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

Abstract

A battery diagnostic device according to the present invention is for diagnosing a cell group including a plurality of battery cells connected in series mounted on an electric vehicle, and includes a voltage sensing circuit configured to periodically generate a voltage signal indicating a cell voltage of each battery cell while the electric vehicle is running, and a control circuit configured to accumulate and store cell voltages determined from the voltage signal in a memory unit and generate time series data indicating a change in the cell voltage of each battery cell over time using the accumulated cell voltage of each battery cell. 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 a difference between the first average cell voltage and the second average cell voltage.

Description

[Technical field]

[0001] The present invention relates to a technique for diagnosing voltage abnormalities in a battery.

[0002] This application claims priority to Korean Patent Application No. 10-2022-0061076, filed on May 18, 2022, the entire contents of which are incorporated herein by reference in their entirety in the specification and drawings. [Background technology]

[0003] Recently, as the demand for portable electronic products such as notebook PCs, video cameras, and mobile phones has increased sharply and the development of electric vehicles, energy storage batteries, robots, satellites, etc. has progressed in earnest, research into high-performance batteries that can be repeatedly charged and discharged is progressing actively.

[0004] Currently, commercial batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, and lithium batteries. Of these, lithium batteries are in the spotlight due to their advantages over nickel-based batteries, such as almost no memory effect, freedom to charge and discharge, a very low self-discharge rate, and high energy density.

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

[0006] An abnormal voltage of a battery cell refers to a fault state in which the cell voltage drops and / or rises abnormally due to an internal short circuit, an external short circuit, a failure of a voltage sensing line, a poor connection with a charge / discharge line, etc.

[0007] In the past, there have been attempts to diagnose voltage abnormalities in each battery cell by comparing a cell voltage, which is a voltage applied across each battery cell at a specific time, with an average cell voltage of the battery cells at the same time as the specific time. However, since the cell voltage of each battery cell also depends on the temperature, current, and / or SOH (State Of Health) of the battery, it is difficult to accurately diagnose voltage abnormalities in each battery cell by simply preparing cell voltages measured for the battery cells at a specific time. For example, even if a battery cell has no voltage abnormality, if the temperature deviation or SOH deviation from the remaining battery cells is large, the difference between the cell voltage of the battery cell and the average cell voltage may also be large.

[0008] In order to solve this problem, it may be proposed to use additional parameters such as charge / discharge current, temperature of each battery cell, and / or SOC (State Of Charge) of each battery cell in addition to the cell voltage of each battery cell when diagnosing the voltage abnormality of each battery cell. However, a diagnosis method using additional parameters has limitations in that it is relatively complicated and takes a long time compared to a diagnosis method using cell voltage as a single parameter because it requires a process of detecting and comparing each parameter. Summary of the Invention [Problem to be solved by the invention]

[0009] The present invention has been made in consideration of the above problems, and has an object to provide a battery diagnostic device, a battery diagnostic method, a battery pack, and an automobile that determine a moving average of cell voltages of each of a plurality of battery cells for at least one moving window having a predetermined time length for each unit time, and efficiently and accurately diagnose voltage abnormalities of each battery cell based on the moving average of each battery cell.

[0010] Other objects and advantages of the present invention will become apparent from the following description and the accompanying drawings, in which: FIG. 1 is a block diagram of a method for manufacturing a semiconductor device according to the present invention; [Means for solving the problem]

[0011] A battery diagnostic device for achieving the above object may be a battery diagnostic device for a cell group including a plurality of battery cells connected in series mounted on an electric vehicle, and may include: a voltage sensing circuit configured to periodically generate a voltage signal indicating a cell voltage of each battery cell while the electric vehicle is operated; and a control circuit configured to accumulate and store in a memory unit a cell voltage determined from the voltage signal, and generate time series data indicating a change over time in the cell voltage of each battery cell using the accumulated cell voltage of each battery cell.

[0012] Desirably, the control circuit may be 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 anomaly of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

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

[0014] Preferably, the control circuit may be configured to generate time series data of cell diagnostic deviation for each battery cell, and detect a voltage abnormality of the battery cell from the time when the cell diagnostic deviation exceeds a diagnostic critical value or the number of data of the cell diagnostic deviation exceeding the diagnostic critical value.

[0015] In another aspect, the control circuit may be configured to determine, for each battery cell, a long-term and short-term average difference which is a difference between the first average cell voltage and the second average cell voltage, determine a cell diagnostic deviation for each battery cell by calculating a deviation between an average value of the long-term and short-term average differences of all battery cells and the long-term and short-term average difference of the battery cell, determine a statistical variable critical value depending on a standard deviation for the cell diagnostic deviation of all battery cells, generate time series data of a filtered diagnostic value by filtering time series data related to the cell diagnostic deviation of each battery cell based on the statistical variable critical value, and detect a voltage abnormality of the battery cell from a time when the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

[0016] In still another aspect, the control circuit may be configured to determine, for each battery cell, a long-term and short-term average difference which is a difference between the first average cell voltage and the second average cell voltage, determine, for each battery cell, a normalized value of the long-term and short-term average difference as a normalized cell diagnostic deviation, determine a statistical variable critical value depending on a standard deviation for the normalized cell diagnostic deviation of all battery cells, generate time series data of a filtered diagnostic value by filtering time series data related to the normalized cell diagnostic deviation of each battery cell based on the statistical variable critical value, and detect a voltage abnormality of the battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

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

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

[0019] In yet another aspect, the control circuit may be configured to generate time series data indicating a change over time in the cell voltage of each battery cell using a differential voltage between an average cell voltage of all battery cells and the cell voltage of each battery cell, measured per unit time.

[0020] In still another aspect, the control circuit determines a long-term and short-term average difference, which is a difference between the first average cell voltage and the second average cell voltage, for each battery cell, determines a normalized value of the long-term and short-term average difference as a normalized cell diagnosis deviation for each battery cell, generates time series data of the normalized cell diagnosis deviation for each battery cell, and generates time series data of the normalized cell diagnosis deviation for each battery cell by recursively repeating the following (i) to (iv) at least once or more times, (i) determining a first moving average and a second moving average for the time series data of the normalized cell diagnosis deviation for each battery cell (wherein the first moving average is a short-term moving average and the second moving average is a long-term moving average); (ii) determining a long-term and short-term average difference, which is the difference between the first moving average and the second moving average, for each battery cell; (iii) determining a normalized value of the long-term and short-term average difference as the normalized cell diagnosis deviation for each battery cell; and (iv) generating time series data of the normalized cell diagnosis deviation for each battery cell. The present invention may be configured to determine a statistical variable critical value depending on a standard deviation for normalized cell diagnostic deviations of all battery cells, generate time series data of a filtered diagnostic value by filtering time series data related to the cell diagnostic deviation of each battery cell based on the statistical variable critical value, and detect a voltage abnormality of a battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or the number of data of the filtered diagnostic value that exceed the diagnostic critical value.

[0021] In the battery diagnostic device according to the present invention, a profile relating to time-series data of the cell voltage of each battery cell may include voltage data equal to or lower than a preset diagnosis start voltage, and may include an inflection point after the time point at which the voltage data is measured.

[0022] To achieve the above technical objective, a battery diagnostic method according to the present invention is a battery diagnostic method for a cell group including a plurality of battery cells connected in series mounted on an electric vehicle, and may include the steps of: (a) generating a voltage signal indicating a cell voltage of each battery cell while the electric vehicle is operating; (b) accumulating and storing cell voltages determined from the voltage signal in a memory unit; (c) periodically generating time series data indicating changes in the cell voltage of each battery cell over time; (d) 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 (e) detecting a voltage abnormality of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

[0023] In one aspect, step (e) may include: (e1) determining, for each battery cell, a long-term / short-term average difference which is a difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery cell, a cell diagnostic deviation which is a deviation between an average value of the long-term / short-term average differences of all battery cells and the long-term / short-term average difference of the battery cell; and (e3) detecting a battery cell which satisfies a condition that the cell diagnostic deviation exceeds a diagnostic critical value as an abnormal voltage cell.

[0024] Preferably, step (e) may include: (e1) generating time series data of cell diagnostic deviation for each battery cell; and (e2) detecting a voltage abnormality of the battery cell from the time when the cell diagnostic deviation exceeds a diagnostic critical value or the number of data of the cell diagnostic deviation exceeding the diagnostic critical value.

