Battery management apparatus and operating method thereof

Abstract

A battery management apparatus according to an embodiment disclosed in the present document may include: a data acquisition unit for obtaining voltage data related to a voltage change of each of a plurality of battery cells over time; a preprocessing unit for obtaining, from a data set composed of the voltage data of each of the plurality of battery cells, voltage data reconstructed for a predetermined time interval by using a data decomposition algorithm; a noise processing unit for generating battery cell data by performing noise processing based on the characteristics of the reconstructed voltage data; and an abnormal cell detection unit for detecting an abnormal battery cell on the basis of a voltage deviation of each of the battery cell data.

Description

Battery management device and its operating method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority from Republic of Korea Patent Application No. 10-2023-0172719, filed December 1, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery management device and a method of operating the same.

[0005] Recently, research and development on secondary batteries has been actively underway. Here, secondary batteries are defined as rechargeable and dischargeable batteries, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Electric vehicles receive external electricity to charge battery cells and modules, which are then discharged to power the motor. During production and use, battery cells and modules undergo internal deformation and transformation through various charging and discharging cycles, altering their physical and chemical properties. This degradation and deterioration of batteries necessitates the development of technologies to manage the operation of battery cells and modules.

[0007] Charge-discharge tests can be performed on batteries for various purposes, including performance diagnosis and condition analysis. For example, a test voltage can be applied to the battery, and a test voltage can be measured from the battery in response to the test voltage. However, the actual measured voltage data also contains noise, making it difficult to clearly analyze the data for battery condition management.

[0008] One purpose of the embodiments disclosed in this document is to provide a battery management device and an operating method thereof capable of managing an abnormal battery cell based on voltage data obtained from a battery unit included in a battery pack.

[0009] One object of the embodiments disclosed in this document is to provide a battery management device and an operating method thereof based on a data processing algorithm for reducing the influence of noise included in voltage data.

[0010] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0011] A battery management device according to an embodiment disclosed in the present document may include a data acquisition unit that acquires voltage data related to a voltage change over time of each of a plurality of battery cells; a preprocessing unit that acquires reconstructed voltage data for a predetermined time interval using a data decomposition algorithm from a data set composed of the voltage data of each of the plurality of battery cells; a noise processing unit that generates battery cell data by performing noise processing based on a characteristic of the reconstructed voltage data; and an abnormal cell detection unit that detects an abnormal battery cell based on a voltage deviation of each of the battery cell data.

[0012] According to an embodiment, the preprocessing unit can generate a plurality of sub-data sets of the length of the predetermined time interval from the data set, and obtain the reconstructed voltage data from each of the plurality of sub-data sets based on the data decomposition algorithm.

[0013] According to an embodiment, the data decomposition algorithm may include singular value decomposition.

[0014] According to an embodiment, the noise processing unit may perform the noise processing based on the number of zero crossings, which is the number of times the voltage crosses a specific voltage level, in each of the reconstructed voltage data.

[0015] According to an embodiment, the noise processing unit may compare the number of zero crossings of each of the reconstructed voltage data with a first threshold value, detect the reconstructed data having the number of zero crossings greater than or equal to the first threshold value as a noise component, and remove the noise component from the reconstructed voltage data to generate the battery cell data.

[0016] According to an embodiment, the specific voltage level may include 0.

[0017] According to an embodiment, the noise processing unit may perform the noise processing based on a zero-crossing period, which is a period in which a voltage crosses a specific voltage level, in each of the reconstructed voltage data.

[0018] According to an embodiment, the noise processing unit may compare the zero crossing period of each of the reconstructed voltage data with a second threshold value, detect the reconstructed data having the zero crossing period less than or equal to the second threshold value as a noise component, and remove the noise component from the reconstructed voltage data to generate the battery cell data.

[0019] According to an embodiment, the specific voltage level may include 0.

[0020] According to an embodiment, the abnormal cell detection unit may compare the voltage deviation, which is the difference between the minimum value and the maximum value of the voltage of each of the battery cell data in the predetermined time interval, with a third threshold value, and detect a battery cell having the voltage deviation greater than or equal to the third threshold value as an abnormal battery cell.

[0021] A battery management method according to an embodiment disclosed in the present document may include: a step of obtaining voltage data related to a change in voltage over time of each of a plurality of battery cells; a step of obtaining reconstructed voltage data for a predetermined time interval using a data decomposition algorithm from a data set composed of the voltage data of each of the plurality of battery cells; a step of generating battery cell data by performing noise processing based on a characteristic of the reconstructed voltage data; and a step of detecting an abnormal battery cell based on a voltage deviation of each of the battery cell data.

