Battery diagnosis device, battery diagnosis method, and battery diagnosis system

Abstract

According to some embodiments disclosed in this document, a battery diagnostic apparatus includes: a sensor configured to measure time-based first battery data from a battery to be diagnosed; and a controller configured to generate pre-processed data by pre-processing the first battery data, generate frequency-based second battery data based on the pre-processed data, and diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

Description

TECHNICAL FIELD

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2023-0096958, filed on July 25, 2023, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] Embodiments disclosed herein relate to a battery diagnosis apparatus, a battery diagnosis method, and a battery diagnosis system. BACKGROUND

[0004] Recently, research and development of secondary batteries have been actively conducted. In the present document, a secondary battery, which is a chargeable / dischargeable battery, can be interpreted to include all conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, etc., as well as recent lithium ion batteries. Among the secondary batteries, lithium ion batteries can have higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, etc., and can be manufactured to be small and light, allowing them to have high usability in power sources of mobile devices. Recently, lithium ion batteries are attracting attention as a next-generation energy storage medium as their use range is extended to power sources of electric vehicles.

[0005] Diagnosis of whether a battery is abnormal can be performed based on a signal measured in a time domain. For example, voltage, current, or temperature changes over time can be measured from a battery, and based on them, the presence of errors or defects in the battery can be diagnosed. However, time-based signals can include various noise and measurement errors, and in order to analyze long-term patterns, a part of data can be lost due to the application of a filter. SUMMARY

[0006] TECHNICAL PROBLEM

[0007] Embodiments disclosed herein aim to provide a battery diagnosis apparatus, a battery diagnosis method, and a battery diagnosis system that can diagnose a battery in other aspects to solve a problem of time-domain-based battery diagnosis.

[0008] Technical problems of embodiments disclosed herein are not limited to the above-mentioned technical problems, and other unmentioned technical problems will be clearly understood by those skilled in the art from the following description.

[0009] TECHNICAL SOLUTION

[0010] According to some embodiments disclosed herein, a battery diagnostic apparatus includes: a sensor configured to measure time-based first battery data from a target battery for diagnosis; 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 target battery for diagnosis is abnormal based on statistical data associated with the second battery data.

[0011] According to some implementations, the controller may also be configured to calculate a statistical value of the amplitude value of the second battery data corresponding to the frequency value of the battery cell of the diagnostic target battery and the second battery data; and to diagnose whether the diagnostic target battery is abnormal based on the statistical value.

[0012] According to some implementations, the controller may also be configured to calculate an error value of the amplitude value based on the statistical value; and to diagnose whether the target battery is abnormal based on the number of error values ​​that exceed the error reference value.

[0013] According to some implementations, the controller may also be configured to calculate the median of the amplitude values ​​for each of the frequency values ​​to calculate a median corresponding to the frequency values; and to calculate an error value of the amplitude values ​​based on the median.

[0014] According to some implementations, the controller may also be configured to calculate the average and variance of the error values ​​for each battery cell; and to diagnose whether each battery cell is abnormal based on the average and variance of the error values.

[0015] According to some implementations, the controller may also be configured to count the number of frequency values ​​in the frequency values ​​whose error values ​​exceed an error reference value for each battery cell; and to identify battery cells in the battery cells whose number of frequency values ​​whose error values ​​exceed the error reference value exceeds a quantity reference value as abnormal battery cells.

[0016] According to some implementations, the controller may also be configured to identify the abnormal battery cells for each test cycle, count the number of abnormal battery cells, and identify the diagnostic target battery as an abnormal battery when the number of abnormal battery cells exceeds a reference value for abnormal battery cells after a preset number of test cycles have been completed.

[0017] According to some implementations, the controller may also be configured to perform time aggregation preprocessing on the first battery data to generate time-uniform data; and to perform missing value imputation preprocessing on the time-uniform data to generate the preprocessed data.

[0018] According to some embodiments disclosed herein, a battery diagnostic method includes the following steps: measuring time-based first battery data from a target battery for diagnosis; preprocessing the first battery data to generate preprocessed data; generating frequency-based second battery data based on the preprocessed data; and diagnosing whether the target battery for diagnosis is abnormal based on statistical data associated with the second battery data.

[0019] According to some implementations, the step of diagnosing whether the target battery is abnormal may include the following steps: calculating a statistical value of the amplitude value of the second battery data corresponding to the frequency value of the battery cell and the second battery data of the target battery; and diagnosing whether the target battery is abnormal based on the statistical value.

[0020] According to some implementations, the step of diagnosing whether the target battery is abnormal may include the following steps: calculating an error value of the amplitude value based on the statistical value; and diagnosing whether the target battery is abnormal based on the number of error values ​​that exceed the error reference value.

[0021] According to some implementations, the step of calculating the error value may include the following steps: calculating the median of the amplitude values ​​for each of the frequency values ​​to calculate the median corresponding to the frequency values; and calculating the error value of the amplitude values ​​based on the median.

