Battery management device and method of operating the same

Abstract

A battery management device according to the embodiments disclosed in this document may include: a data acquisition unit for acquiring voltage data related to the voltage change of each of a plurality of battery cells over time; a preprocessing unit for acquiring reconstructed voltage data for a predetermined time interval from a dataset consisting of voltage data of each of the plurality of battery cells 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 abnormal battery cells based on the voltage deviation of each of the battery cell data.

Description

Technical Field

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0172719, filed on December 1, 2023, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to battery management devices and methods of operation thereof. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively pursued. In this article, rechargeable batteries, as rechargeable / dischargeable batteries, can be interpreted as including all conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, and more recently, lithium-ion batteries. With the recent expansion of lithium-ion battery applications into the power source of electric vehicles, lithium-ion batteries are attracting attention as a next-generation energy storage medium.

[0005] Electric vehicles are powered externally to charge battery cells / modules, which then discharge to drive the motor and obtain power. During production and use, battery cells / modules undergo internal deformation and degradation through various charging / discharging processes, altering their physical and chemical properties. A technology is needed to diagnose and manage the operation of battery cells / modules due to battery degradation and deterioration.

[0006] Batteries can be charged / discharged for various purposes, such as battery performance diagnostics and state 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 includes noise and errors, making it impossible to clearly analyze the data used to manage battery state. Summary of the Invention

[0007] Technical issues

[0008] The embodiments disclosed herein are intended to provide a battery management device and its operating method that can manage abnormal battery cells based on voltage data obtained from battery cells included in a battery pack.

[0009] The embodiments disclosed herein aim to provide a battery management device and its operation method based on a data processing algorithm for reducing the impact of noise included in voltage data.

[0010] The technical problems of the embodiments disclosed herein are not limited to those described above, and those skilled in the art will clearly understand from the following description other unmentioned technical problems.

[0011] Technical solution

[0012] A battery management device according to an embodiment disclosed herein includes: a data acquisition unit configured to acquire voltage data of each of a plurality of battery cells relating to voltage changes over time; a preprocessing unit configured to acquire reconstructed voltage data within a predetermined time interval from a dataset consisting of voltage data of each of the plurality of battery cells using a data decomposition algorithm; a noise processing unit configured to generate battery cell data by performing noise processing based on characteristics of the reconstructed voltage data; and an abnormal cell detection unit configured to detect abnormal battery cells based on voltage deviations of each of the battery cell data.

[0013] According to the implementation, the preprocessing unit may also be configured to generate multiple subsets of data from the dataset, each having a length corresponding to a predetermined time interval, and to obtain reconstructed voltage data from each of the multiple subsets based on a data decomposition algorithm.

[0014] According to the implementation method, the data decomposition algorithm may include the singular value decomposition (SVD) algorithm.

[0015] According to an implementation, the noise processing unit can also be configured to perform noise processing on each of the reconstructed voltage data based on the number of zero-crossings, where the number of zero-crossings is the number of times the voltage crosses a specific voltage level.

[0016] According to the implementation, the noise processing unit may also be configured to: compare the number of zero crossovers of each of the reconstructed voltage data with a first threshold; detect reconstructed data with a number of zero crossovers greater than or equal to the first threshold as noise components; and generate battery cell data by removing noise components from the reconstructed voltage data.

[0017] According to the implementation method, a specific voltage level may include 0 V.

[0018] According to an implementation, the noise processing unit may also be configured to perform noise processing on each of the reconstructed voltage data based on a zero-crossing period, which is the period during which the voltage crosses a specific voltage level.

[0019] According to the implementation, the noise processing unit may also be configured to: compare the zero-crossing period of each of the reconstructed voltage data with a second threshold; detect the reconstructed data with a zero-crossing period less than or equal to the second threshold as noise components; and generate battery cell data by removing the noise components from the reconstructed voltage data.

[0020] According to the implementation method, a specific voltage level may include 0 V.

[0021] According to the implementation method, the abnormal cell detection unit can also be configured to: compare the voltage deviation with a third threshold, wherein the voltage deviation is the difference between the minimum and maximum voltage values ​​of each of the battery cell data within a predetermined time interval; and detect battery cells with voltage deviations greater than or equal to the third threshold as abnormal battery cells.

[0022] A battery management method according to the embodiments disclosed herein includes the following steps: obtaining voltage data of each of a plurality of battery cells in relation to voltage changes over time; obtaining reconstructed voltage data within a predetermined time interval from a dataset consisting of voltage data of each of the plurality of battery cells using a data decomposition algorithm; generating battery cell data by performing noise processing based on the characteristics of the reconstructed voltage data; and detecting abnormal battery cells based on voltage deviations of each of the battery cell data.

[0023] According to the implementation method, the step of obtaining reconstructed voltage data may include the following steps: generating multiple sub-datasets with lengths corresponding to predetermined time intervals from the dataset; and obtaining reconstructed voltage data from each of the multiple sub-datasets based on a data decomposition algorithm.

