target battery cells included in the target battery unit. n may be a natural number greater than or equal to 1. It can represent the total capacity of the target battery cell. It can represent the predicted total capacity of the target battery cell.

[0092] The deviation of the battery cells included in the target battery unit in the first section may be approximately 0.321.

[0093] FIG. 5 illustrates an example of a graph showing the predicted total capacity and the deviation of the total capacity of each of the target battery cells when the discharge capacity is a second specified capacity in a battery diagnostic device and battery diagnostic method according to one embodiment of the present document.

[0094] Referring to FIG. 5, the graph (501) may show the distribution of the predicted total capacity of each of the target battery cells based on the total capacity of each of the target battery cells and the change capacity of the target battery cell in the second section (e.g., the section where the voltage of the target battery cell changes from the third voltage to the fourth voltage). The third voltage in the second section may be the voltage of a reference battery cell with a DOD of about 0%, and the fourth voltage in the second section may be the voltage of a reference battery cell with a DOD of about 20%.

[0095] As described in FIG. 4, at least one processor (205) can identify differences between the predicted total capacity and the total capacity, identify deviations between the predicted total capacity and the total capacity based on the differences, and diagnose the temperature of the target battery unit based on the deviation (e.g., root mean square error).

[0096] The deviation of the battery cells included in the target battery unit in the second section may be approximately 0.298.

[0097] Although not illustrated in FIG. 5, the deviation of the battery cells included in the target battery unit in the third section, which is different from the first and second sections (e.g., the section where the voltage of the target battery cell changes from the fifth voltage to the sixth voltage), may be about 0.276. The fifth voltage in the third section may be the voltage of a reference battery cell with a DOD of about 0%, and the sixth voltage in the third section may be the voltage of a reference battery cell with a DOD of about 30%.

[0098] FIG. 6 illustrates an example of a graph showing the correlation between the temperature and deviation of a target battery unit in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0099] Referring to FIG. 6, the graph (601) may show the deviation of the battery unit according to DOD and the temperature of the battery unit. The first data points (603) may show the deviation according to DOD. The second data points (605) may show the measured temperature of the target battery unit according to DOD.

[0100] According to one embodiment, at least one processor (205) can obtain first data points (603) when the deviation identified in the manner described in FIG. 4 and 5 is represented according to the DOD corresponding to the interval in which the deviation was identified.

[0101] According to one embodiment, at least one processor (205) can acquire second data points (605) according to the average temperature of the target battery unit.

[0102] Referring to the first data points (603) and the second data points (605), the average temperature of the target battery unit may have a negative correlation with the deviation.

[0103] Therefore, at least one processor (205) can identify the average temperature of the target battery unit based on the predicted total capacity of each of the target battery cells and the shape of the deviation of the total capacity.

[0104] Although the target battery unit is described as including a plurality of target battery cells, the embodiments of this document are not limited thereto. According to one embodiment, the target battery unit may include a single target battery cell, and the contents described in this document may apply. Therefore, when the target battery unit includes a single target battery cell, at least one processor (205) can also identify the temperature of the individual battery cell. For example, when the target battery unit includes a single target battery cell, at least one processor (205) can omit the process of calculating the deviation and identify the temperature of the target battery cell based on the difference between the predicted total capacity and the total capacity of the target battery cell. In other words, at least one processor (205) can identify the change capacity at which the target battery cell changes from a first voltage to a second voltage and the total capacity of the target battery cell, identify the difference between the predicted total capacity (which is the total capacity of the target battery cell predicted through a learning model) and the total capacity of the target battery cell, and identify the temperature of the target battery cell based on the difference. The difference can have a negative correlation with the temperature of the target battery cell.

[0105] FIG. 7 illustrates the flow of operation of a battery diagnostic device for estimating the temperature of a target battery unit in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0106] 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. 7. Additionally, in the description of FIG. 7, the operation described as being performed by the battery diagnostic device (201) can be understood as being controlled by at least one processor (205) included in the battery diagnostic device (201).