[0022] According to some implementations, the step of diagnosing whether the target battery is abnormal may include the following steps: calculating the average and variance of the error value for each battery cell; and diagnosing whether each battery cell is abnormal based on the average and variance of the error value.

[0023] According to some implementations, the step of diagnosing whether the target battery is abnormal may include the following steps: for each battery cell, counting the number of frequency values ​​whose error value exceeds an error reference value; and identifying battery cells whose number of frequency values ​​whose error value exceeds the error reference value exceeds a quantity reference value as abnormal battery cells.

[0024] According to some implementations, the step of diagnosing whether the target battery is abnormal may include the following steps: identifying the abnormal battery cells for each test cycle to count the number of abnormal battery cells; and determining the target battery as an abnormal battery when the number of abnormal battery cells exceeds a reference value for abnormal battery cells after a preset number of test cycles have been completed.

[0025] According to some implementations, the step of generating the preprocessed data may include the following steps: performing time aggregation preprocessing on the first battery data to generate time-uniform data; and performing missing value imputation preprocessing on the time-uniform data to generate the preprocessed data.

[0026] According to some embodiments disclosed herein, a battery diagnostic system includes: a target battery for diagnosis; a charger / discharger configured to apply a test cycle voltage to the target battery for diagnosis; a battery diagnostic device configured to measure time-based first battery data from the target battery for diagnosis, preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the target battery for diagnosis is abnormal based on statistical data associated with the second battery data; and a management server configured to manage the diagnostic results of the target battery for diagnosis.

[0027] According to some embodiments, the battery diagnostic device may also be configured to calculate a statistical value of the amplitude value of the second battery data corresponding to the frequency value of the battery cell of the target battery and the second battery data; and to diagnose whether the target battery is abnormal based on the statistical value.

[0028] According to some implementations, the battery diagnostic device may also be configured to calculate an error value of the amplitude value based on the statistical value; and to diagnose whether the target battery is abnormal based on the number of error values ​​that exceed the error reference value.

[0029] According to some embodiments, the battery diagnostic device may also be configured to calculate the median of the amplitude values ​​for each of the frequency values ​​to calculate a median corresponding to the frequency values; and to calculate an error value of the amplitude values ​​based on the median.

[0030] According to some implementations, the battery diagnostic device may also be configured to calculate the average and variance of the error values ​​for each battery cell; and to diagnose whether each battery cell is abnormal based on the average and variance of the error values.

[0031] According to some embodiments, the battery diagnostic device may also be configured to, for each battery cell, count the number of frequency values ​​in which the error value exceeds an error reference value; and identify battery cells in which the number of frequency values ​​in which the error value exceeds the error reference value exceeds a quantity reference value as abnormal battery cells.

[0032] According to some embodiments, the battery diagnostic device may also be configured to identify the abnormal battery cells for each test cycle, to count the number of abnormal battery cells; and to identify the target battery as an abnormal battery when the number of abnormal battery cells exceeds a reference value for abnormal battery cells after a preset number of test cycles have been completed.

[0033] According to some embodiments, the battery diagnostic device may also be configured to perform time aggregation preprocessing on the first battery data to generate time-uniform data; and to perform missing value imputation preprocessing on the time-uniform data to generate the preprocessed data.

[0034] Beneficial effects

[0035] According to the embodiments disclosed herein, a battery diagnostic device, a battery diagnostic method, and a battery diagnostic system can be provided, which can diagnose batteries from other aspects to solve time-domain-based battery diagnostic problems.

[0036] The technical effects of the embodiments disclosed in this document are not limited to the effects described above, and other effects not mentioned will be clearly understood by those skilled in the art based on the disclosure of this document. Attached Figure Description

[0037] Figure 1 Components of a battery diagnostic system according to some embodiments are shown.

[0038] Figure 2 The components of a battery diagnostic device according to some embodiments and the operation of the battery diagnostic device are shown.

[0039] Figure 3 The detailed operation process of a battery diagnostic device according to some embodiments is shown.

[0040] Figure 4 The process of generating preprocessed data by performing preprocessing on first battery data is shown according to some embodiments.

[0041] Figure 5 The process of generating frequency-based second battery data by performing domain transformation on preprocessed data, according to some embodiments, is illustrated.

[0042] Figure 6 The amplitude values ​​A for battery cells C1, ..., Cn and frequency values ​​f1, ..., fm are shown according to some embodiments. 11 A nm .

[0043] Figure 7The process of calculating the median Mj of amplitude values ​​A1j, ..., Anj for each frequency value fj is shown according to some implementations.

[0044] Figure 8 The process of calculating the error values ​​E1j, ..., Enj of amplitude values ​​A1j, ..., Anj based on the median Mj according to some implementations is shown.

[0045] Figure 9 The process of calculating the error reference value (xi1 + 4*xi2) for each battery cell ci according to some implementations is shown.

[0046] Figure 10 The process of filtering battery cells C1, ..., Cn from those with a frequency value exceeding an error reference value that is greater than a quantity reference value, according to some embodiments, is illustrated.

