Battery diagnostic device, battery diagnostic method, battery pack, and vehicle

Abstract

The invention relates to a battery diagnosis device, a battery diagnosis method, a battery pack and a vehicle. The battery diagnosis apparatus according to the present invention is used for diagnosing a battery pack including a plurality of battery cells connected in series, and includes: a voltage sensing circuit configured to periodically generate a voltage signal indicating a cell voltage of each battery cell; and a control circuit configured to generate, based on the voltage signal, time series data indicative of a change in cell voltage of each battery cell over time. The control unit is configured to: (i) determine a first average cell voltage and a second average cell voltage for each battery cell on the basis of time series data [wherein the first average cell voltage corresponds to a short-term moving average value and the second average cell voltage corresponds to a long-term moving average value]; and (ii) detecting an abnormality in the voltage of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

Description

[0001] This application is a divisional application of the patent application No. 202180030996.9 (International application No. PCT / KR2021 / 017684, International filing date: November 26, 2021, Invention name: "Battery diagnostic device, battery diagnostic method, battery pack, and vehicle") with the invention name of "Battery diagnostic device, battery diagnostic method, battery pack, and vehicle". TECHNICAL FIELD

[0002] The present disclosure relates to a battery diagnostic device, a battery diagnostic method, a battery pack including the battery diagnostic device, and a vehicle including the battery pack for a technology of abnormal voltage diagnosis of a battery.

[0003] This application claims the benefit of Korean Patent Application No. 10-2020-0163366, filed November 27, 2020, the disclosure of which is incorporated herein in its entirety by reference. BACKGROUND

[0004] Recently, the demand for portable electronic products such as notebook computers, camcorders, mobile phones, etc. has rapidly increased, and with the widespread development of electric vehicles, energy storage batteries, robots, and satellites, many studies are being conducted on high-performance batteries that can be repeatedly charged.

[0005] Currently, commercialized batteries include nickel-cadmium batteries, nickel-hydrogen batteries, nickel-zinc batteries, lithium batteries, etc., and among them, lithium batteries have almost no memory effect, and are more and more concerned than nickel-based batteries due to the advantages that they can be charged at any time, have a very low self-discharge rate, and have a high energy density.

[0006] Recently, as applications requiring high voltage (e.g., energy storage systems, electric vehicles) are widely popularized, the demand for accurate diagnosis of abnormal voltage of each of a plurality of battery cells connected in series in a battery pack is increasing.

[0007] The abnormal voltage condition of the battery cell refers to a failure state caused by an abnormal decrease and / or increase in the cell voltage due to internal short circuit, external short circuit, defects in the voltage sensing line, poor connection with the charging / discharging line, etc.

[0008] An abnormal voltage diagnosis of each battery cell has been attempted by comparing a voltage across each battery cell at a certain time (i.e., a cell voltage) with an average cell voltage of a plurality of battery cells at the same time as the certain time. However, the cell voltage of each battery cell is dependent on a temperature, a current, and / or a state of health (SOH) of the corresponding battery cell, and thus it is difficult to accurately diagnose an abnormal voltage of each battery cell by simply comparing the cell voltages of the plurality of battery cells measured at a certain time. For example, when there is a large difference in temperature or SOH between a battery cell having no abnormal voltage and the remaining battery cells, a difference between the cell voltage of the corresponding battery cell and the average cell voltage can also be large.

[0009] To solve this problem, in addition to the cell voltage of each battery cell, an additional parameter of each battery cell, such as a charge / discharge current, a temperature of each battery cell, and / or a state of charge (SOC), can also be used for the abnormal voltage diagnosis of each battery cell. However, the diagnosis method using the additional parameter involves a process of detecting each parameter and a process of comparing the parameters, and thus requires more complexity and a longer time than the diagnosis method using the cell voltage as the only parameter. SUMMARY

[0010] TECHNICAL PROBLEM

[0011] The present disclosure aims to solve the above problems, and thus the present disclosure relates to a battery diagnosis apparatus, a battery diagnosis method, a battery pack, and a vehicle for providing efficient and accurate abnormal voltage diagnosis of battery cells, in which, for each of at least one moving window having a given length of time, a moving average of a cell voltage of each of a plurality of battery cells is determined per unit time, and an abnormal voltage diagnosis of each battery cell is performed based on each moving average of each battery cell.

[0012] These and other objects and advantages of the present disclosure can be understood from the following description, and will be apparent from the embodiments of the present disclosure. Furthermore, it will be easily understood that the objects and advantages of the present disclosure can be realized by the means set forth in the appended claims and combinations thereof.

[0013] TECHNICAL SOLUTION

[0014] A battery diagnosis apparatus for achieving the above object is a battery diagnosis apparatus for a cell group including a plurality of battery cells connected in series, and can include a voltage sensing circuit configured to periodically generate a voltage signal indicating a cell voltage of each battery cell, and a control circuit configured to generate time series data indicating a change in the cell voltage of each battery cell over time based on the voltage signal.

[0015] Preferably, the control circuit can be configured to (i) determine, for each battery cell, a first average cell voltage and a second average cell voltage based on the time series data, wherein the first average cell voltage is a short-term moving average and the second average cell voltage is a long-term moving average, and (ii) detect abnormal voltage for each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

[0016] In one aspect, the control circuit can be configured to: determine, for each battery cell, a short-term / long-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; determine, for each battery cell, a cell diagnostic deviation corresponding to a deviation between an average of the short-term / long-term average differences of all battery cells and the short-term / long-term average difference of the battery cell; and detect a battery cell that satisfies a requirement that the cell diagnostic deviation exceeds a diagnostic threshold as an abnormal voltage cell.

[0017] Preferably, the control circuit can be configured to generate, for each battery cell, a time series data of the cell diagnostic deviation, and detect abnormal voltage for the battery cell according to a time period that the cell diagnostic deviation exceeds a diagnostic threshold or a number of data of the cell diagnostic deviation that exceeds the diagnostic threshold.

[0018] In another aspect, the control circuit can be configured to: determine, for each battery cell, a short-term / long-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; determine, for each battery cell, a cell diagnostic deviation by calculating a deviation between an average of the short-term / long-term average differences of all battery cells and the short-term / long-term average difference of the battery cell; determine a statistical adaptive threshold that depends on a standard deviation of the cell diagnostic deviations for all battery cells; generate a time series data of a filter diagnostic value by filtering the time series data of the cell diagnostic deviation for each battery cell based on the statistical adaptive threshold; and detect abnormal voltage for the battery cell according to a time period that the filter diagnostic value exceeds a diagnostic threshold or a number of data of the filter diagnostic value that exceeds the diagnostic threshold.

[0019] In yet another aspect, the control circuit can be configured to determine, for each battery cell, a short / long term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; determine, for each battery cell, a normalized value of the short / long term average difference as a normalized cell diagnostic bias; determine a statistical adaptive threshold that depends on a standard deviation of the normalized cell diagnostic biases for all battery cells; generate time series data of filter diagnostic values by filtering, based on the statistical adaptive threshold, the time series data of the normalized cell diagnostic bias for each battery cell; and detect abnormal voltage of a battery cell from a time period in which the filter diagnostic value exceeds a diagnostic threshold or a number of data of the filter diagnostic value that exceeds the diagnostic threshold.

[0020] Preferably, the control circuit can normalize the short / long term average difference for each battery cell by dividing the short / long term average difference by an average of the short / long term average differences of all battery cells.

[0021] Alternatively, the control circuit can normalize the short / long term average difference for each battery cell by taking a logarithmic calculation of the short / long term average difference.

[0022] In another aspect, the control circuit can be configured to generate time series data indicative of a change in cell voltage of each battery cell over time using voltage corresponding to a voltage difference between a cell voltage average of all battery cells measured at each unit time and a cell voltage of each battery cell.

[0023] In yet another aspect, the control circuit can be configured to determine, for each battery cell, a short / long term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; determine, for each battery cell, a normalized value of the short / long term average difference as a normalized cell diagnostic bias; generate time series data of the normalized cell diagnostic bias for each battery cell by recursively repeating at least once (i) to (iv),

[0024] (i) determining, for the time series data of the normalized cell diagnostic bias of each battery cell, a first moving average value and a second moving average value, wherein the first moving average value is a short term moving average value and the second moving average value is a long term moving average value, (ii) determining, for each battery cell, a short / long term average difference corresponding to a difference between the first moving average value and the second moving average value, (iii) determining, for each battery cell, a normalized value of the short / long term average difference as a normalized cell diagnostic bias; and (iv) generating, for each battery cell, time series data of the normalized cell diagnostic bias;

[0025] determining a statistical adaptive threshold value depending on a standard deviation of the normalized cell diagnostic bias for all battery cells; generating time series data of filter diagnostic values by filtering the time series data of the normalized cell diagnostic bias for each battery cell based on the statistical adaptive threshold value; and detecting abnormal voltage of the battery cell from a time period in which the filter diagnostic value exceeds a diagnostic threshold value or a number of data of the filter diagnostic value that exceeds the diagnostic threshold value.

