Battery diagnosis apparatus, battery diagnosis method, and battery diagnosis system

Abstract

According to some embodiments disclosed in the present document, a battery diagnosis apparatus comprises: a sensor configured to measure time-based first battery data from a battery to be diagnosed; and a controller configured to generate preprocessed data by preprocessing the first battery data, generate frequency-based second battery data on the basis of the preprocessed data, and diagnose whether the battery to be diagnosed is abnormal on the basis of statistical data related to the second battery data.

Description

Battery diagnostic device, battery diagnostic method, and battery diagnostic system

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority to Republic of Korea Patent Application No. 10-2023-0096958, filed July 25, 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 diagnostic device, a battery diagnostic method, and a battery diagnostic system.

[0005] Recently, active research and development has been conducted on secondary batteries. The term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries can boast higher energy densities than conventional Ni / Cd and Ni / MH batteries. They can be manufactured in small and lightweight designs, making them highly versatile power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Battery abnormalities can be diagnosed based on signals measured in the time domain. For example, changes in voltage, current, and temperature over time can be measured from a battery, allowing for diagnosis of battery errors or defects. However, time-based signals can contain various noises and measurement errors, and when analyzing long-term patterns, filtering can result in loss of data.

[0007] One purpose of the embodiments disclosed in this document is to provide a battery diagnosis device, a battery diagnosis method, and a battery diagnosis system capable of diagnosing a battery from a different perspective in order to solve problems of battery diagnosis based on a time domain.

[0008] The technical objectives of the embodiments disclosed in this document are not limited to the technical tasks mentioned above, and other technical tasks not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0009] According to some embodiments disclosed in the present document, a battery diagnostic device includes a sensor configured to measure time-based first battery data from a battery to be diagnosed; and a controller configured to preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

[0010] According to some embodiments, the controller is configured to calculate statistical values ​​for battery cells of the battery to be diagnosed and amplitude values ​​of the second battery data corresponding to frequency values ​​of the second battery data, and to diagnose whether there is an abnormality based on the statistical values.

[0011] According to some embodiments, the controller is configured to calculate error values ​​of the amplitude values ​​based on the statistical values, and to diagnose whether there is an abnormality based on the number of error values ​​exceeding an error reference value among the error values.

[0012] According to some embodiments, the controller is configured to calculate a median of the amplitude values ​​for each of the frequency values, calculate median values ​​corresponding to the frequency values, and calculate error values ​​of the amplitude values ​​based on the median value.

[0013] According to some embodiments, the controller is configured to calculate an average and a variance of the error values ​​for each battery cell, and to diagnose whether each battery cell is abnormal based on the average and the variance of the error values.

[0014] According to some embodiments, the controller is configured to count, for each battery cell, the number of frequency values ​​among the frequency values ​​whose error value exceeds an error reference value, and determine, among the battery cells, a battery cell in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value as an abnormal battery cell.

[0015] According to some embodiments, the controller is configured to determine the abnormal battery cells for each test cycle, count the number of abnormal cells, and determine the battery to be diagnosed as an abnormal battery if the number of abnormal cells exceeds an abnormal cell threshold value when a preset number of test cycles are completed.

[0016] According to some embodiments, the controller is configured to perform time aggregation preprocessing on the first battery data to generate temporally uniform data, and to perform imputation preprocessing on the temporally uniform data to generate the preprocessed data.

[0017] According to some embodiments disclosed in the present document, a battery diagnosis method includes the steps of: measuring time-based first battery data from a battery to be diagnosed; preprocessing the first battery data to generate preprocessed data; generating frequency-based second battery data based on the preprocessed data; and diagnosing whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

[0018] According to some embodiments, the step of diagnosing whether there is an abnormality includes the step of calculating statistical values ​​for amplitude values ​​of the second battery data corresponding to the battery cells of the battery to be diagnosed and the frequency values ​​of the second battery data; and the step of diagnosing whether there is an abnormality based on the statistical values.

[0019] According to some embodiments, the step of diagnosing whether there is an abnormality includes the step of calculating error values ​​of the amplitude values ​​based on the statistical values; and the step of diagnosing whether there is an abnormality based on the number of error values ​​exceeding an error reference value among the error values.

[0020] According to some embodiments, the step of calculating the error values ​​includes the step of calculating the median of the amplitude values ​​for each frequency value of the frequency values ​​to calculate median values ​​corresponding to the frequency values; and the step of calculating the error values ​​of the amplitude values ​​based on the median value.

[0021] According to some embodiments, the step of diagnosing whether there is an abnormality includes the step of calculating an average and a variance of the error values ​​for each battery cell; and the step of diagnosing whether there is an abnormality in each battery cell based on the average and the variance of the error values.

[0022] According to some embodiments, the step of diagnosing whether there is an abnormality includes, for each battery cell, counting the number of frequency values ​​among the frequency values ​​whose error value exceeds an error reference value; and determining, among the battery cells, a battery cell in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value as an abnormal battery cell.