[0047] Figure 11 The process of identifying abnormal battery cells for each test cycle and counting the number of abnormal cells is shown according to some embodiments.

[0048] Figure 12 This illustrates a process, according to some embodiments, for determining whether a target battery is abnormal based on the number of abnormal cells when a preset number of test cycles have been completed.

[0049] Figure 13 The operation of a battery diagnostic method according to some embodiments is shown. Detailed Implementation

[0050] In the following description, embodiments disclosed herein will be described with reference to the accompanying drawings. However, this description is not intended to limit the disclosure of this document to the specific embodiments, and it should be construed as including various modifications, equivalents, and / or substitutions to the embodiments described herein.

[0051] 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 modifications, 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 the singular form of the noun corresponding to an item may include one or more things, unless the relevant context clearly indicates otherwise.

[0052] As used herein, each of 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” can include any one or all possible combinations of the items listed together in the corresponding phrase. Unless otherwise stated, phrases such as “1” st “2” nd Terms such as “first”, “second”, “A”, “B”, “(a)” or “(b)” can be used to simply distinguish one part from another and do not limit the parts in other respects (e.g., importance or order).

[0053] In this document, it should be understood that, with or without the terms “operably” or “communically”, 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), it means that the element can be connected to the other element directly (e.g., wired or wirelessly) or indirectly (e.g., via a third element).

[0054] Methods according to the various embodiments disclosed herein 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. If distributed online, at least a portion of the computer program product can be temporarily generated or at least temporarily stored in a machine-readable storage medium, such as the memory of a manufacturer's server, an app store's server, or a relay server.

[0055] According to the embodiments disclosed herein, each of the above-described components (e.g., a module or program) may include a single entity or multiple entities, and some of the multiple entities may be separately located in different components. According to various embodiments disclosed herein, one or more of the above-described components may be omitted, or one or more other components may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In this case, according to various embodiments, the integrated component may still perform one or more functions of each of the multiple components in the same or similar manner as performed by a corresponding component of the multiple components prior to integration. According to the embodiments disclosed herein, operations performed by a module, program, or other component may be performed sequentially, in parallel, repeatedly, or heuristically, or one or more operations may be performed in a different order or omitted, or one or more other operations may be added.

[0056] Figure 1 Components of a battery diagnostic system according to some embodiments are shown.

[0057] Reference Figure 1 The battery diagnostic system 100 may include a charger / discharger 110, a target battery 120 for inspection, a battery diagnostic device 130, and a management server 140. However, it is not limited to this; some components may be omitted from the battery diagnostic system 100, or other common components may be further included in the battery diagnostic system 100.

[0058] The battery diagnostic system 100 can refer to a system used to diagnose the condition of a target battery 120. According to an embodiment, a test voltage can be applied to the target battery 120 by a charger / discharger 110, and the time-domain signal output by the target battery 120 in response to the test voltage can be measured by the battery diagnostic device 130. The time-domain signal can be converted into frequency-domain data, and it can be determined whether the target battery 120 is abnormal based on the frequency-domain data.

[0059] According to one embodiment, the charger / discharger 110 can be configured to apply a test cycle voltage to the diagnostic target battery 120. The test cycle voltage can have a profile for charging and discharging the diagnostic target battery 120 and can be applied to the diagnostic target battery 120 multiple times. The number of times the test cycle voltage is repeatedly applied can be set to 50, 100, 200, or other suitable values. For example, the charger / discharger 110 may include a power supply device for applying various types of voltage or current to the diagnostic target battery 120.

[0060] According to an embodiment, the target battery 120 for diagnosis can be the diagnostic target of the battery diagnostic system 100. The target battery 120 for diagnosis may include multiple battery cells. For example, the target battery 120 for diagnosis may include multiple battery modules, and each of the multiple battery modules may include multiple battery cells. According to an embodiment, when the number of faulty battery cells in each of the multiple test cycles is greater than or equal to a specific number, the corresponding cycle can be counted as a faulty cycle, and when the count of faulty cycles is greater than or equal to a specific value when the multiple test cycles are completed, it can be finally determined that the target battery 120 for diagnosis is abnormal.

[0061] The battery diagnostic device 130 can perform a process for determining whether the target battery 120 is abnormal. According to one embodiment, the battery diagnostic device 130 can convert data in the time domain to data in the frequency domain and perform additional processing on the frequency domain data to determine abnormalities.

[0062] According to an implementation, the management server 140 can be configured to manage the diagnostic results of the target battery 120. The management server 140 can be connected to the battery diagnostic device 130 via wired / wireless data communication, and can provide and record data such as the status, anomalies, and diagnostic results of the target battery 120 from the battery diagnostic device 130. The management server 140 can control the battery diagnostic device 130 or manage the target battery 120 upon request from a system administrator or battery user.

