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

Abstract

The present invention provides a battery diagnostic device, a battery diagnostic method, a battery pack, and an automobile for efficiently and accurately diagnosing abnormal voltage in battery cells. [Solution] The battery diagnostic device includes a voltage sensing circuit configured to generate a voltage signal that periodically indicates the cell voltage of a plurality of battery cells connected in series, and a control circuit configured to generate time-series data showing the change in the cell voltage of each battery cell over time based on the voltage signal, wherein the control circuit is configured to (i) determine a first average cell voltage and a second average cell voltage of each battery cell based on the time-series data (where 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 a voltage abnormality of each battery cell based on the difference between the first average cell voltage and the second average cell voltage.

Description

[Technical Field]

[0001] This invention relates to a technique for diagnosing abnormal battery voltage.

[0002] This application claims priority based on Korean Patent Application No. 10-2020-0163366, filed on November 27, 2020, and all contents disclosed in the specification and drawings of said application are incorporated herein by reference. [Background technology]

[0003] In recent years, demand for portable electronic products such as laptops, video cameras, and mobile phones has grown rapidly, and as development of electric vehicles, energy storage batteries, robots, and satellites intensifies, research into high-performance batteries that can be repeatedly charged and discharged is becoming increasingly active.

[0004] Currently, commercially available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, and lithium batteries. Among these, lithium batteries are attracting attention because they exhibit almost no memory effect compared to nickel-based batteries, allowing for flexible charging and discharging, and they have the advantages of a very low self-discharge rate and high energy density.

[0005] In recent years, as applications requiring high voltage (e.g., energy storage systems, electric vehicles) have become widespread, there is a growing need for diagnostic techniques that can accurately detect voltage anomalies in each of the multiple battery cells connected in series in a battery pack.

[0006] Battery cell voltage abnormality refers to a fault condition in which the cell voltage drops and / or rises abnormally due to internal short circuits, external short circuits, failures in the voltage sensing line, poor connections with the charge / discharge line, etc.

[0007] Conventionally, attempts have been made to diagnose voltage abnormalities in individual battery cells by comparing the cell voltage—the voltage across each battery cell at a specific point in time—with the average cell voltage of multiple battery cells at the same point in time. However, since the cell voltage of each battery cell also depends on the temperature, current, and / or state of health (SOH) of that battery cell, it is difficult to accurately diagnose voltage abnormalities in individual battery cells simply by comparing the cell voltages measured for multiple battery cells at a specific point in time. For example, even a battery cell without voltage abnormalities may have a large difference between its cell voltage and the average cell voltage if there is a large temperature or SOH deviation compared to the remaining battery cells.

[0008] To solve this problem, when diagnosing voltage abnormalities in each battery cell, it is conceivable to use additional parameters such as the charge / discharge current, the temperature of each battery cell, and / or the State of Charge (SOC) of each battery cell, along with the cell voltage of each battery cell. However, diagnostic methods that utilize additional parameters have limitations in that they are relatively more complex and time-consuming compared to diagnostic methods that use cell voltage as a single parameter, because they must involve detection and comparison processes for each parameter. [Overview of the Initiative] [Problems that the invention aims to solve]

[0009] The present invention was devised to solve the above-mentioned problems, and aims to provide a battery diagnostic device, a battery diagnostic method, a battery pack, and an automobile for efficiently and accurately diagnosing voltage abnormalities in each battery cell by determining the moving average of the cell voltage of each of a plurality of battery cells for each of at least one moving windows having a predetermined time length for each unit time, and based on the moving average of each battery cell.

[0010] Other objects and advantages of the present invention can be understood from the following description and will be more clearly evident from the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims. [Means for solving the problem]

[0011] A battery diagnostic device for achieving the above technical objectives is a battery diagnostic device for a cell group including a plurality of battery cells connected in series, and may include a voltage sensing circuit configured to generate a voltage signal that periodically indicates the cell voltage of each battery cell, and a control circuit configured to generate time-series data that indicates the change in the cell voltage of each battery cell over time based on the voltage signal.

[0012] Preferably, the control circuit may be configured to (i) determine a first average cell voltage and a second average cell voltage for each battery cell based on the time-series data (where 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 a voltage anomaly in each battery cell based on the difference between the first average cell voltage and the second average cell voltage.

[0013] In one embodiment, the control circuit may be configured to determine a long- and short-term average difference for each battery cell, which corresponds to the difference between the first average cell voltage and the second average cell voltage; to determine a cell diagnostic deviation for each battery cell, which corresponds to the deviation between the average value of the long- and short-term average differences of all battery cells and the long- and short-term average difference of the battery cell; and to detect battery cells that satisfy the condition that the cell diagnostic deviation exceeds a diagnostic threshold as voltage abnormal cells.

[0014] Preferably, the control circuit may be configured to generate time-series data of cell diagnostic deviations for each battery cell and to detect a voltage abnormality in a battery cell from the time the cell diagnostic deviation exceeds a diagnostic threshold or the number of data points of cell diagnostic deviations that exceed the diagnostic threshold.

[0015] In other embodiments, the control circuit may be configured to determine, for each battery cell, a long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage; for each battery cell, the deviation between the average value of the long- and short-term average differences of all battery cells and the long- and short-term average difference of the battery cell to determine a cell diagnostic deviation; to determine a statistically variable threshold that depends on the standard deviation of the cell diagnostic deviation of all battery cells; to filter the time-series data relating to the cell diagnostic deviation of each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values; and to detect a voltage abnormality of a battery cell from the time the filtered diagnostic value exceeds the diagnostic threshold or the number of data points of filtered diagnostic values ​​that exceed the diagnostic threshold.

[0016] In yet another embodiment, the control circuit may be configured to determine, for each battery cell, a long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage; determine a normalized value of the long- and short-term average difference for each battery cell as the normalized cell diagnostic deviation; determine a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviations of all battery cells; filter the time-series data relating to the normalized cell diagnostic deviations of each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values; and detect a voltage abnormality of a battery cell from the time the filtered diagnostic value exceeds the diagnostic threshold or the number of data points of filtered diagnostic values ​​that exceed the diagnostic threshold.

[0017] Preferably, the control circuit may normalize the long- and short-term average difference for each battery cell by dividing the long- and short-term average difference by the average value of the long- and short-term average differences of all battery cells.

[0018] Alternatively, the control circuit may normalize the long-term and short-term average differences for each battery cell by performing a logarithmic calculation of the long-term and short-term average differences.

[0019] In yet another embodiment, the control circuit may be configured to generate time-series data showing the change in the cell voltage of each battery cell over time, using a voltage corresponding to the difference between the average cell voltage of all battery cells measured at each unit time and the cell voltage of each battery cell.

[0020] In yet another embodiment, the control circuit determines, for each battery cell, the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage, determines the normalized value of the long- and short-term average difference for each battery cell as the normalized cell diagnostic deviation, and generates time-series data of the normalized cell diagnostic deviation for each battery cell.

[0021] (i) Determine the first and second moving averages for the time series data of the normalized cell diagnostic deviation for each battery cell [where the first moving average is a short-term moving average and the second moving average is a long-term moving average]; (ii) For each battery cell, determine the long- and short-term average difference corresponding to the difference between the first and second moving averages; (iii) For each battery cell, determine the normalized value of the long- and short-term average difference as the normalized cell diagnostic deviation; and (iv) For each battery cell, generate the time series data of the normalized cell diagnostic deviation. These steps are repeated recursively at least once to generate the time series data of the normalized cell diagnostic deviation for each battery cell.

[0022] The system may be configured to determine a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation of all battery cells, to filter time-series data of the normalized cell diagnostic deviation of each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values, and to detect battery cell voltage anomalies from the time the filtered diagnostic value exceeds the diagnostic threshold or the number of data points of filtered diagnostic values ​​that exceed the diagnostic threshold.

[0023] A battery diagnostic method according to the present invention for achieving the above technical problems is a battery diagnostic method for a cell group including a plurality of battery cells connected in series, and may include: (a) the step of periodically generating time-series data showing the change in the cell voltage of each battery cell over time; (b) the step of 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) the step of detecting a voltage abnormality of each battery cell based on the difference between the first average cell voltage and the second average cell voltage.

[0024] In one embodiment, step (c) may include (c1) determining the long- and short-term average difference for each battery cell, which corresponds to the difference between the first average cell voltage and the second average cell voltage; (c2) determining the cell diagnostic deviation for each battery cell, which corresponds to the deviation between the average value of the long- and short-term average differences of all battery cells and the long- and short-term average difference of the battery cell; and (c3) detecting battery cells that satisfy the condition that the cell diagnostic deviation exceeds a diagnostic threshold as voltage abnormal cells.

[0025] Preferably, step (c) may include (c1) generating time-series data of cell diagnostic deviation for each battery cell, and (c2) detecting a voltage abnormality of a battery cell from the time the cell diagnostic deviation exceeds a diagnostic threshold or the number of data points of cell diagnostic deviation exceeding the diagnostic threshold.