[0023] According to some embodiments, the step of diagnosing whether there is an abnormality includes: determining the abnormal battery cell for each test cycle and counting the number of abnormal cells; and determining the battery to be diagnosed as an abnormal battery if the number of abnormal cells exceeds an abnormal cell threshold value when a preset number of test cycles are completed.

[0024] In some embodiments, the step of generating the preprocessed data includes: performing time aggregation preprocessing on the first battery data to generate temporally uniform data; and performing missing value replacement preprocessing on the temporally uniform data to generate the preprocessed data.

[0025] According to some embodiments disclosed in the present document, a battery diagnosis system includes: a battery to be diagnosed; a charger / discharger configured to apply a test cycle voltage to the battery to be diagnosed; a battery diagnosis device configured to measure time-based first battery data from the battery to be diagnosed, preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data; and a management server configured to manage a diagnosis result for the battery to be diagnosed.

[0026] According to some embodiments, the battery diagnostic device is configured to calculate statistical values ​​for amplitude values ​​of the second battery data corresponding to the battery cells of the battery to be diagnosed and the frequency values ​​of the second battery data, and to diagnose whether there is an abnormality based on the statistical values.

[0027] According to some embodiments, the battery diagnostic device is configured to calculate error values ​​of the amplitude values ​​based on the statistical values, and to diagnose whether there is an abnormality based on the number of error values ​​exceeding an error reference value among the error values.

[0028] According to some embodiments, the battery diagnostic device is configured to calculate a median of the amplitude values ​​for each frequency value of the frequency values, calculate median values ​​corresponding to the frequency values, and calculate error values ​​of the amplitude values ​​based on the median value.

[0029] According to some embodiments, the battery diagnostic device is configured to calculate an average and a variance of the error values ​​for each battery cell, and diagnose whether each battery cell is abnormal based on the average and the variance of the error values.

[0030] According to some embodiments, the battery diagnostic device is configured to count, for each battery cell, the number of frequency values ​​whose error value exceeds an error reference value among the frequency values, and determine, among the battery cells, a battery cell whose number of frequency values ​​exceeding the error reference value exceeds the number reference value as an abnormal battery cell.

[0031] According to some embodiments, the battery diagnostic device is configured to determine the abnormal battery cells for each test cycle, count the number of abnormal cells, and determine the battery to be diagnosed as an abnormal battery when the number of abnormal cells exceeds an abnormal cell reference value when a preset number of test cycles are completed.

[0032] According to some embodiments, the battery diagnostic device is configured to perform time aggregation preprocessing on the first battery data to generate temporally uniform data, and to perform missing value replacement preprocessing on the temporally uniform data to generate the preprocessed data.

[0033] According to the embodiments disclosed in this document, a battery diagnosis device, a battery diagnosis method, and a battery diagnosis system capable of diagnosing a battery from a different perspective can be provided to solve problems of battery diagnosis based on a time domain.

[0034] The technical effects according to the embodiments disclosed in this document are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art according to the disclosure of this document.

[0035] FIG. 1 may illustrate elements constituting a battery diagnostic system according to some embodiments.

[0036] FIG. 2 may illustrate elements constituting a battery diagnostic device and the operation of the battery diagnostic device according to some embodiments.

[0037] FIG. 3 can illustrate a detailed operation process of a battery diagnostic device according to some embodiments.

[0038] FIG. 4 may illustrate a process of generating preprocessed data by performing preprocessing on first battery data according to some embodiments.

[0039] FIG. 5 illustrates a process of generating frequency-based second battery data by performing domain transformation on preprocessed data according to some embodiments.

[0040] FIG. 6 may illustrate amplitude values ​​(A11, ..., Anm) for battery cells (c1, ..., cn) and frequency values ​​(f1, ..., fm) according to some embodiments.

[0041] FIG. 7 can illustrate a process of calculating a median value (Mj) of amplitude values ​​(A1j, ..., Anj) for each frequency value (fj) according to some embodiments.

[0042] Fig. 8 can illustrate a process of calculating error values ​​(E1j, ..., Enj) of amplitude values ​​(A1j, ..., Anj) based on a median value (Mj) according to some embodiments.

[0043] FIG. 9 may illustrate a process of calculating an error reference value (xi1 + 4*xi2) for each battery cell (ci) according to some embodiments.

[0044] FIG. 10 illustrates a process of selecting a battery cell among battery cells (c1, ..., cn) according to some embodiments, in which the number of frequency values ​​exceeding an error criterion value exceeds a number criterion value.

[0045] FIG. 11 illustrates a process of determining abnormal battery cells and counting the number of abnormal cells for each test cycle according to some embodiments.

[0046] FIG. 12 may illustrate a process for determining whether a battery to be diagnosed is abnormal based on the number of abnormal cells when a preset number of test cycles have been completed according to some embodiments.