[0063] According to one embodiment, the management server 140 can perform at least some of the processes executed by the battery diagnostic device 130 on behalf of the battery diagnostic device 130. The management server 140 can receive data from the battery diagnostic device 130 required for diagnosing the target battery 120, perform the diagnostic process, and send the results to the battery diagnostic device 130. According to another embodiment, the management server 140 can install energy management software required for diagnosing the target battery 120 on the battery diagnostic device 130 and provide update information for the energy management software to the battery diagnostic device 130.

[0064] Figure 2 The components of a battery diagnostic device according to some embodiments and the operation of the battery diagnostic device are shown.

[0065] Reference Figure 2 The battery diagnostic device 130 may include a sensor 131 and a controller 132. However, it is not limited to this; some components may be omitted from the battery diagnostic device 130, or other common components may be further included in the battery diagnostic device 130.

[0066] According to an embodiment, in the battery diagnostic device 130, the sensor 131 and the controller 132 can be electrically connected to each other through communication between devices such as a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), mobile industrial processor interface (MIPI).

[0067] The sensor 131 of the battery diagnostic device 130 can be configured to measure time-based first battery data from the target battery 120. When the charger / discharger 110 applies a test cycle voltage to the target battery 120, an output voltage can be generated in the target battery 120 in response to the test cycle voltage, and the sensor 131 can measure this output voltage to generate the first battery data. For this purpose, the sensor 131 may include measuring devices such as a voltmeter, ammeter, thermometer, etc. According to embodiments, the first battery data may include signals in the time domain. For example, the first battery data may include voltage data, current data, and / or temperature data in the time domain.

[0068] The controller 132 may have a structure for executing instructions that implement the operations of the battery diagnostic device 130. The controller 132 may be implemented using multiple logic gate arrays or a general-purpose microprocessor for handling various operations, and may include a single processor or multiple processors. For example, the controller 132 may be implemented in the form of at least one of a microprocessor, CPU, GPU, and AP.

[0069] The controller 132 can be configured separately from or integrated with a memory (not shown) configured to store instructions, and can perform various operations by executing instructions stored in the memory. The memory can store various types of data, instructions, mobile applications, computer programs, etc. For example, the memory can be implemented as non-volatile memory such as ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, FRAM, etc., or as volatile memory such as DRAM, SRAM, SDRAM, RRAM, HDD, SSD, SD, microSD, etc., or a combination thereof.

[0070] The controller 132 of the battery diagnostic device 130 can be configured to preprocess first battery data and generate preprocessed data. The preprocessing performed on the first battery data may include a preparation process for converting the first battery data in the time domain into second battery data in the frequency domain. For example, preprocessing may include equalizing the data relative to time and / or filling in missing values.

[0071] The controller 132 of the battery diagnostic device 130 can be configured to generate frequency-based second battery data based on preprocessed data. To generate the second battery data, the domain transformation performed on the preprocessed data can mean converting a signal / data in the time domain into a signal / data in the frequency domain. Depending on the implementation, the domain transformation may include a Fast Fourier Transform (FFT). The second battery data may include amplitude values ​​that vary with a frequency value, which may be continuous or discrete.

[0072] The controller 132 of the battery diagnostic device 130 can be configured to diagnose whether the target battery 120 is abnormal based on statistical data associated with second battery data. The second battery data can indicate the amplitude values ​​of the battery cells of the target battery 120 within a given frequency range, and whether the target battery 120 is abnormal can be determined by using statistical variables based on the amplitude values. According to embodiments, the statistical data associated with the second battery data may include the mean, median, first quartile, third quartile, variance, standard deviation, etc., of the values ​​for the second battery data.

[0073] Figure 3 The detailed operation process of a battery diagnostic device according to some embodiments is shown.

[0074] Reference Figure 3 The detailed operation process of the battery diagnostic device 130 may include a first operation 310 for data measurement, a second operation 320 for time aggregation, a third operation 330 for missing value interpolation, a fourth operation 340 for domain transformation, a fifth operation 350 for statistical value calculation, and a sixth operation 360 for anomaly diagnosis.

[0075] Unlike existing diagnostic schemes that use time-based signals, operations 310 to 360 can diagnose whether the target battery 120 is abnormal by using frequency-based data. While existing time-based schemes utilize methods to detect instantaneous differences in signals, the frequency-based schemes according to operations 310 to 360 can detect frequency components of the signal hidden by noise. Based on these frequency components, cells exhibiting patterns outside the normal range can be detected. For example, considering that battery cells have similar characteristics, an abnormal cell may have a frequency pattern different from that of other cells. Therefore, by defining the normal range based on statistical values, the continuous occurrence of abnormal patterns can be detected even after noise cancellation.

[0076] Figure 4 The process of generating preprocessed data by performing preprocessing on first battery data is shown according to some embodiments.

[0077] Reference Figure 4 The graph can show a first graph 410 indicating the waveform of the first battery measured in the time domain, a second graph 420 indicating the waveform of time-uniform data generated as a result of time aggregation preprocessing of the first battery data, and a third graph 430 indicating the waveform of preprocessed data generated as a result of missing value interpolation preprocessing of the time-uniform data.