[0024] According to the implementation method, the data decomposition algorithm may include the singular value decomposition (SVD) algorithm.

[0025] According to an implementation, the step of generating battery cell data may include performing noise processing on each of the reconstructed voltage data based on the number of zero-crossings, where the number of zero-crossings is the number of times the voltage crosses a specific voltage level.

[0026] According to an implementation, the battery management method may further include the following steps: comparing the number of zero crossovers of each of the reconstructed voltage data with a first threshold; detecting reconstructed data with a number of zero crossovers greater than or equal to the first threshold as noise components; and generating battery cell data by removing noise components from the reconstructed voltage data.

[0027] According to the implementation method, a specific voltage level may include 0 V.

[0028] According to an implementation, the step of generating battery cell data may include performing noise processing on each of the reconstructed voltage data based on a zero-crossing period, which is a period during which the voltage crosses a specific voltage level.

[0029] According to an implementation, the battery management method may further include the following steps: comparing the zero-crossing period of each of the reconstructed voltage data with a second threshold; detecting reconstructed data with zero-crossing periods less than or equal to the second threshold as noise components; and generating battery cell data by removing noise components from the reconstructed voltage data.

[0030] According to the implementation method, a specific voltage level may include 0 V.

[0031] According to the implementation method, the step of detecting abnormal battery cells includes the following steps: comparing the voltage deviation with a third threshold, wherein the voltage deviation is the difference between the minimum and maximum voltage values ​​of each battery cell in the battery cell data within a predetermined time interval; and detecting battery cells with voltage deviations greater than or equal to the third threshold as abnormal battery cells.

[0032] Beneficial effects

[0033] The battery diagnostic equipment and operating methods disclosed in this article can manage abnormal battery cells based on voltage data obtained from battery cells included in a battery pack.

[0034] The battery management device and its operating method according to the embodiments disclosed herein can be based on a data processing algorithm for reducing the impact of noise included in voltage data.

[0035] In addition, various effects that can be identified directly or indirectly through this document can be provided. Attached Figure Description

[0036] Figure 1 A battery pack according to an embodiment disclosed herein is shown.

[0037] Figure 2 This is a block diagram of a battery management device according to the embodiments disclosed herein.

[0038] Figure 3 This is a diagram illustrating a dataset according to the embodiments disclosed herein.

[0039] Figure 4 This is a graph illustrating the reconstructed voltage data according to the embodiments disclosed herein.

[0040] Figure 5 This is a graph illustrating the reconstructed voltage data according to the embodiments disclosed herein.

[0041] Figure 6 This is a graph illustrating the reconstructed voltage data according to the embodiments disclosed herein.

[0042] Figure 7This is a flowchart illustrating the operation of a battery management device according to an embodiment disclosed herein.

[0043] Figure 8 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a battery management device according to the embodiments disclosed herein. Detailed Implementation

[0044] In the following description, various embodiments of the present disclosure will be described with reference to the accompanying drawings. However, this description is not intended to limit the present disclosure to specific embodiments, and it should be construed as including various modifications, equivalents, and / or substitutions of embodiments according to the present disclosure.

[0045] It should be understood that the embodiments described in this document and the terminology used therein are not intended to limit the technical features set forth herein to a particular embodiment and include various changes, equivalents, or substitutions to the corresponding embodiments. Regarding the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that, unless the relevant context clearly indicates otherwise, the singular form of the noun corresponding to an item may include one or more things.

[0046] As used herein, 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 or all possible combinations of the items listed together in the corresponding phrase. Unless otherwise stated, terms such as “firstly,” “secondly,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used only to distinguish the corresponding component from another component and do not limit the components in other respects (e.g., importance or order).

[0047] In this document, it should be understood that when an element (e.g., a first element) is referred to as being “connected,” “linked,” or “coupled,” or “attached to” or “connected to” another element (e.g., a second element) with or without the terms “operably” or “communically”, it means that the element can be connected to the other element directly (e.g., wired), wirelessly, or via a third element.

[0048] According to embodiments of this disclosure, methods according to various embodiments of this disclosure can be included and provided in a computer program product. The computer program product can be traded as a product between a seller and a buyer. The computer program product can be distributed in the form of a machine-readable storage medium (e.g., an optical disc read-only memory (CD-ROM)), or distributed online via an app store (e.g., downloaded or uploaded), or distributed directly between two user devices. In the case of online distribution, at least a portion of the computer program product can be stored at least temporarily in a machine-readable storage medium, such as the memory of a manufacturer's server, an app store's server, or a relay server, or can be temporarily generated.