[0026] A battery diagnostic method according to the present disclosure for achieving the above object is a battery diagnostic method for a cell group including a plurality of battery cells connected in series, and can include: (a) periodically generating time series data indicating a change in voltage signal of a cell voltage of each battery cell over time; (b) determining a first average cell voltage and a second average cell voltage of each battery cell based on the time series data, wherein the first average cell voltage is a short-term moving average, and the second average cell voltage is a long-term moving average; and (c) detecting abnormal voltage of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

[0027] In one aspect, step (c) can include: (c1) determining, for each battery cell, a short-term / long-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; (c2) determining, for each battery cell, a cell diagnostic bias corresponding to a deviation between an average value of the short-term / long-term average differences of all battery cells and the short-term / long-term average difference of the battery cell; and (c3) detecting the battery cell satisfying a requirement that the cell diagnostic bias exceeds a diagnostic threshold value as an abnormal voltage cell.

[0028] Preferably, step (c) can include (c1) generating, for each battery cell, time series data of the cell diagnostic bias; and (c2) detecting abnormal voltage of the battery cell from a time period in which the cell diagnostic bias exceeds a diagnostic threshold value or a number of data of the cell diagnostic bias that exceeds the diagnostic threshold value.

[0029] On the other hand, step (c) can include: (cl) determining, for each battery cell, a short-term / long-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; (c2) determining, for each battery cell, a cell diagnostic deviation by calculating a deviation between an average of the short-term / long-term average differences of all battery cells and the short-term / long-term average difference of the battery cell; (c3) determining a statistical adaptive threshold that depends on a standard deviation of the cell diagnostic deviations for all battery cells; (c4) generating time series data of filter diagnostic values by filtering time series data of the cell diagnostic deviations for each battery cell based on the statistical adaptive threshold; and (c5) detecting abnormal voltage of a battery cell according to a time period in which the filter diagnostic values exceed a diagnostic threshold or a number of data of the filter diagnostic values that exceed the diagnostic threshold.

[0030] In yet another aspect, step (c) can include: (cl) determining, for each battery cell, a short-term / long-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; (c2) determining a normalized value of the short-term / long-term average difference as a normalized cell diagnostic deviation; (c3) determining a statistical adaptive threshold that depends on a standard deviation of the normalized cell diagnostic deviations for all battery cells; (c4) generating time series data of filter diagnostic values by filtering time series data of the normalized cell diagnostic deviations for each battery cell based on the statistical adaptive threshold; and (c5) detecting abnormal voltage of a battery cell according to a time period in which the filter diagnostic values exceed a diagnostic threshold or a number of data of the filter diagnostic values that exceed the diagnostic threshold.

[0031] Preferably, step (c2) can be a step of normalizing, for each battery cell, the short-term / long-term average difference by dividing the short-term / long-term average difference by an average of the short-term / long-term average differences of all battery cells.

[0032] Alternatively, step (c2) can be a step of normalizing, for each battery cell, the short-term / long-term average difference by performing a logarithmic calculation on the short-term / long-term average difference.

[0033] In another aspect, step (a) can be a step of generating time series data indicating a change in cell voltage of each battery cell over time using voltage differences between cell voltage averages of all battery cells measured at each unit time and cell voltage of each battery cell.

[0034] In yet another aspect, the step (c) can include: (c1) determining, for each battery cell, a short-term / long-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; (c2) determining, for each battery cell, a normalized value of the short-term / long-term average difference as a normalized cell diagnostic bias; (c3) generating, for each battery cell, time series data of the normalized cell diagnostic bias; (c4) generating, for each battery cell, time series data of the normalized cell diagnostic bias by recursively repeating the following (i) to (iv) at least once:

[0035] (i) determining, for the time series data of the normalized cell diagnostic bias of each battery cell, a first moving average value and a second moving average value, wherein the first moving average value is a short-term moving average value and the second moving average value is a long-term moving average value, (ii) determining, for each battery cell, a short-term / long-term average difference corresponding to a difference between the first moving average value and the second moving average value, (iii) determining, for each battery cell, a normalized value of the short-term / long-term average difference as a normalized cell diagnostic bias, and (iv) generating, for each battery cell, time series data of the normalized cell diagnostic bias;

[0036] (c5) determining a statistical adaptive threshold value depending on a standard deviation of the normalized cell diagnostic biases for all battery cells; (c6) generating time series data of filter diagnostic values by filtering the time series data of the normalized cell diagnostic bias for each battery cell based on the statistical adaptive threshold value; and (c7) detecting abnormal voltage of the battery cell according to a period in which the filter diagnostic value exceeds a diagnostic threshold value or a number of data of the filter diagnostic value exceeding the diagnostic threshold value.

[0037] The above technical objects can also be achieved by a battery pack including the battery diagnostic apparatus and a vehicle including the battery pack.

[0038] Technical Effects

[0039] According to one aspect of the disclosure, effective and accurate diagnosis of abnormal voltage of each battery cell can be achieved by determining two cell voltage moving average values for each battery cell at two different lengths of time per unit time, and implementing abnormal voltage diagnosis of each battery cell based on a difference between the two moving average values for each of the plurality of battery cells.

[0040] According to another aspect of the disclosure, accurate diagnosis of abnormal voltage of each battery cell can be achieved by analyzing a difference in a change trend of the two moving average values for each battery cell by applying advanced techniques such as normalization and / or a statistical adaptive threshold value.

[0041] According to still another aspect of the present disclosure, a time region in which an abnormal voltage of each battery cell occurs and / or an abnormal voltage detection count can be accurately detected by analyzing time series data of a filter diagnostic value determined based on a statistical adaptive threshold.

[0042] Effects of the present disclosure are not limited to the above-mentioned effects, and these and other effects will be clearly understood by those skilled in the art from the appended claims, based on the disclosure provided herein. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings, which are included to provide a further understanding of the technical aspects of the present disclosure and are incorporated in and constitute a part of this specification, illustrate preferred embodiments of the present disclosure and together with the description given below, serve to provide further understanding of the technical aspects of the present disclosure, and therefore the present disclosure should not be construed as being limited only to the drawings.

[0044] Figure 1 FIG. 1 is a graph exemplarily illustrating a plurality of battery cells according to an embodiment of the present disclosure.

[0045] Figures 2a to 2h FIG. 2 is a graph referred to in a process of diagnosing an abnormal voltage of each battery cell with respect to time series data of a change in a cell voltage of each of the plurality of battery cells illustrated in FIG. 1, according to an indication Figure 1

[0046] Figure 3 FIG. 3 is a flowchart exemplarily illustrating a battery diagnostic method according to a first embodiment of the present disclosure.

[0047] Figure 4 FIG. 4 is a flowchart exemplarily illustrating a battery diagnostic method according to a second embodiment of the present disclosure.

[0048] Figure 5 FIG. 5 is a flowchart exemplarily illustrating a battery diagnostic method according to a third embodiment of the present disclosure.

[0049] Figure 6 FIG. 6 is a flowchart exemplarily illustrating a battery diagnostic method according to a fourth embodiment of the present disclosure.

[0050] Figure 7 FIG. 7 is a flowchart exemplarily illustrating a battery diagnostic method according to a fifth embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before the description, it should be understood that the terms or words used in the specification and the appended claims should not be construed as being limited to the generally and dictionary meanings but interpreted based on the meanings and concepts corresponding to the technical aspects of the present disclosure on the basis of the principle that the inventor is allowed to define the terms appropriately for the best explanation of the invention. Accordingly, the description proposed herein is just a preferable example for the purpose of illustrations only and thus should not be used in explanation for only the preferred example and should not be used to limit the scope of the disclosure. ​

[0052] Therefore, the embodiments described herein and the illustrations shown in the drawings are merely most preferred embodiments of the present disclosure, and are not intended to completely describe the technical aspects of the present disclosure, and thus it should be understood that various other equivalents and modifications can be made thereto at the time of filing the present application.

[0053] The terms including ordinal numbers such as "first", "second", etc. are used to distinguish one element from the other elements among various elements, but are not intended to limit the elements by the terms.

[0054] Unless the context clearly indicates otherwise, it will be understood that the term "comprising" as used in the present specification specifies the presence of the stated elements, but does not exclude the presence or addition of one or more other elements. In addition, the term "control unit" used herein refers to a processing element having at least one function or operation, and this can be realized by hardware and software individually or in combination.

[0055] In addition, throughout the specification, it will be further understood that when an element is referred to as being "connected to" another element, it can be directly connected to the other element or an intervening element can be present.

[0056] Figure 1 is an exemplary view illustrating an electric vehicle according to an embodiment of the present disclosure.

[0057] Referring to Figure 1 , the electric vehicle 1 includes a battery pack 2, an inverter 3, an electric motor 4, and a vehicle controller 5.

[0058] The battery pack 2 includes a cell group CG, a switch 6, and a battery management system 100.

[0059] The cell group CG can be coupled to the inverter 3 through a pair of power supply terminals provided in the battery pack 2. The cell group CG includes a plurality of battery cells BC1 to BC N (N is a natural number of 2 or more). Each battery cell BC i is not limited to a specific type, and can include any battery cell that can be recharged, such as a lithium ion battery cell. i is an index of cell identification. i is a natural number and is between 1 and N.

