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

Abstract

The battery diagnosis device according to the present invention is used for diagnosing a battery cell group including a plurality of battery cells mounted on an electric vehicle and connected in series, and comprises: a voltage sensing circuit, the voltage sensing circuit is configured to periodically generate a voltage signal indicative of a cell voltage of each battery cell while the electric vehicle is traveling; and a control circuit configured to accumulate and store cell voltages determined according to the voltage signal in the memory unit, and generate time series data indicating a change in the cell voltage of each battery cell over time by using the accumulated cell voltages of each battery cell. The control circuit is configured to: (i) determine a first average cell voltage and a second average cell voltage for each battery cell based on the time series data, where the first average cell voltage corresponds to a short-term moving average value and the second average cell voltage corresponds to a long-term moving average value, where the first average cell voltage corresponds to the short-term moving average value and the second average cell voltage corresponds to the long-term moving average value; and (ii) detecting a voltage anomaly for 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 disclosure relates to a technology for diagnosing voltage abnormality of a battery.

[0002] This application claims the benefit of Korean Patent Application No. 10-2022-0061076 filed on May 18, 2022, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. Background Art

[0003] In recent years, the demand for portable electronic products such as laptop computers, camcorders, and mobile phones has increased rapidly, and with the widespread development of electric vehicles, energy storage accumulators, robots, and satellites, many studies have been conducted on high-performance batteries that can be repeatedly charged.

[0004] Currently, commercially available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, lithium-ion batteries, etc. Among them, lithium batteries have almost no memory effect or no memory effect. Therefore, they have received more attention than nickel-based batteries because they can be recharged as long as it is convenient, have a very low self-discharge rate, and have high energy density.

[0005] Recently, as applications requiring high voltage (eg, energy storage systems, electric vehicles) become widespread, demand for diagnostic technology that accurately detects voltage abnormality in each of a plurality of battery cells connected in series within a battery pack is increasing.

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

[0007] Conventionally, attempts have been made to diagnose voltage abnormality of each battery cell by comparing a cell voltage, which is a voltage across each battery cell at a specific time point, with an average cell voltage of a plurality of battery cells at the same time point as the specific time point. However, since the cell voltage of each battery cell depends on the temperature, current, and / or SOH (state of health) of the corresponding battery cell, it is difficult to accurately diagnose voltage abnormality of each battery cell by simply comparing the cell voltages measured for a plurality of battery cells at a specific time point. For example, even in a battery cell without 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 be large.

[0008] In order to solve this problem, when diagnosing the voltage abnormality of each battery cell, it can be assumed that additional parameters such as charging / discharging current, temperature of each battery cell and / or SOC (state of charge) of each battery cell and cell voltage of each battery cell are used. However, since the diagnosis method using additional parameters must be accompanied by a parameter detection process and a parameter comparison process, there is a limitation that the diagnosis method is relatively complicated and takes a long time compared to the diagnosis method using cell voltage as a single parameter. Summary of the invention

[0009] Technical issues

[0010] The present disclosure aims to solve the problems of the related art, and therefore the present disclosure aims to provide a battery diagnosis device, a battery diagnosis method, a battery pack and a vehicle, which can determine the moving average value of the cell voltage of each battery cell in a plurality of battery cells for each moving window in at least one moving window with a given time length per unit time, and efficiently and accurately diagnose the voltage abnormality of each battery cell based on the moving average value of each battery cell.

[0011] These and other purposes and advantages of the present disclosure can be understood from the following detailed description, and will become more fully apparent from the exemplary embodiments of the present disclosure. Moreover, it will be easily understood that the purposes and advantages of the present disclosure can be achieved by the means and combinations thereof shown in the appended claims.

[0012] Technical Solution

[0013] In one aspect of the present disclosure, there is provided a battery diagnostic device for a battery cell group, the battery cell group comprising a plurality of battery cells connected in series and installed in an electric vehicle, the battery diagnostic device comprising: a voltage sensing circuit configured to periodically generate a voltage signal representing a cell voltage of each battery cell when the electric vehicle is operating; and a control circuit configured to cumulatively store the cell voltage determined according to the voltage signal in a memory unit, and to generate time series data representing a change in the cell voltage of each battery cell over time by using the cumulative cell voltage of each battery cell.

[0014] Preferably, the control circuit can 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, wherein the first average cell voltage is a short-term moving average and the second average cell voltage is a long-term moving average, and (ii) detect voltage abnormality of each battery cell based on the difference between the first average cell voltage and the second average cell voltage.

[0015] In an embodiment, the control circuit may be configured to determine, for each battery cell, a short-term and long-term average value difference corresponding to the difference between the first average cell voltage and the second average cell voltage, and, for each battery cell, the control circuit may be configured to determine a cell diagnostic deviation corresponding to a deviation between the short-term and long-term average value difference of the battery cell and an average value of the short-term and long-term average value differences of all battery cells, and the control circuit may be configured to detect a battery cell that satisfies the condition that the cell diagnostic deviation exceeds a diagnostic threshold as a battery cell with abnormal voltage.

[0016] Preferably, for each battery cell, the control circuit can be configured to generate time series data of the battery cell diagnostic deviation, and detect the voltage abnormality of the battery cell based on the time when the battery cell diagnostic deviation exceeds the diagnostic threshold or the amount of data of the battery cell diagnostic deviation exceeding the diagnostic threshold.

[0017] In another embodiment, the control circuit may be configured to determine, for each battery cell, a short-term and long-term average value difference corresponding to a difference between the first average cell voltage and the second average cell voltage, and, for each battery cell, the control circuit may be configured to determine a cell diagnostic deviation by calculating a deviation between the short-term and long-term average value difference of the battery cell and an average value of the short-term and long-term average value differences of all battery cells, and the control circuit may be configured to determine a statistical variable threshold value that depends on a standard deviation of the cell diagnostic deviations of all battery cells, and the control circuit may be configured to generate time series data of filtered diagnostic values ​​by filtering time series data of the cell diagnostic deviations of each battery cell based on the statistical variable threshold value, and the control circuit may be configured to detect voltage abnormalities of the battery cells based on the time at which the filtered diagnostic value exceeds the diagnostic threshold value or the amount of data of the filtered diagnostic value that exceeds the diagnostic threshold value.

[0018] In another embodiment, for each battery cell, the control circuit is configured to determine short-term and long-term average value differences corresponding to the difference between the first average cell voltage and the second average cell voltage, and for each battery cell, the control circuit may be configured to determine a normalized value of the short-term and long-term average value differences as a normalized cell diagnostic deviation, and determine a statistical variable threshold value depending on the standard deviation of the normalized cell diagnostic deviations of all battery cells, the control circuit may be configured to generate time series data of the filtered diagnostic value by filtering the time series data of the normalized cell diagnostic deviations of each battery cell based on the statistical variable threshold value, and the control circuit may be configured to detect voltage abnormalities of the battery cell based on the time when the filtered diagnostic value exceeds the diagnostic threshold or the amount of data of the filtered diagnostic value that exceeds the diagnostic threshold.

[0019] Preferably, the control circuit may be configured to normalize the short-term and long-term average value difference of each battery cell by dividing the short-term and long-term average value difference of each battery cell by the average value of the short-term and long-term average value differences of all battery cells.

[0020] Alternatively, the control circuit may be configured to normalize the difference between the short-term and long-term average values ​​for each battery cell by calculating the logarithm of the difference between the short-term and long-term average values.

[0021] In yet another embodiment, the control circuit may be configured to generate time series data representing a change in the cell voltage of each battery cell over time using a voltage difference between an average value of the cell voltages of all battery cells measured per unit time and the cell voltage of each battery cell.

[0022] In yet another embodiment, for each battery cell, the control circuit may be configured to determine a short-term and long-term average value difference corresponding to the difference between the first average cell voltage and the second average cell voltage, for each battery cell, the control circuit may be configured to determine a normalized value of the short-term and long-term average value difference as a normalized cell diagnostic deviation, and generate time series data of the normalized cell diagnostic deviation for each battery cell, and the control circuit may be configured to generate the time series data of the normalized cell diagnostic deviation for each battery cell by recursively repeating the following steps (i) to (iv) at least once:

[0023] (i) determining a first moving average and a second moving average of time series data of the 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, for each battery cell, a short-term and long-term average difference corresponding to the difference between the first moving average and the second moving average, (iii) determining, for each battery cell, a normalized value of the short-term and long-term average difference as a normalized cell diagnostic deviation, and (iv) generating time series data of the normalized cell diagnostic deviation for each battery cell.