[0025] In another aspect, the step (e) may include: (e1) determining, for each battery cell, a long-term and short-term average difference which is a difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery cell, a cell diagnostic deviation by calculating a deviation between an average value 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; (e3) determining a statistical variable critical value depending on a standard deviation of the cell diagnostic deviation of all battery cells; (e4) generating time series data of a filtered diagnostic value for each battery cell by filtering time series data related to the cell diagnostic deviation of each battery cell based on the statistical variable critical value; and (e5) detecting a voltage abnormality of the battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

[0026] In yet another aspect, step (e) may include: (e1) determining a long-term and short-term average difference for each battery cell, which is a difference between the first average cell voltage and the second average cell voltage; (e2) determining a normalized value of the long-term and short-term average difference for each battery cell as a normalized cell diagnostic deviation; (e3) determining a statistical variable critical value depending on a standard deviation for the normalized cell diagnostic deviation of all battery cells; (e4) generating time series data of a filtered diagnostic value by filtering time series data related to the normalized cell diagnostic deviation of each battery cell based on the statistical variable critical value; and (e5) detecting a voltage abnormality of the battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

[0027] Preferably, step (e2) may be a step of normalizing the long-term and short-term average difference for each battery cell by dividing the long-term and short-term average difference by an average value of the long-term and short-term average differences of all the battery cells.

[0028] Alternatively, step (e2) may be a step of normalizing the long-term and short-term average differences by a logarithmic calculation of the long-term and short-term average differences for each battery cell.

[0029] In yet another aspect, step (a) may be a step of generating time series data indicating a change over time in the cell voltage of each battery cell using a differential voltage between an average value of the cell voltages of all the battery cells and the cell voltage of each battery cell, the differential voltage being measured every unit time.

[0030] In still another aspect, the step (e) includes: (e1) determining, for each battery, a long-term and short-term average difference, which is a difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery, a normalized value of the long-term and short-term average difference as a normalized cell diagnosis deviation; (e3) generating time series data of the normalized cell diagnosis deviation for each battery cell; and (e4) generating time series data of the normalized cell diagnosis deviation for each battery cell by recursively repeating the following (i) to (iv) at least once: (i) determining a first moving average and a second moving average for the time series data of the normalized cell diagnosis deviation for each battery cell (wherein the first moving average is a short-term moving average and the second moving average is a long-term moving average); (ii) determining a long-term and short-term average difference, which is the difference between the first moving average and the second moving average, for each battery cell; (iii) determining a normalized value of the long-term and short-term average difference as the normalized cell diagnosis deviation for each battery cell; and (iv) generating time series data of the normalized cell diagnosis deviation for each battery cell. (e5) determining a statistical variable critical value depending on a standard deviation for normalized cell diagnostic deviations of all battery cells; (e6) generating time series data of a filtered diagnostic value by filtering time series data related to the normalized cell diagnostic deviations of each battery cell based on the statistical variable critical value; and (e7) detecting a voltage abnormality of a battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

[0031] In the battery diagnosis method according to the present invention, a profile relating to time series data of cell voltage of each battery cell may include voltage data equal to or lower than a preset diagnosis start voltage, and may include an inflection point after the time point at which the voltage data is measured.

[0032] The above technical object can also be achieved by a battery pack including the above-mentioned battery diagnostic device and a vehicle including the same. Effect of the Invention

[0033] According to one aspect of the present invention, while an electric vehicle is operating, two moving averages of the cell voltage of each battery cell for two different time lengths are determined for each unit time, and a voltage abnormality of each battery cell can be efficiently and accurately diagnosed based on a difference between the two moving averages of each of a plurality of battery cells.

[0034] According to another aspect of the present invention, in analyzing the change trend difference between the two moving averages of each battery cell, advanced techniques such as normalization and / or statistical variable thresholds can be applied to accurately diagnose voltage abnormalities of each battery cell.

[0035] According to yet another aspect of the present invention, by analyzing time series data of a filter diagnosis value determined based on a statistical variable critical value, it is possible to accurately detect a time period during which a voltage abnormality occurred in each battery cell and / or a voltage abnormality detection count.

[0036] The effects of the present invention are not limited to the effects described above, and other effects of the present invention not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0037] The following drawings attached to this specification illustrate preferred embodiments of the present invention and, together with the detailed description of the invention, serve to further understand the technical ideas of the present invention. Therefore, the present invention should not be interpreted as being limited to only the matters described in the drawings. [Brief description of the drawings]

[0038] [Figure 1] 1 is a diagram illustrating an example of the configuration of an electric vehicle according to an embodiment of the present invention. [Figure 2a] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Figure 2b] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Figure 2c] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Figure 2d] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Figure 2e] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Figure 2f] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Figure 2g] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Figure 2h] 2 is a graph for explaining a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG. 1. [Diagram 3] 2 is a flowchart showing a battery diagnostic method according to a first embodiment of the present invention. [Figure 4]5 is a flowchart showing a battery diagnostic method according to a second embodiment of the present invention. [Diagram 5] 10 is a flowchart showing a battery diagnostic method according to a third embodiment of the present invention. [Figure 6] 10 is a flowchart showing a battery diagnostic method according to a fourth embodiment of the present invention. [Figure 7] 13 is a flowchart illustrating a battery diagnostic method according to a fifth embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the attached drawings. Prior to this, the terms and words used in the specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings, but should be interpreted as having meanings and concepts according to the technical ideas 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.

[0040] Therefore, it should be understood that the embodiments described in this specification and the configurations shown in the drawings are merely the most preferred embodiment of the present invention and do not represent the entire technical idea of ​​the present invention, and that there may be various equivalents and modifications that can be substituted for them at the time of this application.

[0041] Terms including ordinal numbers such as "first", "second", etc. are used for the purpose of distinguishing one of various components from the rest, and are not intended to limit the components.

[0042] In addition, throughout the specification, when a part "includes" a certain element, this does not mean excluding other elements, but means that other elements may be further included, unless otherwise specified. Furthermore, terms such as "controller" in the specification refer to a unit that processes at least one function or operation, which may be embodied by hardware, software, or a combination of hardware and software.

[0043] Furthermore, throughout this specification, when a part is said to be "connected" to another part, this includes not only when it is "directly connected" to another part, but also when it is "indirectly connected" through another element in between.

[0044] FIG. 1 is a diagram showing the configuration of an electric vehicle according to an embodiment of the present invention.

[0045] Referring to FIG. 1 , an electric vehicle 1 includes a battery pack 2 , an inverter 3 , an electric motor 4 and a vehicle controller 5 .

[0046] The battery pack 2 includes a cell group CG, a switch 6 and a battery management system 100.

[0047] The cell group CG can be coupled to the inverter 3 via a pair of power supply terminals provided on the battery pack 2. The cell group CG includes a plurality of battery cells BC1 to BC N (N is a natural number equal to or greater than 2). Each battery cell BC i The type of the battery is not particularly limited as long as it is rechargeable, such as a lithium-ion battery cell. i is an index for identifying the cell. i is a natural number ranging from 1 to N.

[0048] The switch 6 is connected in series to the cell group CG. The switch 6 is provided in a current path for charging and discharging the cell group CG. The switch 6 is controlled to be turned on and off in response to a switching signal from the battery management system 100. The switch 6 may be a mechanical relay that is turned on and off by the magnetic force of a coil, or may be a semiconductor switch such as a MOSFET (Metal Oxide Semiconductor Field Effect Transistor).

[0049] The inverter 3 is provided to switch the direct current from the cells CG to alternating 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.

[0050] The battery management system 100 is provided to take charge of overall control related to charging and discharging of the cell group CG while the electric vehicle 1 is operated. Here, the operation of the electric vehicle 1 may include the movement of the electric vehicle 1, parking, waiting at a traffic light, etc.

[0051] 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.

[0052] The battery diagnostic device 200 detects the temperature of the battery cells BC1 to BC N The battery diagnostic device 200 includes a voltage sensing circuit 210 and a control circuit 220, and is provided to diagnose each voltage abnormality.