[0060] The switch 6 is connected in series to the cell group CG. The switch 6 is installed on a current path for charging / discharging of the cell group CG. The switch 6 controls between an on state and an off state in response to a switching signal from the battery management system 100. The switch 6 can be a mechanical relay turned on / off by electromagnetic force of a coil, or a semiconductor switch such as a metal oxide semiconductor field effect transistor (MOSFET).

[0061] Inverter 3 is configured to convert direct current (DC) power from cell bank CG into alternating current (AC) power in response to a command from battery management system 100. Motor 4 may be, for example, a three-phase AC motor. Motor 4 operates using AC power from inverter 3.

[0062] The battery management system 100 is configured to perform overall control related to the charging / discharging of the cell group CG.

[0063] The battery management system 100 includes a battery diagnostic device 200. The battery management system 100 may also include at least one of a current sensor 310, a temperature sensor 320, and an interface unit 330.

[0064] Battery diagnostic device 200 is configured for multiple battery cells BC1 to BC2. N Abnormal voltage diagnosis for each of the components. The battery diagnostic device 200 includes a voltage sensing circuit 210 and a control circuit 220.

[0065] Voltage sensing circuit 210 is connected to multiple battery cells BC1 to BC2 via multiple voltage sensing lines. N Each of the cells has a positive and a negative terminal. The voltage sensing circuit 210 is configured to measure the cell voltage across each cell BC and generate a voltage signal indicating the measured cell voltage.

[0066] Current sensor 310 is connected in series to cell group CG via a current path. Current sensor 310 is configured to detect the battery current flowing through cell group CG and generate a current signal indicating the detected battery current.

[0067] Temperature sensor 320 is configured to detect the temperature of unit group CG and generate a temperature signal indicating the detected temperature.

[0068] The control circuit 220 can be implemented in hardware using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a microprocessor, and an electrical unit for performing other functions.

[0069] The control circuit 220 can have a storage unit. The storage unit can include at least one type of storage medium of a flash memory type, a hard disk type, a solid state disk (SSD) type, a silicon disk driver (SDD) type, a micro multi media card type, a random access memory (RAM), a static random access memory (SRAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), and a programmable read only memory (PROM). The storage unit can store data and programs required for the control circuit 220 to perform calculations. The storage unit can store data indicating the results of calculations performed by the control circuit 220. Specifically, the control circuit 220 can record at least one of a plurality of parameters calculated at each unit time as described below in the storage unit.

[0070] The control circuit 220 can be operatively coupled to the voltage sensing circuit 210, the temperature sensor 320, the current sensor 310, the interface unit 330, and / or the switch 6. The control circuit 220 can collect sensing signals from the voltage sensing circuit 210, the current sensor 310, and the temperature sensor 320. The sensing signals refer to voltage signals, current signals, and / or temperature signals detected in a synchronous manner.

[0071] The interface unit 330 can include a communication circuit configured to support wired or wireless communication between the control circuit 220 and a vehicle controller 5 (e.g., an electronic control unit (ECU)). The wired communication can be, for example, controller area network (CAN) communication, and the wireless communication can be, for example, Zigbee or Bluetooth communication. The communication protocol is not limited to a specific type and can include any communication protocol that supports wired / wireless communication between the control circuit 220 and the vehicle controller 5.

[0072] The interface unit 330 can be coupled to an output device (e.g., a display, a speaker) that provides information received from the vehicle controller 5 and / or the control circuit 220 in an identifiable format. The vehicle controller 5 can control the inverter 3 based on battery information (e.g., voltage, current, temperature, SOC) collected via communication with the battery management system 100.

[0073] Figures 2a to 2h is an example of a graph illustrating a process of performing abnormal voltage diagnosis of each battery cell based on time series data indicating changes in cell voltage of each of a plurality of battery cells over time. Figure 1

[0074] Figure 2a is an example of a graph illustrating a process of performing abnormal voltage diagnosis of each battery cell based on time series data indicating changes in cell voltage of each of a plurality of battery cells over time. N ​The voltage curve for each of the cells is shown. The number of battery cells is 14. The control circuit 220 collects voltage signals from the voltage sensing circuit 210 every unit time and sets the voltage of each battery cell BC... i The voltage value of the unit voltage is recorded in the storage unit. The unit time can be an integer multiple of the voltage measurement cycle of the voltage sensing circuit 210.

[0075] Control circuit 220 can be based on each battery cell BC recorded in the storage cell. i The cell voltage values ​​are used to generate cell voltage time series data indicating the cell voltage history over time for each cell. The number of cell voltage time series data points is increased by 1 each time a cell voltage is measured.

[0076] Figure 2a The multiple voltage curves shown correspond to multiple battery cells BC1 to BC2. N They are linked in a one-to-one relationship. Therefore, each voltage curve indicates the cell voltage change history of any battery cell BC associated with it.

[0077] Control circuit 220 can use one or two moving windows to determine multiple battery cells BC1 to BC2 in each unit of time. N The moving average of each of the moving windows. When using two moving windows, the duration of either moving window differs from the duration of the other moving window.

[0078] Here, the duration of each moving window is an integer multiple of a unit of time, and the end point of each moving window is the current time, while the start point of each moving window is a time point that is a given duration earlier than the current time.

[0079] In the following text, for ease of description, the moving window associated with the shorter time duration will be referred to as the first moving window, and the moving window associated with the longer time duration will be referred to as the second moving window.

[0080] The control circuit 220 can use the first moving window alone or both the first and second moving windows to execute the control for each battery cell BC. i Diagnosis of abnormal voltage.

[0081] The control circuit 220 can be based on the i-th battery cell BC collected in each unit time. i The cell voltage is compared with that of the i-th cell BC in each unit time. i The short-term and long-term trends of the unit voltage.

[0082] Control circuit 220 can determine the i-th battery cell BC in each unit time using either Equation 1 or Equation 2 via the first moving window. ithe first average cell voltage (i.e., moving average) at the current time.

[0083] Equation 1 is a moving average calculation formula according to an arithmetic average method, and Equation 2 is a moving average calculation formula according to a weighted average method.

[0084] < Equation 1 >

[0085]

[0086] < Equation 2 >

[0087]

[0088] In Equations 1 and 2, k is a time index indicating a current time, SMA i [k] is the i-th battery cell BC i the first average cell voltage at the current time, S is a value obtained by dividing the time length of the first moving window by a unit time, and V i [k] is the i-th battery cell BC i the cell voltage at the current time. For example, when the unit time is 1 second and the time length of the first moving window is 10 seconds, S is 10. When x is a natural number that is k or less, V i [k-x] and SMA i [k-x] respectively indicate the cell voltage and the first average cell voltage of the i-th battery cell BC i at the time index k-x. For reference, the control circuit 220 can be set to increase the time index by 1 at each unit time.

[0089] The control circuit 220 can determine the second average cell voltage, which is a moving average, of the i-th battery cell BC i at each unit time through a second moving window using Equation 3 or 4 below.

[0090] Equation 3 is a moving average calculation formula according to an arithmetic average method, and Equation 4 is a moving average calculation formula according to a weighted average method.

[0091] < Equation 3 >

[0092]

[0093] < Equation 4 >

[0094]

[0095] In Equations 3 and 4, k is a time index indicating a current time, LMA i [k] is the i-th battery cell BC iThe second average unit voltage at the current time, L, is the value obtained by dividing the length of the second moving window by the unit time, while V... i [k] is the i-th battery cell BC i The unit voltage at the current time. For example, when the unit time is 1 second and the second moving window duration is 100 seconds, L is 100. When x is k or a smaller natural number, LMA i [kx] represents the second average cell voltage when the time index is kx.

[0096] In this implementation, the control circuit 220 can be input with respect to the reference cell voltage of the current time unit group CG and the battery cell BC. i The difference between the unit voltages is used as V in equations 1 to 4. i [k], instead of each battery cell BC at the current time. i The unit voltage.

[0097] The reference cell voltage for the current time cell group CG is from multiple battery cells BC1 to BC2. N The average value of multiple cell voltages at the current time. In a variant, the average value of multiple cell voltages can be replaced by its median.

[0098] Specifically, control circuit 220 can convert VD in equation 5 below. i [k] is set as V in equations 1 to 4. i [k].

[0099] <Formula 5>

[0100] VD i [k] = V av [k] - V i [k]

[0101] In Equation 5, V av [k] is the reference cell voltage of the current time cell group CG, and is the average value of multiple cell voltages.

[0102] When the duration of the first moving window is less than the duration of the second moving window, the first average cell voltage can be called the "short-term moving average" of the cell voltage, while the second average cell voltage can be called the "long-term moving average" of the cell voltage.

[0103] Figure 2b It shows according to Figure 2a The i-th battery cell BC is determined by the multiple voltage curves shown. i The short-term moving average and long-term moving average of the unit voltage. Figure 2bIn this figure, the horizontal axis represents time, and the vertical axis represents the short-term moving average and the long-term moving average of the cell voltage.

