Battery diagnostic apparatus and method of operating same

Abstract

According to an embodiment disclosed in this document, a battery diagnosis apparatus may include: an information obtaining unit for obtaining time series data of a voltage of each of a plurality of battery cells; and a controller for calculating a moving average value and a standard deviation for a standard unit from the time series data of each of the battery cells, and diagnosing whether each of the battery cells is abnormal based on the moving average value and the standard deviation.

Description

Technical Field

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0063907, filed with the Korean Intellectual Property Office on May 17, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to battery diagnostic equipment and its operating methods. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively underway. Here, rechargeable batteries, as rechargeable batteries, can include all conventional nickel (Ni) / cadmium (Cd) batteries, nickel / metal hydride (MH) batteries, and more recently, lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have a significantly higher energy density compared to conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured to be small and lightweight, making them suitable for use as power sources in mobile devices. Recently, their applications have expanded to power electric vehicles, and they are attracting considerable attention as a next-generation energy storage medium.

[0005] When battery cells within a battery pack experience defects such as short circuits, negative electrode exposure, or lithium deposition, there is a risk of fire. This necessitates early detection of defective cells and proactive measures to prevent fires. Conventionally, defects such as short circuits or negative electrode exposure in battery cells may result in abnormal voltage behavior, allowing for detection and diagnosis on a battery pack basis. However, a diagnostic method is needed that can detect abnormal voltage behavior at the individual cell level. Summary of the Invention

[0006] Technical issues

[0007] The embodiments disclosed herein aim to provide a battery diagnostic device and its operating method, wherein abnormalities can be diagnosed on a per-cell basis within a battery pack.

[0008] The embodiments disclosed herein aim to provide a battery diagnostic device and its operating method, wherein battery cells can be accurately diagnosed by masking values ​​in a specific range when diagnosing battery cells.

[0009] The technical problems of the embodiments disclosed herein are not limited to those described above. Other unmentioned technical problems will be clearly understood by those skilled in the art from the following description.

[0010] Technical solutions

[0011] A battery diagnostic device according to an embodiment disclosed herein includes: an information acquisition unit configured to acquire time-series data of voltage for each of a plurality of battery cells; and a controller configured to calculate a moving average and standard deviation for a reference unit based on the time-series data of each battery cell, and to diagnose whether each battery cell is abnormal based on the moving average and standard deviation.

[0012] According to the implementation, the controller can also be configured to: normalize the voltage value at the current time point for each battery cell using the moving average and standard deviation at previous time points; and calculate the error rate based on the normalized voltage value for each battery cell.

[0013] According to the implementation, the controller can also be configured to calculate the error rate by applying an error function (erf) to the normalized voltage value.

[0014] According to the implementation, the controller can also be configured to determine battery cells whose absolute error rate exceeds a first value as defective.

[0015] According to the implementation method, the first value can be set to 0.65.

[0016] According to the implementation, the controller can also be configured to mask the error rate with 0 for intervals in the time series data where the rate of change of current is greater than a threshold.

[0017] According to the implementation, the controller can also be configured to: calculate the deviation of the error rate of each battery cell; and determine battery cells whose absolute value of the deviation exceeds a second value as defective.

[0018] According to the implementation method, the second value can be set to 0.25.

[0019] The battery diagnostic method according to the embodiments disclosed herein includes the following steps: obtaining time series data of voltage for each of a plurality of battery cells; calculating a moving average and standard deviation for a reference unit based on the time series data of each battery cell; and diagnosing whether each battery cell is abnormal based on the moving average and standard deviation.

[0020] According to the implementation, the step of diagnosing whether each battery cell is abnormal may include the following steps: normalizing the voltage value at the current time point for each battery cell using the moving average and standard deviation at previous time points; and calculating the error rate based on the normalized voltage value for each battery cell.

[0021] According to the implementation method, the step of diagnosing whether each battery cell is abnormal may further include the following steps: determining battery cells whose absolute value of error rate exceeds a first value as defective.