[0047] FIG. 13 may illustrate steps of a battery diagnosis method according to some embodiments.

[0048] Hereinafter, embodiments described in this document are described with reference to the attached drawings. However, this is not intended to limit the disclosure of this document to specific embodiments, and it should be understood that various modifications, equivalents, and / or alternatives of the embodiments described in this document are included.

[0049] The embodiments and terminology used in this document are not intended to limit the technical features described in this document to a specific embodiment, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the item, unless the context clearly indicates otherwise.

[0050] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding component from other corresponding components, and do not limit the corresponding components in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0051] In this document, when a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired or wirelessly), or indirectly (e.g., via a third component).

[0052] The methods 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.

[0053] According to the embodiments disclosed in this document, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to the embodiments disclosed in this document, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments disclosed in this document, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0054] FIG. 1 may illustrate elements constituting a battery diagnostic system according to some embodiments.

[0055] Referring to FIG. 1, the battery diagnosis system (100) may include a charger / discharger (110), a battery to be inspected (120), a battery diagnosis device (130), and a management server (140). However, the present invention is not limited thereto, and some components may be omitted from the battery diagnosis system (100), or other general-purpose components may be further included in the battery diagnosis system (100).

[0056] The battery diagnosis system (100) may refer to a system for diagnosing the condition of a battery (120) to be inspected. According to an embodiment, a test voltage may be applied to the battery (120) to be inspected by a charger / discharger (110), and a time-domain signal output by the battery (120) to be inspected in response to the test voltage may be measured by a battery diagnosis device (130). The time-domain signal may be converted into frequency-domain data, and based on the frequency-domain data, whether the battery (120) to be inspected is abnormal may be determined.

[0057] According to an embodiment, the charger / discharger (110) may be configured to apply a test cycle voltage to the battery to be diagnosed (120). The test cycle voltage may have a profile of charging and re-discharging the battery to be diagnosed (120) and may be applied to the battery to be diagnosed (120) a plurality of times. The number of repeated application of the test cycle voltage may be set to 50, 100, 200, or any other suitable value. For example, the charger / discharger (110) may include a power supply device for applying various types of voltages or currents to the battery to be diagnosed (120).

[0058] According to an embodiment, a battery to be diagnosed (120) may be a diagnosis target of a battery diagnosis system (100). The battery to be diagnosed (120) may include a plurality of battery cells. For example, the battery to be diagnosed (120) may include a plurality of battery modules, and each module of the plurality of battery modules may include a plurality of battery cells. According to an embodiment, if the number of battery cells having errors in each cycle of a plurality of test cycles is greater than or equal to a specific number, the corresponding cycle may be counted as a cycle having errors, and if the counted number of cycles having errors is greater than or equal to a specific value when the plurality of test cycles are completed, it may be determined that the battery to be diagnosed (120) is ultimately abnormal.

[0059] The battery diagnostic device (130) can perform procedures for determining whether a battery (120) to be diagnosed is abnormal. According to an embodiment, the battery diagnostic device (130) can convert time domain data into frequency domain data and perform additional processing on the frequency domain data to determine whether there is an abnormality.

[0060] According to an embodiment, the management server (140) may be configured to manage the diagnosis results for the battery (120) to be diagnosed. The management server (140) may be connected to the battery diagnosis device (130) via wired / wireless data communication, and may receive data such as the status, abnormality, and diagnosis results of the battery (120) to be diagnosed from the battery diagnosis device (130) and record the data. The management server (140) may control the battery diagnosis device (130) or manage the battery (120) to be diagnosed at the request of a system administrator or a battery user.

[0061] According to an embodiment, the management server (140) may perform at least some of the procedures performed by the battery diagnosis device (130) instead of the battery diagnosis device (130). The management server (140) may receive data necessary for diagnosing the battery (120) to be diagnosed from the battery diagnosis device (130), perform the diagnostic procedures, and transmit the results to the battery diagnosis device (130). According to an embodiment, the management server (140) may install energy management software necessary for diagnosing the battery (120) to be diagnosed in the battery diagnosis device (130), and provide update information of the energy management software to the battery diagnosis device (130).

[0062] FIG. 2 may illustrate elements constituting a battery diagnostic device and the operation of the battery diagnostic device according to some embodiments.

[0063] Referring to FIG. 2, the battery diagnostic device (130) may include a sensor (131) and a controller (132). However, the present invention is not limited thereto, and some components may be omitted from the battery diagnostic device (130), or other general-purpose components may be further included in the battery diagnostic device (130).

[0064] According to an embodiment, the sensor (131) and the controller (132) in the battery diagnostic device (130) may be electrically connected to each other through a device-to-device communication method such as a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), MIPI (mobile industry processor interface), etc.