[0024] The control circuit can be configured to determine a statistical variable threshold value that depends on the standard deviation of the normalized cell diagnostic deviations of all battery cells, the control circuit can be configured to generate time series data of the filtered diagnostic value by filtering the time series data of the cell diagnostic deviations of each battery cell based on the statistical variable threshold value, and the control circuit can be configured to detect voltage abnormalities of the battery cells based on the time when the filtered diagnostic value exceeds the diagnostic threshold value or the amount of data of the filtered diagnostic value that exceeds the diagnostic threshold value.

[0025] In the battery diagnosis device according to the present disclosure, the curve of the time series data of the cell voltage of each battery cell includes voltage data equal to or less than a preset diagnosis start voltage and includes an inflection point after a time point of measuring the voltage data.

[0026] In another aspect of the present disclosure, a battery diagnosis method for a battery cell group is also provided, the battery cell group comprising a plurality of battery cells connected in series and installed in an electric vehicle, the battery diagnosis method comprising the following steps: (a) generating a voltage signal representing the cell voltage of each battery cell when the electric vehicle is operating; (b) cumulatively storing the cell voltage determined based on the voltage signal in a memory unit; (c) periodically generating time series data representing the change of 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 voltage abnormality of each battery cell based on the difference between the first average cell voltage and the second average cell voltage.

[0027] In one embodiment, the step (e) may include: (e1) determining, for each battery cell, a short-term and long-term average value difference corresponding to the difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery cell, a cell diagnostic deviation corresponding to the deviation between the short-term and long-term average value difference of the battery cell and the average value of the short-term and long-term average value differences of all battery cells; and (e3) detecting a battery cell that satisfies the condition that the cell diagnostic deviation exceeds a diagnostic threshold as a cell with abnormal voltage.

[0028] Preferably, step (e) may include: (e1) generating time series data of the battery cell diagnostic deviation for each battery cell; and (e2) detecting voltage abnormality of the battery cell based on the time when the battery cell diagnostic deviation exceeds the diagnostic threshold or the amount of data of the battery cell diagnostic deviation exceeding the diagnostic threshold.

[0029] In another embodiment, step (e) may include: (e1) determining, for each battery cell, short-term and long-term average value differences corresponding to the 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 the deviation of the short-term and long-term average value differences of the battery cell from the average of the short-term and long-term average value differences of all battery cells; (e3) determining a statistical variable threshold value that depends on the standard deviation of the cell diagnostic deviations of all battery cells; (e4) generating time series data of a filtered diagnostic value for each battery cell by filtering the time series data of the cell diagnostic deviations of each battery cell based on the statistical variable threshold value, and (e5) detecting voltage abnormality of the battery cell based on the time when the filtered diagnostic value exceeds the diagnostic threshold or the amount of data of the filtered diagnostic value that exceeds the diagnostic threshold.

[0030] In another embodiment, the step (e) may include: (e1) determining, for each battery cell, short-term and long-term average value differences corresponding to the difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery cell, a normalized value of the short-term and long-term average value differences as a normalized cell diagnostic deviation; (e3) determining a statistical variable threshold value that depends on the standard deviation of the normalized cell diagnostic deviation of all battery cells; (e4) filtering the time series data of the normalized cell diagnostic deviation of each battery cell based on the statistical variable threshold value to generate the time series data of the filtered diagnostic value; and (e5) detecting the voltage abnormality of the battery cell based on the time when the filtered diagnostic value exceeds the diagnostic threshold or the amount of data of the filtered diagnostic value that exceeds the diagnostic threshold.

[0031] Preferably, the step (e2) may be a step of normalizing the short-term and long-term average value difference of each battery cell by dividing the short-term and long-term average value difference of each battery cell by an average value of the short-term and long-term average value differences of all battery cells.

[0032] Alternatively, the step (e2) may be a step of normalizing the difference between the short-term and long-term average values ​​of each battery cell by calculating the logarithm of the difference between the short-term and long-term average values.

[0033] In yet another embodiment, the step (a) may be a step of generating time series data representing the change of the cell voltage of each battery cell over time using a voltage difference between an average value of the cell voltages of all battery cells measured per unit time and the cell voltage of each battery cell.

[0034] In another embodiment, the step (e) may include: (e1) determining, for each battery cell, a short-term and long-term average value difference corresponding to the difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery cell, a normalized value of the short-term and long-term average value difference as a normalized cell diagnostic deviation; (e3) generating time series data of the normalized cell diagnostic deviation for each battery cell; (e4) generating time series data of the normalized cell diagnostic deviation for each battery cell by recursively repeating the following steps (i) to (iv) at least once:

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

[0036] (e5) determining a statistical variable threshold value that depends on the standard deviation of the normalized cell diagnostic deviations of all battery cells; (e6) generating time series data of the filtered diagnostic value by filtering the time series data of the cell diagnostic deviations of each battery cell based on the statistical variable threshold value; and (e7) detecting voltage abnormality of the battery cell based on the time when the filtered diagnostic value exceeds the diagnostic threshold value or the amount of data of the filtered diagnostic value that exceeds the diagnostic threshold value.

[0037] In the battery diagnosis method according to the present disclosure, the curve of the time series data of the cell voltage of each battery cell may include voltage data equal to or less than a preset diagnosis start voltage and include an inflection point after a time point of measuring the voltage data.

[0038] In another aspect of the present disclosure, a battery pack including the battery diagnostic device and a vehicle including the battery pack are also provided.

[0039] Beneficial Effects

[0040] According to an embodiment of the present disclosure, while the electric vehicle is operating, two moving average values ​​of the cell voltage of each battery cell within two different time lengths can be determined per unit time, and the voltage abnormality of each battery cell can be efficiently and accurately diagnosed based on the difference between the two moving average values ​​of each battery cell in a plurality of battery cells.

[0041] According to another embodiment of the present disclosure, by applying advanced techniques such as normalization and / or statistical variable thresholds when analyzing the difference in change trends between two moving average values ​​of each battery cell, voltage abnormality of each battery cell may be accurately diagnosed.

[0042] According to yet another embodiment of the present disclosure, a time period during which a voltage abnormality occurs and / or a voltage abnormality detection count of each battery cell may be accurately detected by analyzing time series data of a filtered diagnosis value determined based on a statistical variable threshold.

[0043] The effects of the present disclosure are not limited to the above-mentioned effects, and these and other effects not mentioned herein will be clearly understood by those skilled in the art from the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, are used to provide a further understanding of the technical features of the present disclosure; therefore, the present disclosure is not to be construed as being limited to the accompanying drawings.

[0045] Figure 1 is a schematic diagram showing an electric vehicle according to an embodiment of the present disclosure.

[0046] Figure 2a to Figure 2h is a graph showing a process of diagnosing voltage abnormality of each battery cell based on time series data showing Figure 1 A variation of a cell voltage over time for each of a plurality of battery cells is shown.

[0047] Figure 3 is an exemplary flowchart illustrating a battery diagnosis method according to the first embodiment of the present disclosure.

[0048] Figure 4 is an exemplary flowchart illustrating a battery diagnosis method according to a second embodiment of the present disclosure.

[0049] Figure 5 is an exemplary flowchart illustrating a battery diagnosis method according to a third embodiment of the present disclosure.

[0050] Figure 6 is an exemplary flowchart illustrating a battery diagnosis method according to a fourth embodiment of the present disclosure.

[0051] Figure 7 is an exemplary flowchart illustrating a battery diagnosis method according to a fifth embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. Before the description, it should be understood that the terms or words used in the specification and the appended claims should not be interpreted as limited to the general meaning and dictionary meaning, but are interpreted based on the principle of allowing the inventor to appropriately define the terms for the best interpretation, based on the meanings and concepts corresponding to the various technical aspects of the present disclosure.

[0053] Therefore, the embodiments described herein and the illustrations shown in the accompanying drawings are only the most preferred embodiments of the present disclosure, and are not intended to fully describe multiple technical aspects of the present disclosure, and it should be understood that various other equivalents and modifications may be made thereto when the present application is filed.

[0054] Terms including ordinal numbers such as “first,” “second,” etc. are used to distinguish one element from another of various elements, but are not intended to limit the elements by the terms.