[0026] In other embodiments, step (c) may include (c1) determining a long- and short-term average difference for each battery cell, corresponding to the difference between the first average cell voltage and the second average cell voltage; (c2) determining a cell diagnostic deviation for each battery cell by calculating the deviation between the average value of the long- and short-term average differences of all battery cells and the long- and short-term average difference of the battery cell; (c3) determining a statistically variable threshold that depends on the standard deviation of the cell diagnostic deviation of all battery cells; (c4) filtering the time-series data relating to the cell diagnostic deviation of each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values ​​for each battery cell; and (c5) detecting a voltage anomaly of a battery cell from the time the filtered diagnostic value exceeds the diagnostic threshold or the number of data points of filtered diagnostic values ​​that exceed the diagnostic threshold.

[0027] In further embodiments, step (c) may include (c1) determining a long- and short-term average difference for each battery cell, corresponding to the difference between the first average cell voltage and the second average cell voltage; (c2) determining a normalized value of the long- and short-term average difference for each battery cell as a normalized cell diagnostic deviation; (c3) determining a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation for all battery cells; (c4) filtering time-series data relating to the normalized cell diagnostic deviation for each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values; and (c5) detecting a voltage anomaly in a battery cell from the time the filtered diagnostic value exceeds the diagnostic threshold or the number of data points of filtered diagnostic values ​​that exceed the diagnostic threshold.

[0028] Preferably, step (c2) may be a step in which the long- and short-term average difference for each battery cell is divided by the average value of the long- and short-term average differences of all battery cells to normalize the long- and short-term average difference.

[0029] Alternatively, step (c2) may be a step in which the long- and short-term average differences are normalized for each battery cell by logarithm calculation of the long- and short-term average differences.

[0030] In other embodiments, step (a) may be a step of generating time-series data showing the change in the cell voltage of each battery cell over time, using a voltage corresponding to the difference between the average cell voltage of all battery cells measured at each unit time and the cell voltage of each battery cell.

[0031] In yet another embodiment, step (c) includes (c1) determining the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage for each battery cell, (c2) determining the normalized value of the long- and short-term average difference for each battery cell as the normalized cell diagnostic deviation, and (c3) generating time-series data of the normalized cell diagnostic deviation for each battery cell.

[0032] (c4)(i) Determine the first moving average and the second moving average for the time series data of the normalized cell diagnostic deviation for each battery cell [where the first moving average is a short-term moving average and the second moving average is a long-term moving average], (ii) For each battery cell, determine the long- and short-term average difference corresponding to the difference between the first moving average and the second moving average, (iii) For each battery cell, determine the normalized value of the long- and short-term average difference as the normalized cell diagnostic deviation, and (iv) For each battery cell, generate the time series data of the normalized cell diagnostic deviation. The above steps are repeated recursively at least once to generate the time series data of the normalized cell diagnostic deviation for each battery cell.

[0033] (c5) The steps may include determining a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation of all battery cells, (c6) filtering time-series data of the normalized cell diagnostic deviation of each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values, and (c7) detecting a voltage anomaly of a battery cell from the time the filtered diagnostic value exceeds the diagnostic threshold or the number of data points of filtered diagnostic values ​​that exceed the diagnostic threshold.

[0034] The above technical challenges can also be addressed by a battery pack including the aforementioned battery diagnostic device and an automobile containing it. [Effects of the Invention]

[0035] According to one aspect of the present invention, two moving averages of the cell voltage of each battery cell are determined for each unit time over two different time lengths, and voltage abnormalities in each battery cell can be efficiently and accurately diagnosed based on the difference between the two moving averages of each of the multiple battery cells.

[0036] According to another aspect of the present invention, voltage anomalies in each battery cell can be accurately diagnosed by applying advanced techniques such as normalization and / or statistical variable thresholding when analyzing the difference in the trend of change of two moving averages of each battery cell.

[0037] According to yet another aspect of the present invention, time-series data of filter diagnostic values ​​determined based on a statistically variable threshold can be analyzed to accurately detect the time interval in which voltage anomalies occurred in each battery cell and / or the voltage anomaly detection count.

[0038] The effects of the present invention are not limited to those described above, and any other effects not mentioned will be clearly understood by those skilled in the art from the claims. [Brief explanation of the drawing]

[0039] The following drawings accompanying this specification illustrate preferred embodiments of the present invention and, together with the detailed description of the invention later, serve to further illustrate the technical concept of the present invention. Therefore, the present invention should not be construed as being limited solely to what is depicted in such drawings.

[0040] [Figure 1] This diagram illustrates the configuration of an electric vehicle according to one embodiment of the present invention. [Figure 2a]Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 2b] Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 2c] Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 2d] Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 2e] Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 2f] Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 2g] Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 2h] Figure 1 is a graph used to explain the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells over time. [Figure 3] This flowchart illustrates a battery diagnostic method according to the first embodiment of the present invention. [Figure 4] This flowchart illustrates a battery diagnostic method according to a second embodiment of the present invention. [Figure 5]This flowchart illustrates a battery diagnostic method according to a third embodiment of the present invention. [Figure 6] This flowchart illustrates a battery diagnostic method according to a fourth embodiment of the present invention. [Figure 7] This flowchart illustrates a battery diagnostic method according to a fifth embodiment of the present invention. [Modes for carrying out the invention]

[0041] Preferred embodiments of the present invention will now be described in detail with reference to the attached drawings. Prior to this, terms and words used in this specification and in the claims should not be interpreted in a manner limited to their usual or dictionary meanings, but rather in a manner corresponding to the technical idea of ​​the present invention, in accordance with the principle that the inventor himself can appropriately define the concepts of terms in order to best describe the invention.

[0042] Therefore, the embodiments described herein and the configurations shown in the drawings represent only one of the most preferred embodiments of the present invention and do not represent the entire technical concept of the present invention. It should be understood that there are various equivalents and modifications that can be substituted for these at the time of filing this application.

[0043] Terms including ordinal numbers such as "1st" and "2nd" are used to distinguish one of the various constituent elements from the others, and these terms do not limit the constituent elements.

[0044] Throughout the specification, when a part "includes" a component, this means, unless otherwise specified, that it may include other components rather than excluding them. Furthermore, terms such as "control unit" in the specification mean a unit that processes at least one function or operation, and can be embodied in hardware, software, or a combination of hardware and software.

[0045] Furthermore, when a part of the specification is described as being "connected" to another part, this includes not only cases where the parts are "directly connected," but also cases where they are "indirectly connected" with other elements in between.

[0046] Figure 1 is a diagram illustrating the configuration of an electric vehicle according to one embodiment of the present invention.

[0047] Referring to Figure 1, the electric vehicle 1 includes a battery pack 10, an inverter 3, an electric motor 4, and a vehicle controller 5.

[0048] The battery pack 10 includes a cell group CG, a switch 6, and a battery management system 100.

[0049] The cell group CG may be coupled to the inverter 3 via a pair of power terminals provided on the battery pack 10. The cell group CG consists of multiple battery cells BC1 to BC connected in series. N、 (N is a natural number greater than or equal to 2) Each battery cell BC i It is not particularly limited to any type as long as it can be recharged, like a lithium-ion battery cell. i is an index for identifying the cell. i is a natural number from 1 to N.

[0050] Switch 6 is connected in series with the cell group CG. Switch 6 is located in the current path for charging and discharging the cell group CG. Switch 6 is controlled to be on or off in response to a switching signal from the battery management system 100. Switch 6 may be a mechanical relay that is switched on or off by the magnetic force of a coil, or it may be a semiconductor switch such as a metal oxide semiconductor field effect transistor (MOSFET).

[0051] The inverter 3 is provided to convert the DC current from the cell group CG into AC current in response to a command from the battery management system 100. The electric motor 4 may be, for example, a three-phase AC motor. The electric motor 4 is driven using the AC power from the inverter 3.

[0052] The battery management system 100 is provided to be responsible for the overall control related to the charging and discharging of the cell group CG.

[0053] The battery management system 100 includes a battery diagnostic device 200. The battery management system 100 may further include at least one of a current sensor 310, a temperature sensor 320, and an interface unit 330.

[0054] The battery diagnostic device 200 has multiple battery cells BC1~BC N It is provided to diagnose each voltage anomaly. The battery diagnostic device 200 includes a voltage sensing circuit 210 and a control circuit 220.

[0055] The voltage sensing circuit 210 transmits signals to multiple battery cells BC1~BC via multiple voltage sensing lines. N It is connected to the anode and cathode of each of the batteries. The voltage sensing circuit 210 is configured to measure the cell voltage across each battery cell BC and to generate a voltage signal representing the measured cell voltage.

[0056] The current sensor 310 is connected in series to the cell group CG via a current path. The current sensor 310 is configured to detect the battery current flowing through the cell group CG and to generate a current signal representing the detected battery current.

[0057] The temperature sensor 320 is configured to detect the temperature of the cell group CG and generate a temperature signal representing the detected temperature.

[0058] The control circuit 220 may be implemented in hardware using at least one of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), microprocessors, and other electrical units for performing functions.

[0059] The control circuit 220 may have a memory section. The memory section may include at least one type of storage medium from among flash memory type, hard disk type, solid state disk type (SSD type), silicon disk drive type (SDD type), multimedia card micro type, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and programmable read-only memory (PROM). The memory section can store data and programs necessary for the calculation operations performed by the control circuit 220. The memory section can store data indicating the results of the calculation operations performed by the control circuit 220. In particular, the control circuit 220 can record at least one of the various parameters calculated per unit time, as described later, in the memory section.

[0060] The control circuit 220 can be operably 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 the synchronously detected voltage signals, current signals, and / or temperature signals.