[0022] According to the implementation method, the step of calculating the error rate may include the following steps: calculating the error rate by applying an error function (erf) to a normalized voltage value.

[0023] According to the implementation method, the step of calculating the error rate may include the following steps: for the interval in the time series data where the rate of change of current is greater than a threshold, the error rate is masked with 0.

[0024] According to the implementation method, the step of diagnosing whether each battery cell is abnormal may further include the following steps: calculating the deviation of the error rate of each battery cell; and identifying battery cells whose absolute value of the deviation exceeds a second value as defective.

[0025] Beneficial effects

[0026] The battery diagnostic device and its operating method according to the embodiments disclosed herein can diagnose abnormalities on a per-cell basis.

[0027] The battery diagnostic device and its operating method according to the embodiments disclosed herein can perform diagnostics on a per-cell basis or on a per-pack basis, thereby improving the accuracy and efficiency of the diagnostics.

[0028] In addition, it can provide various effects that are directly or indirectly derived from publicly available content. Attached Figure Description

[0029] Figure 1 This is a block diagram illustrating the configuration of a typical battery pack.

[0030] Figure 2 This is a block diagram of a battery diagnostic device according to the embodiments disclosed herein.

[0031] Figure 3a An example of time-series data on voltage for each of multiple battery cells is shown.

[0032] Figures 3b to 3d An example of the error rate calculated for a battery cell is shown.

[0033] Figure 4 An example of the error rate deviation of a battery cell is shown.

[0034] Figure 5 This is a flowchart of a battery diagnostic method according to the embodiments disclosed herein.

[0035] Figure 6This is a flowchart illustrating in detail the battery diagnostic process according to the embodiments disclosed herein.

[0036] Figure 7 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a battery management device according to an embodiment disclosed herein. Detailed Implementation

[0037] In the following description, various embodiments of the present disclosure will be disclosed with reference to the accompanying drawings. However, the description is not intended to limit the present disclosure to specific embodiments and should be construed as including various modifications, equivalents, and / or substitutions of embodiments according to the present disclosure.

[0038] Here it should be understood that, unless the relevant context explicitly states otherwise, the singular form of the noun corresponding to an item may include one or more of that thing. 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” may include any one or all possible combinations of items listed together in the corresponding phrase. Terms such as “first,” “second,” or “first,” “second” may be used to simply distinguish one component from another without otherwise limiting the components (e.g., importance or order). It should be understood that if an element (e.g., the first element) is referred to as being “connected,” “linked to,” “connected to,” or “attached to” another element (e.g., the second element), whether or not the terms “operationally” or “communically” are used, it means that the element can be connected to the other element directly (e.g., wired), wirelessly, or via a third element.

[0039] Each of the components described above (e.g., a module or program) may include a single entity or multiple entities. According to various implementations, one or more 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 implementations, the integrated component may still perform one or more functions of each of the multiple components in the same or similar manner as the corresponding components of the multiple components performed functions prior to integration. According to various implementations, operations performed by a module, program, or other component may be performed sequentially, in parallel, repeatedly, or heuristically, or may be performed in a different order, or one or more operations may be omitted, or one or more other operations may be added.

[0040] As used herein, the terms "module" or "unit" can include units implemented in hardware, software, or firmware, and are used interchangeably with other terms such as "logic," "logic block," "component," or "circuit." A module can be a single integrated component suitable for performing one or more functions, or it can be its smallest unit or component. For example, depending on the implementation, a module can be implemented as an application-specific integrated circuit (ASIC).

[0041] Various implementations of this document can be implemented as software (e.g., a program or application) comprising one or more instructions stored in a machine-readable storage medium (e.g., memory). For example, a machine's processor can invoke at least one of the one or more instructions stored in the storage medium and execute that instruction with or without one or more other components under the processor's control. This allows the machine to operate according to the invoked at least one instruction to perform at least one function. The one or more instructions may contain compiler-generated code or interpreter-executable code. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. The term "non-transitory" simply means that the storage medium is a tangible device and does not include signals (e.g., electromagnetic waves), but this term does not distinguish between cases where data is semi-permanently stored in the storage medium and cases where data is temporarily stored in the storage medium.