[0055] Unless the context clearly indicates otherwise, it will be understood that the term "comprising" when used in this specification specifies the presence of the elements described, but does not exclude the presence or addition of one or more other elements. In addition, the term "control unit" used herein refers to a processing unit of at least one function or operation, and can be implemented by hardware and software alone or in combination.

[0056] Furthermore, throughout the specification, it will be understood that when an element is referred to as being “connected to” another element, it can be directly connected to the other element or intervening elements may be present.

[0057] Figure 1 is a schematic diagram showing an electric vehicle according to an embodiment of the present disclosure.

[0058] Reference Figure 1 , the electric vehicle 1 includes a battery pack 2 , an inverter 3 , an electric motor 4 and a vehicle controller 5 .

[0059] The battery pack 2 includes a cell group CG, a switch 6 and a battery management system 100 .

[0060] The cell group CG may be connected to the inverter 3 through a pair of power terminals provided to the battery group 2. The cell group CG includes a plurality of battery cells BC1 to BC N (N is a natural number of 2 or greater). Battery cell BC i It is not particularly limited by its type as long as it is rechargeable, such as a lithium-ion battery cell. i is an index for cell identification. i is a natural number from 1 to N.

[0061] The switch 6 is connected in series with the cell group CG. The switch 6 is installed in the current path to charge and discharge the cell group CG. The switch 6 is controlled to be turned on and off in response to a switch 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 a semiconductor switch such as a MOSFET (metal oxide semiconductor field effect transistor).

[0062] The inverter 3 is configured to convert direct current (DC) from the cell group CG into alternating current (AC) 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 operates using the AC from the inverter 3 .

[0063] The battery management system 100 is configured to be responsible for overall control related to charging and discharging of the cell group CG when the electric vehicle 1 is operating. Here, the operation of the electric vehicle 1 may include moving the electric vehicle 1, parking, or waiting for a sign.

[0064] The battery management system 100 includes a battery diagnosis 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.

[0065] The battery diagnosis device 200 is configured to diagnose the plurality of battery cells BC1 to BC2 when the electric vehicle 1 is operating. N The voltage in each battery cell in the battery diagnosis device 200 is abnormal. The battery diagnosis device 200 includes a voltage sensing circuit 210 and a control circuit 220.

[0066] The voltage sensing circuit 210 is connected to a plurality of battery cells BC1 to BC N The voltage sensing circuit 210 is configured to measure the cell voltage across each battery cell BC at regular time intervals while the electric vehicle 1 is operating, and to generate a voltage signal representing the measured cell voltage.

[0067] 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 when the electric vehicle 1 is operating, and to generate a current signal representing the detected battery current.

[0068] 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 operating, and to generate a temperature signal representing the detected temperature.

[0069] The control circuit 220 can be implemented in hardware using at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a microprocessor, or an electrical unit for performing other functions.

[0070] The control circuit 220 may have a memory unit 221. The memory unit 221 may include, for example, at least one type of storage medium of a flash memory type, a hard disk type, a solid state disk (SSD) type, a silicon disk drive (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), or a programmable read-only memory (PROM). The memory unit 221 may store data and programs required for the control circuit 220 to calculate. The memory unit 221 may store data indicating the calculation results of the control circuit 220. In particular, the control circuit 220 may record at least one parameter among several parameters calculated at each unit time described later in the memory unit 221.

[0071] The control circuit 220 may be operably coupled to the voltage sensing circuit 210, the temperature sensor 320, the current sensor 310, the interface unit 330, and / or the switch 6. The control circuit 220 may collect sensing signals from the voltage sensing circuit 210, the current sensor 310, and the temperature sensor 320 at regular time intervals when the electric vehicle 1 is operating. The sensing signal refers to a voltage signal, a current signal, and / or a temperature signal detected synchronously. The control circuit 220 may convert the voltage signal and / or the current signal and / or the temperature signal into digital data and cumulatively store them in the memory unit 221 to generate time series data on the voltage and / or current and / or temperature of the battery cell.

[0072] 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., an electronic control unit (ECU)). The wired communication may be, for example, a controller area network (CAN) communication, and the wireless communication may be, for example, Zigbee or Bluetooth communication. The communication protocol is not limited to a specific type, and may include any communication protocol capable of supporting wired / wireless communication between the control circuit 220 and the vehicle controller 5.

[0073] The interface unit 330 may be coupled to an output device (e.g., display, speaker) that provides information received from the vehicle controller 5 and / or the control circuit 220 in a user-recognizable form. The vehicle controller 5 may control the inverter 3 based on battery information (e.g., voltage, current, temperature, SOC) collected by communicating with the battery management system 100.

[0074] Figure 2a to Figure 2h is a graph showing a process of diagnosing voltage abnormality of each battery cell based on time series data showing Figure 1 A variation of a cell voltage over time for each of a plurality of battery cells is shown.

[0075] Figure 2a A plurality of battery cells BC1 to BC N In this embodiment, the number of battery cells whose voltage curves are shown is 14. However, the present disclosure is not limited by the number of battery cells. The control circuit 220 collects voltage signals from the voltage sensing circuit 210 per unit time while the electric vehicle 1 is operating, and converts each battery cell BC i The voltage value of the cell voltage is cumulatively 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.

[0076] The control circuit 220 may be based on each battery cell BC recorded in the memory unit 221. i The cell voltage time series data representing the history of the cell voltage of each battery cell over time is generated by measuring the cell voltage value of the battery cell. The amount of the cell voltage time series data increases by 1 each time the cell voltage is measured.

[0077] Figure 2a The multiple voltage curves shown are related to multiple battery cells BC1 to BC N Therefore, each voltage curve represents the battery cell BC associated with it. i The cell voltage change history.

[0078] The control circuit 220 may use one moving window or two moving windows to determine the number of battery cells BC1 to BC N When two moving windows are used, the time length of one moving window is different from the time length of the other moving window.

[0079] 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 point, and the starting point of each moving window is a time point before the current time point by a given time length.

[0080] Hereinafter, for convenience of explanation, one of the two moving windows associated with the shorter time length will be referred to as a first moving window, and the other associated with the longer time length will be referred to as a second moving window.

[0081] The control circuit 220 may use only the first moving window or both the first moving window and the second moving window to diagnose each battery cell BC i The voltage is abnormal.

[0082] The control circuit 220 can be based on the i-th battery cell BC collected per unit time when the electric vehicle 1 is operating. i The cell voltage is compared with the BC of the i-th battery cell per unit time. i The short-term and long-term change trends of the battery cell voltage.

[0083] The control circuit 220 may use the following equation 1 or equation 2 to determine the first average cell voltage per unit time, where the first average cell voltage is the voltage of the i-th battery cell BC i The moving average of the first moving window of the cell voltage.

[0084] Equation 1 is a moving average calculation formula using an arithmetic average method, and Equation 2 is a moving average calculation formula using a weighted average method.

[0085] <Equation 1>

[0086]

[0087] <Equation 2>

[0088]

[0089] In Equation 1 and Equation 2, k is the time index representing the current time point, and SMA i [k] is the ith battery cell BC at the current time point i The first average cell voltage is , S is the value obtained by dividing the time length of the first moving window by the unit time, and V i [k] is the ith battery cell BC at the current time point i For example, if the unit time is 1 second and the time length of the first moving window is 10 seconds, S is 10. When x is a natural number equal to or less than k, V i [kx] and SMA i [kx] represents the ith battery cell BC when the time index is kx i For reference, the control circuit 220 may be configured to increase the time index by 1 at each unit time.

[0090] The control circuit 220 may use the following equation 3 or equation 4 to determine the second average cell voltage per unit time, which is the second average cell voltage of the i-th battery cell BC i The moving average of the second moving window of the cell voltage.

[0091] Equation 3 is a moving average calculation formula using an arithmetic average method, and Equation 4 is a moving average calculation formula using a weighted average method.

[0092] <Equation 3>

[0093]

[0094] <Equation 4>

[0095]

[0096] In Equations 3 and 4, k is the time index representing the current time point, and LMA i [k] is the ith battery cell BC at the current time point i The second average cell voltage is L, which is the value obtained by dividing the time length of the second moving window by the unit time, and V i [k] is the ith battery cell BC at the current time pointi For example, if the unit time is 1 second and the time length of the second moving window is 100 seconds, L is 100. When x is a natural number equal to or less than k, LMA i [kx] represents the second average cell voltage when the time index is kx.

[0097] In one embodiment, V as in Equations 1 to 4 i [k], the control circuit 220 can input the standard cell voltage of the cell group CG and the battery cell BC i The difference between the cell voltages at the current time point, not the BC of each battery cell at the current time point. i The cell voltage.