[0061] The interface unit 330 may include a communication circuit configured to support wired or wireless communication between the control circuit 220 and the vehicle controller 5 (for example, an Electronic Control Unit (ECU)). Wired communication could be, for example, controller area network (CAN) communication, and wireless communication could be, for example, ZigBee® or Bluetooth® communication. Needless to say, the type of communication protocol is not particularly limited as long as it supports wired or wireless communication between the control circuit 220 and the vehicle controller 5.

[0062] The interface unit 330 can be combined with 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 a user-recognizable format. The vehicle controller 5 can control the inverter 3 based on battery information (e.g., voltage, current, temperature, SOC) collected through communication with the battery management system 100.

[0063] Figures 2a to 2h are illustrative graphs showing the process of diagnosing voltage abnormalities in each battery cell from time-series data showing the change in the cell voltage of each of the multiple battery cells shown in Figure 1 over time.

[0064] Figure 2a shows multiple battery cells BC1~BC N The voltage curves for each are shown. There are 14 battery cells. The control circuit 220 collects voltage signals from the voltage sensing circuit 210 at unit time intervals, and calculates the voltage for each battery cell BC. i The voltage value of the cell voltage is recorded in the memory unit. The unit time may be an integer multiple of the voltage measurement period of the voltage sensing circuit 210.

[0065] The control circuit 220 controls each battery cell BC recorded in the memory unit. iBased on the voltage value of the cell voltage, cell voltage time-series data indicating the history of the cell voltage of each battery cell over time can be generated. Each time the cell voltage is measured, the number of cell voltage time-series data increases by one.

[0066] The plurality of voltage curves shown in FIG. 2a are associated one-to-one with the plurality of battery cells BC1 to BC N Therefore, each voltage curve shows the change history of the cell voltage of any one of the battery cells BC associated with it.

[0067] The control circuit 220 can determine the moving average of each of the plurality of battery cells BC1 to BC N per unit time using one moving window or two moving windows. When using two moving windows, the time length of one moving window is different from the time length of the other moving window.

[0068] Here, the time length of each moving window is an integer multiple of the unit time, the end point of each moving window is the current time, and the start point of each moving window is a time point that precedes the current time by a predetermined time length.

[0069] Hereinafter, for the sake of convenience of explanation, the one related to the shorter time length among the two moving windows is called the first moving window, and the one related to the longer time length is called the second moving window.

[0070] The control circuit 220 can diagnose the voltage abnormality of each battery cell BC i using only the first moving window or both the first moving window and the second moving window.

[0071] The control circuit 220 can compare the short-term change trend and the long-term change trend of the cell voltage of the i-th battery cell BC i collected per unit time based on the cell voltage of the i-th battery cell BC i per unit time.

[0072] The control circuit 220 uses either Equation 1 or Equation 2 to determine the i-th battery cell BC by the first moving window. i The first average cell voltage, which is a moving average, can be determined for each unit time.

[0073] Formula 1 is the formula for calculating the moving average using the arithmetic mean method, and Formula 2 is the formula for calculating the moving average using the weighted mean method.

[0074] <Formula 1>

number

[0075] <Formula 2>

number

[0076] In equations 1 and 2, k is the time index representing the current time, and SMA i [k] is the current i-th battery cell BC i V is the first average cell voltage, S is the value obtained by dividing the time length of the first moving window by the unit time, and V is the first average cell voltage. i [k] is the current i-th battery cell BC i This is the cell voltage. For example, if the unit time is 1 second and the time length of the first moving window is 10 seconds, then S is 10. When x is a natural number less than or equal to k, V i [kx] and SMA i [kx] represents the i-th battery cell BC when the time index is kx. i The cell voltage and the first average cell voltage are shown. For reference, the control circuit 220 may be set to increment the time index by 1 for each unit of time.

[0077] The control circuit 220 uses the following equation 3 or equation 4 to determine the i-th battery cell BC by the second moving window. iThe second average cell voltage, which is a moving average of the two values, can be determined for each unit of time.

[0078] Formula 3 is the formula for calculating the moving average using the arithmetic mean method, and Formula 4 is the formula for calculating the moving average using the weighted average method.

[0079] <Formula 3>

number

[0080] <Formula 4>

number

[0081] In equations 3 and 4, k is the time index representing the current time, LMA i [k] is the current i-th battery cell BC i The second average cell voltage, L is the value obtained by dividing the time length of the second moving window by the unit time, V i [k] is the current i-th battery cell BC i This is the cell voltage. For example, if the unit time is 1 second and the time length of the second moving window is 100 seconds, then L is 100. If x is a natural number less than or equal to k, then LMA i [kx] represents the second average cell voltage when the time index is kx.

[0082] In one embodiment, the control circuit 220 is V in equations 1 to 4. i [k] represents each battery cell BC at present i Instead of the cell voltage, use the current reference cell voltage of cell group CG and battery cell BC. i The difference between the cell voltage and the input can be used.

[0083] The current reference cell voltage for cell group CG is the voltage of multiple battery cells BC1~BC NThis is the average value of multiple cell voltages determined at the current time. In a modified example, the average value of multiple cell voltages can be replaced with the median value.

[0084] Specifically, the control circuit 220 is VD in the following equation 5. i [k] V in formulas 1-4 i It can be set as [k].

[0085] <Formula 5> VD i [k]=V av [k]-V i [k]

[0086] In equation 5, V av [k] is the reference cell voltage of cell group CG at the current time, and is the average value of multiple cell voltages.

[0087] If the time length of the first moving window is shorter than the time length of the second moving window, the first average cell voltage can be called the "short-term moving average" of the cell voltage, and the second average cell voltage can be called the "long-term moving average" of the cell voltage.

[0088] Figure 2b shows the i-th battery cell BC, determined from the multiple voltage curves shown in Figure 2a. i The short-term and long-term moving averages for the cell voltage are shown. In Figure 2b, the horizontal axis represents time, and the vertical axis represents the short-term and long-term moving averages of the cell voltage.

[0089] Referring to Figure 2b, the multiple moving average lines Si shown by the dotted lines represent multiple battery cells BC1~BC N It is associated one-to-one with the first average cell voltage (SMA) of each battery cell BC. i [k]) shows the history of changes over time. Also, multiple moving averages L are shown as solid lines. i This consists of multiple battery cells BC1~BC N It is associated one-to-one with the second average cell voltage (LMA) of each battery cell BC. i This shows the time-dependent changes in [k].

[0090] The dotted line graph and the solid line graph were obtained using Equation 2 and Equation 4, respectively. Furthermore, the V in Equations 2 and 4... i As [k], use VDi[k] from formula 5, V av [k] was set as the average of multiple cell voltages. The time length of the first moving window was 10 seconds, and the time length of the second moving window was 100 seconds.

[0091] Figure 2c shows the first average cell voltage (SMA) of each battery cell shown in Figure 2b. i [k]) and second average cell voltage (LMA i [k]) The graph shows the change over time of the long-term average difference (absolute value) corresponding to the difference. In Figure 2c, the horizontal axis represents time, and the vertical axis represents each battery cell BC i This shows the difference between the long-term and short-term averages.

[0092] Each battery cell BC i The long-term average difference is for each battery cell BC per unit time. i The first average cell voltage (SMA) i [k]) and second average cell voltage (LMA i This is the difference from [k]). For example, the i-th battery cell BC i The difference between the long and short-term averages is the SMA. i [k] and LMA i It may also be the same as the difference between one of the values ​​of [k] (e.g., the larger one) and the other (e.g., the smaller one).

[0093] First battery cell BC i The long-term average difference is the i-th battery cell BC i It depends on the short-term and long-term change history of the cell voltage.

[0094] First battery cell BC i The temperature and SOH steadily increase not only in the short term but also in the long term for the i-th battery cell BC i This affects the cell voltage of the i-th battery cell BC. i If there is no abnormality in the voltage, then the i-th battery cell BC iThe long-term average difference for this cell is not significantly different from the long-term average difference for the remaining battery cells.

[0095] Meanwhile, the i-th battery cell BC i Voltage abnormalities that suddenly occur due to internal and / or external short circuits, etc., are classified as the second average cell voltage (LMA). i [k]) is less than the first average cell voltage (SMA i [k]) has a significant impact. As a result, the i-th battery cell BC i The long-term average difference of [cell name] shows a large deviation from the long-term average difference of the remaining battery cells that do not exhibit voltage abnormalities.

[0096] The control circuit 220 controls each battery cell BC at each unit time interval. i The short-term and long-term average difference (SMA) i [k]-LMA i The control circuit 220 can determine the long-term average difference (SMA). i [k]-LMA i The mean value of [k]|) can be determined. Below, the mean value is |SMA i [k]-LMA i [k]| av This is expressed as follows. The control circuit 220 also calculates the average value of the short-term average difference (|SMA). i [k]-LMA i [k]| av ) and the short-term / long-term average difference (|SMA) i [k]-LMA i [k]|) The deviation from the cell diagnostic deviation (D diag,i [k]) can be determined. In addition, the control circuit 220 determines the cell diagnostic deviation (D diag,i [k]) based on each battery cell BC i It can diagnose voltage anomalies.