[0078] The first graph 410, the second graph 420, and the third graph 430 can all indicate the voltage along the vertical axis relative to time along the horizontal axis, such that the first battery data, the time-uniform data, and the preprocessed data can all be data in the time domain. The first battery data, the time-uniform data, and the preprocessed data can all be periodic, with one period corresponding to a test cycle.

[0079] According to one embodiment, the controller 132 of the battery diagnostic device 130 can be configured to perform time aggregation preprocessing on the first battery data to generate time-uniform data, and to perform missing value imputation preprocessing on the time-uniform data to generate preprocessed data. According to the time aggregation preprocessing, the values ​​of the first battery data can be adjusted time-uniformly, and thus, time-uniform data can be generated. According to the missing value imputation preprocessing, when there are measurement missing values ​​in the time-uniform data based on a given time step, the missing values ​​can be imputed by polarization.

[0080] Figure 5 The process of generating frequency-based second battery data by performing domain transformation on preprocessed data, according to some embodiments, is illustrated.

[0081] Reference Figure 5 A third graph 430 can be shown indicating the waveform of the preprocessed data, and a fourth graph 510 can be shown indicating the second battery data in the frequency domain generated due to the domain transformation of the preprocessed data. The fourth graph 510 can show the amplitude value along the vertical axis relative to the frequency along the horizontal axis.

[0082] When a domain transformation is performed on the time-based preprocessed data shown in the third chart 430, the frequency-based second battery data shown in the fourth chart 510 can be generated. According to an implementation, the domain transformation performed on the preprocessed data to generate the second battery data may include a Fast Fourier Transform (FFT) or other various types of time-frequency transformations.

[0083] Figure 6 The amplitude values ​​A for battery cells C1, ..., Cn and frequency values ​​f1, ..., fm are shown according to some embodiments. 11 Anm .

[0084] Reference Figure 6 The first to third charts 610, 620 and 630 may be shown indicating 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 diagnostic target battery 120, respectively.

[0085] The second battery data shown in the first to third charts 610, 620 and 630 can be reconfigured to resemble the amplitude graph 640 of the frequency values ​​f1, ..., fm of the battery cells c1, ..., cn and the second battery data for the diagnostic target battery 120. When the diagnostic target battery 120 includes n battery cells c1, ..., cn and the second battery data includes m frequency values ​​f1, ..., fm, each battery cell c1 can have amplitude values ​​Ai1, ..., Aim for the m frequency values ​​f1, ..., fm.

[0086] According to an embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate the amplitude value A of the second battery data corresponding to the frequency values ​​f1, ..., fm of the battery cells c1, ..., cn of the target battery 120 and the second battery data. 11 A nm The statistical value is used to diagnose abnormalities. Amplitude value A 11 A nm The statistical values ​​can be derived in various ways. According to the implementation, in amplitude diagram 640, the statistical values ​​can be calculated for the same frequency values ​​in the vertical direction and for all battery cells, or the statistical values ​​can be calculated for the same battery cells in the horizontal direction and for all frequency values.

[0087] Figure 7 The process of calculating the median Mj of amplitude values ​​A1j, ..., Anj for each frequency value fj is shown according to some implementations.

[0088] Reference Figure 7 As an example of calculating statistical values ​​for all battery cells in the same frequency and amplitude diagram in the vertical direction, 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 shown.

[0089] Based on the calculation of the first statistical value 710, the amplitude A of the first battery cell c1 at the first frequency f1 can be calculated. 11 The amplitude A of the second battery cell c2 at the first frequency f1 21The amplitude A of the third battery cell c3 at the first frequency f1 31 The statistical values ​​can be the median. Depending on the implementation, the statistical values ​​may also include the mean, first quartile, third quartile, variance, standard deviation, etc. The statistical values ​​for all frequency values ​​f1, ..., fm can be calculated in the same manner as for the first frequency f1.

[0090] According to an embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate the median Mj of amplitude values ​​A1j, ..., Anj for each frequency value fj, to calculate the median M1, ..., Mm corresponding to the frequency values ​​f1, ..., fm, and to calculate the error values ​​E1j, ..., Enj of amplitude values ​​A1j, ..., Anj based on the median Mj. According to an embodiment, other types of representative values ​​such as the average, first quartile, third quartile, variance, standard deviation, etc., can be used instead of the median Mj.

[0091] Figure 8 The process of calculating the error values ​​E1j, ..., Enj of amplitude values ​​A1j, ..., Anj based on the median Mj according to some implementations is shown.

[0092] Reference Figure 8 The error calculation 810, which calculates the error value of the amplitude value of the battery cell for a frequency value in the amplitude diagram, can be shown with respect to the first statistical value 710.