[0098] At the current time point, the standard cell voltage of the cell group CG is from multiple cells BC1 to BC N The average value of the multiple cell voltages at the current time point is determined. In a variant example, the average value of the multiple cell voltages may be replaced by a median value.

[0099] Specifically, the control circuit 220 can convert VD of the following equation 5 into i [k] is set to V in Equation 1 to Equation 4 i [k].

[0100] <Equation 5>

[0101] VD i [k] = V av [k]-V i [k]

[0102] In Equation 5, V av [k] is an average value of a plurality of cell voltages which is a standard cell voltage of the cell group CG at the current time point.

[0103] If the time length of the first moving window is shorter than the time length of the second moving window, the first average cell voltage may be referred to as a “short-term moving average” of the cell voltage, and the second average cell voltage may be referred to as a “long-term moving average” of the cell voltage.

[0104] Figure 2b Shown according to Figure 2a The multiple voltage curves shown determine the i-th battery cell BC i The short-term moving average and long-term moving average of the cell voltage. Figure 2b In the graph, the horizontal axis represents time, and the vertical axis represents the short-term moving average and long-term moving average of the cell voltage.

[0105] Reference Figure 2b, multiple moving average lines S shown in dotted lines i With multiple battery cells BC1 to BC N In a one-to-one relationship, each battery cell BC i The first average cell voltage SMA i [k] changes over time. In addition, the multiple moving average lines L shown in solid lines i With multiple battery cells BC1 to BC N In a one-to-one relationship, each battery cell BC i The second average cell voltage LMA i [k] History of change over time.

[0106] The dashed and solid line graphs are obtained using Equation 2 and Equation 4, respectively. In addition, VD in Equation 5 i [k] is used as V in Equation 2 and Equation 4 i [k], and V av [k] is set to the average value of multiple cell voltages. The time length of the first moving window is 10 seconds, and the time length of the second moving window is 100 seconds.

[0107] Figure 2c Shows the corresponding Figure 2b The first average cell voltage SMA of each battery cell depicted in i [k] and the second average cell voltage LMA i The difference between the short-term and long-term averages of the difference between [k] (in absolute value) over time. Figure 2c In the figure, the horizontal axis represents time and the vertical axis represents each battery cell BC. i The difference between the short-term and long-term averages.

[0108] Each battery cell BC i The difference between the short-term and long-term average values ​​is per unit time per battery cell BC i The first average cell voltage SMA i and the second average cell voltage LMA i As an example, the ith battery cell BC i The difference between the short-term and long-term averages can be compared by applying the SMA i [k] and LMA i The values ​​obtained by subtracting one of [k] (for example, the larger one) from the other (for example, the smaller one) are the same.

[0109] The i-th battery cell BC i The difference between the short-term and long-term average values ​​depends on the i-th battery cell BC i The short-term and long-term change history of the battery cell voltage.

[0110] The i-th battery cell BC i The temperature or SOH of the i-th battery cell BC has a stable effect not only in the short term but also in the long term. i Therefore, if the i-th battery cell BC i If there is no voltage abnormality, then the i-th battery cell BC i The short-term and long-term mean differences of the remaining battery cells are not significantly different from the short-term and long-term mean differences of the remaining battery cells.

[0111] In addition, since the i-th battery cell BC i The voltage abnormality caused by the internal short circuit and / or external short circuit in the first average cell voltage SMA i [k] has a greater impact on the second average cell voltage LMA i [k]. Therefore, the i-th battery cell BC i The short-term and long-term average value difference of has a large deviation from the short-term and long-term average value difference of the remaining battery cells without voltage abnormality.

[0112] When the electric vehicle 1 is operating, the control circuit 220 may determine the value of each battery cell BC per unit time. i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k]|. In addition, the control circuit 220 can determine the difference between the short-term and long-term average values ​​|SMA i [k]-LMA i [k]|. In the following, the average value is denoted as |SMA i [k]-LMA i [k]| av The control circuit 220 can also determine the difference between the short-term and long-term average values ​​|SMA i [k]-LMA i [k]|Average of the difference from the short-term and long-term averages|SMA i [k]-LMA i [k]| av Compared with the deviation, as the battery cell diagnosis deviation D diag,i [k] In addition, the control circuit 220 can be based on the battery cell diagnosis deviation D diag，i [k] to diagnose each battery cell BC i The voltage is abnormal.

[0113] In one embodiment, when for the i-th battery cell BC i Cell diagnostic deviation D diag,i [k] exceeds a preset diagnostic threshold (eg, 0.015), the control circuit 220 may diagnose the i-th battery cell BCi A voltage abnormality has occurred.

[0114] Preferably, the control circuit 220 may use a normalized standard value to normalize each battery cell BC i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k]| to diagnose voltage abnormality. Preferably, the normalized standard value is the average value of the difference between the short-term and long-term average values ​​|SMA i [k]-LMA i [k]| av .

[0115] Specifically, the control circuit 220 can control the first to Nth battery cells BC i To BC N The average difference between the short-term and long-term averages | SMA i [k]-LMA i [k]| av The control circuit 220 can also set each battery cell BC i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k] | Normalize the difference between the short-term and long-term averages by dividing by the normalized standard value | SMA i [k]-LMA i [k]|.

[0116] The following equation 6 is used to normalize each battery cell BC i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k]|. In this embodiment, the value calculated by equation 6 may be referred to as the normalized cell diagnostic deviation D* diag，i [k].

[0117] <Equation 6>

[0118] D * diag，i [k]=(|SMA i [k]-LMA i [k]|)÷(|SMA i [k]-LMA i [k]| av )

[0119] In Equation 6, |SMA i [k]-LMA i [k]| represents the ith battery cell BC at the current time point iThe difference between the short-term and long-term averages, |SMA i [k]-LMA i [k]| av represents the average value (normalized standard value) of the difference between the short-term and long-term average values ​​of all battery cells, and D* diag,i [k] represents the i-th battery cell BC at the current time point i Normalized cell diagnostic deviation. The symbol “*” indicates that the parameter has been normalized.

[0120] Each battery cell BC i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k]| can also be normalized by the logarithmic operation in the following equation 7. In this embodiment, the value calculated by equation 7 can also be referred to as the normalized cell diagnosis deviation D* diag，i [k].

[0121] <Equation 7>

[0122] D * diag,i [k] = Log|SMA i [k]-LMA i [k]|

[0123] Figure 2d Each battery cell BC is shown i Normalized cell diagnostic deviation D* diag,i [k] changes over time. Use Equation 6 to calculate the cell diagnosis deviation D* diag，i [k]. Figure 2d In the figure, the horizontal axis represents time and the vertical axis represents each battery cell BC. i Cell diagnostic deviation D* diag，i [k].

[0124] refer to Figure 2d , it can be found that each battery cell BC i The variation of the difference between the short-term and long-term average values ​​has been magnified based on the average value, because each battery cell BC i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k]| is normalized. Therefore, the voltage abnormality of the battery cell can be diagnosed more accurately.

[0125] Preferably, the control circuit 220 can be configured to normalize the cell diagnostic deviation D* diag，i [k] and statistical variable threshold D threshold [k] Compare and diagnose each battery cell BC i The voltage is abnormal.

[0126] Preferably, the control circuit 220 can set the statistical variable threshold D per unit time using the following equation 8: threshold [k].

[0127] <Equation 8>

[0128] D threshold [k] = β * Sigma (D * diag，i [k])

[0129] In Equation 8, Sigma is the normalized cell diagnostic deviation D* calculated for all battery cells BC at time index k. diag，i [k] is a function of the standard deviation of β. In addition, β is a constant determined experimentally. β is a factor that determines the diagnostic sensitivity. β can be appropriately determined by experiment so that when the present disclosure is implemented on a battery cell group containing battery cells that actually have voltage anomalies, the corresponding battery cells can be detected as voltage abnormality cells. In one example, β can be set to at least 5 or greater, or at least 6 or greater, or at least 7 or greater, or at least 8 or greater, or at least 9 or greater. D* generated by Equation 8 diag，i [k] is a complex number, so it constitutes time series data.

[0130] In addition, the normalized cell diagnostic deviation D* of the battery cell with abnormal voltage diag，i [k] is relatively larger than the normalized cell diagnostic deviation of a normal battery cell. Therefore, in order to improve the accuracy and reliability of diagnosis, it is recommended to calculate Sigma(D* diag,i [k]), excluding max(D* diag，i [k]). Here, max is a function that returns the maximum value for multiple input variables, and the input variable is the normalized cell diagnostic deviation D* of all battery cells. diag，i [k].