[0097] In one embodiment, the control circuit 220 controls the i-th battery cell BC i Cell diagnostic deviation (D diag,i If [k]) exceeds a predetermined diagnostic threshold (e.g., 0.015), the i-th battery cell BC i It can be diagnosed that a voltage anomaly has occurred.

[0098] Preferably, the control circuit 220 calculates the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) of each battery cell BC for voltage abnormality diagnosis, and can normalize it using a normalization reference value. Preferably, the normalization reference value is the average value of the long-term and short-term average differences (|SMA i [k]-LMA i [k]| i ). av )

[0099] Specifically, the control circuit 220 can set the average value of the long-term and short-term average differences (|SMA i [k]-LMA N [k]|) of the first to Nth battery cells BC~BC as the normalization reference value. The control circuit 220 can also divide the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) of each battery cell BC by the normalization reference value to normalize the long-term and short-term average difference (|SMA av [k]-LMA i [k]|). i [k]-LMA i [k]|) by the normalization reference value to normalize the long-term and short-term average difference (|SMA i [k]-LMA i [k]|).

[0100] The following Equation 6 shows an equation for normalizing the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) of each battery cell BC. In an embodiment, the value calculated by Equation 6 can be named the normalized cell diagnosis deviation (D i [k]). * diag,i [k])

[0101] <Equation 6> D * diag,i [k] = (|SMA i [k]-LMA i [k]|) ÷ (|SMA i [k]-LMA i [k]| av )<​​In formula 6, |SMA i [k]-LMA i [k]| is the current i-th battery cell BC i The difference between the long-term and short-term averages, |SMA i [k]-LMA i [k]| av D is the average value (normalized baseline) of the long-term average difference of all battery cells. * diag,i [k] is the current i-th battery cell BC i This is the normalized cell diagnostic deviation. The symbol "*" indicates that the parameter has been normalized.

[0103] Each battery cell BC i The short-term and long-term average difference (SMA) i [k]-LMA i [k]|) can also be normalized by the logarithm operation of formula 7 below. In this embodiment, the value calculated by formula 7 is also a normalized cell diagnostic deviation (D * diag,i It can be named [k]).

[0104] <Formula 7> D * diag,i [k]=Log|SMA i [k]-LMA i [k]|

[0105] Figure 2d shows each battery cell BC. i Normalized cell diagnostic deviation (D * diag,i [k]) shows the change over time. Cell diagnostic deviation (D * diag,i [k]) was calculated using formula 6. In Figure 2d, the horizontal axis represents time, and the vertical axis represents each battery cell BC. i Cell diagnostic deviation (D * diag,i [k]) represents

[0106] Referring to Figure 2d, each battery cell BC i The short-term and long-term average difference (SMA) i [k]-LMA i[k]|) is normalized, and each battery cell BC i It can be seen that the change in the long-term average difference is amplified relative to the average value. This allows for more accurate diagnosis of battery cell voltage abnormalities.

[0107] Preferably, the control circuit 220 controls each battery cell BC i Normalized cell diagnostic deviation (D * diag,i [k]) and statistically variable threshold (D threshold [k]) Compare each battery cell BC i It can perform voltage anomaly diagnosis.

[0108] Preferably, the control circuit 220 uses the following formula 8 to determine a statistically variable threshold (D) at each unit time interval. threshold [k]) can be set.

[0109] <Formula 8> D threshold [k] = β * Sigma(D * diag,i [k])

[0110] In Equation 8, Sigma is the normalized cell diagnostic deviation (D) of all battery cells BC at time index k. * diag、i This is a function for calculating the standard deviation of [k]). β is an experimentally determined constant. β is a factor that determines the diagnostic sensitivity. β can be appropriately determined through trial and error so that when the present invention is applied to a cell group containing a battery cell where an actual voltage anomaly has occurred, the battery cell can be detected as a voltage anomaly cell. In one 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. D generated by Equation 8 threshold Since [k] is plural, it constitutes time-series data.

[0111] On the other hand, battery cells with voltage abnormalities are normalized to the cell diagnostic deviation (D *diag、i [k]) is relatively larger than that of a normal battery cell. Therefore, to improve the accuracy and reliability of the diagnosis, Sigma(D) is calculated using the time index k. * diag,i When calculating the maximum value of [k]), the max(D * diag,i It is preferable to exclude [k]). Here, max is a function that returns the maximum value of multiple input variables, and the input variables are the normalized cell diagnostic deviation (D) of all battery cells. * diag,i [k]) is the case.

[0112] In Figure 2d, the statistical variable threshold (D threshold The time-series data showing the time evolution of [k]) corresponds to the profile displayed in the darkest color among all profiles.

[0113] The control circuit 220 sets a statistically variable threshold (D) at time index k. threshold After determining [k]), use the following formula 9 to determine each battery cell BC i Normalized cell diagnostic deviation (D * diag,i [k]) By filtering, the filtered diagnostic value (D filter、i [k]) can be determined.

[0114] Each battery cell BC i Filter diagnostic value (D) filter、i Two values ​​can be assigned to [k]. That is, the cell diagnostic deviation (D * diag,i [k]) is the statistically variable threshold (D threshold If it is greater than [k], the cell diagnostic deviation (D * diag,i [k]) and statistically variable threshold (D Threshold [k]) The difference value is the filter diagnostic value (D filter、i [k]) is assigned to it. On the other hand, the cell diagnostic deviation (D * diag,i [k]) is the statistically variable threshold (D threshold If [k]) is less than or equal to 0 (zero), the filter diagnostic value (D filter、iIt is assigned to [k]).

[0115] <Formula 9> D filter,i [k]=D * diag,i [k]-D threshold [k](IF D * diag,i [k]>D threshold [k]) D filter,i [k]=0(IF D * diag,i [k]≦D threshold [k])

[0116] Figure 2e shows the cell diagnostic deviation (D) at time index k. * diag,i [k]) Filter diagnostic values ​​obtained by filtering (D filter、i This figure shows the time series data of [k]).

[0117] Referring to Figure 2e, the filter diagnostic value for a specific battery cell (D filter、i An irregular pattern is observed where [k] has positive values ​​around 3000 seconds. For reference, the specific battery cell exhibiting this irregular pattern is the battery cell with the time-series data shown as A in Figure 2d.

[0118] In one example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i By accumulating time intervals where [k]) is greater than a diagnostic threshold (e.g., 0), battery cells that satisfy the condition that the accumulated time is greater than a predetermined reference time can be diagnosed as having a voltage abnormality.

[0119] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,i The control circuit 220 can accumulate time intervals in which the condition [k]) is continuously met and greater than the diagnostic threshold. If there are multiple such time intervals, the control circuit 220 can independently calculate the accumulated time for each time interval.

[0120] In another example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i The number of data points included in a time interval where [k]) is greater than a diagnostic threshold (e.g., 0) is accumulated, and battery cells that satisfy the condition that the accumulated data value is greater than a predetermined reference count can be diagnosed as having a voltage abnormality.

[0121] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,i Only the number of data points included in a time interval in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can independently accumulate the number of data points for each time interval.

[0122] On the other hand, the control circuit 220 is V in equations 1 to 5. i [k] is each battery cell BC shown in Figure 2d. i Normalized cell diagnostic deviation (D * diag,i [k]) can be replaced. In addition, the control circuit 220 controls the cell diagnostic deviation (D) at time index k. * diag,i [k]) Long-term and short-term mean difference (|SMA) i [k]-LMA i [k]|)Calculation, Cell diagnostic deviation (D * diag,i [k]) Long-term and short-term mean difference (|SMA) i [k]-LMA i [k]|) Calculation of the average value, short-term and long-term average difference (|SMA) i [k]-LMA i [k]|) The cell diagnostic deviation (D) corresponds to the difference from the mean. diag,i [k]) calculation, using formula 6 for the short-term and long-term average difference (|SMA) i [k]-LMA i [k]|) Normalized cell diagnostic deviation (D * diag,i[k]) calculation, normalized cell diagnostic deviation (D) using formula 8. * diag,i [k]) statistical variable threshold (D threshold [k]) determination, cell diagnostic deviation (D) using formula 9 * diag,i Filter diagnostic values ​​(D) obtained by filtering [k]) filter,i [k]) determination, and filter diagnostic value (D filter,i [k]) can be used to recursively perform voltage anomaly diagnosis of battery cells using time-series data.

[0123] Figure 2f shows the normalized cell diagnostic deviation (D * diag,i [k]) Time series data (Figure 2d) with respect to the short-term and long-term mean difference (|SMA) i [k]-LMA i This is a graph showing the time change of the short-term average difference (SMA). i [k]-LMA i In formulas 2, 4, and 5 used in the calculation of [k]|), V i [k] is D * diag,i [k] may be substituted, V av [k] is D * diag,i [k] may be replaced with its mean value.

[0124] Figure 2g shows the normalized cell diagnostic deviation (D) calculated using formula 6. * diag,i [k]) is shown in the graph in Figure 2g, where the statistical variable threshold (D threshold The time series data for [k]) corresponds to the profile displayed in the darkest color.

[0125] Figure 2h shows the cell diagnostic deviation (D) using formula 9. * diag,i [k]) Filter diagnostic values ​​obtained by filtering the time series data (D filter、i This is a profile showing the time series data of [k]).

[0126] In one example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i By accumulating time intervals where [k]) is greater than a diagnostic threshold (e.g., 0), battery cells that satisfy the condition that the accumulated time is greater than a predetermined reference time can be diagnosed as having a voltage abnormality.