[0042] Figure 1 This is a block diagram illustrating the configuration of a typical battery pack.

[0043] Reference Figure 1 The illustration schematically shows a battery control system according to an embodiment of the present disclosure, including a battery pack 1 and an advanced controller 2 included in an advanced system.

[0044] like Figure 1 As shown, the battery pack 1 may include: a plurality of battery cells 10, each comprising one or more battery cells, and capable of charging and discharging; a switching unit 14, connected in series with the positive (+) terminal or negative (-) terminal of the plurality of battery cells 10 to control the current flow during charging and discharging of the plurality of battery cells 10; and a battery management system 20, used to control and manage the battery pack 1 by monitoring its voltage, current, temperature, etc., to prevent overcharging and over-discharging. The battery pack 1 may include a plurality of battery cells 10, a sensor 12, a switching unit 14, and a plurality of battery management systems 20.

[0045] Here, the switching unit 14, which is used as an element for controlling the current flow of charging and discharging of multiple battery cells 10, can be, for example, at least one relay, magnetic contactor, etc., depending on the specifications of the battery pack 1.

[0046] The battery management system 20, serving as an interface for receiving measured values ​​of the aforementioned various parameters, may include multiple terminals and circuitry connected thereto for processing input values. The battery management system 20 can control the opening and closing of the switching unit 14 (e.g., a relay, contactor, etc.) and can be connected to multiple battery cells 10 to monitor the state of each battery cell 10. According to an embodiment, the battery management system 20 may include... Figure 2 The battery management device 100. According to another embodiment, the battery management system 20 may be different from... Figure 2 Battery management device 100. That is to say, Figure 2 The battery management device 100 may be included in the battery pack 1, or may be configured as another device outside the battery pack 1. The following operations of the battery management device 100 may also be performed in various devices, such as not only in the battery management system (BMS) in a vehicle, but also in servers, the cloud, chargers, chargers / dischargers, etc.

[0047] The advanced controller 2 can send control signals to the battery management system 20 regarding the multiple battery cells 10. Therefore, in terms of the operation of the battery management system 20, the battery management system 20 can also be controlled based on the signals applied from the advanced controller 2.

[0048] Figure 2 This is a block diagram of a battery diagnostic device according to the embodiments disclosed herein.

[0049] Reference Figure 2 The battery diagnostic device 100 according to the embodiments disclosed herein may include an information acquisition unit 110 and a controller 120. Depending on the embodiment, the battery diagnostic device 100 may include... Figure 1 In the battery management system 20, or it may be different Figure 1 Another device of the battery management system 20.

[0050] The battery diagnostic device 100 can analyze time-series data of voltage for each of the multiple battery cells included in the battery pack 1, thereby diagnosing voltage anomalies on a per-cell basis. The battery diagnostic device 100 can diagnose whether a battery cell is abnormal by determining the anomalousness of its voltage behavior.

[0051] Information acquisition unit 110 can acquire time-series data on the voltage of each of a plurality of battery cells. For example, information acquisition unit 110 can acquire time-series data by acquiring voltage values ​​for each battery cell at defined time intervals. For example, information acquisition unit 100 can acquire time-series data on m voltage values ​​obtained at m time points by acquiring the voltage of each battery cell at defined time intervals. In addition to the voltage of each battery cell, the information acquisition unit 110 can also acquire cell-related data, such as current and temperature.

[0052] The controller 120 can calculate the moving average and standard deviation relative to a reference unit based on the time-series data of each battery cell. The moving average can refer to the average calculated simultaneously from a subset of the moving time-series data of a specific size, where the size of the subset can refer to the number of data points belonging to that subset. For example, when the subset size is 5, the controller 120 can calculate the moving average of five adjacent data points in the time-series data of each battery cell. The moving average calculated at time point t can refer to the voltage data from time point (t-4) to time point t. The average value.