[0053] The voltage sensing circuit 210 detects a voltage between a plurality of battery cells BC1 to BC2 through a plurality of voltage sensing lines. N The voltage sensing circuit 210 is configured to measure a cell voltage across each battery cell BC at regular time intervals while the electric vehicle 1 is in operation, and to generate a voltage signal indicative of the measured cell voltage.

[0054] The current sensor 310 is connected in series to the cell group CG through a current path. The current sensor 310 is configured to detect a battery current flowing through the cell group CG at regular time intervals while the electric vehicle 1 is in operation, and to generate a current signal indicative of the detected battery current.

[0055] The temperature sensor 320 is configured to detect the temperature of the cell group CG at regular time intervals while the electric vehicle 1 is in operation, and to generate a temperature signal indicative of the detected temperature.

[0056] The control circuit 220 may be implemented in hardware using at least one of an ASIC (application specific integrated circuit), a DSP (digital signal processor), a DSPD (digital signal processing device), a PLD (programmable logic device), an FPGA (field programmable gate array), a microprocessor, or other electrical unit for performing a function.

[0057] The control circuit 220 may include a memory unit 221. The memory unit 221 may include at least one type of storage medium, including a flash memory type, a hard disk type, a solid state disk type (SSD type), a silicon disk drive type (SDD type), a multimedia card micro type, a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), and a programmable read-only memory (PROM). The memory unit 221 may store data and programs required for the calculation operation by the control circuit 220. The memory unit 221 may store data indicating the result of the calculation operation by the control circuit 220. In particular, the control circuit 220 may record in the memory unit 221 at least one of various parameters calculated per unit time, which will be described later.

[0058] The control circuit 220 may be operatively coupled to the voltage sensing circuit 210, the temperature sensor 320, the current sensor 310, the interface unit 330, and / or the switch 6. The control circuit 220 may collect sensing signals from the voltage sensing circuit 210, the current sensor 310, and the temperature sensor 320 at regular time intervals while the electric vehicle 1 is running. The sensing signals refer to synchronously detected voltage signals, current signals, and / or temperature signals. The control circuit 220 may convert the voltage signals, current signals, and / or temperature signals into digital data and accumulate and store them in the memory unit 221 to generate time-series data related to the voltage, current, and / or temperature of the battery cell.

[0059] 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 (e.g., ECU: Electronic Control Unit). The wired communication may be, for example, CAN (controller area network) communication, and the wireless communication may be, for example, ZigBee (registered trademark) or Bluetooth (registered trademark) communication. Of course, 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.

[0060] The interface unit 330 may be coupled to an output device (e.g., a display, a speaker) that provides information received from the vehicle controller 5 and / or the control circuit 220 in a form that can be recognized by a user. The vehicle controller 5 may control the inverter 3 based on battery information (e.g., voltage, current, temperature, SOC) collected through communication with the battery management system 100.

[0061] 2a to 2h are graphs showing a process of diagnosing a voltage abnormality in each battery cell from time-series data showing a change over time in the cell voltage of each of the plurality of battery cells shown in FIG.

[0062] FIG. 2a shows a plurality of battery cells BC1 to BC N The voltage curves of the battery cells BC are shown in FIG. 1. Although the number of battery cells for which the voltage curves are shown for the embodiment is 14, the present invention is not limited by the number of battery cells. The control circuit 220 collects voltage signals from the voltage sensing circuit 210 for each unit time while the electric vehicle 1 is running, and detects the voltages of the battery cells BC i The voltage values ​​of the cell voltages are accumulated and recorded in the memory unit 221. The unit time may be an integer multiple of the voltage measurement period of the voltage sensing circuit 210.

[0063] The control circuit 220 detects the voltage of each battery cell BC i Based on the voltage values ​​of the cell voltages, cell voltage time series data indicating the history of the cell voltage of each battery cell over time may be generated. Each time a cell voltage is measured, the number of cell voltage time series data is increased by one.

[0064] The voltage curves shown in FIG. 2a are the voltage curves of the battery cells BC1 to BC N Therefore, each voltage curve indicates a change history of the cell voltage of any one of the battery cells BC associated therewith.

[0065] The control circuit 220 uses one moving window or two moving windows to control the battery cells BC1 to BC N Each moving average may be determined for each unit of time. When two moving windows are used, the length of time for any one moving window is different from the length of time for the remaining moving window.

[0066] 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 a given time length before the current time.

[0067] Hereinafter, for ease of explanation, the one of the two moving windows relating to the shorter time length will be referred to as a first moving window, and the one relating to the longer time length will be referred to as a second moving window.

[0068] The control circuit 220 uses only the first moving window, or both the first moving window and the second moving window, to control each battery cell BC i It is possible to diagnose voltage abnormalities.

[0069] The control circuit 220 detects the current of the i-th battery cell BC collected per unit time while the electric vehicle 1 is in operation. i Based on the cell voltage of the i-th battery cell BC i The short-term and long-term trends of the cell voltages can be compared for each unit time.

[0070] The control circuit 220 calculates the current of the i-th battery cell BC by the first moving window using the following Equation 1 or 2: i A first average cell voltage, which is a moving average of the first average cell voltage, may be determined per unit time.

[0071] Equation 1 is a moving average formula based on the arithmetic average method, and Equation 2 is a moving average formula based on the weighted average method.

number

number

[0072] In Equation 1 and Equation 2, k is the time index indicating the current time, SMA i [k] is the current i-th battery cell BC i is the first average cell voltage of the moving window, S is the time length of the first moving window divided by unit time, V i [k] is the current i-th battery cell BC iFor example, if the unit time is 1 second and the length of the first moving window is 10 seconds, S is 10. When x is a natural number less than or equal to k, V i [kx] and SMA i [kx] indicates the cell voltage and the first average cell voltage of the i-th battery cell BCi when the time index is kx. For reference, the control circuit 220 may be set to increase the time index by 1 every unit time.

[0073] The control circuit 220 calculates the current of the i-th battery cell BC by the second moving window using the following Equation 3 or 4: i A second average cell voltage, which is a moving average of the first average cell voltage, may be determined per unit time.

[0074] Equation 3 is a moving average formula based on the arithmetic average method, and Equation 4 is a moving average formula based on the weighted average method.

number

number

[0075] In Equation 3 and Equation 4, k is a time index indicating the current time, LMA i [k] is the current i-th battery cell BC i is the second average cell voltage, L is the length of the second moving window divided by unit time, V i [k] is the current i-th battery cell BC i In one example, if the unit time is 1 second and the length of the second moving window is 100 seconds, L is 100. When x is a natural number less than or equal to k, LMA i [kx] denotes the second average cell voltage when the time index was kx.

[0076] In one embodiment, the control circuit 220 controls V i [k], where each battery cell BCi Instead of the cell voltage of i The difference between the cell voltage of the

[0077] The reference cell voltage of the cell group CG at this time is the reference cell voltage of the multiple battery cells BC1 to BC N In a variant, the average value of the cell voltages can be replaced by a median value.

[0078] Specifically, the control circuit 220 calculates the VD i [k] is V in Equations 1 to 4 i [k].

number

[0079] In formula 5, V av [k] is the reference cell voltage of the cell group CG at the current time, and is the average value of multiple cell voltages.

[0080] If the length of time of the first moving window is less than the length of time of the second moving window, the first average cell voltage may be referred to as the "short-term moving average" of the cell voltage, and the second average cell voltage may be referred to as the "long-term moving average" of the cell voltage.

[0081] FIG. 2b illustrates the voltage curves of the i-th battery cell BC i 2b shows the short-term moving average line and the long-term moving average line for the cell voltage of . In FIG. 2b, the horizontal axis shows time, and the vertical axis shows the short-term moving average line and the long-term moving average line for the cell voltage of .

[0082] Referring to Figure 2b, the dotted lines of multiple moving averages S i is multiple battery cells BC1 to BC N a first average cell voltage SMA of each battery cell BC, iThe time history of [k] is shown. Also, the multiple moving average lines L i is multiple battery cells BC1 to BC N a second average cell voltage LMA of each battery cell BC, i The time-dependent change history of [k] is shown.

[0083] The dotted line graph and the solid line graph are obtained by using Equation 2 and Equation 4, respectively. i [k] is the VD of Equation 5 i [k] is used, V av [k] was set to 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.