[0022] According to an embodiment, the step of obtaining the reconstructed voltage data may include the step of generating a plurality of sub-data sets of the length of the predetermined time interval from the data set; and the step of obtaining the reconstructed voltage data from each of the plurality of sub-data sets based on the data decomposition algorithm.

[0023] According to an embodiment, the data decomposition algorithm may include singular value decomposition.

[0024] According to an embodiment, the step of generating the battery cell data may include a step of performing the noise processing based on the number of zero crossings, which is the number of times the voltage crosses a specific voltage level, in each of the reconstructed voltage data.

[0025] According to an embodiment, the method may include: comparing the number of zero crossings of each of the reconstructed voltage data with a first threshold value; detecting reconstructed data having the number of zero crossings greater than or equal to the first threshold value as a noise component; and generating the battery cell data by removing the noise component from the reconstructed voltage data.

[0026] According to an embodiment, the specific voltage level may include 0.

[0027] According to an embodiment, the step of generating the battery cell data may include a step of performing the noise processing based on a zero-crossing period, which is a period in which a voltage crosses a specific voltage level, in each of the reconstructed voltage data.

[0028] According to an embodiment, the method may include: comparing the zero crossing period of each of the reconstructed voltage data with a second threshold value; detecting reconstructed data having the zero crossing period less than or equal to the second threshold value as a noise component; and generating the battery cell data by removing the noise component from the reconstructed voltage data.

[0029] According to an embodiment, the specific voltage level may include 0.

[0030] According to an embodiment, the step of detecting the abnormal battery cell may include the step of comparing the voltage deviation, which is the difference between the minimum value and the maximum value of the voltage of each of the battery cell data in the predetermined time interval, with a third threshold value; and the step of detecting a battery cell in which the voltage deviation is greater than or equal to the third threshold value as an abnormal battery cell.

[0031] The battery management device and its operating method disclosed in this document can manage abnormal battery cells based on voltage data obtained from a battery unit included in a battery pack.

[0032] The battery management device and its operating method disclosed in this document may be based on a data processing algorithm for reducing the influence of noise included in voltage data.

[0033] In addition, various effects may be provided, either directly or indirectly, through this document.

[0034] FIG. 1 is a drawing showing a battery pack according to one embodiment disclosed in this document.

[0035] FIG. 2 is a block diagram showing a battery management device according to one embodiment disclosed in this document.

[0036] FIG. 3 is a diagram showing a data set according to one embodiment disclosed in this document.

[0037] FIG. 4 is a diagram showing reconstructed voltage data according to one embodiment disclosed in this document.

[0038] FIG. 5 is a diagram showing reconstructed voltage data according to one embodiment disclosed in this document.

[0039] FIG. 6 is a diagram showing reconstructed voltage data according to one embodiment disclosed in this document.

[0040] FIG. 7 is a flowchart showing the operation of a battery management device according to one embodiment disclosed in this document.

[0041] FIG. 8 is a block diagram showing the hardware configuration of a computing system for performing an operating method of a battery management device according to one embodiment disclosed in this document.

[0042] Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that various modifications, equivalents, and / or alternatives of the embodiments of the present invention are included.

[0043] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0044] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0045] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0046] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0047] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0048] FIG. 1 is a diagram showing a battery pack according to one embodiment disclosed in this document. FIG. 1 schematically illustrates a battery control system including a battery pack (1) and an upper controller (2) included in an upper system.

[0049] Referring to FIG. 1, a battery pack (1) may include a plurality of battery cells (10), a switching unit (14) connected in series to the (+) terminal side or the (-) terminal side of the plurality of battery cells (10) to control the charge and discharge current flow of the plurality of battery cells (10), and a battery management system (BMS) (20) that monitors the voltage, current, temperature, etc. of the battery pack (1) to control and manage the prevention of overcharge and overdischarge, etc. In this case, the battery pack (1) may be equipped with a plurality of battery cells (10), sensors (12), switching units (14), and battery management systems (20).

[0050] According to an embodiment, a plurality of battery cells (10) can supply power to a target device (not shown). To this end, the plurality of battery cells (10) can be electrically connected to the target device. Here, the target device can include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (1). For example, the target device can be, but is not limited to, an electric vehicle (EV) or an energy storage system (ESS).