[0053] The reference unit can be a unit that indicates the size of the subset of time series data used to calculate the moving average. For example, when the size of the subset used to calculate the moving average is 5, the reference unit could be 5 seconds. In another example, when the size of the subset is 5 and the time series data is obtained at 1-second intervals, the reference unit could be 5 seconds. Similarly, the controller 120 can calculate the standard deviation relative to the reference unit.

[0054] For example, when the information acquisition unit 110 obtains the time series data of the voltage of a specific battery cell, When the time series data of the moving average and standard deviation are calculated, the controller 120 can use these data to calculate the moving average and standard deviation. For a reference unit of 5, the calculated moving average and standard deviation can be expressed as follows: and .

[0055] In this case, the moving average and standard deviation can satisfy the following formula.

[0056] [Formula 1]

[0057]

[0058] [Equation 2]

[0059]

[0060] in, and These can refer to the moving average and standard deviation at time point t, respectively. The term "n" can refer to the time series data item of the voltage at time point t, and "n" can refer to the reference unit. Therefore, the controller 120 can calculate the moving average and standard deviation, and examine the distribution, changes, and behavior of the moving average and standard deviation, thereby identifying the voltage distribution trend of each battery cell.

[0061] The controller 120 can diagnose whether each battery cell is abnormal based on moving averages and standard deviations. For example, when the change in the moving average or standard deviation of a particular cell at a certain point in time exceeds a threshold, the controller 120 can determine that the cell is defective or that more detailed analysis is needed. In another example, the controller 120 can calculate parameters for cell diagnosis (e.g., error rates described below) based on the moving averages and standard deviations, and diagnose whether the cell is abnormal based on the calculated parameters.

[0062] According to the implementation, the controller 120 can normalize the voltage value at the current time point for each battery cell using the moving average and standard deviation from previous time points. The controller 120 can normalize the voltage value of each item in the time series data using the moving average and standard deviation calculated at previous time points, thereby calculating a normalized voltage value. For example, the normalized voltage value can be expressed as... The controller 120 can normalize each item of the time series data, thereby identifying the distribution of voltage behavior by applying the characteristics of voltage behavior (moving average and standard deviation) at previous time points to each item of data.

[0063] According to the implementation, the controller 120 can calculate an error rate based on the normalized voltage value of each battery cell. The controller 120 can calculate the error rate based on the normalized voltage value, thereby quantifying and identifying the degree to which the voltage behavior of the cell deviates from a normal distribution. Compared to simple voltage value comparison, the controller 120 can normalize the voltage value of each item and calculate the error rate, thereby more clearly identifying whether a cell is abnormal and improving the accuracy of diagnosis.

[0064] According to one implementation, the controller 120 can calculate the error rate by applying an error function to a normalized voltage value. In this implementation, the error function can be expressed as... The error rate can be expressed as In this case, the error rate can have a range of [-1, 1], where the closer the error rate is to 0, the closer it is to the existing distribution, and the closer the error rate is to -1 or 1, the further it is from the existing distribution. This is just an example; the controller 120 can calculate error rates with different distributions and apply a different error function than the example above.

[0065] According to an implementation, controller 120 can determine that a battery cell whose absolute error rate exceeds a first value is defective. Controller 120 can determine that a cell is defective by identifying an anomaly occurring within the cell, as a larger absolute error rate may indicate a significant deviation of the cell's voltage behavior from its existing distribution. For example, controller 120 can determine that a particular battery cell is defective when the absolute error rate exceeds the first value at any given time. In another example, controller 120 can determine that a particular battery cell is defective when the absolute error rate of a particular cell exceeds the first value a preset number or more times.