[0093] In error calculation 810, the first median M1 calculated for the first frequency f1 can be used to calculate the amplitude value A. 11 The error can be used to calculate other amplitude values ​​A in the same way. 21 A 31 The error can be calculated using other representative values, such as the median. According to the implementation, the mean, first quartile, third quartile, variance, standard deviation, etc., can be used as representative values ​​for error calculation. According to the implementation, the difference between the median and the variance can be expressed as the error value for each frequency.

[0094] According to an embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate the error value of the amplitude value based on statistical values, and to diagnose anomalies based on the number of error values ​​that exceed an error reference value. When all error values ​​are calculated in the error calculation 810, the number of frequencies in each battery cell that exceed the error reference value can be analyzed. For example, for the first battery cell c1, the number of frequency values ​​among 100 frequency values ​​f1 to f100 that exceed the error reference value can be counted, and when the count value exceeds a quantity reference value (e.g., 10), the first battery cell c1 can be classified as an abnormal cell.

[0095] Figure 9 The process of calculating the error reference value (xi1 + 4*xi2) for each battery cell ci according to some implementations is shown.

[0096] Reference Figure 9 The error value E can be calculated in the first battery cell 910. 11 E 12 E 13 The average value x of ... 11 and variance x 12 And it can be based on the average value x 11 and variance x 12 The error reference value for the first battery cell 910 is calculated. Similarly, the error reference values ​​can be calculated for the second battery cell 920 and other battery cells.

[0097] According to an embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate the average value 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 value xi1 and variance xi2 of the error values ​​Ei1, ..., Eim. According to an embodiment, in addition to the average value and variance, other values ​​such as the median, first quartile, third quartile, variance, standard deviation, etc., can also be used to calculate the error reference value.

[0098] According to an embodiment, the controller 132 of the battery diagnostic device 130 can be configured to, for each battery cell ci, count the number of frequency values ​​fj in which the error value Eij exceeds the error reference value (xi1 + 4 * xi2) among the frequency values ​​f1, ..., fm, and determine the battery cells c1, ..., cn in which the number of frequency values ​​exceeding the error reference value exceeds a quantity reference value as abnormal battery cells. According to an embodiment, if necessary, the counts 1 and 4 of the error reference value (xi1 + 4 * xi2) can be changed to other appropriate values.

[0099] The quantity reference value can be a preset value based on the diagnostic performance requirements of the battery diagnostic device 130, and the diagnostic performance can increase as the number of reference values ​​decreases. For example, when the quantity reference value is set to 10 and the error value of 12 out of 100 frequency values ​​f1 to f100 exceeds the error reference value, the first battery cell c1 can be classified as a defective cell.

[0100] Figure 10 The process of filtering battery cells C1, ..., Cn from those with a frequency value exceeding an error reference value that is greater than a quantity reference value, according to some embodiments, is illustrated.

[0101] Reference Figure 10 If the number of frequencies in the first battery cell 1010 where the error exceeds the error reference value (e.g., 2) exceeds the quantity reference value (e.g., 1), the first battery cell 1010 can be classified as a defective cell.

[0102] Similarly, if the number of frequencies in the second battery cell 1020 where the error exceeds the error reference value (e.g., 1) does not exceed the quantity reference value (e.g., 1), the second battery cell 1020 can be classified as a normal cell, and other battery cells can also be classified as defective or normal. Therefore, at the end of a test cycle, the number of battery cells in the target battery 120 that are classified as defective can be derived.

[0103] Figure 11 The process of identifying abnormal battery cells for each test cycle and counting the number of abnormal cells is shown according to some embodiments.

[0104] Reference Figure 11 The process of counting the number of abnormal battery cells for each test cycle can be repeated for multiple test cycles.

[0105] For example, the number of battery cells classified as defective in test cycle 1 could be 3, the number of battery cells classified as defective in test cycle 2 could be 2, and the number of battery cells classified as defective in other test cycles could be 0. In this case, the cumulative count value 5 can be compared with a reference value to ultimately determine whether the target battery 120 is defective. According to an embodiment, the total number of test cycles can be 100, and the reference value to be compared with the cumulative count value can be 3. However, other appropriate values ​​can be used depending on the change in diagnostic performance requirements. According to an embodiment, when high diagnostic performance is required, the total number of test cycles can be increased, and the reference value to be compared can be decreased.

[0106] According to an embodiment, the controller 132 of the battery diagnostic device 130 can be configured to count the number of abnormal battery cells by identifying abnormal battery cells for each test cycle, and to determine that the target battery 120 is an abnormal battery when the number of abnormal battery cells exceeds a reference value for abnormal battery cells after a preset number of test cycles have been completed. For example, when the count value of the number of abnormal battery cells exceeds 3 after 100 test cycles, the target battery 120 can be finally diagnosed as abnormal.

[0107] Figure 12 This illustrates a process, according to some embodiments, for determining whether a target battery is abnormal based on the number of abnormal cells when a preset number of test cycles have been completed.

[0108] Reference Figure 12 The flowchart 1200 is shown to describe the change in the count value indicating the number of battery cells classified as defective when a preset number of test cycles are performed.