[0065] The sensor (131) of the battery diagnostic device (130) may be configured to measure time-based first battery data from the battery to be diagnosed (120). When a test cycle voltage is applied to the battery to be diagnosed (120) by the charger / discharger (110), an output voltage in response thereto may be generated in the battery to be diagnosed (120), and the sensor (131) may measure this to generate first battery data. To this end, the sensor (131) may include a measuring means such as a voltmeter, an ammeter, a thermometer, etc. According to an embodiment, the first battery data may include a signal in the time domain. For example, the first battery data may include voltage data, current data, and / or temperature data in the time domain.

[0066] The controller (132) may have a structure for executing commands that implement the operations of the battery diagnostic device (130). The controller (132) may be implemented as an array of multiple logic gates for processing various operations or as a general-purpose microprocessor, and may be composed of a single processor or multiple processors. For example, the controller (132) may be implemented in the form of at least one of a microprocessor, a CPU, a GPU, and an AP.

[0067] The controller (132) may be configured separately from or integrally with a memory (not shown) configured to store commands, and may process various operations by executing commands stored in the memory. The memory may store various data, commands, mobile applications, computer programs, etc. For example, the memory may be implemented as a non-volatile memory such as ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, FRAM, etc., or a volatile memory such as DRAM, SRAM, SDRAM, RRAM, etc., and may be implemented in the form of an HDD, SSD, SD, Micro-SD, etc., or in the form of a combination thereof.

[0068] The controller (132) of the battery diagnostic device (130) may be configured to preprocess first battery data to generate preprocessed data. The preprocessing performed on the first battery data may include a preparatory process for converting first battery data in the time domain into second battery data in the frequency domain. For example, the preprocessing may include processing to normalize data over time and / or fill in missing values.

[0069] The controller (132) of the battery diagnostic device (130) may be configured to generate frequency-based second battery data based on preprocessed data. The domain transformation performed on the preprocessed data to generate the second battery data may mean converting time-domain signals / data into frequency-domain signals / data. According to an embodiment, the domain transformation may include a fast Fourier transform (FFT). The second battery data may include amplitude values ​​that vary according to frequency values, and the corresponding frequency values ​​may be composed of continuous values ​​or discrete values.

[0070] The controller (132) of the battery diagnosis device (130) may be configured to diagnose whether the battery (120) to be diagnosed is abnormal based on statistical data related to second battery data. The second battery data may represent amplitude values ​​of battery cells of the battery (120) to be diagnosed in a range of given frequency values, and whether the battery (120) to be diagnosed is abnormal may be determined using statistical variables based on these values. According to an embodiment, the statistical data related to the second battery data may include an average, a median, a first quartile, a third quartile, a variance, a standard deviation, etc. for values ​​of the second battery data.

[0071] FIG. 3 can illustrate a detailed operation process of a battery diagnostic device according to some embodiments.

[0072] Referring to FIG. 3, the detailed operation process of the battery diagnosis device (130) may include a first step (310) for data measurement, a second step (320) for time aggregation, a third step (330) for missing value replacement, a fourth step (340) for area transformation, a fifth step (350) for statistical value calculation, and a sixth step (360) for abnormality diagnosis.

[0073] Unlike the existing diagnostic method using time-based signals, steps 1 (310) to 6 (360) can diagnose whether the battery (120) to be diagnosed is abnormal by using frequency-based data. In the existing time-based method, a method of detecting an instantaneous difference in a signal was used, but the frequency-based method according to steps 1 (310) to 6 (360) can detect the frequency component of a signal hidden by noise, and based on this, can detect a cell having a pattern outside the normal range. For example, considering that the characteristics of battery cells are similar, an abnormal cell may exhibit a frequency pattern that is different from the patterns of other cells. Therefore, if the normal range is defined based on statistical values, the continuous occurrence of an abnormal pattern can be detected even after removing noise.

[0074] FIG. 4 may illustrate a process of generating preprocessed data by performing preprocessing on first battery data according to some embodiments.

[0075] Referring to FIG. 4, a first graph (410) representing a waveform of first battery data measured in the time domain, a second graph (420) representing a waveform of temporally uniform data generated as a result of time aggregation preprocessing for the first battery data, and a third graph (430) representing a waveform of preprocessed data generated as a result of missing value replacement preprocessing for the temporally uniform data may be illustrated.

[0076] The first graph (410), the second graph (420), and the third graph (430) can all represent voltage on the vertical axis against time on the horizontal axis, and accordingly, the first battery data, the temporally uniform data, and the preprocessing data can all be data in the time domain. The first battery data, the temporally uniform data, and the preprocessing data can all have periodicity, where one period can correspond to one test cycle.

[0077] According to an embodiment, the controller (132) of the battery diagnostic device (130) may be configured to perform time aggregation preprocessing on the first battery data to generate temporally uniform data, and to perform imputation preprocessing on the temporally uniform data to generate preprocessed data. Through the time aggregation preprocessing, the values ​​of the first battery data can be uniformly adjusted over time, thereby generating temporally uniform data. Through the imputation preprocessing, if a measurement missing value exists in the temporally uniform data based on a given time step value, the missing value can be supplemented through polarization.