[0066] The first value can be set statistically and experimentally, and can be set to clearly distinguish between normal and abnormal behavior of battery cell voltage. For example, the first value can be set to be larger than the maximum error rate that typically occurs in the normal behavior of battery cell voltage. For example, the first value can be set to 0.65.

[0067] In this way, the controller 120 can analyze the voltage behavior of each battery cell, thereby diagnosing whether a cell is abnormal individually. The controller 120 can analyze each battery cell individually to diagnose whether a battery cell is abnormal, and analyze the relative characteristics of each battery cell in the battery pack to diagnose whether a battery cell is abnormal.

[0068] According to the implementation method, the controller 120 can calculate the error rate deviation of each battery cell. For example, the controller 120 can calculate the average error rate of each battery cell at the same point in time, and calculate the error rate deviation based on the difference between the error rate of each battery cell and the average value.

[0069] According to the implementation, the controller 120 can determine that a battery cell whose absolute value of the error rate deviation exceeds a second value is defective. When the deviation of a particular battery cell is large in the same environment, the behavior of that battery cell is different from that of other battery cells, therefore the controller 120 can determine that the battery cell is defective.

[0070] This second value can be set statistically and experimentally. For example, it can be set to be larger than the relative deviation that is typically possible in the normal behavior of the battery cell voltage. For example, the second value can be set to 0.25.

[0071] In this way, the controller 120 can diagnose whether each battery cell is abnormal by relatively analyzing the battery cells in the battery pack 1. For example, when the voltage behavior of a battery cell deviates significantly from the previous behavior distribution, but all cells have the same voltage behavior, it may be inappropriate to identify only a specific cell as defective. In the same case, the controller 120 can relatively compare the battery cells to diagnose whether the battery cells are abnormal.

[0072] According to the implementation, when the number or percentage of battery cells identified as defective exceeds a preset number, the controller 120 can determine that the battery pack 1 is defective or requires precise diagnosis. For example, as a result of diagnosing each battery cell, when the number of battery cells identified as defective exceeds a preset percentage of the total number of battery cells contained in the battery pack 1, the controller 120 can identify the battery pack 1 as a target for precise diagnosis.

[0073] When a battery cell is identified as defective as a result of diagnosis, the controller 120 can provide the user with information about the defective battery cell. For example, the controller 120 can provide information about the defective battery cell to a user terminal via a communication unit (not shown), and can also provide information about the defective battery cell via a display installed in the vehicle, charger, etc.

[0074] According to the implementation, the controller 120 can mask the error rate with 0 for intervals in the time series data where the rate of change of current is greater than a threshold. That is, the controller 120 can perform masking by replacing the error rate calculated in the interval corresponding to the large current change of each battery cell with 0. The current of each battery cell can be obtained from the information acquisition unit 110. For example, when the rate of change of current of a battery cell is greater than a threshold at a time point in the time series, the controller 120 can mask the error rate calculated at that time point with 0. For example, in the charging / discharging interval of a battery cell, the voltage change of the battery cell may be large due to the rapid change in charging / discharging current. However, the large voltage change of the battery cell occurring in this case may correspond to normal behavior rather than abnormal behavior, and therefore may need to be distinguished from abnormal behavior corresponding to actual defects. Therefore, the controller 120 can mask the error rate with 0 in intervals where the rate of change of current of a battery cell is greater than a threshold, thereby preventing the battery cell from being identified as defective even if its voltage behavior is normal. Therefore, the battery diagnostic device 100 can prevent normal behavior from being misdiagnosed as abnormal behavior during the charging / discharging range.

[0075] In another embodiment, the controller 120 can perform diagnostics on the battery cell within a range where the rate of change of current in the battery cell is less than or equal to a predetermined level. For example, the controller 120 can... The controller 120 performs diagnostics on the battery cells within a certain range. For example, the controller 120 can perform the diagnostic process during the idle range of the battery pack 1. As described above, the controller 120 can perform the diagnostic process in a range where the voltage behavior is stable due to the low rate of change of current, such as during idle periods, to prevent abrupt voltage changes that occur in certain ranges based on charging / discharging modes, even if the voltage behavior of the battery cells is normal, from being misdiagnosed as abnormal behavior.