[0049] According to various embodiments of this disclosure, each of the above-described components (e.g., modules or programs) may include a single entity or multiple entities, some of which may be individually disposed on other components. According to various embodiments of this disclosure, one or more of the above-described components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as the corresponding components in the multiple components prior to integration. According to various embodiments of this disclosure, operations performed by modules, programs, or other components may be performed sequentially, in parallel, repeatedly, or heuristically, or may be performed in a different order, or one or more operations may be omitted, or one or more other operations may be added.

[0050] Figure 1 A battery pack according to an embodiment disclosed herein is shown. Figure 1 A battery control system according to an embodiment of the present disclosure is schematically shown. The battery control system includes a battery pack 1 and a higher-level controller 2 included in a higher-level system.

[0051] Reference Figure 1 The battery pack 1 may include a plurality of battery cells 10, a switching unit 14 connected in series with the positive (+) terminal side and / or negative (-) terminal side of the plurality of battery cells 10 to control the charging / discharging current flow of the plurality of battery cells 10, and a battery management system (BMS) 20 for controlling and managing the battery pack 1 by monitoring the voltage, current, temperature, etc. to prevent overcharging and over-discharging. The battery pack 1 may include a plurality of battery cells 10, a sensor 12, a switching unit 14, and a battery management system 20.

[0052] According to an embodiment, a plurality of battery cells 10 can supply power to a target device (not shown). For this purpose, the plurality of battery cells 10 can be electrically connected to the target device. In this document, 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, but is not limited to, an electric vehicle (EV) or an energy storage system (ESS).

[0053] According to the embodiments, the plurality of battery cells 10 may include one or more battery cells. In this document, a battery cell can be a basic unit of a battery cell that is usable through the charging / discharging of electrical energy. For example, a battery cell may be, but is not limited to, a lithium-ion (Li-ion) battery, a Li-ion polymer battery, a nickel-cadmium (Ni-Cd) battery, a nickel-metal hydride (Ni-MH) battery, etc.

[0054] According to an embodiment, sensor 12 can acquire information related to the plurality of battery cells 10. For example, sensor 12 can acquire values ​​(or information) related to the state of each of the plurality of battery cells 10. Hereinafter, state-related values ​​may include one or more values ​​of the battery cell's voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature, or combinations thereof.

[0055] According to the implementation, sensor 12 can provide information about each of the plurality of battery cells 10 to battery management system 20.

[0056] According to various embodiments, a particular sensor among the sensors 12 connected to the plurality of battery cells 10 may have a higher noise level than another sensor. A particular sensor among the sensors 12 connected to the plurality of battery cells 10 may have a lower signal resolution than another sensor. For the voltage signal of the battery cell obtained from a particular sensor 12 with a high noise level or low signal resolution, the voltage data obtained from the voltage signal measured in the particular sensor 12 may include a portion with a larger deviation than the actual voltage data of the battery cell.

[0057] Therefore, although the actual voltage behavior of the battery cell may be normal, the voltage measured in a specific sensor 12 with a high noise level may show abnormal behavior. The battery management device 100 may misdiagnose a normal battery cell as an abnormal battery cell based on the measured voltage. Therefore, the battery management device 100 can obtain voltage data reconstructed by preprocessing voltage data, perform noise processing on the reconstructed voltage data, and detect abnormal battery cells based on the battery cell data from which noise has been removed. In this way, the battery management device 100 can prevent misdiagnosis due to noise from the sensor 12.

[0058] According to the embodiments, the switching unit 14, which is an element for controlling the current flow for charging or discharging the plurality of battery cells 10, may include, for example, at least one relay and / or magnetic contactor, depending on the specifications of the battery pack 1.

[0059] According to the embodiment, the battery management system 20, which serves as an interface for receiving measured values ​​of the various parameters described above, may include multiple terminals and circuitry connected to the terminals to process input values. The battery management system 20 can control the switching unit 14 (e.g., a relay, contactor, etc.) to turn on / off, and can be connected to multiple battery cells 10 to monitor the state of each of the multiple battery cells 10.

[0060] According to an implementation, the battery management system 20 may include Figure 2 The battery management device 100. According to another embodiment, the battery management system 20 may be different from... Figure 2 Battery management device 100. That is to say, Figure 2 The battery management device 100 can be included in the battery pack 1 and can be configured as another device outside the battery pack 1. The following operations of the battery management device 100 can also be performed in various devices such as battery management systems (BMS) in vehicles, as well as servers, cloud, chargers, chargers / dischargers, etc.

[0061] The upper-level controller 2 can send control signals regarding the multiple battery cells 10 to the battery management system 20. Therefore, the battery management system 20 can be controlled in its operation based on the signals applied from the upper-level controller 2.

[0062] Figure 2 This is a block diagram of a battery management device according to the embodiments disclosed herein. Referring below... Figures 3 to 6 Detailed description Figure 2 Operation of the battery management device 100 shown.

[0063] Figure 3 This is a diagram illustrating a dataset according to the embodiments disclosed herein. Figures 4 to 6 This is a graph illustrating the reconstructed voltage data according to the embodiments disclosed herein.