[0084] FIG. 2c shows the first average cell voltage SMA of each battery cell shown in FIG. 2b. i [k] and second average cell voltage LMA i The graph shows the change over time in the long-term and short-term average difference (absolute value) between the battery cells BC and BC. i The long-term and short-term average differences are shown.

[0085] Each battery cell BC i The long-term and short-term average difference of each battery cell BC i First average cell voltage SMA i and the second average cell voltage LMA i For example, the i-th battery cell BC i The long-term and short-term average difference is the SMA i [k] and LMA i It may be equal to any one (e.g., a larger one) of [k] minus the remaining (e.g., a smaller one).

[0086] i-th battery cell BC i The long-term and short-term average difference of the i-th battery cell BC i The change in the cell voltage depends on the short-term and long-term history of the cell voltage.

[0087] i-th battery cell BC i The temperature and SOH of the i-th battery cell BC i Therefore, the cell voltage of the i-th battery cell BC i If there is no voltage abnormality, the i-th battery cell BC i The long-term and short-term average differences of the remaining battery cells do not show any meaningful difference from the long-term and short-term average differences of the remaining battery cells.

[0088] On the other hand, the i-th battery cell BC i In the case of a sudden voltage abnormality caused by an internal short circuit and / or an external short circuit, the second average cell voltage LMA i [k] than the first average cell voltage SMA i As a result, the i-th battery cell BC i The long-term and short-term average differences of the remaining battery cells without voltage abnormality have a large deviation from the long-term and short-term average differences of the remaining battery cells without voltage abnormality.

[0089] The control circuit 220 controls each battery cell BC i Long-term and short-term average difference | SMA i [k]-LMA i [k]|. The control circuit 220 may also determine the long-term and short-term average difference |SMA i [k]-LMA i [k]| can be determined. In the following, the average value is |SMA i [k]-LMA i [k]| av The control circuit 220 also calculates the average value of the long-term and short-term average differences |SMA i [k]-LMA i [k]| av and Long-term and Short-term Average Difference | SMA i [k]-LMA i [k]| cell diagnostic deviation D diag,i [k]. Also, the control circuit 220 may determine the cell diagnostic deviation D diag,i [k] for each battery cell BC i It is possible to diagnose voltage abnormalities.

[0090] 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 preset diagnostic critical value (e.g., 0.015), the i-th battery cell BC i It can be diagnosed that a voltage abnormality has occurred.

[0091] Preferably, the control circuit 220 controls each battery cell BC for voltage abnormality diagnosis. i Long-term and short-term average difference | SMA i [k]-LMA i [k]| may be normalized using a normalization criterion. Preferably, the normalization criterion is the average of the long-term and short-term average differences |SMA i [k]-LMA i [k]| av ).

[0092] Specifically, the control circuit 220 controls the first to Nth battery cells BC i -BC N Average of the long-term and short-term average difference | SMA i [k]-LMA i [k]| av The control circuit 220 may set each battery cell BC to a normalized reference value. i Long-term and short-term average difference | SMA i [k]-LMA i [k]| is divided by the normalization standard value to calculate the long-term and short-term average difference |SMA i [k]-LMA i [k]| can be normalized.

[0093] The following formula 6 is for each battery cell BC i Long-term and short-term average difference | SMA i [k]-LMA i [k]| is normalized. In the embodiment, the value calculated by Equation 6 is the normalized cell diagnosis deviation D * diag,i It can be said that it is [k].

number

[0094] In Equation 6, |SMA i [k]-LMA i [k]| is the current i-th battery cell BC i The long-term and short-term average difference, |SMA i [k]-LMA i [k]| av is the average value of the long-term and short-term average differences of all battery cells (normalized reference value), D * diag,i [k] is the current i-th battery cell BC i The symbol "*" indicates that the parameter is normalized.

[0095] Each battery cell BC i Long-term and short-term average difference | SMA i [k]-LMA i [k]| can also be normalized by a logarithmic calculation of the following Equation 7. In the embodiment, the value calculated by Equation 7 is also the normalized cell diagnosis deviation D * diag,i It can be said that it is [k].

number

[0096] Figure 2d shows each battery cell BC i Normalized cell diagnostic deviation D * diag,i [k] over time. Cell diagnosis deviation D * d iag,i [k] was calculated using Equation 6. In FIG. 2d, the horizontal axis indicates time, and the vertical axis indicates the charge of each battery cell BC i Cell diagnostic deviation D * diag,i [k] is indicated.

[0097] Referring to FIG. 2d, each battery cell BC i Long-term and short-term average difference | SMA i [k]-LMA i [k]| is normalized to each battery cell BC iIt can be seen that the change in the long-term and short-term average difference is amplified based on the average value. This makes it possible to more accurately diagnose voltage abnormalities in battery cells.

[0098] Preferably, the control circuit 220 controls each battery cell BC i Normalized cell diagnostic deviation D * diag,i [k] and the statistically variable critical value D threshold [k] for each battery cell BC i It is possible to diagnose voltage abnormalities.

[0099] Preferably, the control circuit 220 calculates the statistical variable threshold D per unit time using the following Equation 8: threshold [k] can be set.

number

[0100] In Equation 8, Sigma is the normalized cell diagnostic deviation D of the entire battery cell BC at time index k. * diag,i is a function that calculates the standard deviation for [k]. And, β is a constant that is determined experimentally. β is a factor that determines the diagnostic sensitivity. β can be appropriately determined by trial and error so that when the present invention is implemented on a cell group including a battery cell in which an abnormal voltage has actually occurred, the battery cell is detected as a cell with an abnormal voltage. In one example, β can be set to at least 5 or more, or at least 6 or more, or at least 7 or more, or at least 8 or more, or at least 9 or more. D generated by Equation 8 threshold Since [k] is multiple, it constitutes time series data.

[0101] On the other hand, the battery cells with abnormal voltage have a normalized cell diagnostic deviation D * diag,i [k] is relatively larger than that of a normal battery cell. Therefore, in order to improve the accuracy and reliability of the diagnosis, Sigma(D *diag,i When calculating the maximum value max(D * diag,i [k]) is preferably excluded. Here, max is a function that returns the maximum value for multiple input variables, and the input variables are the normalized cell diagnosis deviation D * diag,i [k].

[0102] In Fig. 2d, the statistically variable critical value D threshold The time series data showing the change in [k] over time is the profile shown in the darkest color among the overall profiles.

[0103] The control circuit 220 calculates a statistical variable critical value D at a time index k. threshold After determining [k], the following equation 9 is used to calculate the i Normalized cell diagnostic deviation D * diag,i By filtering [k], the filter diagnostic value D filter,i [k] can be determined.

[0104] Each battery cell BC i Filter diagnostic value D for filter,i Two values ​​can be assigned to [k]: cell diagnostic deviation D * diag,i [k] is the statistical variable critical value D threshold If it is greater than [k], the cell diagnostic deviation D * diag,i [k] and the statistically variable critical value D Threshold The difference between [k] and the filter diagnostic value D filter,i [k]. In contrast, the cell diagnostic deviation D * diag,i [k] is the statistical variable critical value D threshold If it is equal to or smaller than [k], 0 is the filter diagnostic value D filter,i [k] is assigned to

number

[0105] Fig. 2e shows the cell diagnostic deviation D * diag,i Filter diagnostic value D obtained by filtering [k] filter,i This is a diagram showing the time series data of [k].

[0106] Referring to FIG. 2e, the filter diagnostic value D of a particular battery cell filter,i An irregular pattern is observed in which [k] has a positive value around 3,000 seconds. For reference, a particular battery cell having an irregular pattern is the battery cell having time series data indicated by A in FIG. 2d.

[0107] In one example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i A time period in which [k] is greater than a diagnosis critical value (eg, 0) is accumulated, and a battery cell for which a condition is established in which the accumulated time is greater than a preset reference time may be diagnosed as an abnormal voltage cell.

[0108] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i The control circuit 220 may accumulate the time intervals during which the condition that [k] is greater than the diagnostic critical value is continuously satisfied. If there are a plurality of time intervals, the control circuit 220 may independently calculate the accumulated time for each time interval.

[0109] In another example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i The number of data included in a time period in which [k] is greater than a diagnosis critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated data value is greater than a preset reference count is satisfied can be diagnosed as a cell with an abnormal voltage.