[0076] Figure 3a An example of time-series data on voltage for each of multiple battery cells is shown.

[0077] Reference Figure 3a A graph 310 shows time-series data of voltage for each battery cell. In graph 310, each line indicates a graph of the time-series data for each battery cell. The purpose of the battery diagnostic device 100 is to detect anomalies in voltage behavior occurring in a specific battery cell, such as… Figure 3a The part indicated by the circle.

[0078] Figures 3b to 3d An example of the error rate calculated for a battery cell is shown. More specifically, Figure 3c and Figure 3d They were enlarged and expressed respectively Figure 3b The intervals C_1 and C_2 in the given information.

[0079] Reference Figure 3b The graph 320 shows the error rate and the voltage of a specific battery cell. Figure 3b In the diagram, the x-axis example indicates time, the left y-axis example indicates voltage, and the right y-axis example indicates error rate. The battery diagnostic device 100 can determine battery cell anomalies based on the absolute value of the error rate calculated for each battery cell. For example, the battery diagnostic device 100 can detect points where the absolute value of the battery cell's error rate exceeds a first value.

[0080] The controller 120 can mask the error rate with 0 when the current change rate of the battery cell exceeds a threshold level. Figure 3b As can be seen from graph 330 regarding voltage, in the charging / discharging range where voltage changes rapidly (voltage behavior is normal, but the voltage change rate is also high due to the high current change rate), the error rate is treated as 0 in graph 320 regarding error rate.

[0081] Figure 3c and Figure 3d They were enlarged and expressed respectively Figure 3b The intervals C_1 and C_2. Figure 3c Graphs of error rates 350 and Figure 3d The graph of the error rate is 370. Figure 3b The graph of error rate is shown in section 320. Figure 3c Graph 340 and related to voltage Figure 3d The 360-degree graph of voltage is Figure 3b The graph of voltage, section 330. Figure 3c and Figure 3dIn the diagram, the x-axis indicates relative time to 0, which serves as the starting point for each of intervals C_1 and C_2. The battery diagnostic device 100 can detect errors where the absolute value of the error rate is greater than or equal to a threshold. Figure 3c Point P_1 and Figure 3d Point P_2. In this case, the threshold can be set, for example, between 0.4 and 0.65.

[0082] Figure 4 An example of the error rate deviation of a battery cell is shown.

[0083] Reference Figure 4 Graph 410 shows the voltage of each battery cell with respect to charging / discharging, and graph 420 shows the error rate deviation of each battery cell. Although Figure 4 The example shows eight battery cells, but the number of battery cells is not limited to this.

[0084] Since the voltage of each battery cell included in battery pack 1 varies according to the same charging / discharging mode, controller 120 can detect a specific cell with a large error rate deviation during this processing based on graph 420 regarding the error rate deviation. For example, battery diagnostic device 100 can detect point P_3, where the absolute value of the error rate deviation exceeds a second value. In this case, the second value can be set, for example, between 0.24 and 0.28.

[0085] The controller 120 can use 0 to mask the error rate in the range where the current change rate of the battery cell is greater than a threshold, so that as the error rate of each cell in this range is masked by 0, the error rate deviation is 0.

[0086] Figure 5 This is a flowchart of a battery diagnostic method according to an embodiment disclosed herein. According to the embodiment, Figure 5 The operation shown can be performed by Figure 2 The battery management device 100 is executed.

[0087] Reference Figure 5 The battery diagnostic method may include: operation S100, obtaining time series data of voltage for each of a plurality of battery cells; operation S200, calculating a moving average and standard deviation for a reference unit based on the time series data of each battery cell; and operation S300, diagnosing whether each battery cell is abnormal based on the moving average and standard deviation.

[0088] In operation S100, the information acquisition unit 110 can acquire time-series data on the voltage of each of the multiple battery cells.