[0131] exist Figure 2d In the figure, it represents the statistical variable threshold D threshold The time series data of the temporal variation of [k] corresponds to the curve displayed in the darkest color among all the curves.

[0132] The control circuit 220 may determine the statistical variable threshold D at time index k threshold [k], and then for each battery cell BC by using the following equation 9 i Normalized cell diagnostic deviation D* diag,i [k] Filter to determine the filter diagnostic value D filter,i [k].

[0133] Each battery cell BC i The filtered diagnostic value D filter,i [k] is assigned two values. In other words, if the cell diagnosis deviation D* diag,i [k] is greater than the statistical variable threshold D threshold [k], then the cell diagnostic deviation D* diag,i [k] and statistical variable threshold D threshold The difference between [k] is assigned to the filter diagnostic value D filter,i [k]. In addition, if the cell diagnosis deviation D* diag,i [k] is equal to or less than the statistical variable threshold D threshold [k], then 0 is assigned to the filter diagnostic value D filter,i [k].

[0134] <Equation 9>

[0135] D filter,i [k] = D * diag,i [k]-D threshold [k](If D * diag,i [k]>D threshold [k])

[0136] D filter,i [k]=0(if D * diag,i [k]≤D threshold [k])

[0137] Figure 2e is obtained by filtering the cell diagnosis deviation D* at time index k diag,i [k] The obtained filtered diagnostic value D filter,i [k] time series data.

[0138] Reference Figure 2e , confirm the filter diagnostic value D of a specific battery cell filter,i [k] has an irregular pattern with a positive value near 3000 seconds. For reference, a specific battery cell with an irregular pattern is one having Figure 2d A in the figure indicates the time series data of the battery cells.

[0139] In one example, the control circuit 220 may provide a control circuit for each battery cell BC. i Filter diagnostic value D filter,i [k] is greater than the filter diagnostic value D filter,i The time period of the diagnosis threshold (for example, 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the integration time is greater than the preset standard time is diagnosed as a battery cell with abnormal voltage.

[0140] Preferably, the control circuit 220 can continuously meet the filtering diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can calculate the integration time for each time period independently.

[0141] In another example, the control circuit 220 may provide a control circuit for each battery cell BC. i Include diagnostic values ​​D in the filter filter,i [k] is greater than the filter diagnostic value D filter,i The amount of data within the time period of the diagnostic threshold (e.g., 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the amount of integrated data is greater than the preset standard count is diagnosed as a battery cell with abnormal voltage.

[0142] Preferably, the control circuit 220 may only perform the filtering of the diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can independently integrate the amount of data in each time period.

[0143] In addition, the control circuit 220 can convert V in Equations 1 to 5 into i [k] is replaced by Figure 2d Each battery cell BC shown i Normalized cell diagnostic deviation D* diag,i [k]. In addition, at time index k, the control circuit 220 can recursively calculate the cell diagnosis deviation D* diag,i [k] short-term and long-term average difference | SMA i [k]-LMA i [k]|, calculate the cell diagnosis deviation D* diag,i [k] short-term and long-term average difference | SMA i [k]-LMA i [k]|The average value, calculated from the short-term and long-term average values|SMA i [k]-LMA i [k]|Deviation of cell diagnosis corresponding to the difference compared to the average value* diag,i [k], and use Equation 6 to calculate the normalized cell diagnostic deviation D* for the difference between the short-term and long-term average values ​​|SMAi[k]-LMAi[k] diag,i [k], using Equation 8 to determine the normalized cell diagnostic deviation D* diag,i The statistical variable threshold D of [k] threshold [k], by filtering the cell diagnostic deviation D* by using Equation 9 diag,i [k] to determine the filter diagnostic value Dfilter,i [k], and using the filtered diagnostic value D filter,i [k] time series data to diagnose battery cell voltage anomalies.

[0144] Figure 2f It shows the normalized cell diagnosis deviation D* diag,i [k] time series data ( Figure 2d )'s short-term and long-term average difference | SMA i [k]-LMA i [k]|Chart over time. Used to calculate the difference between the short-term and long-term averages|SMA i [k]-LMA i [k]| in Equations 2, 4, and 5, V i [k] can be used with D* diag,i [k] instead, and V av [k] can be used with D* diag,i The average value of [k] is used instead.

[0145] Figure 2g is the normalized cell diagnostic deviation D* calculated using Equation 6. diag,i [k] is a graph of time series data. Figure 2g In the statistical variable threshold D threshold The time series data of [k] corresponds to the curve shown in the darkest color.

[0146] Figure 2h It is shown that by using Equation 9, the cell diagnosis deviation D* diag,i The filtered diagnostic value D obtained by filtering the time series data of [k] filter,i [k] is a curve of the time series data.

[0147] In one example, the control circuit 220 may provide a control circuit for each battery cell BC. i Filter diagnostic value D filter,i [k] is greater than the filter diagnostic value D filter,i The time period of the diagnosis threshold (for example, 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the integration time is greater than the preset standard time is diagnosed as a battery cell with abnormal voltage.

[0148] Preferably, the control circuit 220 can continuously meet the filtering diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can calculate the integration time in each time period independently.

[0149] In another example, the control circuit 220 may provide a control circuit for each battery cell BC. iInclude diagnostic values ​​D in the filter filter,i [k] is greater than the filter diagnostic value D filter,i The amount of data within the time period of the diagnostic threshold (e.g., 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the amount of integrated data is greater than the preset standard count is diagnosed as a battery cell with abnormal voltage.

[0150] Preferably, the control circuit 220 may only continuously satisfy the conditions included in the filter diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can independently integrate the amount of data in each time period.

[0151] The control circuit 220 may repeat the above recursive calculation process as many times as the standard number. That is, the control circuit 220 may use the normalized cell diagnostic deviation D* diag,i [k] time series data (e.g., Figure 2g ) instead of Figure 2a The voltage time series data shown. In addition, at time index k, the control circuit 220 can recursively calculate the short-term and long-term average difference |SMA i [k]-LMA i [k]|, calculate the difference between the short-term and long-term average |SMA i [k]-LMA i [k]|The average value, calculated from the short-term and long-term average values|SMA i [k]-LMA i [k]|Deviation of cell diagnosis corresponding to the difference compared to the average value* dig,i [k], calculated using Equation 6 for the difference between the short-term and long-term averages | SMA i [k]-LMA i Normalized cell diagnosis deviation D* of [k]| dig,i [k], use Equation 8 to determine the cell diagnostic deviation D* dig,i The statistical variable threshold D of [k] threshold [k], by using Equation 9 to diagnose the cell deviation D* dig,i [k] Filter to determine the filter diagnostic value D threshold [k], and use the filtered diagnostic value D filter,i [k] time series data to diagnose battery cell voltage anomalies.

[0152] If the above recursive calculation process is repeated, the voltage abnormality of the battery cell can be diagnosed more accurately. Figure 2e , only in the case of abnormal voltage, the filter diagnostic value D filter,iA positive curve pattern is observed in two time periods in the time series data of [k]. However, referring to Figure 2h , the filter diagnostic value D of the battery cell with abnormal voltage filter,i In the time series data of [k], Figure 2e Therefore, if the recursive calculation process is repeated, the time point at which the voltage abnormality of the battery cell occurs can be detected more accurately.

[0153] The battery diagnosis operation of the control circuit 220 according to the above embodiment (variation) is preferably performed under the following diagnosis conditions, that is, Figure 2a As shown, for each battery cell BC i The curve of the time series data of the cell voltage includes voltage data equal to or less than a preset diagnosis start voltage and includes an inflection point after the time point of measuring the voltage data. The diagnosis start voltage may be set to be higher than each battery cell BC i The lower discharge limit voltage is higher than a predetermined level. The predetermined level can be based on the battery cell BC i Types and cells of BC i The degree of degradation of Figure 2a In the curve related to the time series data of the cell voltage shown, the diagnosis start voltage can be set to 3.35V. i When the discharge is stopped for a certain period of time while the battery is being discharged, an inflection point may occur. In one example, when the electric vehicle 1 stops waiting for a sign in the on state, an inflection point may be included in the battery cell BC. i The curve of the time series data of the battery cell voltage. i When the discharge stops, an inflection point may occur in the process of resolving the polarization of the electrode.