[0104] Referring to Figure 2b In this figure, the broken lines S i are associated in a one-to-one relationship, and represent the first average cell voltage SMA N [k] of each battery cell BC i In addition, the solid lines L i are associated in a one-to-one relationship, and represent the second average cell voltage LMA N [k] of each battery cell BC i In addition, the solid lines L i [k] are used as V i [k] in Formulas 2 and 4. In addition, V av [k] is set to the average value of the plurality of cell voltages. The time length of the first moving window is 10 seconds, and the time length of the second moving window is 100 seconds.

[0105] The broken line curve and the solid line curve are obtained using Formulas 2 and 4, respectively. In addition, VD i [k] is used as V i [k] in Formulas 2 and 4. In addition, V av [k] is set to the average value of the plurality of cell voltages. The time length of the first moving window is 10 seconds, and the time length of the second moving window is 100 seconds.

[0106] Figure 2c The difference between the first average cell voltage SMA Figure 2b [k] and the second average cell voltage LMA i [k] of each battery cell is shown. In this figure, the horizontal axis represents time, and the vertical axis represents the short-term / long-term average difference (absolute value) corresponding to the difference between the first average cell voltage SMA i [k] and the second average cell voltage LMA i [k] of each battery cell BC Figure 2c In this figure, the horizontal axis represents time, and the vertical axis represents the short-term / long-term average difference of each battery cell BC i .

[0107] The short-term / long-term average difference of each battery cell BC i is the difference between the first average cell voltage SMA i [k] and the second average cell voltage LMA i [k] of each battery cell BC i . For example, the short-term / long-term average difference of the i-th battery cell BC i may be equal to the value obtained by subtracting one (e.g., the smaller one) from the other (e.g., the larger one) of SMA i [k] and LMA i [k].

[0108] The short-term / long-term average difference of the i-th battery cell BC i depends on the i-th battery cell BCi short-term and long-term change history of the cell voltage of the i-th battery cell BCi.

[0109] i-th battery cell BCi i temperature or SOH stably affects the cell voltage of the i-th battery cell BCi i for a short term as well as for a long term. Therefore, in the case where there is no abnormal voltage in the i-th battery cell BCi i , there is no significant difference between the short-term / long-term average difference of the i-th battery cell BCi i and the short-term / long-term average difference of the remaining battery cells.

[0110] In contrast, since an abnormal voltage suddenly occurs in the i-th battery cell BCi i due to an internal short circuit and / or an external short circuit, the influence on the first average cell voltage SMA i [k] is greater than the influence on the second average cell voltage LMA i [k]. As a result, the short-term / long-term average difference of the i-th battery cell BCi i is greatly deviated from the short-term / long-term average difference of the remaining battery cells having no abnormal voltage.

[0111] The control circuit 220 can determine the short-term / long-term average difference |SMA i [k]-LMA i [k]| of each battery cell BC i at every unit time. Additionally, the control circuit 220 can determine an average value of the short-term / long-term average difference |SMA i [k]-LMA i [k]|. Hereinafter, the average value is denoted as |SMA i [k]-LMA i [k]| av . Additionally, the control circuit 220 can determine a deviation of the short-term / long-term average difference |SMA i [k]-LMA i [k]| from the average value of the short-term / long-term average difference |SMAi[k]-LMAi[k]|av as a cell diagnosis deviation D diag,i [k]. Additionally, the control circuit 220 can perform an abnormal voltage diagnosis of each battery cell BC i based on the cell diagnosis deviation D diag,i [k].

[0112] In an embodiment, when the cell diagnosis deviation D diag,i [k] of the i-th battery cell BCi i exceeds a preset diagnosis threshold (for example, 0.015), the control circuit 220 can diagnose that an abnormal voltage occurs in the corresponding i-th battery cell BCii An abnormal voltage exists in the middle.

[0113] Preferably, the control circuit 220 can normalize the short-term / long-term average difference |SMA i [k]-LMA i [k] of each battery cell BC i using a normalized reference value for abnormal voltage diagnosis. Preferably, the normalized reference value is the average of the short-term / long-term average differences |SMA i [k]-LMA i [k]| av .

[0114] Specifically, the control circuit 220 can set the average of the short-term / long-term average differences |SMA i [k]-LMA N [k] of the first battery cell to the Nth battery cell (BC i 1 to BC i N) to a normalized reference value. Additionally, the control circuit 220 normalizes the short-term / long-term average difference |SMA av [k]-LMA i [k] of each battery cell BC i i by dividing the short-term / long-term average difference |SMA i [k]-LMA i [k] of each battery cell BC i i by the normalized reference value.

[0115] Equation 6 below is a formula for normalizing the short-term / long-term average difference |SMA i [k]-LMA * [k] of each battery cell BC diag,i i. In an embodiment, the product of Equation 6 can be referred to as a normalized cell diagnosis deviation D * diag,i i. i [k]-LMA i [k]| i [k]| i ) av .

[0118] In Equation 6, |SMA i [k]-LMA i [k] is the short-term / long-term average difference of the i-th battery cell BC i i at the current time, and |SMA i [k]-LMA i [k] is the average of the short-term / long-term average differences of the first battery cell to the Nth battery cell (BC av 1 to BC * N) at the current time.i [k] - LMA i [k] av is the average of the short-term / long-term average differences of all battery cells (normalized reference value), while D * diag,i [k] is the short-term / long-term average difference |SMA i normalized cell diagnostic deviation. The symbol "*" indicates that the parameter has been normalized.

[0119] each battery cell BC i i [k] - LMA i [k] | can be normalized by a logarithmic calculation of the following equation 7. In embodiments, the product of equation 7 can also be referred to as the normalized cell diagnostic deviation D * diag,i [k].

[0120] <equation 7>

[0121] D * diag,i [k] = Log |SMA i [k] - LMA i [k] |

[0122] Figure 2d shows the normalized cell diagnostic deviation D i * diag,i [k] of each battery cell BC * diag,i [k]. In Figure 2d , the horizontal axis represents time, while the vertical axis represents the cell diagnostic deviation D i * diag,i [k] of each battery cell BC

[0123] Referring to Figure 2d , it can be seen that, on the basis of the normalized average of the short-term / long-term average difference |SMA i i [k] - LMA i [k] |, the change in the short-term / long-term average difference of each battery cell BC i is amplified. Thus, a more accurate diagnosis of abnormal voltages of battery cells can be achieved.

[0124] Preferably, the control circuit 220 can diagnose the abnormal voltage of each battery cell BC i * diag,i ​​​​​[k] and statistical adaptive threshold D threshold [k] compares to implement each battery cell BC i Diagnosis of abnormal voltage.

[0125] Preferably, the control circuit 220 can set the statistical adaptive threshold D using Equation 8 at each unit time. threshold [k].

[0126] <Formula 8>

[0127] D threshold [k] = β*Sigma(D) * diag,i [k])

[0128] In Equation 8, Sigma is the normalized cell diagnostic deviation D of all battery cells BC at time index k. * diag,i The standard deviation of [k] is a function. Additionally, β is an experimentally determined constant. β is a factor determining diagnostic sensitivity. When this disclosure is applied to a group of cells including battery cells in which abnormal voltages actually occur, β can be appropriately determined by trial and error to detect the corresponding battery cell as an abnormal voltage cell. In the example, β can be set to at least 5, or at least 6, or at least 7, or at least 8, or at least 9. The D generated by Equation 8... threshold [k] consists of multiple [k] values ​​and is used to construct time series data.

[0129] Meanwhile, the normalized cell diagnostic bias D of battery cells in abnormal voltage conditions * diag,i [k] is greater than the normalized cell diagnostic bias of a normal battery cell. Therefore, to improve the accuracy and reliability of the diagnosis, Sigma(D) is calculated at time index k. * diag,i When [k]), we want to exclude max(D) corresponding to the maximum value. * diag,i [k]). Here, max is a function that returns the maximum value for multiple input parameters, and the input parameters are the normalized cell diagnostic bias D of all battery cells. * diag,i [k].

[0130] exist Figure 2d In the middle, D represents the statistical adaptive threshold. threshold [k] The time series data that changes over time corresponds to the darkest color among all the distributions (profiles).

[0131] The statistical adaptive threshold D at time index k was determined. threshold[k] After that, the control circuit 220 can use the following formula 9 to control each battery cell BC i Normalized unit diagnostic bias D * diag,i [k] Perform filtering to determine the filter diagnostic value D. filter,i [k].

[0132] It can be done for each battery cell BC i Filter diagnostic value D filter,i [k] is assigned two values. That is, in the unit diagnostic deviation D * diag,i [k] is greater than the statistical adaptive threshold D threshold In the case of [k], the unit diagnostic deviation D * diag,i [k] and statistical adaptive threshold D Threshold The difference between [k] is assigned to the filter diagnostic value D. filter,i [k]. Conversely, in the unit diagnostic deviation D * diag,i [k] is equal to or less than the statistical adaptive threshold D threshold In the case of [k], assign 0 to the filter diagnostic value D. filter,i [k].