[0110] Preferably, the control circuit 220 determines the filter diagnostic value D filter,iOnly the number of data included in the time interval in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be accumulated. If there are multiple time intervals, the control circuit 220 may independently accumulate the number of data in each time interval.

[0111] On the other hand, the control circuit 220 controls V i [k] is each battery cell BC- i Normalized cell diagnostic deviation D * diag,i [k]. Also, the control circuit 220 may replace the cell diagnostic deviation D * diag,i [k] long-term and short-term average difference | SMA i [k]-LMA i Calculation of [k]|, cell diagnostic deviation D * diag,i [k] long-term and short-term average difference | SMA i [k]-LMA i Calculation of average value of [k]|, long-term and short-term average difference |SMA i [k]-LMA i The cell diagnostic deviation D, which is the difference between |[k]| and the mean diag,i Calculate [k] using formula 6: Long-term and short-term average difference |SMA i [k]-LMA i Normalized cell diagnostic deviation D against |[k]| * diag,i Calculation of [k], normalized cell diagnostic deviation D using Equation 8 * diag,i Statistical variable critical value D for [k] threshold [k] is determined, and the cell diagnosis deviation D is calculated using Equation 9. * diag,i Filter diagnostic value D by filtering [k] filter,i Determination of [k] and filter diagnostic value D filter,i [k] time series data can be used to recursively diagnose voltage abnormalities in battery cells.

[0112] Figure 2f shows the normalized cell diagnostic deviation D * diag,i Long-term and short-term average difference |SMA for the time series data of [k] (Fig. 2d) i[k]-LMA i This is a graph showing the time course of |[k]|. i [k]-LMA i In the formulas 2, 4, and 5 used to calculate |[k]|, V i [k] is D * diag,i [k] is replaced by V av [k] is D * diag,i It can be replaced by the average value of [k].

[0113] Figure 2g shows the normalized cell diagnostic deviation D calculated using Eq. * diag,i In Fig. 2g, the statistical variable critical value D threshold The time series data for [k] is the profile shown in the darkest color.

[0114] Figure 2h shows the cell diagnosis deviation D using Equation 9. * diag,i Filter diagnostic value D obtained by filtering the time series data of [k] filter,i This is a profile showing the time series data of [k].

[0115] In one example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i A time period in which [k] is greater than a diagnosis critical value (eg, 0) is accumulated, and a battery cell for which the accumulated time is greater than a preset reference time is diagnosed as an abnormal voltage cell.

[0116] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i The control circuit 220 may accumulate the time intervals during which the condition that [k] is greater than the diagnostic critical value is continuously satisfied. If there are a plurality of time intervals, the control circuit 220 may independently calculate the accumulated time for each time interval.

[0117] In another example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i The number of data included in a time period in which [k] is greater than a diagnosis critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated data value is greater than a preset reference count is satisfied can be diagnosed as a cell with an abnormal voltage.

[0118] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i Only the number of data included in the time interval in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be accumulated. If there are multiple time intervals, the control circuit 220 may independently accumulate the number of data in each time interval.

[0119] The control circuit 220 may further repeat the above-mentioned recursive calculation process a reference number of times. That is, the control circuit 220 converts the voltage time series data shown in FIG. 2a into a normalized cell diagnosis deviation D * diag,i [k] of time series data (e.g., data in FIG. 2g). Also, the control circuit 220 may use the long-term and short-term average difference |SMA i [k]-LMA i Calculation of [k]|, Long and Short Term Average Difference|SMA i [k]-LMA i Calculation of the average value of [k]|, the long-term and short-term average difference |SMA i [k]-LMA i [k]| diag,i Calculate [k] using formula 6: Long-term and short-term average difference |SMA i [k]-LMA i Normalized cell diagnostic deviation D against |[k]| * diag,i Calculation of [k], cell diagnosis deviation D using formula 8 * diag,i Statistical variable critical value D for [k] threshold [k] is determined, and the cell diagnosis deviation D is calculated using Equation 9. * diag,iFilter diagnostic value D by filtering for [k] filter,i Determination of [k] and filter diagnostic value D filter,i [k] time series data can be used to recursively diagnose voltage anomalies in the battery cells.

[0120] When the recursive calculation process is repeated, the voltage abnormality diagnosis of the battery cell can be performed more accurately. That is, referring to FIG. 2e, the filter diagnosis value D filter,i From the time series data of [k], a positive profile pattern is observed only in two time intervals. However, referring to FIG. 2h, the filter diagnosis value D of the battery cell where the voltage abnormality occurred is filter,i A positive profile pattern is observed in more time intervals from the time series data [k] than in FIG. 2e. Therefore, when the recursive calculation process is repeated, the time point when the voltage abnormality of the battery cell occurs can be detected more accurately.

[0121] The battery diagnosis operation according to the embodiment (variation) of the control circuit 220 described above is performed by: i Preferably, the diagnosis is performed under a diagnosis condition in which a profile relating to time series data of cell voltages of each battery cell BC includes voltage data equal to or lower than a preset diagnosis start voltage and includes an inflection point after the time point at which the voltage data is measured. i The predetermined level may be set higher than the lower limit discharge voltage of the battery cell BC. i Types of battery cells BC i In the profile related to the time series data of the cell voltage shown in FIG. 2a, the diagnosis start voltage may be set to 3.35 V. The inflection points are i During the discharge of the battery cells BC, the discharge may be interrupted for a predetermined period of time. In one example, when the electric vehicle 1 is stopped at a traffic light with the key on, each battery cell BC i The profile relating to the time series data of the cell voltage of each battery cell BC may include an inflection point. iWhen the discharge is interrupted, the polarization of the electrodes may be in the process of being eliminated.

[0122] When the battery diagnosis operation of the control circuit 220 is performed under the above-described desirable diagnosis conditions, the voltage abnormality of a battery cell having a voltage abnormality is amplified more than that of other battery cells, thereby improving the accuracy and reliability of the battery diagnosis.

[0123] Hereinafter, a method for diagnosing a battery using the battery diagnosis device 200 of the present invention will be described in detail. The method for diagnosing a battery may be performed by the battery diagnosis device 200 while the electric vehicle 1 is being operated. Herein, the operation of the electric vehicle 1 may include the movement of the electric vehicle 1, parking, waiting at a traffic light, etc.

[0124] The operation of the control circuit 220 is illustrated by various embodiments of a battery diagnostic method.

[0125] 3 is a flowchart showing a battery diagnostic method according to an embodiment of the present invention. The method of FIG. 3 can be periodically executed per unit time by the control circuit 220 while the electric vehicle 1 is in operation.

[0126] 1 to 3, in step S310, the control circuit 220 detects a plurality of battery cells BC1 to BC N The voltage signals indicating the cell voltages of each of the battery cells BC are collected, and time-series data of the cell voltages of each battery cell BC is generated (see FIG. 2a). The number of data points in the time-series data of the cell voltages increases by one every time a unit time elapses.

[0127] Preferably, the cell voltage is V i [k] or VD in Equation 5 i [k] may be used.

[0128] In step S320, the control circuit 220 controls each battery cell BC i Based on the time series data of the cell voltage of each battery cell BC- iFirst average cell voltage SMA i [k] (see Equation 1 and Equation 2) and the second average cell voltage LMA i [k] (see Equation 3 and Equation 4) is determined (see FIG. 2b). i [k] is the number of times each battery cell BC is subjected to a first moving window having a first time length. i The second average cell voltage LMA is the short-term moving average of the cell voltages. i [k] is the number of times each battery cell BC is subjected to a second moving window having a second time length. i The first average cell voltage SMA is the long-term moving average of the cell voltages. i [k] and the second average cell voltage LMA i When calculating [k], V i [k] or VD i [k] may be used.

[0129] In step S330, the control circuit 220 controls each battery cell BC i Long-term and short-term average difference | SMA i [k]-LMA i Determine |[k]| (see Figure 2c).

[0130] 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 diag,i [k] is the average value of the long-term and short-term average difference for all battery cells |SMA i [k]-LMA i [k]| av and the i-th battery cell BC i Long-term and short-term average difference | SMA i [k]-LMA i [k]| is the deviation from

[0131] In step S350, the control circuit 220 determines whether the diagnostic time has elapsed. The diagnostic time is preset. 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.