[0051] According to an embodiment, the plurality of battery cells (10) may include one or more battery cells. Here, the battery cell may be a basic unit of a battery cell that can charge and discharge electric energy. For example, the battery cell may be a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion polymer) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-metal hydride (Ni-MH) battery, etc., but is not limited thereto.

[0052] According to an embodiment, the sensor (12) can obtain information related to a plurality of battery cells (10). For example, the sensor (12) can obtain values ​​(or information) related to the status of each of the plurality of battery cells (10). Here, the values ​​related to the status may include one or more values ​​for voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature of the battery cell, or a combination thereof.

[0053] According to an embodiment, the sensor (12) can provide information on each of a plurality of battery cells (10) to the battery management system (20).

[0054] According to various embodiments, among the sensors (12) connected to each of the plurality of battery cells (10), a specific sensor may have a higher noise level than other sensors. Furthermore, among the sensors (12) connected to each of the plurality of battery cells (10), a specific sensor may have a lower signal resolution than other sensors. In the case of a voltage signal of a battery cell acquired from a specific sensor (12) having such a high noise level or low signal resolution, the voltage data acquired from the voltage signal measured by the specific sensor (12) may include a portion having a large deviation compared to the voltage data of the actual battery cell.

[0055] Accordingly, even if the voltage behavior of the actual battery cell is normal, the voltage measured by a specific sensor (12) with a high noise level may exhibit abnormal behavior. In addition, the battery management device (100) may incorrectly diagnose a normal battery cell as an abnormal battery cell based on the measured voltage. Therefore, the battery management device (100) can preprocess voltage data to obtain reconstructed voltage data, perform noise processing on the reconstructed voltage data, and detect an abnormal battery cell based on the battery cell data from which noise has been removed. Through this, the battery management device (100) can prevent misdiagnosis due to noise from the sensor (12).

[0056] According to an embodiment, the switching unit (14) is a device for controlling the current flow for charging or discharging of a plurality of battery cells (10), and may be configured with, for example, at least one relay and / or magnetic contactor, depending on the specifications of the battery pack (1).

[0057] According to an embodiment, the battery management system (20) may include a plurality of terminals and a circuit connected to the terminals to process the input values ​​as an interface for receiving values ​​measured from the various parameters described above. In addition, the battery management system (20) may control the ON / OFF of a switching unit (14), for example, a relay or a contactor, and may be connected to a plurality of battery cells (10) to monitor the status of each of the plurality of battery cells (10).

[0058] According to an embodiment, the battery management system (20) may include the battery management device (100) of FIG. 2. According to another embodiment, the battery management system (20) may be a different system from the battery management device (100) of FIG. 2. That is, the battery management device (100) of FIG. 2 may be included in the battery pack (1) or may be configured as another device external to the battery pack (1). In addition, the operation of the battery management device (100) described below may be performed by an in-vehicle BMS (Battery Management System), as well as by various devices such as a server, a cloud, a charger, or a charger / discharger.

[0059] The upper controller (2) can transmit control signals for a plurality of battery cells (10) to the battery management system (20). Accordingly, the battery management system (20) can be controlled for operation based on the signals received from the upper controller (2).

[0060] FIG. 2 is a block diagram illustrating a battery management device according to one embodiment disclosed in this document. The operation of the battery management device (100) illustrated in FIG. 2 may be described in detail with reference to FIGS. 3 to 6 below.

[0061] FIG. 3 is a diagram showing a data set according to one embodiment disclosed in the present document. FIGS. 4 to 6 are diagrams showing reconstructed voltage data according to one embodiment disclosed in the present document.

[0062] First, referring to FIG. 2, the battery management device (100) may be a variety of electronic devices for managing, diagnosing, and testing batteries. According to an embodiment, the battery management device (100) may be included in any one of a battery management system (BMS) within a battery pack, a battery management server, a computer, and a cloud server. According to another embodiment, the battery management device (100) may be included in a device for charge / discharge testing, such as a charge / discharge cycler.

[0063] According to an embodiment, the battery management device (100) may include a data acquisition unit (110), a preprocessing unit (120), a noise processing unit (130), and an abnormal cell detection unit (140).

[0064] According to an embodiment, the data acquisition unit (110) can acquire the voltage of each of the plurality of battery cells (10). For example, the data acquisition unit (110) can acquire the voltage of each of the plurality of battery cells (10) in a time series manner. In another aspect, the data acquisition unit (110) can acquire voltage data related to a change in voltage over time of each of the plurality of battery cells (10). Here, the change in voltage over time may include a change in voltage of the battery cell in one or more of a charging period, a post-charge rest period, a discharging period, and a post-discharging rest period.