[0089] In operation S200, controller 120 can calculate the moving average and standard deviation for a reference unit based on the time series data of each battery cell.

[0090] In operation S300, controller 120 can diagnose whether each battery cell is abnormal based on a moving average and standard deviation. For example, when the moving average and / or standard deviation are greater than a threshold, controller 120 can determine that the cell is defective. In another example, controller 120 can calculate parameters for cell diagnosis (e.g., error rate) based on the moving average and standard deviation, and diagnose whether the cell is abnormal based on the calculated parameters.

[0091] Figure 6 This is a flowchart illustrating in detail the battery diagnostic process according to the embodiments disclosed herein.

[0092] Reference Figure 6 In operation S310, the controller 120 can normalize the voltage value at the current time point for each battery cell using the moving average and standard deviation from previous time points. For example, at the current time point t, the controller 120 can use the moving average from time point (t-1). and standard deviation To adjust the voltage of the battery cells Normalization.

[0093] In operation S320, controller 120 can calculate the error rate based on the normalized voltage value of each battery cell. For example, controller 120 can calculate the error rate by applying an error function to the normalized voltage value.

[0094] In operation S330, controller 120 can determine whether the absolute value of the error rate of each battery cell exceeds a first value.

[0095] If the absolute value of the error rate exceeds the first value in operation S330 (yes), the controller 120 can determine that the corresponding battery cell is defective in operation S340. If the absolute value of the error rate is less than or equal to the first value in operation S330 (no), the controller 120 can determine that the corresponding battery cell is normal in operation S350.

[0096] In operation S360, controller 120 can calculate the error rate deviation of each battery cell.

[0097] In operation S370, controller 120 can determine whether the absolute value of the error rate deviation of each battery cell exceeds a second value.

[0098] When the absolute value of the error rate deviation exceeds the second value in operation S370 (yes), the controller 120 can determine that the corresponding battery cell is defective in operation S380. When the absolute value of the error rate is less than or equal to the first value in operation S370 (no), the controller 120 can determine that the corresponding battery cell is normal in operation S390.

[0099] The controller 120 can selectively, simultaneously or sequentially execute operations S330 and S360.

[0100] Figure 7 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a battery management device according to an embodiment disclosed herein.

[0101] Reference Figure 7 The computing system 1000 according to the embodiments disclosed herein may include an MCU 1010, a memory 1020, an input / output I / F 1030, and a communication I / F 1040.

[0102] The MCU 1010 can be a processor that executes various programs stored in the memory 1020 (e.g., battery cell SOC / OCV collection program, battery cell parameter calculation program, SOC-OCV group generation program, battery cell diagnostic program, etc.). These programs process various information, including battery cell SOC, OCV, and parameters, and execute... Figure 2 The battery management device shown includes a controller with the aforementioned functions.

[0103] The memory 1020 can store various programs such as battery cell SOC / OCV collection programs, battery cell parameter calculation programs, SOC-OCV group generation programs, and battery cell diagnostic programs. Furthermore, the memory 1020 can store various information such as battery cell SOC, OCV, and parameters.

[0104] Multiple memory modules 1020 can be configured as needed. Memory modules 1020 can be volatile or non-volatile. For memory modules 1020 used as volatile memory, random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), etc., can be used. For memory modules 1020 used as non-volatile memory, read-only memory (ROM), programmable ROM (PROM), electrically variable ROM (EAROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, etc., can be used. The examples of memory modules 1020 listed above are merely examples and are not limited to these.

[0105] The Input / Output I / F 1030 provides an interface for sending and receiving data by connecting input devices (not shown) such as a keyboard, mouse, or touchpad, and output devices such as a display (not shown) to the MCU 1010.

[0106] The communication I / F 1040, which serves as a component capable of sending and receiving various data with and from a server, can be any device capable of supporting wired or wireless communication. For example, a battery management device can use the communication I / F 1040 to send and receive various information, including battery cell SOC, OCV, parameters, etc., with a separately configured external server.