[0078] FIG. 5 illustrates a process of generating frequency-based second battery data by performing domain transformation on preprocessed data according to some embodiments.

[0079] Referring to FIG. 5, a third graph (430) representing a waveform of preprocessed data and a fourth graph (510) representing second battery data in a frequency domain generated as a result of domain transformation of the preprocessed data may be illustrated. The fourth graph (510) may depict amplitude values ​​on a vertical axis according to frequency on a horizontal axis.

[0080] When a domain transformation is performed on the time-based preprocessing data illustrated in the third graph (430), frequency-based second battery data illustrated in the fourth graph (510) can be generated. According to an embodiment, the domain transformation performed on the preprocessing data to generate the second battery data may include a fast Fourier transform (FFT), or may include various other types of time-frequency transformations.

[0081] FIG. 6 may illustrate amplitude values ​​(A11, ..., Anm) for battery cells (c1, ..., cn) and frequency values ​​(f1, ..., fm) according to some embodiments.

[0082] Referring to FIG. 6, first to third graphs (610, 620, 630) representing frequency-based second battery data generated for the first battery cell (c1), the second battery cell (c2), and the third battery cell (c3) of the battery to be diagnosed (120) may be illustrated.

[0083] The second battery data illustrated in the first to third graphs (610, 620, 630) can be reconstructed as an amplitude diagram (640) for the battery cells (c1, ..., cn) of the battery to be diagnosed (120) and the frequency values ​​(f1, ..., fm) of the second battery data. When the battery to be diagnosed (120) includes n battery cells (c1, ..., cn) and the second battery data includes m frequency values ​​(f1, ..., fm), each battery cell (ci) can have amplitude values ​​(Ai1, ..., Aim) for the m frequency values ​​(f1, ..., fm).

[0084] According to an embodiment, the controller (132) of the battery diagnosis device (130) may be configured to calculate statistical values ​​for amplitude values ​​(A11, ..., Anm) of second battery data corresponding to battery cells (c1, ..., cn) of the battery to be diagnosed (120) and frequency values ​​(f1, ..., fm) of the second battery data, and diagnose whether there is an abnormality based on the statistical values. The statistical values ​​for the amplitude values ​​(A11, ..., Anm) may be derived in various ways. According to an embodiment, statistical values ​​may be calculated for the same frequency value and all battery cells in the vertical direction in the amplitude diagram (640), or statistical values ​​may be calculated for the same battery cell and all frequency values ​​in the horizontal direction.

[0085] FIG. 7 can illustrate a process of calculating a median value (Mj) of amplitude values ​​(A1j, ..., Anj) for each frequency value (fj) according to some embodiments.

[0086] Referring to FIG. 7, as an example in which statistical values ​​are calculated for the same frequency value and all battery cells in the vertical direction in the amplitude diagram, a first statistical value calculation (710) for a first frequency (f1) and all battery cells (c1, ..., cn) and a second statistical value calculation (720) for a second frequency (f2) and all battery cells (c1, ..., cn) can be illustrated.

[0087] In the case of first statistical value calculation (710), statistical values ​​may be calculated for the amplitude (A11) of the first battery cell (c1) at the first frequency (f1), the amplitude (A21) of the second battery cell (c2) at the first frequency (f1), the amplitude (A31) of the third battery cell (c3) at the first frequency (f1), etc., and at this time, the statistical value may be a median. According to an embodiment, the statistical value may further include a mean, a first quartile value, a third quartile value, a variance, a standard deviation, etc. In the same way as in the case of the first frequency (f1), statistical values ​​may be calculated for all frequency values ​​(f1, ..., fm).

[0088] According to an embodiment, the controller (132) of the battery diagnostic device (130) may be configured to calculate a median (Mj) of amplitude values ​​(A1j, ..., Anj) for each frequency value (fj) of the frequency values, calculate median values ​​(M1, ..., Mm) corresponding to the frequency values ​​(fj, ..., fm), and calculate error values ​​(E1j, ..., Enj) of the amplitude values ​​(A1j, ..., Anj) based on the median value (Mj). According to an embodiment, instead of the median value (Mj), other types of representative values ​​such as a mean, a first quartile value, a third quartile value, a variance, and a standard deviation may be utilized.

[0089] Fig. 8 can illustrate a process of calculating error values ​​(E1j, ..., Enj) of amplitude values ​​(A1j, ..., Anj) based on a median value (Mj) according to some embodiments.

[0090] Referring to FIG. 8, a process of calculating error values ​​of amplitude values ​​of battery cells for one frequency value in an amplitude diagram can be illustrated in relation to error calculation (810) of first statistical value calculation (710).