[0127] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,i The control circuit 220 can accumulate time intervals in which the condition [k]) is continuously met and greater than the diagnostic threshold. If there are multiple such time intervals, the control circuit 220 can independently calculate the accumulated time for each time interval.

[0128] In another example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i The number of data points included in a time interval where [k]) is greater than a diagnostic threshold (e.g., 0) is accumulated, and battery cells that satisfy the condition that the accumulated data value is greater than a predetermined reference count can be diagnosed as having a voltage abnormality.

[0129] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,i Only the number of data points included in a time interval in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can independently accumulate the number of data points for each time interval.

[0130] The control circuit 220 can repeat the above-described recursive calculation process a reference number of times. That is, the control circuit 220 processes the voltage time series data shown in Figure 2a into a normalized cell diagnostic deviation (D * diag,i [k]) can be replaced with time series data (for example, the data in Figure 2g). In addition, the control circuit 220 calculates the long-term average difference (|SMA) at the time index k. i[k]-LMA i [k]|) calculation, long and short term average difference (|SMA i [k]-LMA i [k]|) Calculation of the average value, short-term and long-term average difference (|SMA) i [k]-LMA i [k]|) The cell diagnostic deviation (D) corresponds to the difference relative to the mean. diag,i [k]) calculation, using formula 6 for the short-term and long-term average difference (|SMA) i [k]-LMA i [k]|) Normalized cell diagnostic deviation (D * diag,i [k]) calculation, using formula 8, cell diagnostic deviation (D diag,i [k]) statistical variable threshold (D threshold [k]) determination, cell diagnostic deviation (D) using formula 9 * diag,i [k]) Filter diagnostic value (D filter,i [k]) determination, and filter diagnostic value (D filter,i [k]) can be used to recursively perform voltage anomaly diagnosis of battery cells using time-series data.

[0131] When the above recursive calculation process is repeated, the voltage abnormality diagnosis of the battery cell can be performed more accurately. Specifically, referring to Figure 2e, the filter diagnostic value (D) of the battery cell where the voltage abnormality occurred is filter、i In the time series data of [k]), a positive profile pattern is observed in only two time intervals. However, referring to Figure 2h, the filter diagnostic value (D) of the battery cell where the voltage anomaly occurred is filter、i In the time series data of [k]), a positive profile pattern is observed over more time intervals than in Figure 2e. Therefore, as the recursive calculation process is repeated, the point in time when the battery cell voltage anomaly occurs can be detected more accurately.

[0132] The following describes in detail a battery diagnostic method using the battery diagnostic device 200 of the present invention described above. The operation of the control circuit 220 will be described in more detail in various embodiments of the battery diagnostic method.

[0133] FIG. 3 is a flowchart exemplarily showing a battery diagnosis method according to an embodiment of the present invention. The method of FIG. 3 can be periodically executed by the control circuit 220 every unit time.

[0134] Referring to FIGS. 1 to 3, in step S310, the control circuit 220 collects voltage signals indicating the respective cell voltages of a plurality of battery cells BC1 to BC N and generates time-series data of the cell voltages of each battery cell BC (see FIG. 2a). The time-series data of the cell voltages increases by 1 in the number of data every time a unit time elapses.

[0135] Preferably, V i [k] or VD i [k] of Equation 5 can be used as the cell voltage.

[0136] In step S320, the control circuit 220 determines the first average cell voltage (SMA i [k], see Equations 1 and 2) and the second average cell voltage (LMA i [k], see Equations 3 and 4) of each battery cell BC based on the time-series data of the cell voltages of each battery cell BC i (see FIG. 2b). The first average cell voltage (SMA i [k]) is the short-term moving average of the cell voltages of each battery cell BC over a first moving window having a first time length. The second average cell voltage (LMA i [k]) is the long-term moving average of the cell voltages of each battery cell BC over a second moving window having a second time length. For calculating the first average cell voltage (SMA i [k]) and the second average cell voltage (LMA i [k]), V i [k] or VD i [k] can be used. i [k]) and the second average cell voltage (LMA i [k] or VD i [k] can be used.

[0137] In step S330, the control circuit 220 controls each battery cell BC i The short-term and long-term average difference (SMA) i [k]-LMA i Determine [k]|) (see Figure 2c).

[0138] In step S340, the control circuit 220 controls each battery cell BC i Cell diagnostic deviation (D diag,i Determine the cell diagnostic deviation (D) (k). diag,i [k]) is the average of the long-term and short-term average differences (|SMA) of all battery cells. i [k]-LMA i [k]| av ) and the i-th battery cell (BC i ) Short-term and long-term average difference (|SMA) i [k]-LMA i This is the deviation from [k]|).

[0139] In step S350, the control circuit 220 determines whether the diagnostic time has elapsed. The diagnostic time is set in advance. If the determination in step S350 is YES, the process proceeds to step S360; if the determination in step S350 is NO, steps S310 to S340 are repeated.

[0140] In step S360, the control circuit 220 analyzes each battery cell BC collected during the diagnostic time. i Cell diagnostic deviation (D diag,i Generate time-series data for [k]).

[0141] In step S370, the control circuit 220 calculates the cell diagnostic deviation (D diag,i [k]) analyze the time series data for each battery cell BC i Diagnose voltage anomalies.

[0142] In one example, the control circuit 220 controls each battery cell BC i Cell diagnostic deviation (D diag,i [k]) in time series data, cell diagnostic deviation (D diag,iBy accumulating time intervals where [k]) is greater than a diagnostic threshold (e.g., 0.015), battery cells that satisfy the condition that the accumulated time is greater than a predetermined reference time can be diagnosed as having a voltage abnormality.

[0143] Preferably, the control circuit 220 measures the cell diagnostic deviation (D diag,i Only time intervals in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can independently calculate the accumulated time for each time interval.

[0144] In another example, the control circuit 220 controls each battery cell BC i Cell diagnostic deviation (D diag,i [k]) in time series data, cell diagnostic deviation (D diag,i The number of data points where [k]) is greater than a diagnostic threshold (e.g., 0.015) is accumulated, and battery cells that meet the condition that the accumulated data value is greater than a predetermined reference count can be diagnosed as having a voltage abnormality.

[0145] Preferably, the control circuit 220 measures the cell diagnostic deviation (D diag,i Only the number of data points included in a time interval in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can accumulate the number of data points for each time interval independently.

[0146] Figure 4 is a flowchart illustrating an exemplary battery diagnostic method according to a second embodiment of the present invention. The method in Figure 4 can be periodically executed at unit time intervals by the control circuit 220.

[0147] In the battery diagnostic method of the second embodiment, steps S310 to S360 are substantially the same as those of the first embodiment, so their explanation will be omitted. After step S360, proceed to step S380.

[0148] In step S380, the control circuit 220 uses equation 8 to determine the statistically variable threshold (D thresholdGenerate time series data of [k]). The input to the Sigma function in formula 8 is the cell diagnostic deviation (D) of all battery cells generated in step S360. diag,i [k]) is time series data. Preferably, cell diagnostic deviation (D diag,i The maximum value of [k]) may be excluded from the input values ​​of the Sigma function. Cell diagnostic deviation (D diag,i [k]) is the short-term and long-term average difference (|SMA). i [k]-LMA i This is the mean-to-average deviation for [k]|).

[0149] In step S390, the control circuit 220 uses equation 9 to control each battery cell BC i Cell diagnostic deviation (D diag,i [k]) By filtering, the filtered diagnostic value (D filter,i Generate time series data for [k]).

[0150] When using formula 9, D * diag,i [k] D diag,i It can be replaced with [k].

[0151] In step S400, the control circuit 220 sets the filter diagnostic value (D filter,i [k]) Analyze the time series data of each battery cell BC i Diagnose voltage anomalies.

[0152] In one example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i By accumulating time intervals where [k]) is greater than a diagnostic threshold (e.g., 0), battery cells that satisfy the condition that the accumulated time is greater than a predetermined reference time can be diagnosed as having a voltage abnormality.

[0153] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,iIt is possible to integrate only the time intervals during which the condition that [k] is greater than the diagnostic threshold is continuously satisfied. When there are a plurality of such time intervals, the control circuit 220 can independently calculate the integration time for each time interval.

[0154] In another example, the control circuit 220 i for each battery cell BC filter,i in the time series data of the filter diagnostic value (D filter,i [k]), integrates the number of data included in the time interval in which the filter diagnostic value (D

[0155] [k]) is greater than the diagnostic threshold (for example, 0), and can diagnose a battery cell for which the condition that the data integration value is greater than a predetermined reference count is satisfied as a voltage abnormal cell. filter,i Preferably, the control circuit 220 can integrate only the number of data included in the time interval during which the condition that the filter diagnostic value (D

[0156] [k]) is greater than the diagnostic threshold is continuously satisfied. When there are a plurality of such time intervals, the control circuit 220 can independently integrate the number of data for each time interval.

[0157] The battery diagnosis method according to the third embodiment has substantially the same configuration as that of the first embodiment, except that steps S340, S360, and S370 are changed to steps S340', 360', and step S370', respectively. Therefore, for the third embodiment, only the configuration different from that of the first embodiment will be described.