[0107] Thus, the computer program according to the embodiments disclosed herein can be recorded in the memory 1020 and processed by the MCU 1010, thereby being implemented to execute... Figure 2 The module that provides the indicated function.

[0108] Although all components constituting the embodiments disclosed herein have been described above as operating in combination or in combination, the embodiments disclosed herein are not necessarily limited to these embodiments. That is, within the scope of the purposes of the embodiments disclosed herein, all components may be operated by selectively combining them into one or more.

[0109] Furthermore, terms such as "comprising," "constituting," or "having" described above may indicate that the corresponding component may be inherent unless otherwise stated, and should therefore be interpreted as including other components rather than excluding them. Unless otherwise defined, all terms, including technical or scientific terms, shall have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Terms of ordinary use, as defined in a dictionary, shall be interpreted as having the same meaning as in the context of the relevant art and shall not be interpreted as having an ideal or overly formal meaning unless they are explicitly defined in this document.

[0110] The foregoing description is merely illustrative 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 essential 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. 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 document.

Claims

1. A battery diagnostic device, the battery diagnostic device comprising: An information acquisition unit is configured to acquire time-series data on voltage for each of a plurality of battery cells; as well as A controller configured to calculate a moving average and standard deviation for a reference unit based on the time-series data for each battery cell, and Based on the moving average and the standard deviation, diagnose whether each battery cell is abnormal.

2. The battery diagnostic device according to claim 1, wherein, The controller is also configured to: For each battery cell, the voltage value at the current time point is normalized using the moving average and standard deviation from previous time points; and The error rate is calculated based on the normalized voltage value for each battery cell.

3. The battery diagnostic device according to claim 2, wherein, The controller is also configured to calculate the error rate by applying an error function (erf) to the normalized voltage value.

4. The battery diagnostic device according to claim 2, wherein, The controller is also configured to identify battery cells whose absolute error rate exceeds a first value as defective.

5. The battery diagnostic device according to claim 4, wherein, The first value was set to 0.

65.

6. The battery diagnostic device according to claim 2, wherein, The controller is also configured to mask the error rate with 0 for the interval where the rate of change of the current in the time series data is greater than a threshold.

7. The battery diagnostic device according to claim 2, wherein, The controller is also configured to: Calculate the deviation of the error rate for each battery cell; and Battery cells whose absolute value of the deviation exceeds the second value are identified as defective.

8. The battery diagnostic device according to claim 7, wherein, The second value is set to 0.

25.

9. A battery diagnostic method, the battery diagnostic method comprising the following steps: Obtain time-series data on voltage for each of multiple battery cells; Calculate the moving average and standard deviation against a reference unit based on the time-series data for each battery cell; and Based on the moving average and the standard deviation, diagnose whether each battery cell is abnormal.

10. The battery diagnostic method according to claim 9, wherein, The steps to diagnose whether each battery cell is abnormal include the following: For each battery cell, the voltage value at the current time point is normalized using the moving average and standard deviation from previous time points; and The error rate is calculated based on the normalized voltage value for each battery cell.

11. The battery diagnostic method according to claim 10, wherein, The process of diagnosing whether each battery cell is abnormal also includes the following step: identifying battery cells whose absolute value of the error rate exceeds a first value as defective.

12. The battery diagnostic method according to claim 10, wherein, The steps for calculating the error rate include the following: calculating the error rate by applying an error function (erf) to a normalized voltage value.

13. The battery diagnostic method according to claim 10, wherein, The steps for calculating the error rate include the following: for the interval where the rate of change of current in the time series data is greater than a threshold, the error rate is masked with 0.

14. The battery diagnostic method according to claim 10, wherein, The steps for diagnosing whether each battery cell is abnormal also include the following: Calculate the deviation of the error rate for each battery cell; and Battery cells whose absolute value of the deviation exceeds the second value are identified as defective.

Images