[0091] In the error calculation (810), the first median (M1) calculated for the first frequency (f1) can be utilized to calculate the error of the amplitude value (A11), and can also be utilized in the same way to calculate the errors of the remaining amplitude values ​​(A21, A31, ...). Meanwhile, in addition to the median, various types of representative values ​​can be utilized to calculate the error. According to an embodiment, the mean, the first quartile, the third quartile, the variance, the standard deviation, etc. can be utilized as representative values ​​for calculating the error. According to an embodiment, the difference between the median and the variance can be expressed as the error value for each frequency.

[0092] According to an embodiment, the controller (132) of the battery diagnosis device (130) may be configured to calculate error values ​​of amplitude values ​​based on statistical values, and to diagnose whether there is an abnormality based on the number of error values ​​that exceed an error reference value among the error values. As in error calculation (810), once all error values ​​are calculated, it may be analyzed how many frequencies in each battery cell exceed the error reference value. For example, for the first battery cell (c1), the number of frequency values ​​whose error values ​​exceed the error reference value among 100 frequency values ​​(f1 to f100) is counted, and if the count value exceeds the number reference value (e.g., 10), the first battery cell (c1) may be classified as an abnormal cell.

[0093] FIG. 9 may illustrate a process of calculating an error reference value (xi1 + 4*xi2) for each battery cell (ci) according to some embodiments.

[0094] Referring to FIG. 9, the mean (x11) and variance (x12) of error values ​​(E11, E12, E13, ...) in the first battery cell (910) can be calculated, and an error reference value for the first battery cell (910) can be calculated based on the mean (x11) and variance (x12). In the same manner, error reference values ​​can be calculated for the second battery cell (920) and the remaining battery cells.

[0095] According to an embodiment, the controller (132) of the battery diagnostic device (130) may be configured to calculate the average (xi1) and variance (xi2) of error values ​​(Ei1, ..., Eim) for each battery cell (ci), and diagnose whether each battery cell (ci) is abnormal based on the average (xi1) and variance (xi2) of the error values ​​(Ei1, ..., Eim). According to an embodiment, in addition to the average and variance, other values ​​such as the median, the first quartile, the third quartile, and the standard deviation may be utilized to calculate the error reference value.

[0096] According to an embodiment, the controller (132) of the battery diagnostic device (130) may be configured to count, for each battery cell (ci), the number of frequency values ​​(fj) whose error value (Eij) exceeds an error reference value (xi1 + 4*xi2) among the frequency values ​​(f1, ..., fm), and determine, among the battery cells (c1, ..., cn), a battery cell in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value as an abnormal battery cell. According to an embodiment, coefficients 1 and 4 of the error reference value (xi1 + 4*xi2) may be changed to other appropriate values ​​as needed.

[0097] The count reference value may be a preset value according to the diagnostic performance requirement of the battery diagnostic device (130), and the lower the count reference value, the higher the diagnostic performance. For example, if the count reference value is set to 10, and in the first battery cell (c1), if an error exceeds the reference value in 12 frequency values ​​among 100 frequency values ​​(f1 to f100), the first battery cell (c1) may be classified as a defective cell.

[0098] FIG. 10 illustrates a process of selecting a battery cell among battery cells (c1, ..., cn) according to some embodiments, in which the number of frequency values ​​exceeding an error criterion value exceeds a number criterion value.

[0099] Referring to FIG. 10, if the number of frequencies (ex. 2) having an error exceeding the error criterion value in the first battery cell (1010) exceeds the number criterion value (ex. 1), the first battery cell (1010) may be classified as a defective cell.

[0100] Similarly, if the number of frequencies (e.g., 1) having an error exceeding the error threshold in the second battery cell (1020) does not exceed the number threshold (e.g., 1), the second battery cell (1020) can be classified as a normal cell, and the remaining battery cells can also be classified as defective or normal. Accordingly, when one test cycle is completed, the number of battery cells classified as defective among the battery cells of the battery to be diagnosed (120) can be derived.

[0101] FIG. 11 illustrates a process of determining abnormal battery cells and counting the number of abnormal cells for each test cycle according to some embodiments.

[0102] Referring to FIG. 11, the process of counting the number of abnormal battery cells for each test cycle as described above can be repeated for multiple test cycles.

[0103] For example, in test cycle 1, the number of battery cells classified as defective may be 3, in test cycle 2, the number of battery cells classified as defective may be 2, and in the remaining test cycles, the number of battery cells classified as defective may be 0. In this case, the cumulative count value of 5 may be compared with a reference value to determine the final defect of the battery to be diagnosed (120). According to an embodiment, the total number of test cycles may be 100, and the comparison reference value of the cumulative count value may be 3. However, other appropriate numbers may be utilized depending on changes in the diagnostic performance requirements. According to an embodiment, when high diagnostic performance is required, the total number of test cycles may be increased, and the comparison reference value may be decreased.