[0154] If the battery diagnosis operation of the control circuit 220 is performed under the above-described desired diagnosis conditions, the voltage abnormality of the battery cell having the voltage abnormality is amplified compared to other battery cells, thereby improving the accuracy and reliability of the battery diagnosis.

[0155] Hereinafter, a battery diagnosis method using the battery diagnosis device 200 of the present disclosure will be described in detail. The battery diagnosis method may be performed by the battery diagnosis device 200 when the electric vehicle 1 is operating. Here, the operation of the electric vehicle 1 may include moving, parking, or waiting signs of the electric vehicle 1.

[0156] The operation of the control circuit 220 will be described in more detail in various embodiments of the battery diagnosis method.

[0157] Figure 3is an exemplary flowchart showing a battery diagnosis method according to a first embodiment of the present disclosure. When the electric vehicle 1 is operating, Figure 3 The method may be periodically executed by the control circuit 220 per unit time.

[0158] Reference Figures 1 to 3 In step S310, the control circuit 220 collects voltages representing a plurality of battery cells BC1 to BC from the voltage sensing circuit 210. N The voltage signal of each battery cell voltage is generated, and each battery cell BC i Time series data of cell voltage (see Figure 2a ). In the time series data of the cell voltage, the amount of data increases by 1 every time a unit time passes.

[0159] Preferably, V in Equation 5 i [k] or VD i [k] can be used as the cell voltage.

[0160] In step S320, the control circuit 220 controls the battery cell BC based on the i Time series data of cell voltage (see Figure 2b ) Determine the BC of each battery cell i The first 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). The first average cell voltage SMA i [k] is each battery cell BC i The second average cell voltage LMA is a short-term moving average of the cell voltage within a first moving window having a first time length. i [k] is each battery cell BC i The long-term moving average value of the cell voltage in a second moving window having a second time length. When calculating the first average cell voltage SMA i [k] and the second average cell voltage LMA i [k], you can use V i [k] or VD i [k].

[0161] In step S330, the control circuit 220 determines each battery cell BC i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k]|(See Figure 2c ).

[0162] In step S340, the control circuit 220 determines each battery cell BC i Cell diagnostic deviation D diag,i [k]. Cell diagnosis deviation D diag,i [k] is the i-th battery cell BC i The difference between the short-term and long-term averages | SMA i [k]-LMA i [k]|Average of the short-term and long-term differences from all battery cells|SMA i [k]-LMA i [k]| av The deviation between .

[0163] In step S350, the control circuit 220 determines whether the diagnosis time has passed. The diagnosis 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, the process repeats steps S310 to S340.

[0164] In step S360, the control circuit 220 generates a BC value for each battery cell collected during the diagnosis time. i Cell diagnostic deviation D diag,i [k] time series data.

[0165] In step S370, the control circuit 220 analyzes the cell diagnosis deviation D diag,i [k] time series data to diagnose each battery cell BC i The voltage is abnormal.

[0166] In one example, the control circuit 220 can diagnose the cell deviation D diag,i [k] greater than each battery cell BC i Cell diagnostic deviation D diag,i The time period of the diagnosis threshold (for example, 0.015) in the time series data of [k] is integrated, and the battery cells that meet the condition that the integration time is greater than the preset standard time are diagnosed as abnormal voltage cells.

[0167] Preferably, the control circuit 220 may only continuously meet the battery cell diagnosis deviation D diag,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can calculate the integration time in each time period independently.

[0168] In another example, the control circuit 220 can set the cell diagnosis deviation D diag,i [k] greater than each battery cell BC i Cell diagnostic deviation D diag,iThe amount of data of the diagnosis threshold value (for example, 0.015) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the amount of integrated data is greater than the preset standard count is diagnosed as a battery cell with abnormal voltage.

[0169] Preferably, the control circuit 220 may only continuously meet the battery cell diagnosis deviation D diag,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can independently integrate the amount of data in each time period.

[0170] Figure 4 is an exemplary flowchart showing a battery diagnosis method according to a second embodiment of the present disclosure. When the electric vehicle 1 is operating, Figure 4 The method may be periodically executed by the control circuit 220 per unit time.

[0171] In the battery diagnosis method of the second embodiment, steps S310 to S360 are substantially the same as those of the first embodiment, and thus will not be described in detail. After step S360, the process proceeds to step S380.

[0172] In step S380, the control circuit 220 generates the statistical variable threshold value D using equation 8 threshold The input of the Sigma function in Equation 8 is the cell diagnosis deviation D of all battery cells generated in step S360. diag,i Preferably, the cell diagnosis deviation D can be excluded from the input value of the Sigma function. diag,i The maximum value of [k]. Cell diagnosis deviation D diag,i [k] is the difference between the short-term and long-term averages | SMA i [k]-LMA i [k] | Deviation from the mean.

[0173] In step S390, the control circuit 220 calculates the value of each battery cell BC by using Equation 9. i Cell diagnostic deviation D diag,i [k] is filtered to generate the filtered diagnostic value D filter,i [k] time series data.

[0174] When using Equation 9, D* diag,i [k] can be used with D diag,i [k] instead.

[0175] In step S400, the control circuit 220 analyzes and filters the diagnostic value D filter,i [k] time series data to diagnose each battery cell BC iThe voltage is abnormal.

[0176] In one example, the control circuit 220 may filter the diagnostic value D filter,i [k] greater than each battery cell BC i The filtered diagnostic value D filter,i The time period of the diagnosis threshold (for example, 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the integration time is greater than the preset standard time is diagnosed as a battery cell with abnormal voltage.

[0177] Preferably, the control circuit 220 may only continuously satisfy the filtering diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can calculate the integration time in each time period independently.

[0178] In another example, the control circuit 220 may include the filter diagnostic value D filter,i [k] greater than each battery cell BC i The filtered diagnostic value of BC filter,i The amount of data within the time period of the diagnostic threshold (e.g., 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the amount of integrated data is greater than the preset standard count is diagnosed as a battery cell with abnormal voltage.

[0179] Preferably, the control circuit 220 may only perform the filtering of the diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can independently integrate the amount of data in each time period.

[0180] Figure 5 is an exemplary flowchart showing a battery diagnosis method according to a third embodiment of the present disclosure. When the electric vehicle 1 is operating, Figure 5 The method may be periodically executed by the control circuit 220 per unit time.

[0181] The battery diagnosis method according to the third embodiment is substantially the same as the first embodiment except that steps S340, S360 and S370 are changed to steps S340', S360' and S370', respectively. Therefore, for the third embodiment, only the features having differences will be described.

[0182] In step S340 ′, the control circuit 220 determines each battery cell BC using equation 6. i The difference between the short-term and long-term averages | SMA i [k]-LMA i Normalized cell diagnosis deviation D* of [k]|diag,i [k]. The normalized standard value is the difference between the short-term and long-term average values ​​| SMA i [k]-LMA i The average value of [k]|. Equation 6 can be replaced by Equation 7.

[0183] In step S360', the control circuit 220 generates the BC of each battery cell collected during the diagnosis time. i Normalized cell diagnostic deviation D* diag,i [k] time series data (see Figure 2d ).

[0184] In step S370', the control circuit 220 analyzes the normalized cell diagnosis deviation D* diag,i [k] time series data to diagnose each cell BC i The voltage is abnormal.

[0185] In one example, the control circuit 220 can perform normalized cell diagnostic deviation D* diag,i [k] greater than each battery cell BC i Normalized cell diagnostic deviation D* diag,i The time period of the diagnosis threshold (for example, 4) in the time series data of [k] is integrated, and the battery cells that meet the condition that the integration time is greater than the preset standard time are diagnosed as abnormal voltage cells.

[0186] Preferably, the control circuit 220 may only continuously satisfy the normalized cell diagnostic deviation D* diag,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can calculate the integration time in each time period independently.

[0187] In another example, the control circuit 220 may detect a normalized cell diagnostic deviation greater than each battery cell BC i Normalized cell diagnostic deviation D* diag,i The amount of data of the diagnosis threshold (for example, 4) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the amount of integrated data is greater than the preset standard count is diagnosed as a battery cell with abnormal voltage.

[0188] Preferably, the control circuit 220 may only continuously satisfy the normalized cell diagnostic deviation D* diag,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can independently integrate the amount of data in each time period.