[0132] In step S360, the control circuit 220 receives the battery voltage Vcc of each battery cell BC collected during the diagnosis time. i Cell diagnostic deviation D diag,i Generate time series data for [k].

[0133] In step S370, the control circuit 220 determines the cell diagnosis deviation D diag,i [k] is analyzed for each battery cell BC i Diagnose voltage abnormalities.

[0134] In one example, the control circuit 220 controls each battery cell BC i Cell diagnostic deviation D diag,i Cell diagnosis deviation D for time series data [k] diag,i A time period in which [k] is greater than a diagnosis critical value (eg, 0.015) is accumulated, and a battery cell for which a condition is established in which the accumulated time is greater than a preset reference time may be diagnosed as an abnormal voltage cell.

[0135] Preferably, the control circuit 220 detects the cell diagnostic deviation D diag,i Only the time intervals in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be integrated. If there are multiple time intervals, the control circuit 220 may independently calculate the integrated time for each time interval.

[0136] In another example, the control circuit 220 controls each battery cell BC i Cell diagnostic deviation D diag,i In the time series data for [k], the cell diagnostic deviation D diag,i [k] may accumulate the number of data items greater than a diagnosis critical value (eg, 0.015), and may diagnose a battery cell for which the accumulated data value is greater than a preset reference count as an abnormal voltage cell.

[0137] Preferably, the control circuit 220 detects the cell diagnostic deviation D diag,iOnly the number of data included in the time interval where the condition that [k] is greater than the diagnosis critical value is continuously satisfied may be accumulated. If there are a plurality of time intervals, the control circuit 220 may independently accumulate the number of data in each time interval. Fig. 4 is a flowchart showing a battery diagnosis method according to a second embodiment of the present invention. The method of Fig. 4 may be periodically performed per unit time by the control circuit 220 of the battery diagnosis device 200 while the electric vehicle 1 is operating.

[0138] In the battery diagnosis method of the second embodiment, steps S310 to S360 are substantially the same as those in the first embodiment, so the description thereof will be omitted. After step S360, the method proceeds to step S380.

[0139] In step S380, the control circuit 220 calculates the statistical variable threshold value D threshold The input of the Sigma function in Equation 8 is the cell diagnosis deviation D of the entire battery cell generated in step S360. diag,i [k]. It is preferable to use the cell diagnosis deviation D diag,i The maximum value of [k] can be excluded from the input value of the Sigma function. diag,i [k] is the long-term and short-term average difference | SMA i [k]-LMA i [k]| is the deviation from the mean value.

[0140] In step S390, the control circuit 220 calculates the current of each battery cell BC using Equation 9. i Cell diagnostic deviation D diag,i By filtering [k], the filter diagnostic value D filter,i Generate time series data [k].

[0141] In using Equation 9, D * diag,i [k] is D diag,i Can be replaced with [k].

[0142] In step S400, the control circuit 220 determines the filter diagnostic value D filter,iAnalyze the time series data of [k] and calculate the time series of each battery cell BC i Diagnose voltage abnormalities.

[0143] In one example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i A time period in which [k] is greater than a diagnosis critical value (eg, 0) is accumulated, and a battery cell for which the accumulated time is greater than a preset reference time is diagnosed as an abnormal voltage cell.

[0144] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i Only the time intervals in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be integrated. If there are multiple time intervals, the control circuit 220 may independently calculate the integrated time for each time interval.

[0145] In another example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i The number of data included in a time period in which [k] is greater than a diagnosis critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated data value is greater than a preset reference count is satisfied can be diagnosed as a cell with an abnormal voltage.

[0146] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i Only the number of data included in the time interval in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be accumulated. If there are multiple time intervals, the control circuit 220 may independently accumulate the number of data in each time interval.

[0147] 5 is a flowchart showing a battery diagnostic method according to a third embodiment of the present invention. The method of FIG 5 may be periodically executed per unit time by the control circuit 220 of the battery diagnostic device 200 while the electric vehicle 1 is running.

[0148] The battery diagnosis method according to the third embodiment is substantially the same as that according to the first embodiment, except that steps S340, S360, and S370 are changed to steps S340', S360', and S370', respectively. Therefore, only the different configurations will be described for the third embodiment.

[0149] In step S340′, the control circuit 220 calculates the current of each battery cell BC using Equation 6. i Long-term and short-term average difference | SMA i [k]-LMA i Normalized cell diagnostic deviation D against [k]| * diag,i Determine [k]. The normalization criterion is the long-term and short-term average difference |SMA i [k]-LMA i [k]| is the average value of Equation 6. Equation 6 can be replaced by Equation 7.

[0150] In step S360', the control circuit 220 receives the battery voltage Vcc of each battery cell BC collected during the diagnosis time. i Normalized cell diagnostic deviation D * diag,i Generate time series data for [k] (see Figure 2d).

[0151] In step S370', the control circuit 220 calculates the normalized cell diagnostic deviation D * diag,i [k] is analyzed for each battery cell BC i Diagnose voltage abnormalities.

[0152] In one example, the control circuit 220 controls each battery cell BC i Normalized cell diagnostic deviation D * diag,i In the time series data for [k], the cell diagnostic deviation D * diag,i A time period in which [k] is greater than a diagnosis critical value (eg, 4) is accumulated, and a battery cell for which the accumulated time is greater than a preset reference time is diagnosed as an abnormal voltage cell.

[0153] Preferably, the control circuit 220 calculates a normalized cell diagnostic deviation D * diag,i Only the time intervals in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be integrated. If there are multiple time intervals, the control circuit 220 may independently calculate the integrated time for each time interval.

[0154] In another example, the control circuit 220 controls each battery cell BC i Normalized cell diagnostic deviation D * diag,i In the time series data for [k], the number of data whose cell diagnosis deviation is greater than a diagnosis critical value (e.g., 4) is accumulated, and a battery cell for which the condition that the data accumulation value is greater than a preset reference count is satisfied can be diagnosed as a voltage abnormal cell.

[0155] Preferably, the control circuit 220 calculates a normalized cell diagnostic deviation D * diag,i Only the number of data included in the time interval in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be accumulated. If there are multiple time intervals, the control circuit 220 may independently accumulate the number of data in each time interval.

[0156] 6 is a flowchart showing a battery diagnostic method according to a fourth embodiment of the present invention. The method of FIG. 6 may be periodically executed per unit time by the control circuit 220 of the battery diagnostic device 200 while the electric vehicle 1 is running.

[0157] The battery diagnosis method according to the fourth embodiment is substantially the same as that of 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 differences between the second embodiment and the fourth embodiment will be described.

[0158] In step S340′, the control circuit 220 calculates the current of each battery cell BC using Equation 6. i Long-term and short-term average difference | SMAi [k]-LMA i Normalized cell diagnostic deviation D against [k]| * diag,i Determine [k]. The normalization criterion is the long-term and short-term average difference |SMA i [k]-LMA i [k]| is the average value of Equation 6. Equation 6 can be replaced by Equation 7.

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

[0160] In step S380′, the control circuit 220 calculates the statistical variable threshold value D using Equation 8. threshold The input of the Sigma function in Equation 8 is the normalized cell diagnosis deviation D of the entire battery cell generated in step S360′. * diag,i [k]. Preferably, at 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.

[0161] In step S390′, the control circuit 220 calculates the statistical variable threshold value D using Equation 9. threshold [k] for each battery cell BC i Cell diagnostic deviation D * diag,i By filtering [k], the filter diagnostic value D filter,i Generate time series data [k].

[0162] In step S400', the control circuit 220 determines the filter diagnostic value D filter,i Analyze the time series data of [k] and calculate the time series of each battery cell BC i Diagnose voltage abnormalities.

[0163] In one example, the control circuit 220 controls each battery cell BC-i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i A time period in which [k] is greater than a diagnosis critical value (eg, 0) is accumulated, and a battery cell for which the accumulated time is greater than a preset reference time is diagnosed as an abnormal voltage cell.

[0164] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i The control circuit 220 may accumulate the time intervals during which the condition that [k] is greater than the diagnostic critical value is continuously satisfied. If there are a plurality of time intervals, the control circuit 220 may independently calculate the accumulated time for each time interval.

[0165] In another example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i The number of data included in a time period in which [k] is greater than a diagnosis critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated data value is greater than a preset reference count is satisfied can be diagnosed as a cell with an abnormal voltage.