[0064] Reference Figure 2 The battery management device 100 can be one of various electronic devices used for managing, diagnosing, and testing batteries. According to one embodiment, the battery management device 100 can be included in any of a battery management system (BMS), a battery management server, a computer, and a cloud server within a battery pack. According to another embodiment, the battery management device 100 can be included in a device such as a charge / discharge cycler for charge / discharge testing.

[0065] 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.

[0066] 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 sequence. On the other hand, the data acquisition unit 110 can acquire voltage data of each of the plurality of battery cells 10 related to voltage changes over time. Herein, voltage changes over time can include voltage changes of the battery cell in one or more intervals, including a charging interval, a post-charging resting interval, a discharging interval, and a post-discharging resting interval.

[0067] According to the implementation, the preprocessing unit 120 can obtain voltage data reconstructed by processing the voltage data of multiple battery cells 10.

[0068] Reference Figure 3 The preprocessing unit 120 can manage the voltage data of multiple battery cells 10 in the form of a dataset. In this paper, the dataset can be represented as including multiple battery cells 10 relative to M time series values ​​(t1, ..., t...). M N voltages (v(t1, 1)..., v(t)) M A matrix (e.g., an M×N matrix) of multiple battery cells 10. In this paper, M may correspond to the time at which the voltages of the multiple battery cells 10 are obtained, and N may correspond to the number of multiple battery cells 10. For example, when the time points at which the voltages of the multiple battery cells 10 are obtained (or the sensing time points of the sensor 12) are 180 and the number of multiple battery cells 10 is 14, M may be 180 and N may be 14.

[0069] According to the implementation, when the dataset is represented as a matrix, each entry (value) of the matrix can indicate the voltage of each of the plurality of battery cells 10. In this document, each column (or column vector) of the dataset can indicate the voltage change (e.g., v(t1, 1), ..., v(t2, 1), ..., v(t3, 1), ..., v(t4, 1), ..., v(t5, 1), ..., v(t6, 1), ..., v(t7, 1), ..., v(t8, 1), ..., v(t9, 1), ..., v(t1 ... M Each row (or row vector) of the dataset can indicate the voltage (v(t1, 1), ..., v(t1, N)) of each of the multiple battery cells 10 (N battery cells) obtained at the same time point (or sensing time point).

[0070] Reference Figures 4 to 6 The preprocessing unit 120 can obtain voltage data reconstructed by processing the dataset. For example, Figure 4This is a graph showing the reconfiguration voltage data S1 related to a normal battery cell. Figure 5 It is a graph showing the reconfiguration voltage data S2 related to the abnormal battery cell, and Figure 6 This is a graph showing the reconstructed voltage data S3 related to noise.

[0071] According to the implementation, the preprocessing unit 120 can represent the reconstructed voltage data as a function of the reconstructed voltage over a predetermined time interval. Alternatively, the preprocessing unit 120 can represent the reconstructed voltage data as a graph including time points (or sensing time points of sensor 12) as the x-axis (unit: time) and the resulting values ​​of the preprocessed voltage data for each of the plurality of battery cells 10 as the y-axis (unit: mV).

[0072] According to an implementation, the preprocessing unit 120 can normalize the voltage data of each of the plurality of battery cells 10 by obtaining data reconstructed based on a dataset related to the voltage changes of each of the plurality of battery cells 10. For example, the preprocessing unit 120 can obtain reconstructed data with a normal distribution having a mean of 0 and a standard deviation of 1.

[0073] According to the implementation method, the reconfiguration voltage data S1 related to the normal battery cell (see [link]) Figure 4 The voltage of a normal battery cell can be distributed between the mean of the normal distribution (i.e., 0 mV) and a point deviating from its standard deviation (i.e., 1 mV). In this paper, the reconfiguration voltage data S1 associated with normal battery cells can mainly include values ​​between -1 mV and 1 mV. On the other hand, the reconfiguration voltage data S2 associated with abnormal battery cells (see...) Figure 5 ) or reconstructed voltage data S3 related to the noise of sensor 12 (see Figure 6 The voltage data can include extreme values ​​in a normal distribution. In this context, extreme values ​​can represent values ​​located far from the mean of a normally skewed normal distribution. For example, extreme values ​​can represent voltages less than -1 mV or greater than 1 mV in the reconstructed voltage data. Therefore, the preprocessing unit 120 can provide reconstructed voltage data for detecting whether the voltage data of the battery management device 100 is obtained from a normal battery cell, an abnormal battery cell, or includes noisy voltage data by obtaining the voltage data reconstructed from the voltage data.

[0074] On the other hand, the preprocessing unit 120 can normalize the range of voltage data of multiple battery cells 10 obtained 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 multiple battery cells 10 obtained in various charging / discharging intervals may have large deviations for each charging / discharging interval or for each battery cell. In this case, the preprocessing unit 120 can normalize the range of voltage data of multiple battery cells 10 to a specific range. In this way, the battery management device 100 can improve the reliability of noise processing and abnormal battery cell detection.