[0065] According to an embodiment, the preprocessing unit (120) can process voltage data of a plurality of battery cells (10) to obtain reconstructed voltage data.

[0066] Referring to FIG. 3, the preprocessing unit (120) can manage voltage data of a plurality of battery cells (10) in the form of a data set. Here, the data set can be expressed as a matrix (e.g., an MxN matrix) composed of voltages (v(t1,1), ..., v(tM,N)) of N plurality of battery cells (10) for M time series values ​​(t1, ..., tM). Here, M may correspond to each time at which the voltages of the plurality of battery cells (10) are acquired, and N may correspond to the number of the plurality of battery cells (10). For example, if the number of times at which the voltages of the plurality of battery cells (10) are acquired (or the sensing times of the sensor (12)) is 180 and the number of the plurality of battery cells (10) is 14, M may be 180 and N may be 14.

[0067] According to an embodiment, when a data set is expressed as a matrix, each entry (value) of the matrix may represent a voltage of a plurality of battery cells (10). Here, each column (or column vector) of the data set may represent a voltage change (e.g., v(t1,1), ..., v(tM,1)) obtained at each time point (M) of each of the plurality of battery cells (10). In addition, each row (or row vector) of the data set may represent a voltage (v(t1,1), ..., v(t1,N)) of each of the plurality of battery cells (10) (N) obtained at the same time point (or sensing time point).

[0068] Referring to FIGS. 4 to 6, the preprocessing unit (120) may process a data set to obtain reconstructed voltage data. For example, FIG. 4 may be a graph showing reconstructed voltage data (S1) related to a normal battery cell, FIG. 5 may be a graph showing reconstructed voltage data (S2) related to an abnormal battery cell, and FIG. 6 may be a graph showing reconstructed voltage data (S3) related to noise.

[0069] According to an embodiment, the preprocessing unit (120) can express the reconstructed voltage data as a function of the reconstructed voltage for a predetermined time interval. In another aspect, the preprocessing unit (120) can express the reconstructed voltage data as a graph of the reconstructed voltage (unit: mV), where the x-axis is a point in time (or a sensing point in time of the sensor (12)) (unit: time), and the y-axis is a preprocessed result value of the voltage data of each of the plurality of battery cells (10).

[0070] According to an embodiment, the preprocessing unit (120) can normalize the voltage data of each of the plurality of battery cells (10) by obtaining reconstructed data based on a data set related to voltage changes of each of the plurality of battery cells (10). For example, the preprocessing unit (120) can obtain reconstructed data having a normal distribution with a mean of 0 and a standard deviation of 1.

[0071] According to an embodiment, the voltage of the reconstructed voltage data (S1, see FIG. 4) related to a normal battery cell may be distributed between points that are a standard deviation (i.e., 1 mV) away from the mean (i.e., 0 mV) of the normal distribution. Here, the reconstructed voltage data (S1) related to the normal battery cell may mainly include values ​​between -1 mV and 1 mV. In contrast, the reconstructed voltage data (S2, see FIG. 5) related to an abnormal battery cell or the reconstructed voltage data (S3, see FIG. 6) related to noise of the sensor (12) may include extreme values ​​in the normal distribution. Here, the extreme values ​​may mean values ​​that are distributed at points that are a standard deviation farther away from the mean of the normal distribution. For example, the extreme values ​​may mean values ​​that are less than -1 mV or greater than 1 mV in the voltage of the reconstructed voltage data. Accordingly, the preprocessing unit (120) can obtain reconstructed voltage data from the voltage data, thereby providing reconstructed data for the battery management device (100) to detect whether the voltage data is voltage data obtained from a normal battery cell, voltage data obtained from an abnormal battery cell, or voltage data containing noise.

[0072] In another aspect, the preprocessing unit (120) can normalize the range of voltage data of a plurality of battery cells (10) acquired in various environments to a specific range, thereby scaling the deviation of the voltage data to a consistent range. According to various embodiments, the voltage data of a plurality of battery cells (10) acquired in various charge / discharge periods may have a large deviation for each charge / discharge period or for each battery cell. In this case, the preprocessing unit (120) can normalize the range of voltage data of a plurality of battery cells (10) to a specific range. Through this, the battery management device (100) can increase the reliability of noise processing and abnormal battery cell detection.