[0133] <Form 9>

[0134] D filter,i [k] = D * diag,i [k] - D threshold [k] (if D) * diag,i [k] > D threshold [k])

[0135] D filter,i [k] = 0 (if D) * diag,i [k] ≤ D threshold [k])

[0136] Figure 2e This demonstrates how to diagnose the deviation D of the cell at time index k. * diag,i [k] is the filter diagnostic value D obtained by filtering. filter,i A graph of the time series data of [k].

[0137] Reference Figure 2e The irregular pattern indicates the filter diagnostic value D for a specific battery cell. filter,i [k] has a positive value at approximately 3000 seconds. For reference, a specific battery cell with an irregular pattern is one that has...Figure 2d A in the figure indicates the cell of the time series data.

[0138] In the example, control circuitry 220 can be accumulated in each battery cell BC. i Filter diagnostic value D filter,i The time series data of [k], including the filter diagnostic value D. filter,i [k] is a time step greater than the diagnostic threshold (e.g., 0), and battery cells that meet the requirement of accumulating time greater than a preset reference time are diagnosed as abnormal voltage cells.

[0139] Preferably, the control circuit 220 can accumulate and successively satisfy the filter diagnostic value D filter,i [k] is the time step greater than the diagnostic threshold. When there are multiple corresponding time steps, the control circuit 220 can independently calculate the cumulative time for each time step.

[0140] In another example, control circuitry 220 can be accumulated in each battery cell BC. i Filter diagnostic value D filter,i The time series data of [k], including the filter diagnostic value D. filter,i [k] is the number of data included in a time step that is greater than the diagnostic threshold (e.g., 0), and battery cells that meet the requirement that the cumulative number of data is greater than a preset reference count are diagnosed as abnormal voltage cells.

[0141] Preferably, the control circuit 220 can only accumulate the filter diagnostic values ​​D that are successively satisfied. filter,i [k] is the number of data points contained in the time step that exceeds the diagnostic threshold. When there are multiple corresponding time steps, the control circuit 220 can independently accumulate the number of data points for each time step.

[0142] Meanwhile, the control circuit 220 can be used Figure 2d Each battery cell BC shown i Normalized unit diagnostic bias D * diag,i [k] Replace V in equations 1 to 5 i [k]. Additionally, the control circuit 220 can recursively perform the following at time index k: calculate the unit diagnostic deviation D. * diag,i [k] short-term / long-term mean difference | SMA i [k]-LMA i [k]|;Calculate the diagnostic deviation D of the calculation unit * diag,i [k] short-term / long-term mean difference | SMA i [k]-LMA i[k] the average value; the short-term / long-term average difference |SMA i [k] - LMA i [k] the cell diagnostic bias D corresponding to the difference compared to the average value diag,i [k]; the short-term / long-term average difference |SMA is calculated using equation 6 i [k] - LMA i [k] the normalized cell diagnostic bias D * diag,i [k]; the normalized cell diagnostic bias D is determined using equation 8 * diag,i [k] the statistical adaptive threshold D threshold [k]; the filter diagnostic value D is determined by filtering the cell diagnostic bias D * diag,i [k] using equation 9 filter,i [k]; and the filter diagnostic value D filter,i [k] is used for the abnormal voltage diagnosis of the battery cell.

[0143] Figure 2f is a graph showing the normalized cell diagnostic bias D * diag,i [k] of the time series data Figure 2d i [k] - LMA i [k] the time variation of |SMA. In equations 2, 4 and 5 for calculating the short-term / long-term average difference |SMA i [k] - LMA i [k] can be replaced by D * diag,i [k] can be replaced by V i [k], and D * diag,i [k] can be replaced by V av [k].

[0144] Figure 2g is a graph showing the normalized cell diagnostic bias D * diag,i [k] of the time series data. In Figure 2g threshold [k] the time series data corresponds to the distribution represented in the darkest color.

[0145] Figure 2h is a graph showing the filter diagnostic value D * diag,i ​​The filter diagnostic value D obtained by filtering the time series data of [k] is... filter,i Distribution of time series data for [k].

[0146] In the example, control circuitry 220 can be accumulated in each battery cell BC. i Filter diagnostic value D filter,i The time series data of [k] contains filter diagnostic values ​​(D). filter,i [k]) is greater than the diagnostic threshold (e.g., 0) in time steps, and battery cells that meet the requirement of accumulating time greater than a preset reference time are diagnosed as abnormal voltage cells.

[0147] Preferably, the control circuit 220 can accumulate and successively satisfy the filter diagnostic value D filter,i [k] is the time step required to exceed the diagnostic threshold. When there are multiple corresponding time steps, the control circuit 220 can independently calculate the cumulative time for each time step.

[0148] In another example, control circuitry 220 can be accumulated in each battery cell BC. i Filter diagnostic value D filter,i The time series data of [k], including the filter diagnostic value D. filter,i [k] is the number of data included in a time step that is greater than the diagnostic threshold (e.g., 0), and battery cells that meet the requirement that the cumulative number of data is greater than a preset reference count are diagnosed as abnormal voltage cells.

[0149] Preferably, the control circuit 220 can only accumulate the filter diagnostic values ​​D that are successively satisfied. filter,i [k] is the number of data points included in the time step that exceeds the diagnostic threshold. When there are multiple corresponding time steps, the control circuit 220 can independently accumulate the number of data points for each time step.

[0150] Control circuit 220 can additionally repeat the above recursive calculation process a certain number of times. That is, control circuit 220 can use normalized unit diagnostic bias D. * diag,i [k] time series data (e.g., Figure 2g (data) instead Figure 2a The voltage time series data is shown. Additionally, control circuit 220 can recursively perform the following at time index k: calculate the short-term / long-term average difference |SMA i [k]-LMA i [k]|;Calculate the short-term / long-term mean difference |SMA i [k]-LMA i The average value of [k]; calculate the difference between the short-term / long-term average |SMA|.i [k]-LMA i [k]| The unit diagnostic bias D corresponding to the difference compared to the mean. diag,i [k]; Use Equation 6 to calculate the short-term / long-term mean difference |SMA i [k]-LMA i Normalized unit diagnostic bias D of [k]| * diag,i [k]; Use Equation 8 to determine the unit diagnostic deviation D. * diag,i The statistical adaptive threshold D of [k] threshold [k]; Using Equation 9, the unit diagnostic deviation D is analyzed. * diag,i [k] Perform filtering to determine the filter diagnostic value D filter,i [k]; and using the filter diagnostic value D filter,i [k] time series data is used for abnormal voltage diagnosis of battery cells.

[0151] By repeating the above recursive calculation process, abnormal voltage diagnosis of battery cells can be performed more accurately. That is, referring to... Figure 2e The filter diagnostic value D of the battery cell under abnormal voltage conditions filter,i In the time series data of [k], a positive profile pattern was observed only at two time steps. However, referring to... Figure 2h The filter diagnostic value D of the battery cell under abnormal voltage conditions filter,i In the time series data of [k], in comparison Figure 2e A positive distribution pattern was observed over more time steps. Therefore, when the recursive calculation process is performed iteratively, the timing of abnormal voltage occurrences in battery cells can be detected more accurately.

[0152] The battery diagnostic method using the battery diagnostic apparatus 200 described above will be described in detail below. The operation of the control circuit 220 will be described in more detail in various embodiments of the battery diagnostic method.

[0153] Figure 3 This is an exemplary flowchart of a battery diagnostic method according to a first embodiment of the present disclosure. It can be periodically executed by the control circuit 220 at each unit time. Figure 3 The method.

[0154] Reference Figures 1 to 3 In step S310, the control circuit 220 collects data from the voltage sensing circuit 210 representing multiple battery cells BC1 to BC2. N The voltage signal of each cell in the data is used to generate the voltage signal of each battery cell BC (see [link]).Figure 2a The time series data of the unit voltage is denoted as ). The number of unit voltage time series data points increases by 1 at each unit time.

[0155] Preferably, V in Formula 5 i [k] or VD i [k] can be used as a unit voltage.

[0156] In step S320, the control circuit 220 is based on each battery cell BC i The time series data of the cell voltage determines the BC of each battery cell. i First average cell voltage SMA i [k] (see Equations 1 and 2) and the second average unit voltage LMA i [k] (See Equations 3 and 4) (See also) Figure 2b First average cell voltage SMA i [k] is the value of each battery cell BC i The cell voltage is a short-term moving average over a first moving window with a first time length. The second average cell voltage is LMA. i [k] is the value of each battery cell BC i The long-term moving average of the cell voltage over a second moving window with a second time length. V can be used. i [k] or VD i [k] is used to calculate the first average unit voltage SMA. i [k] and second average unit voltage LMA i [k].

[0157] In step S330, the control circuit 220 determines each battery cell BC i Short-term / long-term average difference | SMA i [k]-LMA i [k]| (see also) Figure 2c ).