[0075] According to the implementation, the preprocessing unit 120 can generate multiple subsets from the dataset. For example, the preprocessing unit 120 can generate multiple subsets from the dataset using a moving window (or sliding window) scheme. In this document, a moving window (or sliding window) scheme can refer to any scheme that extracts a portion of the data array while moving a fixed-size window along the data array. Therefore, the preprocessing unit 120 can determine the size (or length) of the moving window (or sliding window) and the window's moving interval (i.e., the moving interval or sliding interval).

[0076] According to an embodiment, the preprocessing unit 120 can determine the length of a window as a predetermined time interval. For example, the length of the window may include at least 16 sensing time points. In this case, the length of the subset generated by the preprocessing unit 120 may be equal to the length of the predetermined time interval. According to an embodiment, the preprocessing unit 120 can generate the subset by using a square (e.g., 16×16) matrix with a predetermined time interval length (e.g., 16 time points) as a moving window.

[0077] According to the implementation, the preprocessing unit 120 can determine the window's movement interval or movement / slide interval. For example, the movement (or slide) interval may include four sensing time points. In this case, the preprocessing unit 120 can generate multiple subset datasets while moving the 16×16 window by four steps on a 180×14 data set.

[0078] According to the implementation, the preprocessing unit 120 can obtain voltage data reconstructed from a subset of data sets. In this way, the preprocessing unit 120 can increase the number of samples targeted for data decomposition by generating multiple subsets from a finite dataset. The preprocessing unit 120 can reduce bias in the data using a moving window scheme. Therefore, the preprocessing unit 120 can obtain more reconstructed voltage data by obtaining reconstructed voltage data from subsets of data sets, rather than from the dataset itself. Consequently, the battery management device 100 can more accurately detect abnormal battery cells based on more reconstructed voltage data.

[0079] According to an implementation, the preprocessing unit 120 can use any data processing algorithm to obtain the reconstructed voltage data. For example, the preprocessing unit 120 can obtain the reconstructed voltage data by using a data decomposition algorithm. According to an implementation, the data decomposition algorithm can be based on matrix decomposition. For example, the data decomposition algorithm may include the singular value decomposition (SVD) algorithm.

[0080] According to the implementation, the preprocessing unit 120 can use the dataset or subset of datasets obtained from the plurality of battery cells 10 using SVD output as reconstructed data. In this way, the preprocessing unit 120 can normalize the data obtained from each of the plurality of battery cells 10.

[0081] Return to reference Figure 1 , Figure 4 , Figure 5 and Figure 6 The noise processing unit 130 can process noise from the reconstructed voltage data. Herein, noise may include noise from the sensor 12. On the other hand, the characteristics of the reconstructed voltage data may include characteristics related to the noise originating from the sensor 12. Therefore, the noise processing unit 130 can identify patterns of noise originating from the sensor 12 and remove noise based on the characteristics of the reconstructed voltage data. In this way, the noise processing unit 130 can generate battery cell data from which noise has been removed.

[0082] According to various embodiments, a particular sensor among the sensors 12 connected to multiple battery cells 10 may have a higher noise level than another sensor. Therefore, the deviation of the voltage signal obtained from the sensor 12 may increase. In this case, the battery management device 100 may misdiagnose the voltage measured in the sensor with the high noise level as an abnormal voltage of the battery cell. Therefore, the noise processing unit 130 can generate battery cell data by filtering noise from the voltage data of the battery cells. In this way, the battery management device 100 can prevent misdiagnosis due to noise from the sensor 12.

[0083] According to an implementation, the noise processing unit 130 can perform noise processing based on the characteristics of the reconstructed voltage data. In this document, it can be based on... Figures 4 to 6 The reconstructed voltage data curve shown is used to describe the characteristics of the reconstructed voltage data. For example, the noise processing unit 130 can perform noise processing based on at least one of the distribution of the reconstructed voltage data curve, the number of zero crossovers, and the zero crossover time periods.

[0084] 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 time period of the reconstructed voltage data. Herein, a zero-crossing may refer to a point in a predetermined time interval where the reconstructed voltage data crosses a specific voltage level. According to an embodiment, the specific voltage level may include 0 V. According to an embodiment, the noise processing unit 130 may determine the zero-crossing based on the values ​​of the voltage data reconstructed before and after the specific voltage level.

[0085] According to an implementation, the noise processing unit 130 can compare the number of zero-crossings of each of the reconstructed voltage data with a first threshold and detect noise components based on the comparison result. Here, the first threshold can be a value used as a standard to distinguish between data related to battery cells and data related to noise. Data related to battery cells can include data related to normal battery cells and data related to abnormal battery cells.