[0166] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i Only the number of data included in the time interval in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be accumulated. If there are multiple time intervals, the control circuit 220 may independently accumulate the number of data for each time interval.

[0167] FIG. 7 is a flowchart showing a battery diagnostic method according to a fifth embodiment of the present invention.

[0168] In the fifth embodiment, steps S310 to S360' are substantially the same as in the fourth embodiment, so only the configuration of the fifth embodiment that differs from the fourth embodiment will be described.

[0169] In step S410, the control circuit 220 controls each battery cell BC iNormalized cell diagnostic deviation D * diag,i Using the time series data of [k], the cell diagnosis deviation D * diag,i First Moving Average SMA for [k] i [k] time series data and second moving average LMA i Generate time series data [k] (see Figure 2f).

[0170] In step S420, the control circuit 220 calculates the current of each battery cell BC using Equation 6. i First Moving Average SMA i [k] time series data and second moving average LMA i Cell diagnosis deviation D normalized using the time series data [k] * diag,i Generate time series data [k] (see Figure 2g).

[0171] In step S430, the control circuit 220 calculates the statistical variable threshold value D threshold Generate time series data [k] (see Figure 2g).

[0172] In step S440, the control circuit 220 calculates the statistical variable threshold value D threshold [k] for each battery cell BC i Filter diagnostic value D filter,i Generate time series data for [k] (see Figure 2h).

[0173] In step S450, the control circuit 220 controls each battery cell BC i Filter diagnostic value D filter,i Analyze the time series data of [k] and calculate the time series of each battery cell BC i Diagnose voltage abnormalities.

[0174] In one example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,iA time period in which [k] is greater than a diagnosis critical value (eg, 0) is accumulated, and a battery cell for which the accumulated time is greater than a preset reference time is diagnosed as an abnormal voltage cell.

[0175] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i The control circuit 220 may accumulate the time intervals during which the condition that [k] is greater than the diagnostic critical value is continuously satisfied. If there are a plurality of time intervals, the control circuit 220 may independently calculate the accumulated time for each time interval.

[0176] In another example, the control circuit 220 controls each battery cell BC- i Filter diagnostic value D for filter,i In the time series data [k], the filter diagnostic value D filter,i The number of data included in a time period in which [k] is greater than a diagnosis critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated data value is greater than a preset reference count is satisfied can be diagnosed as a cell with an abnormal voltage.

[0177] Preferably, the control circuit 220 determines the filter diagnostic value D filter,i Only the number of data included in the time interval in which the condition that [k] is greater than the diagnostic critical value is continuously satisfied may be accumulated. If there are multiple time intervals, the control circuit 220 may independently accumulate the number of data in each time interval.

[0178] In the fifth embodiment, the control circuit 220 may recursively perform steps S410 and S420 two or more times. That is, the control circuit 220 may recursively perform the normalized cell diagnostic deviation D generated in step S420. * diag,i Using the time series data [k], the cell diagnosis deviation D * diag,i First Moving Average SMA for [k] i [k] time series data and second moving average LMA i [k] of time series data. Then, in step S420, the control circuit 220 further i First Moving Average SMA i[k] time series data and second moving average LMA i The cell diagnosis deviation D normalized based on Equation 6 using the time series data [k] * diag,i [k] time series data may be generated. Such a recursive algorithm may be repeated a fixed number of times.

[0179] When steps S410 and S420 are performed by the recursive algorithm, steps S430 to S450 are performed to obtain the cell diagnosis deviation D * diag,i This can be performed using [k] time series data.

[0180] The battery diagnosis method according to the above embodiment (variant) is performed by dividing each battery cell BC i Preferably, the diagnosis is performed under a diagnosis condition in which a profile relating to time series data of cell voltages of each battery cell BC includes voltage data equal to or lower than a preset diagnosis start voltage and includes an inflection point after the time point at which the voltage data is measured. i The predetermined level may be set higher than the lower limit voltage of the battery cell BC. i Types of battery cells BC i In the profile related to the time series data of the cell voltage shown in FIG. 2a, the diagnosis start voltage may be set to 3.35 V. The inflection points are i During the discharge of the battery cells BC, the discharge may be interrupted for a predetermined period of time. In one example, when the electric vehicle 1 is stopped at a traffic light with the key on, each battery cell BC i The profile relating to the time series data of the cell voltage of each battery cell BC may include an inflection point. i When the discharge is interrupted, the electrodes may be in the process of being depolarized.

[0181] When the battery diagnosis method according to the actual example (variant example) is performed under the above-mentioned desirable diagnosis conditions, the voltage abnormality of a battery cell having a voltage abnormality is amplified compared to other battery cells, thereby improving the accuracy and reliability of the battery diagnosis.

[0182] In an embodiment of the present invention, the control circuit 220 may output diagnosis result information from a display unit (not shown) when a voltage abnormality is diagnosed from a specific battery cell after performing a voltage abnormality diagnosis on all battery cells. In addition, the control circuit 220 may record identification information (ID) of the battery cell diagnosed with a voltage abnormality, a time point when the voltage abnormality was diagnosed, and a diagnosis flag in the memory unit 221.

[0183] Preferably, the diagnosis result information may include a message indicating that a cell with an abnormal voltage is present in the cell group. Optionally, the diagnosis result information may include a warning message indicating that a detailed inspection of the battery cell is required.

[0184] In one example, the display unit may be included in a load device that receives power from the cell group CG. When the load device is an electric vehicle, a hybrid vehicle, a plug-in hybrid vehicle, etc., the diagnosis result information may be output from an integrated information display of the vehicle. In another example, when the battery diagnosis device 200 according to the present invention is included in a diagnosis system, the diagnosis result may be output from a display provided in the diagnosis system.

[0185] Preferably, the battery diagnostic device 200 according to the embodiment of the present invention may be included in the battery management system 100 or a control system (not shown) of a load device.

[0186] According to the above-described embodiment, two moving averages of the cell voltage of each battery cell for two different time lengths are determined for each unit time, and a voltage abnormality of each battery cell can be efficiently and accurately diagnosed based on the difference between the two moving averages of each of the plurality of battery cells.

[0187] According to another aspect, by applying advanced techniques such as normalization and / or statistically variable thresholds in analyzing the difference in change trends between the two moving averages for each battery cell, voltage anomalies in each battery cell can be accurately diagnosed.

[0188] According to another aspect, it is possible to accurately detect a time period during which a voltage abnormality occurred in each battery cell and / or a voltage abnormality detection count by analyzing time series data of a filter diagnosis value determined based on a statistical variable critical value.

[0189] 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 various modifications and variations can be made by those having ordinary skill in the art to which the present invention pertains within the scope of the technical spirit of the present invention and the scope of the claims.

[0190] Furthermore, since the above-mentioned invention can be variously replaced, modified, and altered by a person having ordinary knowledge in the technical field to which the invention belongs without departing from the technical concept of the invention, it is not limited to the above-mentioned embodiments and the attached drawings, and can be configured by selectively combining all or part of each embodiment to make various modifications.

Claims

1. A battery diagnostic device for a cell group including a plurality of battery cells connected in series mounted on an electric vehicle, comprising: a voltage sensing circuit configured to periodically generate a voltage signal indicative of a cell voltage of each battery cell while the electric vehicle is in operation; a control circuit configured to accumulate and store the cell voltages determined from the voltage signals in a memory unit, and generate time series data indicating a change over time in the cell voltage of each battery cell using the accumulated cell voltage of each battery cell; The control circuit includes: (i) determining 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; (ii) a battery diagnostic device configured to detect a voltage abnormality of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

2. The control circuit includes: determining, for each battery cell, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; For each battery cell, a cell diagnostic deviation is determined, which is a deviation between an average value of the long-term and short-term average differences of the entire battery cell and the long-term and short-term average differences of the battery cell; The battery diagnostic device according to claim 1 , further comprising: detecting a battery cell that satisfies a condition in which a cell diagnostic deviation exceeds a diagnostic critical value as an abnormal voltage cell.

3. The control circuit includes:

3. The battery diagnostic device according to claim 2, configured to generate time series data of cell diagnostic deviation for each battery cell, and detect a voltage abnormality of the battery cell from a time during which the cell diagnostic deviation exceeds a diagnostic critical value or a number of data of the cell diagnostic deviation exceeding the diagnostic critical value.