[0073] According to an embodiment, the preprocessing unit (120) can generate a plurality of sub-data sets from a data set. For example, the preprocessing unit (120) can generate a plurality of sub-data sets from a data set using a moving window (or sliding window) method. Here, the moving window (or sliding window) method can mean any method of extracting a partial data array by moving a window of a fixed size along the data array. Accordingly, the preprocessing unit (120) can determine the size (or length) of the moving window (or sliding window) and the movement interval of the window (i.e., the moving interval or sliding interval).

[0074] According to an embodiment, the preprocessing unit (120) may determine a predetermined time interval as the length of a window. For example, the length of the window may include at least 16 sensing points. In this case, the length of the sub-data sets generated by the preprocessing unit (120) may be equal to the length of the predetermined time interval. Furthermore, according to an embodiment, the preprocessing unit (120) may generate the sub-data sets using a square matrix (e.g., 16x16) of the length of the predetermined time interval (e.g., 16 points) as a moving window.

[0075] According to an embodiment, the preprocessing unit (120) can determine the window movement interval or moving / sliding interval. For example, the moving (or sliding) interval can include four sensing points. In this case, the preprocessing unit (120) can generate multiple sub-data sets by moving a 16x16 window by four spaces in a 180x14 data set.

[0076] According to an embodiment, the preprocessing unit (120) can obtain reconstructed voltage data from sub-data sets. Through this, the preprocessing unit (120) can increase the sample target for data decomposition by generating a large number of sub-data sets from a limited data set. In addition, the preprocessing unit (120) can reduce data bias through a moving window method. Therefore, the preprocessing unit (120) can obtain more reconstructed voltage data by obtaining reconstructed voltage data from sub-data sets than by obtaining reconstructed voltage data from a data set. Accordingly, the battery management device (100) can more accurately detect abnormal battery cells based on a larger number of reconstructed voltage data.

[0077] According to an embodiment, the preprocessing unit (120) may obtain reconstructed voltage data using any data processing algorithm. For example, the preprocessing unit (120) may obtain reconstructed voltage data using a data decomposition algorithm. According to an embodiment, the data decomposition algorithm may be based on matrix decomposition. For example, the data decomposition algorithm may include a singular value decomposition (SVD) algorithm.

[0078] According to an embodiment, the preprocessing unit (120) can output a data set or sub-data set acquired from a plurality of battery cells (10) as reconstructed data using singular value decomposition. Through this, the preprocessing unit (120) can normalize the data acquired from each of the plurality of battery cells (10).

[0079] Referring again to FIGS. 1, 4, 5, and 6, the noise processing unit (130) can process noise from the reconstructed voltage data. Here, the noise may include noise from the sensor (12). In another aspect, the characteristics of the reconstructed voltage data may include characteristics related to noise originating from the sensor (12). Accordingly, the noise processing unit (130) can pattern the noise originating from the sensor (12) and remove the noise based on the characteristics of the reconstructed voltage data. Through this, the noise processing unit (130) can generate battery cell data from which noise has been removed.

[0080] According to various embodiments, among the sensors (12) connected to each of the plurality of battery cells (10), a specific sensor may have a higher noise level than other sensors. Therefore, the deviation of the voltage signal obtained from the sensor (12) may increase. In this case, the battery management device (100) may incorrectly diagnose the voltage measured by the sensor with the high noise level as an abnormal voltage of the battery cell itself. Therefore, the noise processing unit (130) may filter out noise from the voltage data of the battery cell to generate battery cell data. Through this, the battery management device (100) can prevent misdiagnosis due to noise from the sensor (12).

[0081] According to an embodiment, the noise processing unit (130) may perform noise processing based on the characteristics of the reconstructed voltage data. Here, the characteristics of the reconstructed voltage data may be explained based on the reconstructed voltage data graphs illustrated in FIGS. 4 to 6. For example, the noise processing unit (130) may perform noise processing based on at least one of the shape of the reconstructed voltage data graph, the number of zero-crossings, and the zero-crossing period.

[0082] According to an embodiment, the noise processing unit (130) may perform noise processing based on at least one of the number of zero-crossings or the zero-crossing period of the reconstructed voltage data. Here, the zero-crossing may refer to a point where the reconstructed voltage data crosses a specific voltage level within a predetermined time interval. According to an embodiment, the specific voltage level may include 0. According to an embodiment, the noise processing unit (130) may determine whether a zero-crossing has occurred based on the value of the reconstructed voltage data before and after the specific voltage level.