[0086] According to the implementation, the noise processing unit 130 can detect reconstructed data with a zero-crossing count greater than or equal to a first threshold as noise components based on the comparison result. For example, the first threshold can be at least 2 (unit: times). In this case, when the number of times the reconstructed voltage data crosses 0 V within a predetermined time interval is greater than or equal to 2, the noise processing unit 130 can detect the reconstructed voltage data as noise components. For example, the noise processing unit 130 may not detect reconstructed voltage data S1 and S2 with a zero-crossing count of 1 as noise components, but may detect reconstructed voltage data S3 with a zero-crossing count of 8 as noise components.

[0087] According to an implementation, the noise processing unit 130 can compare the zero-crossing period of each of the reconstructed voltage data with a second threshold and detect noise components based on the comparison result. Here, the second threshold can be a value used as a standard to distinguish between data related to battery cells and data related to noise. Data related to battery cells can include data related to normal battery cells and data related to abnormal battery cells. On the other hand, the zero-crossing period can refer to the time interval between a first zero-crossing and a second zero-crossing within a predetermined time interval.

[0088] According to an implementation, the noise processing unit 130 can detect reconstructed data with zero-crossing periods less than or equal to a second threshold as noise components based on a comparison result. For example, the second threshold can be at least 10 (unit: time point or sensing time point). In this case, when the time interval in which the reconstructed voltage data crosses 0 V within a predetermined time interval is less than or equal to 10 (unit: time point or sensing time point), the noise processing unit 130 can detect the reconstructed voltage data as noise components. For example, the noise processing unit 130 may not detect reconstructed voltage data S1 and S2 with infinite zero-crossing periods as noise components, and may detect reconstructed voltage data S3 with a zero-crossing period of 4 (or less) as noise components.

[0089] According to one embodiment, the noise processing unit 130 can generate battery cell data by removing noise components from the reconstructed voltage data. In this way, the noise processing unit 130 can remove noise associated with the sensor 12 and generate voltage data associated with the pure battery cell. Therefore, the battery management device 100 can reduce false diagnoses of the sensor 12 due to noise and accurately detect abnormal battery cells.

[0090] Return to reference Figure 1 , Figure 4 and Figure 5 The abnormal cell detection unit 140 can detect abnormal battery cells based on battery cell data S1 and S2 generated from the noise processing unit 130. In this document, the battery cell data generated from the noise processing unit 130 can refer to the reconstructed voltage data from which noise associated with the sensor 12 has been removed.

[0091] Reference Figure 4 and Figure 5 The abnormal cell detection unit 140 can detect abnormal battery cells based on the voltage deviation of each of the battery cell data S1 and S2. In this document, voltage deviation can refer to the difference between the minimum and maximum voltage values ​​of the battery cell data within a predetermined time interval.

[0092] According to the implementation, 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 and detect abnormal battery cells based on the comparison result. In this document, the third threshold can be a value used as a standard to distinguish between data related to normal battery cells and data related to abnormal battery cells.

[0093] According to various embodiments, the voltage deviation of the reconstructed voltage data of the abnormal battery cell can be greater than the voltage deviation of the reconstructed voltage data of the normal battery cell. Therefore, the abnormal cell detection unit 140 can detect normal battery cells and abnormal battery cells based on the voltage deviation of each of the battery cell data S1 and S2.

[0094] According to the implementation, the abnormal cell detection unit 140 can detect battery cells with voltage deviations greater than or equal to a third threshold as abnormal battery cells based on the comparison results. In this document, the third threshold can be the same as the standard deviation of the reconstructed voltage data. For example, the third threshold can be 1 (unit: mV). In this case, when the voltage deviation of the reconstructed voltage data is at least 1 mV within a predetermined time interval, the abnormal cell detection unit 140 can detect the reconstructed voltage data as abnormal battery cells. For example, the abnormal cell detection unit 140 can detect battery cell data S1 with a voltage deviation less than 1 mV as normal battery cell data, and detect battery cell data S2 with a voltage deviation greater than or equal to 1 mV as abnormal battery cell data.

[0095] Figure 7 This is a flowchart illustrating the operation of a battery management device according to an embodiment disclosed herein.

[0096] Reference Figure 7 In operation S101, the battery management device 100 can obtain voltage data related to the voltage change over time for each of the multiple battery cells. In operation S102, reconstructed voltage data within a predetermined time interval is obtained from the dataset consisting of the voltage data of each of the multiple battery cells by using a data decomposition algorithm. In operation S103, battery cell data is generated by performing noise processing based on the characteristics of the reconstructed voltage data. In operation S104, abnormal battery cells are detected based on the voltage deviation of each of the battery cell data.

[0097] In operation S101, the data acquisition unit 110 of the battery management device 100 can acquire voltage data of each of the plurality of battery cells in relation to voltage changes over time.