[0158] In step S340, the control circuit 220 determines each battery cell BC i Unit diagnostic deviation D diag,i [k]. Unit diagnostic deviation D diag,i [k] is the average of the short-term / long-term average differences of all battery cells |SMA i [k]-LMA i [k]| av With the i-th battery cell BC i Short-term / long-term average difference | SMA i [k]-LMA i The deviation between [k] and [k].

[0159] In step S350, the control circuit 220 determines whether the diagnosis time has elapsed. The diagnosis time is preset. When the determination of step S350 is "Yes", step S360 is executed, and when the determination of step S350 is "No", steps S310 to S340 are repeated.

[0160] In step S360, the control circuit 220 generates time series data of the cell diagnosis deviation D i [k] of each battery cell BC diag,i collected for the diagnosis time.

[0161] In step S370, the control circuit 220 performs the abnormal voltage diagnosis on each battery cell BC diag,i by analyzing the time series data of the cell diagnosis deviation D i [k].

[0162] In an example, the control circuit 220 can accumulate the time steps in which the cell diagnosis deviation D i [k] is greater than the diagnosis threshold (for example, 0.015) among the time series data of the cell diagnosis deviation D diag,i [k] of each battery cell BC diag,i and diagnose the battery cell satisfying the requirement that the accumulated time is greater than the preset reference time as an abnormal voltage cell.

[0163] Preferably, the control circuit 220 can accumulate only the time steps successively satisfying the requirement that the cell diagnosis deviation D diag,i [k] is greater than the diagnosis threshold. When the corresponding time steps are plural, the control circuit 220 can independently calculate the accumulated time of each time step.

[0164] In another example, the control circuit 220 can accumulate the number of data in which the cell diagnosis deviation D i [k] is greater than the diagnosis threshold (for example, 0.015) among the time series data of the cell diagnosis deviation D diag,i [k] of each battery cell BC diag,i and diagnose the battery cell satisfying the requirement that the accumulated number of data is greater than the preset reference count as an abnormal voltage cell.

[0165] Preferably, the control circuit 220 can accumulate only the number of data included in the time steps successively satisfying the requirement that the cell diagnosis deviation D diag,i [k] is greater than the diagnosis threshold. When the corresponding time steps are plural, the control circuit 220 can independently accumulate the number of data of each time step.

[0166] Figure 4This is an exemplary flowchart of a battery diagnostic method according to a second embodiment of the present disclosure. It can be periodically executed by the control circuit 220 at each unit time. Figure 4 The method.

[0167] In the battery diagnostic method of the second embodiment, steps S310 to S360 are substantially the same as in the first embodiment, and their descriptions are omitted. After step S360 is completed, step S380 is executed.

[0168] In step S380, control circuit 220 uses Equation 8 to generate a statistical adaptive threshold D. threshold [k] is the time series data. The input to the Sigma function in Equation 8 is the cell diagnostic deviation D of all battery cells generated in step S360. diag,i [k] is time series data. Preferably, the unit diagnostic bias D can be excluded from the input values ​​of the Sigma function. diag,i The maximum value of [k]. Unit diagnostic deviation D diag,i [k] is the short-term / long-term average difference | SMA i [k]-LMA i [k]| Deviation from the mean.

[0169] In step S390, the control circuit 220 uses Equation 9 to control each battery cell BC. i Unit diagnostic deviation D diag,i [k] performs filtering to generate filter diagnostic value D. filter,i [k] is a time series data.

[0170] When using Equation 9, D can be used. diag,i [k] replaces D * diag,i [k].

[0171] In step S400, the control circuit 220 analyzes the filter diagnostic value D. filter,i [k] time series data, for each battery cell BC i Perform abnormal voltage diagnosis.

[0172] In the example, control circuitry 220 can be accumulated in each battery cell BC. i Filter diagnostic value D filter,i The time series data of [k], including the filter diagnostic value D. filter,i [k] is a time step greater than the diagnostic threshold (e.g., 0), and battery cells that meet the requirement of having a cumulative time greater than a preset reference time are diagnosed as abnormal voltage cells.

[0173] Preferably, the control circuit 220 can only accumulate the filter diagnostic values ​​D that are successively satisfied.filter,i [k] the time step in which the filter diagnosis value D i [k] is greater than the diagnosis threshold value. When the corresponding time steps are plural, the control circuit 220 can independently calculate the cumulative time of each time step.

[0174] In another example, the control circuit 220 can accumulate the number of data included in the time step in which the filter diagnosis value D i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D filter,i [k] of each battery cell BC filter,i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D filter,i [k] of each battery cell BC i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D i [k] of each battery cell BC i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D * [k] of each battery cell BC diag,i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D i [k] of each battery cell BC i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D

[0179] The control circuit 220 can independently accumulate the number of data of each time step when the corresponding time steps are plural.

[0175] Preferably, the control circuit 220 can accumulate only the number of data included in the time step in which the filter diagnosis value D filter,i [k] is greater than the diagnosis threshold value. When the corresponding time steps are plural, the control circuit 220 can independently accumulate the number of data of each time step.

[0176] Figure 5 is an exemplary flowchart illustrating a battery diagnosis method according to the third embodiment of the present disclosure. Figure 5 The method of the third embodiment can be periodically performed by the control circuit 220 every unit time.

[0177] The battery diagnosis method according to the third embodiment is substantially the same as the first embodiment except that the steps S340, S360, and S370 are changed to steps S340', 360', and S370'. Therefore, the third embodiment will be described with respect to the differences.

[0178] In step S340', the control circuit 220 determines the normalized cell diagnosis deviation D i [k] of each battery cell BC i [k] using Equation 6. i [k] of each battery cell BC * diag,i [k] of each battery cell BC i [k] of each battery cell BC i [k] of each battery cell BC

[0179] In step S360', the control circuit 220 generates time series data of the normalized cell diagnosis deviation D i [k] of each battery cell BC * diag,i [k] collected at the diagnosis time (see Figure 2d ).

[0180] In step S370', the control circuit 220 diagnoses the battery 200 as an abnormal voltage battery by analyzing the normalized cell diagnosis deviation D * diag,i [k] of the time-series data of each battery cell BC i performs an abnormal voltage diagnosis.

[0181] In an example, the control circuit 220 can accumulate the normalized cell diagnosis deviation D i of each battery cell BC * diag,i [k] of the time-series data of each battery cell BC * diag,i [k] is greater than a diagnosis threshold (e.g., 4) and the battery cell that satisfies the requirement that the accumulated time is greater than a preset reference time is diagnosed as an abnormal voltage cell.

[0182] Preferably, the control circuit 220 can only accumulate the time steps that successively satisfy the requirement that the normalized cell diagnosis deviation D * diag,i [k] is greater than a diagnosis threshold. When there are a plurality of corresponding time steps, the control circuit 220 can independently calculate the accumulated time of each time step.

[0183] In another example, the control circuit 220 can accumulate the number of data in which the normalized cell diagnosis deviation D i of each battery cell BC * diag,i [k] is greater than a diagnosis threshold (e.g., 4) in the time-series data of each battery cell BC

[0184] Preferably, the control circuit 220 can only accumulate the number of data included in the time steps that successively satisfy the requirement that the normalized cell diagnosis deviation D * diag,i [k] is greater than a diagnosis threshold. When there are a plurality of corresponding time steps, the control circuit 220 can independently accumulate the number of data of each time step.

[0185] Figure 6 is an exemplary flowchart illustrating a battery diagnosis method according to the fourth embodiment of the present disclosure. Figure 6 The method of FIG. 10 can be periodically performed by the control circuit 220 at every unit time.

[0186] Except for steps S340, S360, S380, S390, and S400 being changed to steps S340′, S360′, S380′, S390′, and S400′ respectively, the battery diagnostic method according to the fourth embodiment is substantially the same as that of the second embodiment. Therefore, the fourth embodiment will be described with respect to the differences from the second embodiment.

[0187] In step S340', control circuit 220 uses Equation 6 to determine each battery cell BC i Short-term / long-term average difference | SMA i [k]-LMA i Normalized unit diagnostic bias D of [k]| * diag,i [k]. The normalized reference value is the difference between the short-term and long-term averages |SMA i [k]-LMA i The average value of [k]|. Equation 7 can be used instead of Equation 6.

[0188] In step S360', the control circuit 220 targets each battery cell BC collected during the diagnostic time. i Normalized unit diagnostic bias D * diag,i [k], generates time series data (see [k]). Figure 2d ).

[0189] In step S380', control circuit 220 uses Equation 8 to generate a statistical adaptive threshold D. threshold [k] is the time series data. The input to the Sigma function in Equation 8 is the normalized cell diagnostic bias D of all battery cells generated in step S360′. * diag,i [k] is a time series data point. Preferably, at each time index, the cell diagnostic bias D of the cell can be excluded from the input value of the Sigma function. * diag,i The maximum value of [k].

[0190] In step S390', the control circuit 220 uses Equation 9 based on the statistical adaptive threshold D threshold [k] for each battery cell BC i Unit diagnostic deviation D * diag,i [k] performs filtering to generate filter diagnostic value D. filter,i [k] is a time series data.