[0083] According to an embodiment, the noise processing unit (130) may compare the number of zero crossings of each of the reconstructed voltage data with a first threshold value and detect noise components based on the comparison result. Here, the first threshold value may be a value that serves as a reference for distinguishing data related to battery cells from data related to noise. Furthermore, the data related to battery cells may include both data related to normal battery cells and data related to abnormal battery cells.

[0084] According to an embodiment, the noise processing unit (130) may detect reconstructed data having a zero crossing count greater than or equal to a first threshold as a noise component based on the comparison result. For example, the first threshold may be 2 (unit: count) or greater. In this case, the noise processing unit (130) may detect the reconstructed voltage data as a noise component if the number of times the reconstructed voltage data crosses 0 is 2 or more for a predetermined time interval. For example, the noise processing unit (130) may not detect S1 and S2, which have 1 zero crossing count, as noise components, and may detect S3, which has 8 zero crossing counts, as a noise component.

[0085] According to an embodiment, the noise processing unit (130) may compare the zero crossing period of each of the reconstructed voltage data with a second threshold value and detect a noise component based on the comparison result. Here, the second threshold value may be a value that serves as a reference for distinguishing data related to a battery cell from data related to noise. In addition, the data related to a battery cell may include both data related to a normal battery cell and data related to an abnormal battery cell. In another aspect, the zero crossing period may refer to a time interval during which a first zero crossing and a second zero crossing occur during a predetermined time period.

[0086] According to an embodiment, the noise processing unit (130) may detect reconstructed data having a zero-crossing period less than or equal to a second threshold as a noise component as a result of the comparison. For example, the second threshold may be 10 (unit: point in time or sensing point in time) or more. In this case, the noise processing unit (130) may detect the reconstructed voltage data as a noise component if the time interval at which the reconstructed voltage data crosses 0 during a predetermined time period is 10 (unit: point in time or sensing point in time) or less. For example, the noise processing unit (130) may not detect S1 and S2 having infinite zero-crossing periods as noise components, and may detect S3 having a zero-crossing period of 4 (or less than 4) as a noise component.

[0087] According to an embodiment, the noise processing unit (130) can generate battery cell data by removing the noise component from the reconstructed voltage data. Through this, the noise processing unit (130) can remove noise related to the sensor (12) and generate voltage data related to pure battery cells. Accordingly, the battery management device (100) can reduce misdiagnosis due to noise from the sensor (12) and accurately detect abnormal battery cells.

[0088] Referring again to FIGS. 1, 4, and 5, the abnormal cell detection unit (140) can detect an abnormal battery cell based on the battery cell data (S1 and S2) generated from the noise processing unit (130). Here, the battery cell data generated from the noise processing unit (130) can mean reconstructed voltage data from which noise related to the sensor (12) has been removed.

[0089] Referring to FIGS. 4 and 5, the abnormal cell detection unit (140) can detect an abnormal battery cell based on the voltage deviation of each of the battery cell data (S1 and S2). Here, the voltage deviation can mean the difference between the minimum and maximum voltage values ​​of the battery cell data in a predetermined time interval.

[0090] According to an embodiment, the abnormal cell detection unit (140) can compare the voltage deviation of each of the battery cell data (S1 and S2) with a third threshold value and detect an abnormal battery cell based on the comparison result. Here, the third threshold value can be a value that serves as a standard for distinguishing data related to a normal battery cell from data related to an abnormal battery cell.

[0091] According to various embodiments, the reconstructed voltage data of an abnormal battery cell may have a larger voltage deviation than the reconstructed voltage data of a normal battery cell. Accordingly, the abnormal cell detection unit (140) may detect a normal battery cell and an abnormal battery cell based on the voltage deviation of each of the battery cell data (S1 and S2).

[0092] According to an embodiment, the abnormal cell detection unit (140) can detect a battery cell whose voltage deviation as a result of the comparison is greater than or equal to the third threshold value as an abnormal battery cell. Here, the third threshold value may be the same as the standard deviation of the reconstructed voltage data. For example, the third threshold value may be 1 (unit: mV). In this case, the abnormal cell detection unit (140) can detect the reconstructed voltage data as abnormal battery cell data when the voltage deviation of the reconstructed voltage data is greater than or equal to 1 mV for a predetermined time period. For example, the abnormal cell detection unit (140) can detect S1, which has a voltage deviation of less than 1 mV among the battery cell data (S1 and S2), as normal battery cell data, and S2, which has a voltage deviation of 1 mV or more, as abnormal battery cell data.

[0093] FIG. 7 is a flowchart showing the operation of a battery management device according to one embodiment disclosed in this document.