[0098] In operation S102, the preprocessing unit 120 of the battery management device 100 can obtain reconstructed voltage data within a predetermined time interval from a dataset consisting of voltage data of each of multiple battery cells using a data decomposition algorithm. According to an embodiment, the preprocessing unit 120 can generate multiple sub-datasets with lengths corresponding to the predetermined time intervals from the dataset. Reconstructed voltage data can be obtained from each of the multiple sub-datasets based on the data decomposition algorithm.

[0099] In operation S103, the noise processing unit 130 of the battery management device 100 can generate battery cell data by performing noise processing based on the characteristics of the reconstructed voltage data. According to an embodiment, the noise processing unit 130 can detect noise components based on at least one of the number of zero-crossings and the zero-crossing time periods for each of the reconstructed voltage data. The noise processing unit 130 can generate battery cell data by removing noise components from the reconstructed voltage data.

[0100] In operation S104, the abnormal cell detection unit 140 of the battery management device 100 can detect abnormal battery cells based on the voltage deviation of each of the battery cell data.

[0101] Figure 8 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a battery management device according to the embodiments disclosed herein.

[0102] Reference Figure 8 The computing device 200 according to the embodiments disclosed herein may include a microcontroller unit (MCU) 210, a memory 220, an input / output interface (I / F) 230, and a communication I / F 240.

[0103] MCU 210 can be a processor that executes various programs stored in memory 220 (e.g., battery cell data collection program, graphics calculation program, data analysis program, data decomposition algorithm, normalization program, battery cell diagnostic program, etc.). These programs process various information about the battery cells, including characteristic data and latent variables, and execute... Figures 1 to 7 The battery management device 100 shown includes the controller with the above-mentioned functions.

[0104] The memory 220 can store various programs such as battery cell data collection programs, graphics calculation programs, data analysis programs, data decomposition algorithms, normalization programs, and battery cell diagnostic programs.

[0105] Multiple memory units 220 can be configured as needed. Memory units 220 can be volatile or non-volatile. For memory units 220 used as volatile memory, random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), etc., can be used. For memory units 220 used as non-volatile memory, read-only memory (ROM), programmable ROM (PROM), electrically rewritable ROM (EAROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, etc., can be used. The examples of memory units 220 listed above are merely examples and are not limited to these.

[0106] The input / output I / F 230 can provide an interface for sending and receiving data by connecting input devices (not shown) such as a keyboard, mouse, touch panel, etc. and output devices such as a display (not shown) to the MCU 210.

[0107] The communication I / F 240, which is a component capable of sending and receiving various types of data from a server, can be any device capable of supporting wired or wireless communication. For example, the battery management device 100 can send and receive various information, including the shape model of the battery cell, from an external server provided separately via the communication I / F 240.

[0108] Thus, the computer program according to the embodiments disclosed herein can be recorded in memory 220 and processed by MCU 210, thereby being implemented to execute... Figure 2 The module that provides the shown functions.

[0109] Although all components constituting the embodiments disclosed herein have been described above as operating in one or more combinations, the embodiments disclosed herein are not necessarily limited to these embodiments. That is, within the scope of the objectives of the embodiments disclosed herein, all components can be operated by selectively combining them into one or more.

[0110] Furthermore, unless otherwise stated, terms such as "comprising," "constituting," or "having" may mean that the corresponding component may be inherent, and therefore should be interpreted as including other components rather than excluding them. Unless otherwise defined, all terms, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Terms generally used with respect to terms as defined in dictionaries should be interpreted as having the same meaning as in the context of the relevant art, and should not be interpreted as having an ideal or overly formal meaning unless they are explicitly defined in this document.

[0111] The foregoing disclosure provides a rough illustration of features of several embodiments to allow those skilled in the art to better understand various aspects of this disclosure. Those skilled in the art will understand that they can readily use this disclosure as a basis for designing or modifying other structures to achieve the same purpose or realize the same advantages as the embodiments described herein. Furthermore, those skilled in the art will recognize that such equivalent configurations may be made without departing from the scope of this disclosure, and that various changes, substitutions, and modifications may be made herein without departing from the scope of this disclosure.

[0112] [Explanation of reference numerals in the attached figures]

[0113] 1: Battery pack

[0114] 2: Upper-level controller

[0115] 10: Multiple battery cells

[0116] 12: Sensors

[0117] 14: Switching Unit

[0118] 20: BMS

[0119] 100: Battery Management Device

[0120] 110: Data Acquisition Unit

[0121] 120: Preprocessing Unit

[0122] 130: Noise processing unit

[0123] 140: Abnormal Cell Detection Unit

[0124] 200: Computing System

[0125] 210: MCU

[0126] 220: Memory

[0127] 230: Input / Output I / F

[0128] 240: Communication I / F

Claims

1. A battery management device, the battery management device comprising: A data acquisition unit is configured to acquire voltage data for each of a plurality of battery cells in relation to voltage changes over time. A preprocessing unit is configured to obtain reconstructed voltage data within a predetermined time interval from a dataset consisting of voltage data of each of the plurality of battery cells by using a data decomposition algorithm. A noise processing unit configured to generate battery cell data by performing noise processing based on the characteristics of the reconstructed voltage data; as well as An abnormal cell detection unit is configured to detect abnormal battery cells based on the voltage deviation of each of the battery cell data.