[0191] In step S400', the control circuit 220 analyzes the filter diagnostic value D. filter,i [k] time series data, for each battery cell BCi An abnormal voltage diagnosis is performed.

[0192] In an example, the control circuit 220 can accumulate the time steps in which the filter diagnosis value D i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D filter,i [k] of each battery cell BC filter,i , and diagnose the battery cell that satisfies the requirement that the accumulated time is greater than the preset reference time as an abnormal voltage cell.

[0193] Preferably, the control circuit 220 can accumulate the time steps in which the filter diagnosis value D filter,i [k] is greater than the diagnosis threshold value successively. When there are a plurality of corresponding time steps, the control circuit 220 can independently calculate the accumulated time of each time step.

[0194] In another example, the control circuit 220 can accumulate the number of data included in the time steps in which the filter diagnosis value D i [k] is greater than the diagnosis threshold value (for example, 0) among the time series data of the filter diagnosis value D filter,i [k] of each battery cell BC filter,i , and diagnose the battery cell that satisfies the requirement that the accumulated number of data is greater than the preset reference count as an abnormal voltage cell.

[0195] Preferably, the control circuit 220 can accumulate the number of data in the time steps in which the filter diagnosis value D filter,i [k] is greater than the diagnosis threshold value successively. When there are a plurality of corresponding time steps, the control circuit 220 can independently accumulate the number of data of each time step.

[0196] Figure 7 is an example of a flowchart illustrating a battery diagnosis method according to the fifth embodiment of the present disclosure.

[0197] In the fifth embodiment, the steps S310 to S360' are substantially the same as those of the fourth embodiment. Therefore, the fifth embodiment will be described with respect to the differences compared to the fourth embodiment.

[0198] In step S410, the control circuit 220 generates a first moving average value SMA i [k] of the unit diagnosis deviation D * diag,i [k] using the time series data of the normalized unit diagnosis deviation D * diag,i [k] of each battery cell BC i [k] time series data and a second moving average value LMAi [k] Time series data (see Figure 2f ).

[0199] In step S420, the control circuit 220 utilizes Equation 6, using each battery cell BC i First moving average (SMA) i [k] Time series data and second moving average (LMA) i [k] Time series data, generating normalized unit diagnostic bias D * diag,i [k] Time series data (see Figure 2g ).

[0200] In step S430, control circuit 220 uses Equation 8 to generate a statistical adaptive threshold D. threshold [k] time series data (see Figure 2g ).

[0201] In step S440, the control circuit 220 uses Equation 9 based on the statistical adaptive threshold D threshold [k], generate each battery cell BC i Filter diagnostic value D filter,i [k] time series data (see Figure 2h ).

[0202] In step S450, the control circuit 220 analyzes each battery cell BC i Filter diagnostic value D filter,i [k] time series data, for each battery cell BC i Perform abnormal voltage diagnosis.

[0203] In the example, control circuitry 220 can be accumulated in each battery cell BC. i Filter diagnostic value D filter,i The time series data of [k], including the filter diagnostic value D. filter,i [k] is a time step greater than the diagnostic threshold (e.g., 0), and battery cells that meet the requirement of having a cumulative time greater than a preset reference time are diagnosed as abnormal voltage cells.

[0204] Preferably, the control circuit 220 can accumulate and successively satisfy the filter diagnostic value D. filter,i [k] is the time step required to exceed the diagnostic threshold. When there are multiple corresponding time steps, the control circuit 220 can independently calculate the cumulative time for each time step.

[0205] In another example, control circuitry 220 can be accumulated in each battery cell BC. i Filter diagnostic value D filter,ithe number of data included in the time steps in which the filter diagnostic value D filter,i [k] is greater than a diagnostic threshold (e.g., 0), and satisfies a requirement that the cumulative value of the number of data is greater than a preset reference count, the battery cell is diagnosed as an abnormal voltage cell.

[0206] Preferably, the control circuit 220 can accumulate only the data included in the time steps in which the filter diagnostic value D filter,i [k] is greater than a diagnostic threshold. When the corresponding time steps are plural, the control circuit 220 can independently accumulate the number of data for each time step.

[0207] In the fifth embodiment, the control circuit 220 can recursively perform steps S410 and S420 at least twice. That is, the control circuit 220 can use the normalized cell diagnostic deviation D * diag,i [k] time series data, and generate the cell diagnostic deviation D * diag,i [k] time series data, and generate the cell diagnostic deviation D i [k] time series data, and generate the cell diagnostic deviation D i [k] time series data. Subsequently, the control circuit 220 can use the first moving average value SMA i [k] time series data, and generate the cell diagnostic deviation D i [k] time series data, and generate the cell diagnostic deviation D i [k] time series data, and generate the cell diagnostic deviation D * diag,i [k] time series data. The recursive algorithm can be repeated a preset number of times.

[0208] When steps S410 and S420 are performed according to the recursive algorithm, the cell diagnostic deviation D * diag,i [k] time series data to perform steps S430 to S450.

[0209] In the embodiments of the present disclosure, when an abnormal voltage in a specific battery cell is diagnosed after the abnormal voltage diagnosis for all battery cells, the control circuit 220 can output the diagnosis result information through a display unit (not shown). Additionally, the control circuit 220 can record identification information (ID) of the battery cell in which the abnormal voltage has been diagnosed, the time at which the abnormal voltage is diagnosed, and a diagnosis flag in a storage unit.

[0210] Preferably, the diagnosis result information can include a message indicating that there is a cell in an abnormal voltage condition in the battery pack. Alternatively, the diagnosis result information can include a warning message requiring accurate inspection of the battery cell.

[0211] In an example, the display unit can be included in a load device provided with power from the cell group CG. When the load device is an electric vehicle, a hybrid vehicle, or a plug-in hybrid vehicle, the diagnosis result information can be output through the cluster information display. In another example, when the battery diagnosis apparatus 200 according to the present disclosure is included in a diagnosis system, the diagnosis result can be output through a display provided in the diagnosis system.

[0212] Preferably, the battery diagnosis apparatus 200 according to the embodiments of the present disclosure can be included in a control system (not shown) of the battery management system 100 or the load device.

[0213] According to the above-described embodiments, effective and accurate diagnosis of the abnormal voltage of each battery cell can be achieved by determining two cell voltage moving averages over two different lengths of time for each battery cell per unit time, and implementing abnormal voltage diagnosis for each battery cell based on a difference between the two moving averages for each of the plurality of battery cells.

[0214] According to another aspect, accurate diagnosis of the abnormal voltage of each battery cell can be achieved by applying advanced techniques such as normalization and / or statistical adaptive thresholding, analyzing the difference in the trend of change of the two moving averages for each battery cell.

[0215] According to yet another aspect, the time step at which the abnormal voltage of each battery cell occurs and / or the abnormal voltage detection count can be accurately detected by analyzing time series data of the filter diagnosis value determined based on the statistical adaptive thresholding.

[0216] The above-described embodiments of the present disclosure are not implemented only by devices and methods, but can be implemented by a program performing functions corresponding to the configuration of the embodiments of the present disclosure or a recording medium having the program recorded thereon, and those skilled in the art can easily implement such implementation from the disclosure of the above-described embodiments.

[0217] Although the present disclosure has been described above with respect to a limited number of embodiments and drawings, the present disclosure is not limited thereto, and it will be apparent to those skilled in the art that various modifications and variations can be made thereto within the technical aspects of the present disclosure and the equivalent scope of the appended claims.

[0218] Additionally, since a person having ordinary skill in the art can make many substitutions, modifications, and changes to the present disclosure described above without departing from the technical aspects of the present disclosure, the present disclosure is not limited by the above-described embodiments and drawings, and some or all of the embodiments can be selectively combined to allow various modifications.

Claims

1. A battery diagnostic device for multiple battery cells, the battery diagnostic device comprising: One or more processors, wherein the one or more processors are configured to: For at least one of the plurality of battery cells, a first average cell voltage and a second average cell voltage of the battery cell are determined based on a voltage signal indicating the cell voltage of the battery cell; as well as For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the difference between the first average cell voltage and the second average cell voltage.

2. The battery diagnostic device according to claim 1, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, a short-term / long-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell is determined; For at least one of the plurality of battery cells, a cell diagnostic deviation is determined that corresponds to the deviation between the average of the short-term / long-term average differences of the plurality of battery cells and the deviation between the short-term / long-term average differences of the battery cell. as well as A battery cell that meets the requirement that the diagnostic deviation of the cell exceeds the diagnostic threshold will be detected as an abnormal cell.

3. The battery diagnostic device according to claim 2, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, time-series data of the cell diagnostic deviation are generated, and For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the cell diagnostic deviation exceeds the diagnostic threshold or the number of data points showing the cell diagnostic deviation exceeding the diagnostic threshold.