[0107] Referring to FIG. 7, in the first operation (701), at least one processor (205) according to one embodiment can obtain cell data of a target battery cell included in a target battery unit.

[0108] Cell data may include a capacity profile of a target battery cell and a voltage profile of a target battery cell. For example, cell data may include a graph representing a voltage profile according to the capacity profile of a target battery cell.

[0109] In the second operation (703), at least one processor (205) according to one embodiment can set a voltage range and identify a change capacity.

[0110] In the third operation (705), at least one processor (205) according to one embodiment can identify the total predicted capacity through a learning model.

[0111] In the fourth operation (707), at least one processor (205) according to one embodiment can identify a deviation based on the difference between the predicted total capacity and the total capacity.

[0112] In the fifth operation (709), at least one processor (205) according to one embodiment can identify a deviation shape for the target battery cells.

[0113] In the sixth operation (711), at least one processor (205) according to one embodiment can estimate the temperature of the battery unit.

[0114] FIG. 8 illustrates the flow of operation of a battery diagnostic device for diagnosing the state of a target battery unit in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0115] 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. 8. Additionally, in the description of FIG. 8, the operation described as being performed by the battery diagnostic device (201) can be understood as being controlled by at least one processor (205) included in the battery diagnostic device (201).

[0116] In the first operation (801), at least one processor (205) according to one embodiment can identify the change capacities in the process of changing the target battery cells included in the target battery unit from a first voltage to a second voltage, and the total capacities of each of the target battery cells.

[0117] In the second operation (803), at least one processor (205) according to one embodiment can diagnose the state of the target battery unit based on the predicted total capacity, which is the total capacity of each of the target battery cells predicted through a learning model, and the total capacity of the target battery cell corresponding to the predicted total capacity.

[0118] According to one embodiment, at least one processor (205) can diagnose the state of a target battery unit based on the predicted total capacity and the differences between the total capacity.

[0119] FIG. 9 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.

[0120] Referring to FIG. 9, a computing system (900) according to one embodiment disclosed in this document may include an MCU (910), memory (920), an input / output I / F (930), and a communication I / F (940).

[0121] The MCU (910) may be one or more processors that execute various programs stored in memory (920) (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 8.

[0122] The memory (920) 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.

[0123] These memories (920) may be provided in multiple quantities as needed. The memories (920) may be volatile memories or non-volatile memories. As volatile memories, the memory (920) may use RAM, DRAM, SRAM, etc. As non-volatile memories, the memory (920) may use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the listed memories (920) are merely examples and are not limited to these examples.

[0124] The input / output I / F (930) 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 (910).

[0125] The communication I / F (940) 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 (940).

[0126] 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 (920) and processed by an MCU (910).

[0127] 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.

[0128] 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.

[0129] 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 disclosure without departing from the scope of the present disclosure.

Claims

1. Memory storing at least one instruction; and It includes at least one processor that executes the above at least one instruction, and The above at least one processor is, A configuration for diagnosing the state of a target battery unit based on a predicted total capacity, which is the total capacity of each of the target battery cells included in the target battery unit and is predicted through a learning model, and the total capacity of the target battery cell corresponding to the predicted total capacity. Battery diagnostic device.

2. In Claim 1, The above at least one processor is, Identifying the change capacities during the process in which the above target battery cells change from a first voltage to a second voltage, and Configured to input the change capacities into the learning model to identify the total predicted capacity, Battery diagnostic device.

3. In Claim 1, The above at least one processor is, Configured to diagnose the state of the target battery unit based on the above predicted total capacity and the differences between the above total capacity, Battery diagnostic device.

4. In Claim 3, The above at least one processor is, Based on the above differences, configured to identify the temperature of the target battery unit over time, Battery diagnostic device.