[0158] In step S340', the control circuit 220 uses Equation 6 to calculate the normalized cell diagnostic deviation (D i for the long-term and short-term average difference (|SMA i [k] - LMA i [k]|) of each battery cell BC *diag,i Determine the [k]). The normalized reference value is the difference between the short and long mean (|SMA). i [k]-LMA i This is the average value of [k]|). Equation 6 can be replaced with Equation 7.

[0159] In step S360', the control circuit 220 analyzes each battery cell BC collected during the diagnostic time. i Normalized cell diagnostic deviation (D * diag,i Generate time series data for [k]) (see Figure 2d).

[0160] In step S370', the control circuit 220 calculates the normalized cell diagnostic deviation (D * diag,i [k]) analyze the time series data for each battery cell BC i Diagnose voltage anomalies.

[0161] In one example, the control circuit 220 controls each battery cell BC i Normalized cell diagnostic deviation (D * diag,i [k]) in time series data, cell diagnostic deviation (D * diag,i By accumulating time intervals where [k]) is greater than a diagnostic threshold (e.g., 4), a battery cell can be diagnosed as a voltage abnormality cell if the condition is met that the accumulated time is greater than a predetermined reference time.

[0162] Preferably, the control circuit 220 controls the normalized cell diagnostic deviation (D * diag,i Only time intervals in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can independently calculate the accumulated time for each time interval.

[0163] In another example, the control circuit 220 controls each battery cell BC i Normalized cell diagnostic deviation (D * diag,iIn the time-series data for [k]), the number of data points in which the cell diagnostic deviation is greater than a diagnostic threshold (e.g., 4) is accumulated, and battery cells for which the accumulated data value is greater than a predetermined reference count can be diagnosed as voltage abnormal cells.

[0164] Preferably, the control circuit 220 controls the normalized cell diagnostic deviation (D * diag,i Only the number of data points included in a time interval in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can accumulate the number of data points for each time interval independently.

[0165] Figure 6 is a flowchart illustrating an exemplary battery diagnostic method according to a fourth embodiment of the present invention. The method in Figure 6 can be periodically executed at unit time intervals by the control circuit 220.

[0166] The battery diagnostic method according to the fourth embodiment has substantially the same configuration as the second embodiment, except that steps S340, S360, S380, S390, and S400 are changed to steps S340', S360', S380', S390', and S400', respectively. Therefore, only the configurations that differ from the second embodiment will be described for the fourth embodiment.

[0167] In step S340', the control circuit 220 uses equation 6 to determine each battery cell BC i The short-term and long-term average difference (SMA) i [k]-LMA i [k]|) Normalized cell diagnostic deviation (D * diag,i Determine the [k]). The normalized reference value is the difference between the short and long mean (|SMA). i [k]-LMA i This is the average value of [k]|). Equation 6 can be replaced with Equation 7.

[0168] In step S360', the control circuit 220 analyzes each battery cell BC collected during the diagnostic time.i Normalized cell diagnostic deviation (D * diag,i Generate time series data for [k]) (see Figure 2d).

[0169] In step S380', the control circuit 220 uses equation 8 to determine the statistically variable threshold (D threshold [k]) generates time series data. The input to the Sigma function in Equation 8 is the normalized cell diagnostic deviation (D) of all battery cells generated in step S360'. * diag,i [k]) is time series data. Preferably, in each time index, the cell diagnostic deviation (D * diag,i The maximum value of [k]) may be excluded from the input values ​​of the Sigma function.

[0170] In step S390', the control circuit 220 uses equation 9 to determine the statistically variable threshold (D threshold [k]) based on each battery cell BC i Cell diagnostic deviation (D * diag,i [k]) By filtering, the filtered diagnostic value (D filter、i Generate time series data for [k]).

[0171] In step S400', the control circuit 220 sets the filter diagnostic value (D filter,i [k]) Analyze the time series data of each battery cell BC i Diagnose voltage anomalies.

[0172] In one example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i By accumulating time intervals where [k]) is greater than a diagnostic threshold (e.g., 0), battery cells that satisfy the condition that the accumulated time is greater than a predetermined reference time can be diagnosed as having a voltage abnormality.

[0173] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,i The control circuit 220 can accumulate time intervals in which the condition [k]) is continuously met and greater than the diagnostic threshold. If there are multiple such time intervals, the control circuit 220 can independently calculate the accumulated time for each time interval.

[0174] In another example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i The number of data points included in a time interval where [k]) is greater than a diagnostic threshold (e.g., 0) is accumulated, and battery cells that satisfy the condition that the accumulated data value is greater than a predetermined reference count can be diagnosed as having a voltage abnormality.

[0175] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,i Only the number of data points included in a time interval in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can independently accumulate the number of data points for each time interval.

[0176] Figure 7 is a flowchart illustrating an exemplary battery diagnostic method according to a fifth embodiment of the present invention.

[0177] In the fifth embodiment, steps S310 to S360' are substantially the same as in the fourth embodiment. Therefore, only the configurations that differ from the fourth embodiment will be described for the fifth embodiment.

[0178] In step S410, the control circuit 220 controls each battery cell BC i Normalized cell diagnostic deviation (D * diag,i [k]) Cell diagnostic deviation (D) using time series data * diag,i [k]) First Moving Average (SMA) i [k]) Time series data and second moving average (LMA) i[k]) Generate time-series data (see Figure 2f).

[0179] In step S420, the control circuit 220 uses equation 6 to determine each battery cell BC i The first moving average (SMA) i [k]) Time series data and second moving average (LMA) i [k]) Cell diagnostic deviation (D) normalized using time series data * diag,i [k]) Generate time-series data (see Figure 2g).

[0180] In step S430, the control circuit 220 uses equation 8 to determine the statistically variable threshold (D threshold [k]) Time series data is generated (see Figure 2g).

[0181] In step S440, the control circuit 220 uses equation 9 to determine the statistically variable threshold (D threshold [k]) as the reference for each battery cell BC i Filter diagnostic value (D filter,i Generate time-series data for [k]) (see Figure 2h).

[0182] In step S450, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D filter,i [k]) Analyze the time series data of each battery cell BC i Diagnose voltage anomalies.

[0183] In one example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i By accumulating time intervals where [k]) is greater than a diagnostic threshold (e.g., 0), battery cells that satisfy the condition that the accumulated time is greater than a predetermined reference time can be diagnosed as having a voltage abnormality.

[0184] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,iThe control circuit 220 can accumulate time intervals in which the condition [k]) is continuously met and greater than the diagnostic threshold. If there are multiple such time intervals, the control circuit 220 can independently calculate the accumulated time for each time interval.

[0185] In another example, the control circuit 220 controls each battery cell BC i Filter diagnostic value (D) filter,i [k]) In the time series data, the filter diagnostic value (D filter,i The number of data points included in a time interval where [k]) is greater than a diagnostic threshold (e.g., 0) is accumulated, and battery cells that satisfy the condition that the accumulated data value is greater than a predetermined reference count can be diagnosed as having a voltage abnormality.

[0186] Preferably, the control circuit 220 controls the filter diagnostic value (D filter,i Only the number of data points included in a time interval in which the condition [k]) is greater than the diagnostic threshold is continuously met can be accumulated. If there are multiple such time intervals, the control circuit 220 can independently accumulate the number of data points for each time interval.

[0187] In the fifth embodiment, the control circuit 220 can recursively perform steps S410 and S420 two or more times. That is, the control circuit 220 can perform the normalized cell diagnostic deviation (D) generated in step S420. * diag,i [k]) Using the time series data, the cell diagnostic deviation (D * diag,i [k]) First Moving Average (SMA) i [k]) Time series data and second moving average (LMA) i [k]) Time series data can be generated. Next, in step S420, the control circuit 220 again generates time series data for each battery cell BC. i The first moving average (SMA) i [k]) Time series data and second moving average (LMA) i [k]) Cell diagnostic deviation (D) normalized based on formula 6 using time series data * diag,i[k]) Time series data can be generated. Such a recursive algorithm can be repeated a predetermined number of times.

[0188] If steps S410 and S420 are performed according to the recursive algorithm, then steps S430 to S450 are performed to obtain the cell diagnostic deviation (D) finally calculated by the recursive algorithm. * diag,i [k]) This may be done using time series data.

[0189] In an embodiment of the present invention, the control circuit 220 can output diagnostic result information via a display unit (not shown) if a voltage abnormality is diagnosed in a specific battery cell after performing a voltage abnormality diagnosis for all battery cells. The control circuit 220 can also record the identification information (ID) of the battery cell diagnosed with the voltage abnormality, the time when the voltage abnormality was diagnosed, and a diagnostic flag in the memory unit.

[0190] Preferably, the diagnostic information may include a message indicating that there is a cell in the cell group where a voltage anomaly has occurred. Selectively, the diagnostic information may include a warning message indicating that a more thorough inspection of the battery cells is required.

[0191] In one example, the display unit may be included in a load device that receives power from a cell group CG. If the load device is an electric vehicle, a hybrid vehicle, a plug-in hybrid vehicle, etc., the diagnostic result information may be output via the vehicle's integrated information display. In another example, if the battery diagnostic device 200 according to the present invention is included in a diagnostic system, the diagnostic results may be output via a display provided in the diagnostic system.

[0192] Preferably, the battery diagnostic device 200 according to an embodiment of the present invention may be included in a battery management system 100 or a control system for a load device (not shown).