[0189] Figure 6is an exemplary flowchart showing a battery diagnosis method according to a fourth embodiment of the present disclosure. When the electric vehicle 1 is operating, Figure 6 The method may be periodically executed by the control circuit 220 per unit time.

[0190] The battery diagnosis method according to the fourth embodiment is substantially the same as the second embodiment except that steps S340, S360, S380, S390 and S400 are respectively changed to steps S340', S360', S380', S390' and S400'. Therefore, for the fourth embodiment, only the features different from the second embodiment will be described.

[0191] In step S340 ′, the control circuit 220 determines each battery cell BC using equation 6. i The difference between the short-term and long-term averages | SMA i [k]-LMA i Normalized cell diagnosis deviation D* of [k]| diag,i [k]. The normalized standard value is the difference between the short-term and long-term average values ​​| SMA i [k]-LMA i The average value of [k]|. Equation 6 can be replaced by Equation 7.

[0192] In step S360', the control circuit 220 generates the BC of each battery cell collected during the diagnosis time. i Normalized cell diagnostic deviation D* diagi [k] time series data (see Figure 2d ).

[0193] In step S380', the control circuit 220 generates the statistical variable threshold value D using equation 8 threshold The input of the Sigma function in Equation 8 is the normalized cell diagnostic deviation D* of all battery cells generated in step S360'. diagi [k] time series data. Preferably, at each time index, the normalized cell diagnostic deviation D* diagi The maximum value of [k] can be excluded from the input value of the Sigma function.

[0194] In step S390', the control circuit 220 calculates the value of the statistical variable threshold D by using Equation 9. threshold [k] For each battery cell BC i Normalized cell diagnostic deviation D* diag,i [k] is filtered to generate the filtered diagnostic value D filter,i [k] time series data.

[0195] In step S400', the control circuit 220 analyzes and filters the diagnostic value D filter,i [k] time series data to diagnose each battery cell BC i The voltage is abnormal.

[0196] In one example, the control circuit 220 may filter the diagnostic value D filter,i [k] greater than each battery cell BC i The filtered diagnostic value D filter,i The time period of the diagnosis threshold (for example, 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the integration time is greater than the preset standard time is diagnosed as a battery cell with abnormal voltage.

[0197] Preferably, the control circuit 220 may only continuously satisfy the filtering diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can calculate the integration time in each time period independently.

[0198] In another example, the control circuit 220 may include the filter diagnostic value D filter,i [k] is greater than the filter diagnostic value D of each battery cell BCi filter,i The amount of data in the diagnostic threshold (e.g., 0) time period in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the amount of integrated data is greater than the preset standard count is diagnosed as a voltage abnormal cell.

[0199] Preferably, the control circuit 220 may only perform the filtering of the diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can independently integrate the amount of data in each time period.

[0200] Figure 7 is an exemplary flowchart illustrating a battery diagnosis method according to a fifth embodiment of the present disclosure.

[0201] In the fifth embodiment, steps S310 to S360' are substantially the same as those of the fourth embodiment. Therefore, for the fifth embodiment, only the features different from the fourth embodiment will be described.

[0202] In step S410, the control circuit 220 uses each battery cell BC i Normalized cell diagnostic deviation D* diag,i [k] time series data to generate the cell diagnostic deviation D* diag,i The first moving average SMA of [k] i[k] time series data and second moving average LMA i [k] time series data (see Figure 2f ).

[0203] In step S420, the control circuit 220 uses Equation 6 to calculate the value of each battery cell BC i The first moving average SMA i [k] time series data and second moving average LMA i [k] time series data to generate the normalized cell diagnosis deviation D* diag,i [k] time series data (see Figure 2g ).

[0204] In step S430, the control circuit 220 generates the statistical variable threshold value D using equation 8 threshold [k] time series data (see Figure 2g ).

[0205] In step S440, the control circuit 220 uses Equation 9 based on the statistical variable threshold D threshold [k] Generate each battery cell BC i The filtered diagnostic value D filter,i [k] time series data (see Figure 2h ).

[0206] In step S450, the control circuit 220 analyzes each battery cell BC i The filtered diagnostic value D filter,i [k] time series data to diagnose each battery cell BC i The voltage is abnormal.

[0207] In one example, the control circuit 220 may filter the diagnostic value D filteri [k] greater than each battery cell BC i The filtered diagnostic value D filteri The time period of the diagnosis threshold (for example, 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the integration time is greater than the preset standard time is diagnosed as a battery cell with abnormal voltage.

[0208] Preferably, the control circuit 220 can continuously meet the filtering diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can calculate the integration time in each time period independently.

[0209] In another example, the control circuit 220 may include the filter diagnostic value D filter,i [k] greater than each battery cell BCi The filtered diagnostic value D filter,i The amount of data in the time period of the diagnostic threshold (for example, 0) in the time series data of [k] is integrated, and the battery cell that satisfies the condition that the amount of integrated data is greater than the preset standard count is diagnosed as a voltage abnormal cell.

[0210] Preferably, the control circuit 220 may only perform the filtering of the diagnostic value D filter,i [k] is greater than the diagnostic threshold. If there are multiple time periods, the control circuit 220 can independently integrate the amount of data in each time period.

[0211] In the fifth embodiment, the control circuit 220 may recursively perform steps S410 and S420 two or more times. In other words, the control circuit 220 may use the normalized cell diagnostic deviation D* generated in step S420 to diag,i [k] time series data, and in step S410, the cell diagnosis deviation D* is generated again. diag,i The first moving average SMA of [k] i [k] time series data and second moving average LMA i Then, in step S420, the control circuit 220 can use each battery cell BC i The first moving average SMA i The time series data of LMAi[k] and the time series data of the second moving average LMAi[k] are used to generate the normalized cell diagnosis deviation D* again based on Equation 6. diag,i [k] time series data. This recursive algorithm can be repeated a set number of times.

[0212] When steps S410 and S420 are performed according to the recursive algorithm, the normalized cell diagnostic deviation D* finally calculated by the recursive algorithm may be used. diag,i [k] to perform steps S430 to S450.

[0213] The battery diagnosis method according to the above embodiment (variation) is preferably performed under the following diagnosis conditions, that is, Figure 2a As shown, for each battery cell BC i The curve of the time series data of the cell voltage includes voltage data equal to or less than a preset diagnosis start voltage and includes an inflection point after the time point of measuring the voltage data. The diagnosis start voltage may be set to be higher than each battery cell BC i The lower discharge limit voltage is higher than a predetermined level. The predetermined level can be based on the battery cell BC i Types and cells of BC iThe degree of degradation varies with Figure 2a In the curve related to the time series data of the cell voltage shown, the diagnosis start voltage can be set to 3.35V. i When discharging stops for a certain period of time, an inflection point may occur. In one example, when the electric vehicle 1 stops waiting for a sign in the on state, an inflection point may be included in the battery cell BC. i The curve of the time series data of the battery cell voltage. i When the discharge stops, an inflection point can appear in the process of analyzing the polarization of the electrode.

[0214] If the battery diagnosis method according to the above-described embodiment (variation) is performed under the above-described desired diagnosis conditions, the voltage abnormality of the abnormal battery cell is amplified compared to other battery cells, thereby improving the accuracy and reliability of the battery diagnosis.

[0215] In an embodiment of the present disclosure, after the control circuit 220 diagnoses the voltage abnormality related to all battery cells, if the voltage abnormality is diagnosed in a specific battery cell, the control circuit 220 may output the diagnosis result information through a display unit (not shown). In addition, the control circuit 220 may record the identification information (ID) of the battery cell diagnosed with the voltage abnormality, the time point when the voltage abnormality is diagnosed, and the diagnosis flag in the memory unit 221.

[0216] Preferably, the diagnosis result information may include a message indicating that there are cells with abnormal voltage in the cell group. Optionally, the diagnosis result information may include a warning message indicating that a detailed inspection of the battery cells is required.

[0217] In one example, the display unit may be included in a load device that receives power from the battery cell group CG. If the load device is an electric vehicle, a hybrid vehicle, a plug-in hybrid vehicle, etc., the diagnostic result information may be output through an integrated information display of the vehicle. In another example, when the battery diagnostic device 200 according to the present disclosure is included in a diagnostic system, the diagnostic result may be output through a display provided in the diagnostic system.

[0218] Preferably, the battery diagnosis apparatus 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.

[0219] According to the above embodiment, two moving average values ​​of the cell voltage of each battery cell within two different time lengths can be determined in each unit time, and based on the difference between the two moving average values ​​of each battery cell in a plurality of battery cells, the voltage abnormality of each battery cell can be diagnosed efficiently and accurately.