5. In Claim 3, The above at least one processor is, Based on the above differences, identify the deviation between the predicted total capacity and the total capacity, and Configured to diagnose the state of the target battery unit based on the above deviation, Battery diagnostic device.

6. In Claim 5, The above at least one processor is, Based on the above deviation, configured to identify the temperature of the target battery unit having a negative correlation with the above deviation, Battery diagnostic device.

7. In Claim 5, The above deviation is, Including RMSE (root mean squared error), Battery diagnostic device.

8. In Claim 2, The above at least one processor is, Identifying the first voltage corresponding to the first reference change capacity of a reference battery cell not included in the above target battery unit, and A configuration for identifying the second voltage corresponding to the second reference change capacity of the reference battery cell that differs from the first reference change capacity by a specified change capacity. Battery diagnostic device.

9. In Claim 2, The above at least one processor is, Based on the above change capacities, calculate a first predicted total capacity value predicted as the above total capacity, and Calculate a first predicted correction value through a regression equation that calculates a correction value for the total capacity based on error factors causing an error in the first predicted total capacity value, and A configuration configured to calculate a second prediction correction value by applying the first prediction correction value to the first prediction total capacity value. Battery diagnostic device.

10. In Claim 9, The regression coefficients included in the above regression equation are, Calculated based on a light gradient boosting machine (Light GBM), Battery diagnostic device.

11. An operation to diagnose the state of a target battery unit based on a predicted total capacity, which is the total capacity of each of the target battery cells included in the target battery unit and is predicted through a learning model, and the total capacity of the target battery cell corresponding to the predicted total capacity. Battery diagnostic method.

12. In Claim 11, An operation to identify the change capacities during the process in which the above target battery cells change from a first voltage to a second voltage; and A method further comprising the operation of inputting the change capacities into the learning model to identify the total predicted capacity. Battery diagnostic method.

13. In Claim 11, The operation of diagnosing the state of the target battery unit based on the predicted total capacity, which is the total capacity of each of the target battery cells predicted through the learning model, and the total capacity of the target battery cell corresponding to the predicted total capacity, is as follows: The operation of diagnosing the state of the target battery unit based on the above-mentioned total capacity and the differences between the above-mentioned total capacity, Battery diagnostic method.

14. In Claim 13, Based on the above differences, the operation of diagnosing the state of the target battery unit is, Based on the above differences, the operation of identifying the temperature of the target battery unit over time, Battery diagnostic method.

15. In Claim 13, Based on the above differences, the operation of diagnosing the state of the target battery unit is, An operation to identify deviations between the predicted total capacity and the total capacity based on the above differences; and Based on the above deviation, the operation of diagnosing the state of the target battery unit, Battery diagnostic method.

16. In Claim 15, Based on the above deviation, the operation of diagnosing the state of the target battery unit is, Based on the above deviation, the method includes an operation of identifying the temperature of the target battery unit having a negative correlation with the above deviation. Battery diagnostic method.

17. In Claim 15, The above deviation is, Including RMSE (root mean squared error), Battery diagnostic method.

18. In Claim 12, An operation to identify the first voltage corresponding to the first reference change capacity of a reference battery cell not included in the above target battery unit; and The method is configured to further include an operation of identifying the second voltage corresponding to the second reference change capacity of the reference battery cell, which differs from the first reference change capacity by a specified change capacity. Battery diagnostic method.

19. In Claim 12, An operation to calculate a first predicted total capacity value predicted as the total capacity based on the above-mentioned change capacities; The operation of calculating a first predicted correction value through a regression equation that calculates a correction value of the total capacity based on error factors causing an error in the first predicted total capacity value; and The method further includes the operation of calculating a second prediction correction value by applying the first prediction correction value to the first prediction total capacity value. Battery diagnostic method.

20. In Claim 19, A method further comprising the operation of calculating regression coefficients included in the above regression equation based on a light gradient boosting machine (Light GBM), Battery diagnostic method.

Images