[0193] According to the above embodiment, two moving averages of the cell voltage of each battery cell are determined for each unit time for two different time lengths, and voltage anomalies in each battery cell can be efficiently and accurately diagnosed based on the difference between the two moving averages of each of the multiple battery cells.

[0194] In another embodiment, when analyzing the difference in the trend of change of two moving averages for each battery cell, the voltage anomaly of each battery cell can be accurately diagnosed by applying advanced techniques such as normalization and / or statistical variable thresholding.

[0195] In another embodiment, time-series data of filter diagnostic values ​​determined based on a statistically variable threshold can be analyzed to accurately detect the time interval in which voltage anomalies occurred in each battery cell and / or the voltage anomaly detection count.

[0196] The embodiments of the present invention described above are not limited to devices and methods, but may also be realized through a program that implements the functions corresponding to the configuration of the embodiments of the present invention, or through a recording medium on which such a program is recorded. Such implementation can be easily achieved by experts in the art to which the present invention belongs, based on the above description of embodiments.

[0197] Although the present invention has been described above with reference to limited embodiments and drawings, it goes without saying that the present invention is not limited thereto, and that various modifications and variations can be made by persons with ordinary skill in the art to which the present invention pertains, within the equivalent scope of the technical concept and claims of the present invention.

[0198] Furthermore, the present invention described above can be modified and altered in various ways by a person with ordinary skill in the art to which the present invention belongs, without departing from the technical spirit of the invention. Therefore, it is not limited by the embodiments described above and the accompanying drawings, but rather can be constructed by selectively combining all or part of each embodiment for various modifications. [Item 1] In a battery diagnostic device for a cell group including multiple battery cells connected in series, A voltage sensing circuit that generates a voltage signal periodically indicating the cell voltage of each battery cell, A control circuit that generates time-series data showing the change in the cell voltage of each battery cell over time based on the voltage signal, Includes, The aforementioned control circuit is (i) Based on the time series data, determine the first average cell voltage, which is a short-term moving average, and the second average cell voltage, which is a long-term moving average, for each battery cell. (ii) A battery diagnostic device that detects voltage abnormalities in each battery cell based on the difference between the first average cell voltage and the second average cell voltage. [Item 2] The aforementioned control circuit is For each of the aforementioned battery cells, the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage is determined. For each of the aforementioned battery cells, a cell diagnostic deviation is determined, which corresponds to the difference between the average of the long- and short-term average differences of all battery cells and the long- and short-term average differences of the battery cell. The battery diagnostic device according to item 1, which detects battery cells that meet the condition of exceeding a diagnostic threshold in the aforementioned cell diagnostic deviation as voltage abnormal cells. [Item 3] The battery diagnostic device according to item 2, wherein the control circuit generates time-series data of the cell diagnostic deviation for each of the battery cells, and detects a voltage abnormality of a battery cell from the time during which the cell diagnostic deviation exceeds a diagnostic threshold or the number of data points of the cell diagnostic deviation that exceeds the diagnostic threshold. [Item 4] The aforementioned control circuit is For each of the aforementioned battery cells, the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage is determined. For each of the aforementioned battery cells, the cell diagnostic deviation is determined by calculating the difference between the average of the long-term and short-term average differences of all battery cells and the long-term and short-term average differences of the battery cell. A statistically variable threshold dependent on the standard deviation of the cell diagnostic deviation of all battery cells is determined. The time-series data relating to the cell diagnostic deviation of each battery cell is filtered based on the statistically variable threshold to generate time-series data of filtered diagnostic values. A battery diagnostic device according to any one of items 1 to 3, which detects a voltage abnormality in a battery cell from the time the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceeds the diagnostic threshold. [Item 5] The aforementioned control circuit is For each of the aforementioned battery cells, the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage is determined. For each of the aforementioned battery cells, the normalized value of the long-term average difference is determined as the normalized cell diagnostic deviation, and a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation for all battery cells is determined. The time-series data relating to the normalized cell diagnostic deviation of each battery cell is filtered based on the statistically variable threshold to generate time-series data of filtered diagnostic values. A battery diagnostic device according to any one of items 1 to 4, which detects a voltage abnormality in a battery cell from the time the filter diagnostic value exceeds a diagnostic threshold or from the number of filter diagnostic value data that exceeds the diagnostic threshold. [Item 6] The aforementioned control circuit is A battery diagnostic device according to any one of items 2 to 5, wherein for each of the aforementioned battery cells, the long-term average difference is normalized by dividing the long-term average difference by the average value of the long-term average differences of all battery cells. [Item 7] The aforementioned control circuit is A battery diagnostic device according to any one of items 2 to 5, wherein the long-term average difference is normalized for each of the aforementioned battery cells by logarithm calculation of the long-term average difference. [Item 8] The aforementioned control circuit is A battery diagnostic device according to any one of items 1 to 7, which generates time-series data showing the change in the cell voltage of each battery cell over time, using a voltage corresponding to the difference between the average cell voltage of all battery cells measured at each unit time and the cell voltage of each battery cell. [Item 9] The aforementioned control circuit is For each battery cell, the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage is determined. For each of the aforementioned battery cells, the normalized value of the long-term average difference is determined as the normalized cell diagnostic deviation. For each of the aforementioned battery cells, time-series data of the normalized cell diagnostic deviation is generated. (i) For each of the time series data of the normalized cell diagnostic deviation for each battery cell, determine a first moving average, which is a short-term moving average, and a second moving average, which is a long-term moving average; (ii) For each of the battery cells, determine the long- and short-term average difference, which corresponds to the difference between the first moving average and the second moving average; (iii) For each of the battery cells, determine the normalized value of the long- and short-term average difference as the normalized cell diagnostic deviation; and (iv) For each of the battery cells, generate time series data of the normalized cell diagnostic deviation. These steps are repeated recursively at least once to generate time series data of the normalized cell diagnostic deviation for each of the battery cells. Determine a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation for all battery cells. The time-series data relating to the normalized cell diagnostic deviation of each battery cell is filtered based on the statistically variable threshold to generate time-series data of filtered diagnostic values. A battery diagnostic device according to any one of items 1 to 8, which detects a voltage abnormality in a battery cell from the time the filter diagnostic value exceeds a diagnostic threshold or from the number of filter diagnostic value data that exceeds the diagnostic threshold. [Item 10] A battery pack including a battery diagnostic device as described in any one of items 1 through 9. [Item 11] Automobiles, including the battery pack described in item 10. [Item 12] In a battery diagnostic method for a cell group including multiple battery cells connected in series, (a) A step of periodically generating time-series data showing the change in cell voltage of each battery cell over time, (b) A step of determining a first average cell voltage, which is a short-term moving average, and a second average cell voltage, which is a long-term moving average, for each battery cell based on the time-series data, (c) A step of detecting a voltage abnormality in each battery cell based on the difference between the first average cell voltage and the second average cell voltage, Battery diagnostic methods, including those mentioned above. [Item 13] Step (c) above is, (c1) For each of the battery cells, the step of determining the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage, (c2) For each of the aforementioned battery cells, the step of determining the cell diagnostic deviation which corresponds to the difference between the average value of the long-term average difference of all battery cells and the long-term average difference of the battery cell, (c3) The battery diagnostic method according to item 12, comprising the step of detecting a battery cell that satisfies the condition that the cell diagnostic deviation exceeds a diagnostic threshold as a voltage abnormality cell. [Item 14] Step (c) above is, (c1) A step of generating time-series data of the cell diagnostic deviation for each of the battery cells, (c2) The battery diagnostic method according to item 13, comprising the step of detecting a voltage abnormality in a battery cell from the time the cell diagnostic deviation exceeds a diagnostic threshold or the number of data points of the cell diagnostic deviation that exceeds the diagnostic threshold. [Item 15] Step (c) above is, (c1) For each of the battery cells, the step of determining the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage, (c2) For each of the aforementioned battery cells, the step of determining the cell diagnostic deviation by calculating the deviation between the average value of the long-term average differences of all battery cells and the long-term average differences of the battery cell, (c3) A step of determining a statistically variable threshold that depends on the standard deviation of the cell diagnostic deviation of all battery cells, (c4) A step of filtering the time series data relating to the cell diagnostic deviation of each battery cell based on the statistical variable threshold to generate time series data of filtered diagnostic values ​​for each battery cell, (c5) The battery diagnostic method according to item 12, comprising the step of detecting a battery cell voltage abnormality from the time the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceeds the diagnostic threshold. [Item 16] Step (c) above is, (c1) For each of the battery cells, the step of determining the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage, (c2) For each of the battery cells, the normalized value of the long-term average difference is determined as the normalized cell diagnostic deviation. (c3) A step of determining a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation of all battery cells, (c4) A step of generating time series data of filtered diagnostic values ​​by filtering the time series data of the normalized cell diagnostic deviation of each battery cell based on the statistical variable threshold, (c5) The battery diagnostic method according to item 12, comprising the step of detecting a battery cell voltage abnormality from the time the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceeds the diagnostic threshold. [Item 17] The aforementioned (c2) step is, A battery diagnostic method according to any one of items 13 to 16, comprising the step of normalizing the long-term average difference for each of the aforementioned battery cells by dividing the long-term average difference by the average value of the long-term average differences of all battery cells. [Item 18] The aforementioned (c2) step is, A battery diagnostic method according to any one of items 13 to 16, comprising the step of normalizing the long-term average difference for each of the aforementioned battery cells by logarithm calculation of the long-term average difference. [Item 19] Step (a) above is, A battery diagnostic method according to any one of items 12 to 18, comprising the step of generating time-series data showing the change in the cell voltage of each battery cell over time, using a voltage corresponding to the difference between the average cell voltage of all battery cells measured at each unit time and the cell voltage of each battery cell. [Item 20] Step (c) above is, (c1) For each of the battery cells, the step of determining the long- and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage, (c2) For each of the battery cells, the normalized value of the long-term average difference is determined as the normalized cell diagnostic deviation. (c3) A step of generating normalized time-series data of the cell diagnostic deviation for each of the battery cells, (c4)(i) Determine a first moving average, which is a short-term moving average, and a second moving average, which is a long-term moving average, for the time series data of the normalized cell diagnostic deviation of each battery cell; (ii) Determine the long- and short-term average difference, which corresponds to the difference between the first moving average and the second moving average, for each battery cell; (iii) Determine the normalized value of the long- and short-term average difference for each battery cell as the normalized cell diagnostic deviation; and (iv) Generate time series data of the normalized cell diagnostic deviation for each battery cell; and repeat these steps at least once recursively to generate time series data of the normalized cell diagnostic deviation for each battery cell. (c5) A step of determining a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation of all battery cells, (c6) A step of generating time series data of filtered diagnostic values ​​by filtering the time series data of the normalized cell diagnostic deviation of each battery cell based on the statistical variable threshold, (c7) A battery diagnostic method according to item 12 or 19, comprising the step of detecting a battery cell voltage abnormality from the time the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceeds the diagnostic threshold.