[0104] According to an embodiment, the controller (132) of the battery diagnostic device (130) may be configured to determine abnormal battery cells for each test cycle, count the number of abnormal cells, and determine the battery (120) to be diagnosed as an abnormal battery if the number of abnormal cells exceeds an abnormal cell reference value when a preset number of test cycles are completed. For example, if the count value of the number of abnormal cells exceeds 3 when 100 test cycles are completed, the battery (120) to be diagnosed may be finally diagnosed as having an abnormality.

[0105] FIG. 12 may illustrate a process for determining whether a battery to be diagnosed is abnormal based on the number of abnormal cells when a preset number of test cycles have been completed according to some embodiments.

[0106] Referring to FIG. 12, a flowchart (1200) may be illustrated to explain the variation of a count value representing the number of battery cells classified as defective while a preset number of test cycles are performed.

[0107] In the first step (1210), a count value can be verified, and in the second step (1220), the count value can be compared with a reference value. In the third step (1230), if the count value exceeds the reference value, a notification may be generated indicating that there is a problem with the battery to be diagnosed (120). If the count value does not exceed the reference value, in the fourth step (1240), it may be checked whether a preset number of test cycles have been completed. If the preset number of test cycles has been completed, in the fifth step (1250), the count value may be initialized to 0.

[0108] If the preset number of times is not completed, in step 6 (1260), it is determined whether the count value has not increased because no abnormal cells have been detected for a specific number of consecutive test cycles (e.g., 5), and if "Yes", the total accumulated count value may be decreased by 1 in step 7 (1270), and if "No", the total accumulated count value may be maintained in step 8 (1280).

[0109] With respect to steps 6 (1260) to 8 (1280), if no abnormal cells are detected during a specific number of consecutive test cycles (e.g., 5), this may indicate that the battery (120) being diagnosed has stabilized, or that abnormal cells were detected due to a temporary factor. Therefore, through this correction process, the accuracy of the battery diagnostic device (130) can be improved.

[0110] FIG. 13 may illustrate steps of a battery diagnosis method according to some embodiments.

[0111] Referring to FIG. 13, the battery diagnosis method (1300) may include steps (1310) to (1340). However, the present invention is not limited thereto, and some steps may be omitted or other general steps may be added, and the steps of the battery diagnosis method (1300) may be executed in a different order than the illustrated order.

[0112] The battery diagnosis method (1300) may be composed of steps that are processed in a time-series manner in the battery diagnosis device (130). Therefore, even if the content is omitted below, the content described above for the battery diagnosis device (130) may be equally applied to the battery diagnosis method (1300).

[0113] Steps (1310) to (1340) of the battery diagnosis method (1300) can be performed by the sensor (131) and controller (132) of the battery diagnosis device (130).

[0114] In step (1310), the battery diagnostic device (130) can measure time-based first battery data from the battery to be diagnosed through a sensor.

[0115] In step (1320), the battery diagnostic device (130) can preprocess first battery data through a controller to generate preprocessed data.

[0116] In step (1330), the battery diagnostic device (130) can generate frequency-based second battery data based on preprocessing data through the controller.

[0117] In step (1340), the battery diagnostic device (130) can diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data through the controller.

[0118] Meanwhile, the battery diagnosis method (1300) may be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include commands for implementing the battery diagnosis method (1300), and the program commands may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0119] For example, a computer-readable storage medium may include magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM, a DVD, magneto-optical media such as a floptical disk, and hardware devices specially configured to store and execute computer program instructions, such as ROM, RAM, flash memory, and the like. The computer program instructions may include machine language code generated by a compiler and high-level language code that can be executed by a computer using an interpreter, etc.

[0120] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude 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 pertain, unless otherwise defined. Commonly used terms, such as terms defined in dictionaries, should be interpreted to be consistent with their contextual meaning in the relevant art, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0121] The above description is merely an illustrative description of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document. Therefore, the embodiments disclosed in this document are not intended to limit the technical idea of ​​the embodiments disclosed in this document, but to explain it, and the scope of the technical idea disclosed in this document is not limited by these embodiments. The protection scope of the technical idea disclosed in this document should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included in the scope of rights of this document.

[0122] [Explanation of symbols]

[0123] 100: Battery diagnostic system 110: Charger / discharger

[0124] 120: Battery to be inspected 130: Battery diagnostic device

[0125] 131: Sensor 132: Controller

[0126] 140: Management Server

Claims

1. A sensor configured to measure time-based first battery data from a battery to be diagnosed; and Preprocessing the above first battery data to generate preprocessed data, Generate frequency-based second battery data based on the above preprocessing data, A battery diagnostic device comprising a controller configured to diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

2. In paragraph 1, The controller calculates statistical values ​​for the battery cells of the battery to be diagnosed and the amplitude values ​​of the second battery data corresponding to the frequency values ​​of the second battery data, A battery diagnostic device configured to diagnose whether there is an abnormality based on the above statistical values.