[0094] Referring to FIG. 7, a battery management device (100) obtains voltage data related to voltage changes over time of each of a plurality of battery cells (S101), obtains reconstructed voltage data for a predetermined time interval using a data decomposition algorithm from a data set composed of the voltage data of each of the plurality of battery cells (S102), performs noise processing based on characteristics of the reconstructed voltage data to generate battery cell data (S103), and detects an abnormal battery cell based on a voltage deviation of each of the battery cell data (S104).

[0095] In step S101, the data acquisition unit (110) of the battery management device (100) can acquire voltage data related to voltage changes over time of each of a plurality of battery cells (S101).

[0096] In step S102, the preprocessing unit (120) of the battery management device (100) can obtain reconstructed voltage data for a predetermined time interval using a data decomposition algorithm from a data set composed of the voltage data of each of the plurality of battery cells (S102). According to an embodiment, the preprocessing unit (120) can generate a plurality of sub-data sets of a length of a predetermined time interval from the data set. Then, reconstructed voltage data can be obtained from each of the plurality of sub-data sets based on the data decomposition algorithm.

[0097] In step S103, the noise processing unit (130) of the battery management device (100) may perform noise processing based on the characteristics of the reconstructed voltage data to generate battery cell data (S103). According to an embodiment, the noise processing unit (130) may detect noise components based on at least one of the number of zero crossings and the zero crossing period of each of the reconstructed voltage data. In addition, the noise processing unit (130) may remove noise components from the reconstructed voltage data to generate battery cell data.

[0098] In step S104, the abnormal cell detection unit (140) of the battery management device (100) can detect an abnormal battery cell based on the voltage deviation of each of the battery cell data (S104).

[0099] FIG. 8 is a block diagram showing the hardware configuration of a computing system for performing an operating method of a battery management device according to one embodiment disclosed in this document.

[0100] Referring to FIG. 8, a computing system (200) according to one embodiment disclosed in the present document may include an MCU (210), a memory (220), an input / output I / F (230), and a communication I / F (240).

[0101] The MCU (210) may be a processor that executes various programs stored in the memory (220) (e.g., a battery cell data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, a battery cell diagnosis program, etc.), processes various information including battery cell characteristic data and latent variables through these programs, and performs the functions of the battery management device (100) shown in the aforementioned FIGS. 1 to 7.

[0102] The memory (220) 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.

[0103] Such memories (220) may be provided in multiple numbers as needed. The memories (220) may be volatile memories or non-volatile memories. As volatile memories (220), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (220), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (220) listed above are merely examples and are not limited to these examples.

[0104] The input / output I / F (230) can provide an interface that enables data transmission and reception between an input device (not shown) such as a keyboard, mouse, or touch panel, and an output device (not shown) such as a display and the MCU (210).

[0105] The communication I / F (240) is a component capable of transmitting and receiving various data with the server, and may be any device capable of supporting wired or wireless communication. For example, the battery management device (100) can transmit and receive various types of information, including battery cell shape models, from a separately provided external server via the communication I / F (240).

[0106] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a module that performs each function illustrated in FIG. 2, for example, by being recorded in a memory (220) and processed by an MCU (210).

[0107] Although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0108] In addition, terms such as "include," "comprise," or "have" described above, unless specifically stated otherwise, mean that the corresponding component can be included, and therefore should be interpreted to include other components rather than excluding other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0109] The foregoing disclosure outlines 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 readily appreciate that the present disclosure can be readily used as a basis for designing or modifying other structures to achieve the same purposes or advantages of the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent structures do not depart from the scope of the present disclosure, and that various changes, substitutions, and modifications can be made herein without departing from the scope of the present disclosure.

[0110] [Explanation of symbols]

[0111] 1: Battery pack

[0112] 2: Upper controller

[0113] 10: Multiple battery cells

[0114] 12: Sensor

[0115] 14: Switching section

[0116] 20: BMS

[0117] 100: Battery management device

[0118] 110: Data Acquisition Unit

[0119] 120: Preprocessing unit

[0120] 130: Noise Processing Unit

[0121] 140: Abnormal cell detection unit

[0122] 200: Computing Systems

[0123] 210: MCU

[0124] 220: Memory

[0125] 230: Input / Output I / F

[0126] 240: Communication I / F

Claims

1. A data acquisition unit that acquires voltage data related to voltage changes over time of each of a plurality of battery cells; A preprocessing unit that obtains reconstructed voltage data for a predetermined time interval using a data decomposition algorithm from a data set composed of the voltage data of each of the plurality of battery cells; A noise processing unit that generates battery cell data by performing noise processing based on the characteristics of the above-mentioned reconstructed voltage data; and A battery management device including an abnormal cell detection unit that detects an abnormal battery cell based on a voltage deviation of each of the above battery cell data.