2. The battery management device according to claim 1, wherein, The preprocessing unit is further configured to: Multiple sub-datasets with lengths corresponding to the predetermined time intervals are generated from the dataset; and The reconstructed voltage data is obtained from each of the plurality of subset datasets based on the data decomposition algorithm.

3. The battery management device according to claim 2, wherein, The data decomposition algorithm includes Singular Value Decomposition (SVD).

4. The battery management device according to claim 1, wherein, The noise processing unit is also configured to perform the noise processing on each of the reconstructed voltage data based on the number of zero-crossings, where the number of zero-crossings is the number of times the voltage crosses a specific voltage level.

5. The battery management device according to claim 4, wherein, The noise processing unit is further configured to: The number of zero crosses for each of the reconstructed voltage data is compared with a first threshold. The reconstructed data with zero-crossing counts greater than or equal to the first threshold are detected as noise components; and The battery cell data is generated by removing the noise component from the reconstructed voltage data.

6. The battery management device according to claim 5, wherein, The specific voltage level includes 0 V.

7. The battery management device according to claim 1, wherein, The noise processing unit is also configured to perform the noise processing on each of the reconstructed voltage data based on a zero-crossing period, which is a period during which the voltage crosses a specific voltage level.

8. The battery management device according to claim 7, wherein, The noise processing unit is further configured to: The zero-crossing period of each of the reconstructed voltage data is compared with a second threshold; The reconstructed data with zero crossover periods less than or equal to the second threshold is detected as noise components; and The battery cell data is generated by removing the noise component from the reconstructed voltage data.

9. The battery management device according to claim 8, wherein, The specific voltage level includes 0 V.

10. The battery management device according to claim 1, wherein, The abnormal cell detection unit is also configured to: The voltage deviation is compared with a third threshold, the voltage deviation being the difference between the minimum and maximum voltage values ​​of each of the battery cell data within the predetermined time interval; and Battery cells with voltage deviations greater than or equal to the third threshold are detected as abnormal battery cells.

11. A battery management method, the battery management method comprising the following steps: Obtain voltage data for each of multiple battery cells in relation to voltage changes over time; By using a data decomposition algorithm, reconstructed voltage data within a predetermined time interval is obtained from a dataset consisting of voltage data of each of the plurality of battery cells; Battery cell data is generated by performing noise processing based on the characteristics of the reconstructed voltage data; as well as Abnormal battery cells are detected based on the voltage deviation of each of the battery cell data.

12. The battery management method according to claim 11, wherein, The steps to obtain the reconstructed voltage data include the following: Generate multiple sub-datasets from the dataset, each with a length corresponding to the predetermined time interval; and The reconstructed voltage data is obtained from each of the plurality of subset datasets based on the data decomposition algorithm.

13. The battery management method according to claim 12, wherein, The data decomposition algorithm includes Singular Value Decomposition (SVD).

14. The battery management method according to claim 11, wherein, The step of generating the battery cell data includes performing the noise processing on each of the reconstructed voltage data based on the number of zero-crossings, where the number of zero-crossings is the number of times the voltage crosses a specific voltage level.

15. The battery management method according to claim 14, further comprising the following steps: The number of zero crosses for each of the reconstructed voltage data is compared with a first threshold. The reconstructed data with zero-crossing counts greater than or equal to the first threshold are detected as noise components; as well as The battery cell data is generated by removing the noise component from the reconstructed voltage data.

16. The battery management method according to claim 15, wherein, The specific voltage level includes 0 V.

17. The battery management method according to claim 11, wherein, The step of generating the battery cell data includes performing the noise processing on each of the reconstructed voltage data based on a zero-crossing period, where the zero-crossing period is the period during which the voltage crosses a specific voltage level.

18. The battery management method according to claim 17, further comprising the following steps: The zero-crossing period of each of the reconstructed voltage data is compared with a second threshold; The reconstructed data with zero crossover periods less than or equal to the second threshold is detected as noise components; as well as The battery cell data is generated by removing the noise component from the reconstructed voltage data.

19. The battery management method according to claim 18, wherein, The specific voltage level includes 0 V.

20. The battery management method according to claim 11, wherein, The steps for detecting the abnormal battery cell include the following: The voltage deviation is compared with a third threshold, the voltage deviation being the difference between the minimum and maximum voltage values ​​of each of the battery cell data within the predetermined time interval; and Battery cells with voltage deviations greater than or equal to the third threshold are detected as abnormal battery cells.

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