3. In paragraph 2, The above controller calculates error values ​​of the amplitude values ​​based on the above statistical values, A battery diagnostic device configured to diagnose whether there is an abnormality based on the number of error values ​​exceeding an error reference value among the above error values.

4. In paragraph 3, The controller calculates the median of the amplitude values ​​for each frequency value of the frequency values ​​and calculates median values ​​corresponding to the frequency values, A battery diagnostic device configured to calculate error values ​​of the amplitude values ​​based on the median value.

5. In paragraph 4, The above controller calculates the average and variance of the error values ​​for each battery cell, A battery diagnostic device configured to diagnose whether each battery cell is abnormal based on the average and variance of the above error values.

6. In paragraph 5, The above controller counts, for each battery cell, the number of frequency values ​​among the frequency values ​​whose error value exceeds the error reference value, A battery diagnostic device configured to determine, among the above battery cells, a battery cell in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value as an abnormal battery cell.

7. In paragraph 6, The above controller determines the abnormal battery cells for each test cycle and counts the number of abnormal cells. A battery diagnostic device configured to determine the battery to be diagnosed as an abnormal battery when the number of abnormal cells exceeds an abnormal cell threshold value when a preset number of test cycles are completed.

8. In paragraph 1, The above controller performs time aggregation preprocessing on the first battery data to generate temporally uniform data, A battery diagnostic device configured to generate the preprocessed data by performing missing value imputation preprocessing on the above temporal uniform data.

9. A step of measuring time-based first battery data from a battery to be diagnosed; A step of preprocessing the first battery data to generate preprocessed data; A step of generating frequency-based second battery data based on the above preprocessing data; and A battery diagnosis method, comprising a step of diagnosing whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

10. In paragraph 9, The steps to diagnose whether the above abnormality exists are: A step of calculating statistical values ​​for the battery cells of the battery to be diagnosed and the amplitude values ​​of the second battery data corresponding to the frequency values ​​of the second battery data; and A battery diagnosis method, comprising a step of diagnosing whether the above is abnormal based on the above statistical values.

11. In paragraph 10, The steps to diagnose whether the above abnormality exists are: A step of calculating error values ​​of the amplitude values ​​based on the above statistical values; and A battery diagnosis method, comprising a step of diagnosing whether there is an abnormality based on the number of error values ​​exceeding an error reference value among the above error values.

12. In paragraph 11, The steps for calculating the above error values ​​are: A step of calculating the median of the amplitude values ​​for each frequency value of the frequency values ​​and calculating the median values ​​corresponding to the frequency values; and A battery diagnosis method, comprising a step of calculating error values ​​of the amplitude values ​​based on the median value.

13. In paragraph 12, The steps to diagnose whether the above abnormality exists are: A step of calculating the average and variance of the above error values ​​for each battery cell; and A battery diagnosis method, comprising a step of diagnosing whether each battery cell is abnormal based on the average and variance of the above error values.

14. In paragraph 13, The steps to diagnose whether the above abnormality exists are: For each battery cell, a step of counting the number of frequency values ​​among the above frequency values ​​whose error value exceeds an error reference value; and A battery diagnosis method, comprising a step of determining, among the above battery cells, a battery cell in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value as an abnormal battery cell.

15. In paragraph 14, The steps to diagnose whether the above abnormality exists are: A step of determining the above abnormal battery cells for each test cycle and counting the number of abnormal cells; and A battery diagnosis method, comprising a step of determining the battery to be diagnosed as an abnormal battery when the number of abnormal cells exceeds an abnormal cell reference value when a preset number of test cycles are completed.

16. In paragraph 9, The step of generating the above preprocessing data is: A step of performing time aggregation preprocessing on the first battery data to generate temporally uniform data; and A battery diagnosis method, comprising a step of generating the preprocessed data by performing missing value replacement preprocessing on the temporal uniform data.

17. Battery to be diagnosed; A charger / discharger configured to apply a test cycle voltage to the above-mentioned battery to be diagnosed; A battery diagnostic device configured to measure time-based first battery data from the battery to be diagnosed, preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data; and A battery diagnosis system comprising a management server configured to manage diagnosis results for the above-mentioned diagnosis target battery.

18. In paragraph 17, The battery diagnosis device calculates statistical values ​​for the battery cells of the battery to be diagnosed and the amplitude values ​​of the second battery data corresponding to the frequency values ​​of the second battery data, A battery diagnosis system configured to diagnose whether there is an abnormality based on the above statistical values.

19. In paragraph 18, The above battery diagnostic device calculates error values ​​of the amplitude values ​​based on the statistical values, A battery diagnosis system configured to diagnose whether there is an abnormality based on the number of error values ​​exceeding an error reference value among the above error values.

20. In paragraph 19, The above battery diagnostic device calculates the median of the amplitude values ​​for each frequency value of the frequency values ​​and calculates median values ​​corresponding to the frequency values, A battery diagnosis system configured to calculate error values ​​of the amplitude values ​​based on the median value.

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