Claims

1. In a battery diagnostic device including multiple battery cells, A battery diagnostic device including a processor that acquires a voltage signal indicating the cell voltage of each battery cell, determines the moving average voltage of each battery cell based on the voltage signal, and detects an abnormality in each battery cell based on a cell diagnostic deviation based on the moving average voltage of each battery cell and the average value of the moving average voltages of multiple battery cells.

2. The aforementioned processor, The battery diagnostic device according to claim 1, which detects abnormalities in battery cells depending on whether the cell diagnostic deviation satisfies the condition of exceeding a diagnostic threshold.

3. The battery diagnostic device according to claim 2, wherein the processor generates time-series data of the cell diagnostic deviation for each of the battery cells, and detects an abnormality in a battery cell from the time during which the cell diagnostic deviation exceeds a diagnostic threshold or the number of data points of the cell diagnostic deviation that exceeds the diagnostic threshold.

4. The aforementioned processor, Determine a statistically variable threshold that depends on the standard deviation of the cell diagnostic deviation of multiple battery cells. The time-series data relating to the cell diagnostic deviation of each battery cell is filtered based on the statistically variable threshold to generate time-series data of filtered diagnostic values. The battery diagnostic device according to claim 1, which detects a battery cell abnormality from the time the filter diagnostic value exceeds a diagnostic threshold or from the number of data points of the filter diagnostic value that exceeds the diagnostic threshold.

5. The aforementioned processor, For each of the aforementioned battery cells, the normalized value of the cell diagnostic deviation is determined as the normalized cell diagnostic deviation, and a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviations of multiple battery cells is determined. The time-series data relating to the normalized cell diagnostic deviation of each battery cell is filtered based on the statistically variable threshold to generate time-series data of filtered diagnostic values. The battery diagnostic device according to claim 1, which detects a battery cell abnormality from the time the filter diagnostic value exceeds a diagnostic threshold or from the number of filter diagnostic value data exceeding the diagnostic threshold.

6. The aforementioned processor, The battery diagnostic device according to claim 5, wherein the moving average voltage of each battery cell is normalized based on the average value of the moving average voltages of a plurality of battery cells to normalize the cell diagnostic deviation.

7. The aforementioned processor, The battery diagnostic device according to claim 1, which generates time-series data showing the change in the cell voltage of each battery cell over time, using a voltage corresponding to the difference between the average cell voltage of a plurality of battery cells measured at each unit time and the cell voltage of each of the battery cells.

8. The aforementioned processor, For each of the aforementioned battery cells, the normalized value of the cell diagnostic deviation is determined as the normalized cell diagnostic deviation, and time-series data of the normalized cell diagnostic deviation is generated for each of the aforementioned battery cells. (i) Determine a first moving average, which is a short-term moving average, and a second moving average, which is a long-term moving average, for the time series data of the normalized cell diagnostic deviation for each battery cell; (ii) Determine the long- and short-term average difference for each battery cell, which corresponds to the difference between the first moving average and the second moving average; (iii) Determine the normalized value of the long- and short-term average difference for each battery cell as the normalized cell diagnostic deviation; and (iv) Generate time series data of the normalized cell diagnostic deviation for each battery cell. These steps are repeated recursively at least once to generate time series data of the normalized cell diagnostic deviation for each battery cell. Determine a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation of multiple battery cells. The time-series data relating to the cell diagnostic deviation of each battery cell is filtered based on the statistically variable threshold to generate time-series data of filtered diagnostic values. The battery diagnostic device according to claim 1, which detects a battery cell abnormality from the time the filter diagnostic value exceeds a diagnostic threshold or from the number of filter diagnostic value data exceeding the diagnostic threshold.

9. A battery pack comprising a battery diagnostic device according to any one of claims 1 to 8.

10. An automobile driven by a motor that is powered using the power of the battery pack described in claim 9.

11. In a battery diagnostic method that includes multiple battery cells, (a) A step of generating time-series data showing the change in cell voltage of each battery cell over time, (b) A step of determining the moving average voltage of each battery cell based on the time series data, (c) A step of detecting an abnormality in each battery cell based on the cell diagnostic deviation which is based on the moving average voltage of each battery cell and the average value of the moving average voltages of the plurality of battery cells, Battery diagnostic methods, including those mentioned above.

12. Step (c) above is, The battery diagnostic method according to claim 11, further comprising the step of detecting an abnormality in a battery cell depending on whether the cell diagnostic deviation satisfies the condition of exceeding a diagnostic threshold.

13. Step (c) above is, The steps include generating time-series data of the cell diagnostic deviation for each of the aforementioned battery cells, The battery diagnostic method according to claim 12, comprising the step of detecting an abnormality in a battery cell from the time the cell diagnostic deviation exceeds a diagnostic threshold or the number of data points of the cell diagnostic deviation that exceeds the diagnostic threshold.

14. Step (c) above is, The steps include determining a statistically variable threshold that depends on the standard deviation of the cell diagnostic deviation of the plurality of battery cells, The steps include: filtering the time-series data relating to the cell diagnostic deviation of each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values ​​for each battery cell; The battery diagnostic method according to claim 11, comprising the step of detecting an abnormality in a battery cell from the time the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceeds the diagnostic threshold.

15. Step (c) above is, (c1) For each of the battery cells, the step of determining the normalized value of the cell diagnostic deviation as the normalized cell diagnostic deviation, (c2) A step of determining a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation of multiple battery cells, (c3) A step of generating time series data of filtered diagnostic values ​​by filtering the time series data of the normalized cell diagnostic deviation of each battery cell based on the statistical variable threshold, (c4) The battery diagnostic method according to claim 11, comprising the step of detecting an abnormality in a battery cell from the time the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceeds the diagnostic threshold.

16. The battery diagnostic method according to claim 15, wherein step (c1) is a step of normalizing the cell diagnostic deviation based on the average value of the moving average voltages of a plurality of battery cells, wherein the moving average voltage of each battery cell is normalized.

17. Step (a) above is, The battery diagnostic method according to claim 11, comprising the step of generating time-series data showing the change in the cell voltage of each battery cell over time, using a voltage corresponding to the difference between the average value of the cell voltages of a plurality of battery cells measured at each unit time and the cell voltage of each battery cell.

18. Step (c) above is, For each of the aforementioned battery cells, the normalized value of the cell diagnostic deviation is determined as the normalized cell diagnostic deviation. For each of the aforementioned battery cells, the steps include: generating time-series data of normalized cell diagnostic deviations; (i) Determining a first moving average, which is a short-term moving average, and a second moving average, which is a long-term moving average, for the time series data of the normalized cell diagnostic deviation of each battery cell; (ii) Determining the long- and short-term average difference, which corresponds to the difference between the first moving average and the second moving average, for each battery cell; (iii) Determining the normalized value of the long- and short-term average difference for each battery cell as the normalized cell diagnostic deviation; (iv) Generating time series data of the normalized cell diagnostic deviation for each battery cell; and repeating these steps at least once recursively to generate time series data of the normalized cell diagnostic deviation for each battery cell. A step of determining a statistically variable threshold that depends on the standard deviation of the normalized cell diagnostic deviation of multiple battery cells, The steps include: filtering the time-series data relating to the normalized cell diagnostic deviation of each battery cell based on the statistically variable threshold to generate time-series data of filtered diagnostic values; The battery diagnostic method according to claim 11, comprising the step of detecting an abnormality in a battery cell from the time the filter diagnostic value exceeds a diagnostic threshold or the number of data points of the filter diagnostic value that exceeds the diagnostic threshold.

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