4. The control circuit includes: determining, for each battery cell, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; For each battery cell, a deviation between an average value of the long-term and short-term average differences of the entire battery cells and the long-term and short-term average differences of the battery cell is calculated to determine a cell diagnosis deviation; determining a statistical variable critical value depending on a standard deviation for cell diagnosis deviations of all battery cells; The time series data related to the cell diagnosis deviation of each battery cell is filtered based on a statistical variable critical value to generate time series data of a filtered diagnosis value; The battery diagnostic device according to claim 1 , further comprising: a detecting unit configured to detect a voltage abnormality of a battery cell from a time during which a filter diagnostic value exceeds a diagnostic critical value or a number of data of the filter diagnostic value exceeding the diagnostic critical value.

5. The control circuit includes: determining, for each battery cell, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; For each battery cell, a normalized value of the long-term and short-term average difference is determined as a normalized cell diagnosis deviation, and a statistical variable critical value depending on the standard deviation for the normalized cell diagnosis deviation of the entire battery cell is determined; Generate time series data of a filtered diagnostic value by filtering time series data related to a normalized cell diagnostic deviation of each battery cell based on a statistical variable critical value; The battery diagnostic device according to claim 1 , further comprising: a detecting unit configured to detect a voltage abnormality of a battery cell from a time during which a filter diagnostic value exceeds a diagnostic critical value or a number of data of the filter diagnostic value exceeding the diagnostic critical value.

6. The control circuit includes: The battery diagnostic device according to claim 5 , configured to normalize the long-term and short-term average difference for each battery cell by dividing the long-term and short-term average difference by an average value of the long-term and short-term average differences of all the battery cells.

7. The control circuit includes:

6. The battery diagnostic device of claim 5, configured to normalize the long-term and short-term average differences for each battery cell by a logarithmic calculation of the long-term and short-term average differences.

8. The control circuit includes:

2. The battery diagnostic device according to claim 1, configured to generate time series data indicating a change over time in the cell voltage of each battery cell by using a differential pressure between an average cell voltage of all battery cells and the cell voltage of each battery cell, the differential pressure being measured every unit time.

9. The control circuit includes: determining, for each battery cell, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; For each battery cell, a normalized value of the long-term and short-term average difference is determined as a normalized cell diagnostic deviation, and for each battery cell, time series data of the normalized cell diagnostic deviation is generated; Repeating the following steps (i) to (iv) recursively at least once to generate normalized time series data of cell diagnosis deviation for each battery cell; (i) determining a first moving average and a second moving average for time series data of normalized cell diagnostic deviation for each battery cell (wherein the first moving average is a short-term moving average and the second moving average is a long-term moving average); (ii) determining a long-term / short-term average difference, which is the difference between the first moving average and the second moving average, for each battery cell; (iii) determining a normalized value of the long-term / short-term average difference as a normalized cell diagnostic deviation for each battery cell; and (iv) generating time series data of normalized cell diagnostic deviation for each battery cell. determining a statistical variable critical value depending on a standard deviation for normalized cell diagnosis deviations of the entire battery cell; The time series data related to the cell diagnosis deviation of each battery cell is filtered based on a statistical variable critical value to generate time series data of a filtered diagnosis value; The battery diagnostic device according to claim 1 , further comprising: a detecting unit configured to detect a voltage abnormality of a battery cell from a time during which a filter diagnostic value exceeds a diagnostic critical value or a number of data of the filter diagnostic value exceeding the diagnostic critical value.

10. 2. The battery diagnostic device of claim 1, wherein a profile relating to time-series data of cell voltages of each battery cell includes voltage data equal to or lower than a preset diagnosis start voltage, and includes an inflection point after a point at which the voltage data is measured.

11. A battery pack comprising the battery diagnostic device according to any one of claims 1 to 10.

12. A motor vehicle comprising the battery pack of claim 11.

13. A battery diagnostic method for a cell group including a plurality of battery cells connected in series mounted on an electric vehicle, comprising: (a) generating a voltage signal indicative of a cell voltage of each battery cell while the electric vehicle is being operated; (b) accumulating and storing cell voltages determined from the voltage signals in a memory unit; (c) periodically generating time series data indicating a change over time in the cell voltage of each battery cell; (d) determining 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 (e) detecting a voltage abnormality in each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

14. The step (e) comprises: (e1) determining, for each battery cell, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; (e2) determining a cell diagnostic deviation for each battery cell, the cell diagnostic deviation being a deviation between an average value of long-term and short-term average differences of all battery cells and the long-term and short-term average differences of the battery cell; and (e3) detecting a battery cell that satisfies a condition that the cell diagnostic deviation exceeds a diagnostic critical value as an abnormal voltage cell.

15. The step (e) comprises: (e1) generating time series data of cell diagnostic deviation for each battery cell; (e2) detecting a voltage abnormality of a battery cell from a time during which the cell diagnostic deviation exceeds the diagnostic critical value or a number of data of the cell diagnostic deviation exceeding the diagnostic critical value.

16. The step (e) comprises: (e1) determining, for each battery cell, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; (e2) determining a cell diagnosis deviation for each battery cell by calculating a deviation between an average value of long-term and short-term average differences of all battery cells and the long-term and short-term average differences of the battery cell; (e3) determining a statistical variable critical value depending on a standard deviation for the cell diagnosis deviation of the entire battery cells; (e4) generating time series data of a filtered diagnosis value for each battery cell by filtering time series data related to a cell diagnosis deviation of each battery cell based on a statistical variable critical value; (e5) detecting a voltage abnormality of a battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

17. The step (e) comprises: (e1) determining, for each battery cell, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery cell, a normalized value of the long-term and short-term average difference as a normalized cell diagnostic deviation; (e3) determining a statistical variable critical value depending on a standard deviation for normalized cell diagnosis deviations of all battery cells; (e4) generating time series data of a filtered diagnosis value by filtering time series data related to a normalized cell diagnosis deviation of each battery cell based on a statistical variable critical value; (e5) detecting a voltage abnormality of a battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

18. The step (e2) comprises: The method of claim 17, further comprising: normalizing the long-term and short-term average difference for each battery cell by dividing the long-term and short-term average difference by an average value of the long-term and short-term average differences of all battery cells.

19. The step (e2) comprises: The method of claim 17, further comprising the step of normalizing the long-term and short-term average differences for each battery cell by a logarithmic calculation of the long-term and short-term average differences.

20. The step (a) comprises: The method for diagnosing a battery according to claim 13, further comprising the step of generating time series data indicating a change over time in the cell voltage of each battery cell by using a differential voltage between an average cell voltage of all battery cells and the cell voltage of each battery cell, the differential voltage being measured every unit time.

21. The step (e) comprises: (e1) determining, for each battery, a long-term and short-term average difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery, a normalized value of the long-term and short-term average difference as a normalized cell diagnostic deviation; (e3) generating normalized cell diagnostic deviation time series data for each battery cell; (e4) repeating the following steps (i) to (iv) at least once or more recursively to generate normalized time series data of cell diagnosis deviation for each battery cell; (i) determining a first moving average and a second moving average for time series data of normalized cell diagnostic deviation for each battery cell (wherein the first moving average is a short-term moving average and the second moving average is a long-term moving average); (ii) determining a long-term / short-term average difference, which is the difference between the first moving average and the second moving average, for each battery cell; (iii) determining a normalized value of the long-term / short-term average difference as a normalized cell diagnostic deviation for each battery cell; and (iv) generating time series data of normalized cell diagnostic deviation for each battery cell. (e5) determining a statistical variable critical value depending on a standard deviation for normalized cell diagnosis deviations of all battery cells; (e6) generating time series data of a filtered diagnosis value by filtering time series data related to a normalized cell diagnosis deviation of each battery cell based on a statistical variable critical value; (e7) detecting a voltage abnormality of a battery cell from a time during which the filtered diagnostic value exceeds the diagnostic critical value or a number of data of the filtered diagnostic value exceeding the diagnostic critical value.

22. The battery diagnosis method according to claim 13 , wherein a profile relating to time-series data of cell voltages of each battery cell includes voltage data equal to or lower than a preset diagnosis start voltage, and includes an inflection point after a point at which the voltage data is measured.

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