2. In claim 1, The above preprocessing unit, Generating multiple sub-data sets of the length of the predetermined time interval from the above data set, A battery management device that obtains the reconstructed voltage data from each of the plurality of sub-data sets based on the data decomposition algorithm.

3. In claim 2, The above data decomposition algorithm is a battery management device including singular value decomposition.

4. In claim 1, The above noise processing unit, A battery management device that performs the noise processing based on the number of zero crossings, which is the number of times the voltage crosses a specific voltage level, in each of the reconstructed voltage data.

5. In claim 4, The above noise processing unit, The number of zero crossings of each of the above reconstructed voltage data is compared with the first threshold value, Detecting reconstructed data having a number of zero crossings greater than or equal to the first threshold value as a noise component, A battery management device that generates the battery cell data by removing the noise component from the reconstructed voltage data.

6. In claim 5, A battery management device wherein the above specific voltage level includes 0.

7. In claim 1, The above noise processing unit, A battery management device that performs the noise processing based on the zero-crossing cycle, which is a cycle in which the voltage crosses a specific voltage level, in each of the reconstructed voltage data.

8. In claim 7, The above noise processing unit, Comparing the zero crossing period of each of the above reconstructed voltage data with the second threshold value, Detecting reconstructed data having a zero crossing period less than or equal to the second threshold value as a noise component, A battery management device that generates the battery cell data by removing the noise component from the reconstructed voltage data.

9. In claim 8, A battery management device wherein the above specific voltage level includes 0.

10. In claim 1, The above abnormal cell detection unit, The voltage deviation, which is the difference between the minimum and maximum voltage values ​​of each of the battery cell data in the above-determined time interval, is compared with a third threshold value, A battery management device that detects a battery cell having a voltage deviation greater than or equal to the third threshold value as an abnormal battery cell.

11. A step of obtaining voltage data related to voltage changes over time of each of a plurality of battery cells; A step of obtaining reconstructed voltage data for a predetermined time interval using a data decomposition algorithm from a data set composed of the voltage data of each of the plurality of battery cells; A step of generating battery cell data by performing noise processing based on the characteristics of the above reconstructed voltage data; and A battery management method comprising a step of detecting an abnormal battery cell based on a voltage deviation of each of the battery cell data.

12. In claim 11, The step of obtaining the above reconstructed voltage data is: A step of generating a plurality of sub-data sets of the length of the predetermined time interval from the above data set; and A battery management method comprising a step of obtaining the reconstructed voltage data from each of the plurality of sub-data sets based on the data decomposition algorithm.

13. In claim 12, The above data decomposition algorithm is a battery management method including singular value decomposition.

14. In claim 11, The step of generating the above battery cell data is: A battery management method comprising a step of performing noise processing based on the number of zero crossings, which is the number of times the voltage crosses a specific voltage level, in each of the reconstructed voltage data.

15. In claim 14, A step of comparing the number of zero crossings of each of the reconstructed voltage data with a first threshold value; A step of detecting reconstructed data having a zero crossing count greater than or equal to the first threshold value as a noise component; and A battery management method comprising a step of generating the battery cell data by removing the noise component from the reconstructed voltage data.

16. In claim 15, A battery management method wherein the above specific voltage level includes 0.

17. In claim 11, The step of generating the above battery cell data is: A battery management method comprising a step of performing noise processing based on a zero-crossing period, which is a period in which the voltage crosses a specific voltage level, in each of the reconstructed voltage data.

18. In claim 17, A step of comparing the zero crossing period of each of the reconstructed voltage data with a second threshold value; A step of detecting reconstructed data having a zero crossing period less than or equal to the second threshold value as a noise component; and A battery management method comprising a step of generating the battery cell data by removing the noise component from the reconstructed voltage data.

19. In claim 18, A battery management method wherein the above specific voltage level includes 0.

20. In claim 11, The step of detecting the above abnormal battery cell is: A step of comparing the voltage deviation, which is the difference between the minimum and maximum voltage values ​​of each of the battery cell data in the above-determined time interval, with a third threshold value; and A battery management method comprising a step of detecting a battery cell having a voltage deviation greater than or equal to the third threshold value as an abnormal battery cell.

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