4. The battery diagnostic device according to claim 1, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, a short-term / long-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell is determined; For at least one of the plurality of battery cells, a cell diagnostic deviation is determined by calculating the deviation between the average of the short-term / long-term average differences of the plurality of battery cells and the short-term / long-term average difference of the battery cell. For at least one of the plurality of battery cells, a statistical adaptive threshold is determined, the statistical adaptive threshold depending on the standard deviation of the cell diagnostic deviation for the plurality of battery cells; For at least one of the plurality of battery cells, time series data of the cell diagnostic deviation for the battery cell are filtered based on the statistical adaptive threshold to generate time series data of the filter diagnostic value. as well as For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceed the diagnostic threshold.

5. The battery diagnostic device according to claim 1, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, a short-term / long-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell is determined; For at least one of the plurality of battery cells, the normalized value of the short-term / long-term average difference is determined as the normalized cell diagnostic bias of the battery cell, and a statistical adaptive threshold is determined, the statistical adaptive threshold depending on the standard deviation of the normalized cell diagnostic bias for the plurality of battery cells. For at least one of the plurality of battery cells, the normalized time series data of the cell diagnostic deviation for the battery cell is filtered based on the statistical adaptive threshold to generate time series data of the filter diagnostic value. as well as For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceed the diagnostic threshold.

6. The battery diagnostic device according to claim 5, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, the short-term / long-term average difference of the battery cell is normalized by dividing the short-term / long-term average difference by the average of the short-term / long-term average differences of the plurality of battery cells.

7. The battery diagnostic device according to claim 5, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, the short-term / long-term average difference of the battery cell is normalized by performing a logarithmic calculation on the short-term / long-term average difference.

8. The battery diagnostic device according to claim 1, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, time-series data indicating the change of the cell voltage of the battery cell over time is generated using the voltage difference between the average cell voltage of the plurality of battery cells measured at each unit time and the cell voltage of the battery cell.

9. The battery diagnostic device according to claim 1, wherein, The one or more processors are configured to: For at least one of the plurality of battery cells, a short-term / long-term average difference of the battery cell corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell is determined; For at least one of the plurality of battery cells, the normalized value of the short-term / long-term average difference is determined as the normalized cell diagnostic bias of the battery cell, and time series data of the normalized cell diagnostic bias of the battery cell is generated. For at least one of the plurality of battery cells, the normalized cell diagnostic bias time series data of the battery cell is generated by recursively repeating (i) to (iv) at least once: (i) For the time series data of the normalized cell diagnostic deviation of the battery cell, determine a first moving average and a second moving average, wherein the first moving average is a short-term moving average and the second moving average is a long-term moving average; (ii) determine the short-term / long-term average difference corresponding to the difference between the first moving average and the second moving average of the battery cell; (iii) determine the normalized value of the short-term / long-term average difference as the normalized cell diagnostic deviation of the battery cell; and (iv) generate the time series data of the normalized cell diagnostic deviation of the battery cell. For at least one of the plurality of battery cells, a statistical adaptive threshold is determined, the statistical adaptive threshold depending on the standard deviation of the normalized cell diagnostic bias for the plurality of battery cells; For at least one of the plurality of battery cells, the time series data of the normalized cell diagnostic deviation for the battery cell is filtered based on the statistical adaptive threshold to generate time series data of the filter diagnostic value; and For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceed the diagnostic threshold.

10. The battery diagnostic device according to claim 1, wherein, The first average cell voltage is a short-term moving average of the cell voltage of the battery cell over a first moving window with a first time length, and the second average cell voltage is a long-term moving average of the cell voltage of the battery cell over a second moving window with a second time length.

11. The battery diagnostic device according to claim 10, wherein, The first time length is shorter than the second time length.

12. A battery pack comprising a battery diagnostic device according to any one of claims 1 to 11.

13. A vehicle comprising a battery pack according to claim 12.

14. A battery diagnostic method for multiple battery cells, the battery diagnostic method comprising the following steps: (a) For at least one of the plurality of battery cells, a first average cell voltage and a second average cell voltage of each battery cell are determined based on a voltage signal indicating the cell voltage of the battery cell; as well as (b) For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the difference between the first average cell voltage and the second average cell voltage.

15. The battery diagnostic method according to claim 14, wherein, Step (b) includes the following steps: (b1) For at least one of the plurality of battery cells, determine a short-term / long-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell; (b2) For at least one of the plurality of battery cells, determine a cell diagnostic deviation corresponding to the deviation between the average of the short-term / long-term average differences of the plurality of battery cells and the deviation of the short-term / long-term average difference of the battery cell; and (b3) For at least one of the plurality of battery cells, the battery cell that meets the requirement that the cell diagnostic deviation exceeds the diagnostic threshold is detected as an abnormal cell.

16. The battery diagnostic method according to claim 15, wherein, Step (b3) includes the following steps: For at least one of the plurality of battery cells, time-series data of the cell diagnostic deviation of the battery cell are generated; and For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the cell diagnostic deviation exceeds the diagnostic threshold or the number of data points showing the cell diagnostic deviation exceeding the diagnostic threshold.

17. The battery diagnostic method according to claim 14, wherein, Step (b) includes the following steps: (b1) For at least one of the plurality of battery cells, determine a short-term / long-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell; (b2) For at least one of the plurality of battery cells, a cell diagnostic deviation is determined by calculating the deviation between the average of the short-term / long-term average differences of the plurality of battery cells and the short-term / long-term average difference of the battery cell. (b3) For at least one of the plurality of battery cells, a statistical adaptive threshold is determined, the statistical adaptive threshold depending on the standard deviation of the cell diagnostic deviation for the plurality of battery cells; (b4) For at least one of the plurality of battery cells, time-series data of the cell diagnostic deviation for the battery cell are filtered based on the statistical adaptive threshold to generate time-series data of the filtered diagnostic values; and (b5) For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the filter diagnostic value exceeds a diagnostic threshold or the number of data of the filter diagnostic value that exceeds the diagnostic threshold.

18. The battery diagnostic method according to claim 14, wherein, Step (b) includes the following steps: (b1) For at least one of the plurality of battery cells, determine a short-term / long-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell; (b2) For at least one of the plurality of battery cells, the normalized value of the short-term / long-term average difference is determined as the normalized cell diagnostic bias of the battery cell. (b3) For at least one of the plurality of battery cells, a statistical adaptive threshold is determined, the statistical adaptive threshold depending on the standard deviation of the normalized cell diagnostic bias for the plurality of battery cells; (b4) For at least one of the plurality of battery cells, time-series data of the normalized cell diagnostic bias for the battery cell are filtered based on the statistical adaptive threshold to generate time-series data of the filter diagnostic values; and (b5) For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the filter diagnostic value exceeds a diagnostic threshold or the number of data of the filter diagnostic value that exceeds the diagnostic threshold.

19. The battery diagnostic method according to claim 18, wherein, Step (b2) includes the following steps: For at least one of the plurality of battery cells, the short-term / long-term average difference of the battery cell is normalized by dividing the short-term / long-term average difference by the average of the short-term / long-term average differences of the plurality of battery cells.

20. The battery diagnostic method according to claim 18, wherein, Step (b2) includes the following steps: For at least one of the plurality of battery cells, the short-term / long-term average difference of the battery cell is normalized by performing a logarithmic calculation on the short-term / long-term average difference.

21. The battery diagnostic method according to claim 14, further comprising the following steps: For at least one of the plurality of battery cells, time-series data indicating the change of the cell voltage of the battery cell over time is generated using the voltage difference between the average cell voltage of the plurality of battery cells measured at each unit time and the cell voltage of the battery cell.

22. The battery diagnostic method according to claim 14, wherein, Step (b) includes the following steps: (b1) For at least one of the plurality of battery cells, determine a short-term / long-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage of the battery cell; (b2) For at least one of the plurality of battery cells, the normalized value of the short-term / long-term average difference is determined as the normalized cell diagnostic bias of the battery cell. (b3) For at least one of the plurality of battery cells, generate time series data of the normalized cell diagnostic bias of the battery cell; (b4) For at least one of the plurality of battery cells, the normalized cell diagnostic bias time series data of the battery cell is generated by recursively repeating (i) to (iv) at least once: (i) For the time series data of the normalized cell diagnostic deviation of the battery cell, determine a first moving average and a second moving average, wherein the first moving average is a short-term moving average and the second moving average is a long-term moving average; (ii) determine the short-term / long-term average difference corresponding to the difference between the first moving average and the second moving average of the battery cell; (iii) determine the normalized value of the short-term / long-term average difference as the normalized cell diagnostic deviation of the battery cell; and (iv) generate the time series data of the normalized cell diagnostic deviation of the battery cell. (b5) For at least one of the plurality of battery cells, a statistical adaptive threshold is determined, the statistical adaptive threshold depending on the standard deviation of the normalized cell diagnostic bias for the plurality of battery cells; (b6) For at least one of the plurality of battery cells, the normalized time-series data of the cell diagnostic deviation for the battery cell is filtered based on the statistical adaptive threshold to generate time-series data of the filter diagnostic values; and (b7) For at least one of the plurality of battery cells, an anomaly of the battery cell is detected based on the time period during which the filter diagnostic value exceeds a diagnostic threshold or the number of data of the filter diagnostic value that exceeds the diagnostic threshold.

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