[0109] The count value can be identified in the first operation 1210, and compared with a reference value in the second operation 1220. When the count value exceeds the reference value, a notification indicating a diagnostic abnormality in the target battery 120 can be generated in the third operation 1230. When the count value does not exceed the reference value, it can be determined in the fourth operation 1240 whether a preset number of test cycles has been completed. When the preset number of test cycles has been completed, the count value can be initialized to 0 in the fifth operation 1250.

[0110] Unless a preset number of test cycles are not completed, it can be determined in the sixth operation 1260 whether the count value does not increase during a specific number (e.g., 5) of consecutive test cycles due to the absence of detected abnormal battery cells, such that if "yes", the total cumulative count value can be reduced by 1 in the seventh operation 1270, and if "no", the total cumulative count value can be maintained in the eighth operation 1280.

[0111] Regarding operations 1260 to 1280, if no abnormal battery cell is detected during a specific number (e.g., 5) consecutive test cycles, this may indicate that the target battery 120 is stable or that an abnormal battery cell was detected due to a temporary reason. Therefore, through such a calibration process, the accuracy of the battery diagnostic device 130 can be improved.

[0112] Figure 13 The operation of a battery diagnostic method according to some embodiments is shown.

[0113] Reference Figure 13The battery diagnostic method 1300 may include operations 1310 to 1340. However, it is not limited thereto; some operations may be omitted and other general operations may be added, and the operations of the battery diagnostic method 1300 may be performed in a different order than those shown.

[0114] Battery diagnostic method 1300 may include operations processed by battery diagnostic device 130 in a time sequence. Therefore, the matters described above with respect to battery diagnostic device 130, even if omitted below, can be equally applied to battery diagnostic method 1300.

[0115] Operations 1310 to 1340 of the battery diagnostic method 1300 can be performed by the sensor 131 and controller 132 of the battery diagnostic device 130.

[0116] In operation 1310, the battery diagnostic device 130 can use sensors to measure time-based first battery data from the target battery for diagnosis.

[0117] In operation 1320, the battery diagnostic device 130 can generate preprocessed data by the controller through preprocessing the first battery data.

[0118] In operation 1330, the battery diagnostic device 130 can generate frequency-based second battery data by the controller based on preprocessed data.

[0119] In operation 1340, the battery diagnostic device 130 can be used by the controller to diagnose whether the target battery is abnormal based on statistical data related to the second battery data.

[0120] Meanwhile, the battery diagnostic method 1300 can be implemented as a computer program stored in a computer-readable storage medium. That is, the computer program may include instructions for implementing the battery diagnostic method 1300, and the instructions may be stored in a computer-readable storage medium. The computer program may include a mobile application.

[0121] For example, computer-readable storage media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as optical disc read-only memory (CD-ROM) and digital versatile disc (DVD); magneto-optical media such as floppy disks; and hardware devices such as read-only memory (ROM), random access memory (RAM), and flash memory that are specifically configured to store and execute program instructions. Computer program instructions may include machine language code created by a compiler and high-level language code that can be executed by a computer using an interpreter.

[0122] Unless otherwise stated, terms such as “comprising,” “constituting,” or “having” above may mean that the corresponding component may be inherent and should therefore be interpreted as further including rather than excluding other components. 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 idealized or overly formal meaning unless they are clearly defined in this document.

[0123] The above description is merely an illustration of the technical concept of this disclosure, and those skilled in the art to which the embodiments disclosed herein pertain can make various modifications and variations without departing from the basic characteristics of the embodiments disclosed herein. Therefore, the embodiments disclosed herein are intended to describe, not limit, the technical spirit of the embodiments disclosed herein, and the scope of the technical spirit of this disclosure is not limited by these embodiments disclosed herein. The scope of protection of the technical spirit disclosed herein should be interpreted by the appended claims, and all technical spirit within the same scope should be understood to be included within the scope of this disclosure.

[0124] [Symbol Description]

[0125] 100: Battery diagnostic system 110: Charger / discharger

[0126] 120: Inspect the target battery 130: Battery diagnostic device

[0127] 131: Sensor 132: Controller

[0128] 140: Management Server

Claims

1. A battery diagnostic device, the battery diagnostic device comprising: A sensor configured to measure time-based first battery data from a target battery for diagnosis; as well as The controller is configured to: The first battery data is preprocessed to generate preprocessed data; Based on the preprocessed data, generate frequency-based second battery data; as well as The diagnostic target battery is diagnosed as abnormal based on statistical data related to the second battery data.

2. The battery diagnostic device according to claim 1, wherein, The controller is also configured to: Calculate the statistical value of the amplitude value of the second battery data corresponding to the frequency value of the battery cell and the second battery data of the target diagnostic battery; and The statistical values ​​are used to diagnose whether the target battery is abnormal.

3. The battery diagnostic device according to claim 2, wherein, The controller is also configured to: The error value of the amplitude value is calculated based on the statistical values; and The number of error values ​​that exceed the error reference value is used to diagnose whether the target battery is abnormal.

4. The battery diagnostic device according to claim 3, wherein, The controller is also configured to: For each of the frequency values, the median of the amplitude values ​​is calculated to calculate the median corresponding to the frequency value; and The error value of the amplitude value is calculated based on the median.

5. The battery diagnostic device according to claim 4, wherein, The controller is also configured to: Calculate the average and variance of the error values ​​for each battery cell; and The average and variance of the error values ​​are used to diagnose whether each battery cell is abnormal.

6. The battery diagnostic device according to claim 5, wherein, The controller is also configured to: For each battery cell, the number of frequency values ​​whose error value exceeds the error reference value is counted; and A battery cell whose frequency value exceeds the error reference value is identified as an abnormal battery cell if the number of frequency values ​​exceeds the quantity reference value.

7. The battery diagnostic device according to claim 6, wherein, The controller is also configured to: For each test cycle, the abnormal battery cells are identified to count their number; and When the number of abnormal battery cells exceeds the reference value for abnormal battery cells after completing a preset number of test cycles, the target battery for diagnosis is identified as an abnormal battery.

8. The battery diagnostic device according to claim 1, wherein, The controller is also configured to: Perform time aggregation preprocessing on the first battery data to generate time-uniform data; and Missing value imputation preprocessing is performed on the time-uniform data to generate the preprocessed data.

9. A battery diagnostic method, the battery diagnostic method comprising the following steps: Measure first battery data based on time from the target battery for diagnosis; The first battery data is preprocessed to generate preprocessed data; Based on the preprocessed data, generate frequency-based second battery data; as well as The diagnostic target battery is diagnosed as abnormal based on statistical data related to the second battery data.

10. The battery diagnostic method according to claim 9, wherein, The steps for diagnosing whether the target battery is abnormal include the following: Calculate the statistical value of the amplitude value of the second battery data corresponding to the frequency value of the battery cell and the second battery data of the diagnostic target battery; and The statistical values ​​are used to diagnose whether the target battery is abnormal.

11. The battery diagnostic method according to claim 10, wherein, The steps for diagnosing whether the target battery is abnormal include the following: The error value of the amplitude value is calculated based on the statistical values; and The number of error values ​​that exceed the error reference value is used to diagnose whether the target battery is abnormal.

12. The battery diagnostic method according to claim 11, wherein, The steps for calculating the error value include the following: For each of the frequency values, the median of the amplitude values ​​is calculated to calculate the median corresponding to the frequency value; and The error value of the amplitude value is calculated based on the median.

13. The battery diagnostic method according to claim 12, wherein, The steps for diagnosing whether the target battery is abnormal include the following: Calculate the average and variance of the error values ​​for each battery cell; and The average and variance of the error values ​​are used to diagnose whether each battery cell is abnormal.

14. The battery diagnostic method according to claim 13, wherein, The steps for diagnosing whether the target battery is abnormal include the following: For each battery cell, the number of frequency values ​​whose error value exceeds the error reference value is counted; and A battery cell whose frequency value exceeds the error reference value is identified as an abnormal battery cell if the number of frequency values ​​exceeds the quantity reference value.

15. The battery diagnostic method according to claim 14, wherein, The steps for diagnosing whether the target battery is abnormal include the following: For each test cycle, the abnormal battery cells are identified to count their number; and When the number of abnormal battery cells exceeds the reference value for abnormal battery cells after completing a preset number of test cycles, the target battery for diagnosis is identified as an abnormal battery.

16. The battery diagnostic method according to claim 9, wherein, The steps for generating the preprocessed data include the following: Perform time aggregation preprocessing on the first battery data to generate time-uniform data; and Missing value imputation preprocessing is performed on the time-uniform data to generate the preprocessed data.

17. A battery diagnostic system, the battery diagnostic system comprising: Diagnose the target battery; A charger / discharger configured to apply a test cycle voltage to the diagnostic target battery; A battery diagnostic device is configured to measure time-based first battery data from a target battery, preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the target battery is abnormal based on statistical data related to the second battery data. as well as A management server configured to manage the diagnostic results of the target battery.

18. The battery diagnostic system according to claim 17, wherein, The battery diagnostic device is also configured to: Calculate the statistical value of the amplitude value of the second battery data corresponding to the frequency value of the battery cell and the second battery data of the diagnostic target battery; and The statistical values ​​are used to diagnose whether the target battery is abnormal.

19. The battery diagnostic system according to claim 18, wherein, The battery diagnostic device is also configured to: The error value of the amplitude value is calculated based on the statistical values; and The number of error values ​​that exceed the error reference value is used to diagnose whether the target battery is abnormal.

20. The battery diagnostic system according to claim 19, wherein, The battery diagnostic device is also configured to: For each of the frequency values, the median of the amplitude values ​​is calculated to calculate the median corresponding to the frequency value; and The error value of the amplitude value is calculated based on the median.

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