[0220] According to another embodiment of the present disclosure, by applying advanced techniques such as normalization and / or statistical variable thresholds when analyzing the difference in change trends between two moving average values ​​of each battery cell, voltage abnormality of each battery cell may be accurately diagnosed.

[0221] According to yet another embodiment of the present disclosure, a time period during which a voltage abnormality occurs and / or a voltage abnormality detection count of each battery cell may be accurately detected by analyzing time series data of a filtered diagnosis value determined based on a statistical variable threshold.

[0222] The embodiments of the present disclosure described above can be realized not only by the device and method, but also by a program that executes functions corresponding to the configuration of the embodiments of the present disclosure or a recording medium on which the program is recorded, and those skilled in the art can easily complete such realization from the disclosure of the embodiments described above.

[0223] Although the present disclosure has been described above with respect to a limited number of embodiments and drawings, the present disclosure is not limited thereto, and it will be apparent to those skilled in the art that various modifications and changes may be made thereto within its multiple technical aspects and within the equivalent scope of the appended claims.

[0224] In addition, since those skilled in the art can make many substitutions, modifications and changes to the present disclosure without departing from the multiple technical aspects of the present disclosure, the present disclosure is not limited to the above-mentioned embodiments and drawings, and all or some of the embodiments may be selectively combined to allow various modifications.

Claims

1. A battery diagnostic device for a battery pack, the battery pack comprising a plurality of battery cells connected in series and installed in an electric vehicle, the battery diagnostic device include: a voltage sensing circuit configured to periodically generate a voltage signal representative of a cell voltage of each battery cell while the electric vehicle is operating; as well as a control circuit configured to cumulatively store the cell voltage determined from the voltage signal in a memory unit, and generate time series data representing a change in the cell voltage of each battery cell over time by using the cumulative cell voltage of each battery cell, Wherein, the control circuit is configured as follows: (i) determining a first average cell voltage and a second average cell voltage for 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 (ii) detecting voltage abnormality of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

2. The battery diagnostic device according to claim 1, in, For each battery cell, the control circuit is configured to determine a difference in short-term and long-term average values ​​corresponding to a difference between the first average cell voltage and the second average cell voltage, wherein, for each battery cell, the control circuit is configured to determine a cell diagnostic deviation corresponding to a deviation between the short-term and long-term average value difference of the battery cell and an average value of the short-term and long-term average value differences of all battery cells, and The control circuit is configured to detect a battery cell that satisfies the condition that the battery cell diagnosis deviation exceeds a diagnosis threshold as a battery cell with abnormal voltage.

3. The battery diagnostic device according to claim 2, in, For each battery cell, the control circuit is configured to generate time series data of the battery cell diagnostic deviation, and detect voltage abnormality of the battery cell based on the time when the battery cell diagnostic deviation exceeds the diagnostic threshold or the amount of data of the battery cell diagnostic deviation exceeding the diagnostic threshold.

4. The battery diagnostic device according to claim 1, in, For each battery cell, the control circuit is configured to determine a difference in short-term and long-term average values ​​corresponding to a difference between the first average cell voltage and the second average cell voltage, wherein, for each battery cell, the control circuit is configured to determine a cell diagnosis deviation by calculating a deviation between the short-term and long-term average value difference of the battery cell and an average value of the short-term and long-term average value differences of all battery cells, wherein the control circuit is configured to determine a statistical variable threshold value depending on a standard deviation of the cell diagnostic deviations of all battery cells, wherein the control circuit is configured to generate time series data of filtered diagnostic values ​​by filtering the time series data of the cell diagnostic deviation of each battery cell based on the statistical variable threshold, and The control circuit is configured to detect voltage abnormality of the battery cell according to a time when the filtered diagnostic value exceeds a diagnostic threshold or an amount of data of the filtered diagnostic value exceeding the diagnostic threshold.

5. The battery diagnosis device according to claim 1, in, For each battery cell, the control circuit is configured to determine a difference in short-term and long-term average values ​​corresponding to a difference between the first average cell voltage and the second average cell voltage, wherein, for each battery cell, the control circuit is configured to determine a normalized value of the difference between the short-term and long-term average values ​​as a normalized cell diagnostic deviation, and determine a statistical variable threshold value depending on a standard deviation of the normalized cell diagnostic deviations of all battery cells, wherein the control circuit is configured to generate time series data of filtered diagnostic values ​​by filtering the time series data of the normalized cell diagnostic deviation of each battery cell based on the statistical variable threshold, and The control circuit is configured to detect voltage abnormality of the battery cell according to a time when the filtered diagnostic value exceeds a diagnostic threshold or an amount of data of the filtered diagnostic value exceeding the diagnostic threshold.

6. The battery diagnosis device according to claim 5, in, The control circuit is configured to normalize the short term and long term average difference of each battery cell by dividing the short term and long term average difference of each battery cell by an average of the short term and long term average differences of all battery cells.

7. The battery diagnosis device according to claim 5, in, The control circuit is configured to normalize the difference between the short-term and long-term average values ​​for each battery cell by calculating a logarithm of the difference between the short-term and long-term average values.

8. The battery diagnostic device according to claim 1, in, The control circuit is configured to generate time series data representing a change in the cell voltage of each battery cell over time using a voltage difference between an average value of cell voltages of all battery cells measured per unit time and the cell voltage of each battery cell.

9. The battery diagnostic device according to claim 1, in, For each battery cell, the control circuit is configured to determine a difference in short-term and long-term average values ​​corresponding to a difference between the first average cell voltage and the second average cell voltage, wherein, for each battery cell, the control circuit is configured to determine a normalized value of the difference between the short-term and long-term average values ​​as a normalized cell diagnostic deviation, and generate time series data of the normalized cell diagnostic deviation for each battery cell, The control circuit is configured to generate the time series data of the normalized cell diagnostic deviation of each battery cell by recursively repeating the following steps (i) to (iv) at least once: (i) determining a first moving average value and a second moving average value of the time series data of the normalized cell diagnostic deviation for each battery cell, wherein the first moving average value is a short-term moving average value and the second moving average value is a long-term moving average value, (ii) determining, for each battery cell, a short-term and long-term average value difference corresponding to a difference between the first moving average value and the second moving average value, (iii) determining, for each battery cell, a normalized value of the short-term and long-term average value difference as a normalized cell diagnostic deviation, and (iv) generating time series data of the normalized cell diagnostic deviation for each battery cell, wherein the control circuit is configured to determine a statistical variable threshold value that depends on a standard deviation of a normalized cell diagnostic deviation of all battery cells, wherein the control circuit is configured to generate time series data of filtered diagnostic values ​​by filtering the time series data of the cell diagnostic deviation of each battery cell based on the statistical variable threshold, and The control circuit is configured to detect voltage abnormality of the battery cell according to a time when the filtered diagnostic value exceeds a diagnostic threshold or an amount of data of the filtered diagnostic value exceeding the diagnostic threshold.

10. The battery diagnosis device according to claim 1, in, The curve of the time series data of the cell voltage of each battery cell includes voltage data equal to or less than a preset diagnosis start voltage and includes an inflection point after a time point at which the voltage data is measured. 11 . A battery pack, comprising the battery diagnosis device according to claim 1 .

12. A vehicle comprising the battery pack according to claim 11.

13. A battery diagnostic method for a battery pack, the battery pack comprising a plurality of battery cells connected in series and installed in an electric vehicle, the battery diagnostic method The following steps are involved: (a) generating a voltage signal representing a cell voltage of each battery cell when the electric vehicle is operating; (b) cumulatively storing the cell voltage determined according to the voltage signal in a memory unit; (c) periodically generating time series data representing changes in the cell voltage of each battery cell over time; (d) determining a first average cell voltage and a second average cell voltage for 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.

14. The battery diagnosis method according to claim 13, in, The step (e) comprises: (e1) determining, for each battery cell, short-term and long-term average differences corresponding to a difference between the first average cell voltage and the second average cell voltage; (e2) determining, for each battery cell, a cell diagnostic deviation corresponding to a deviation between the short-term and long-term mean difference of the battery cell and an average of the short-term and long-term mean differences of all battery cells; and (e3) A battery cell that satisfies the condition that the battery cell diagnosis deviation exceeds the diagnosis threshold is detected as a battery cell with abnormal voltage.

15. The battery diagnosis method according to claim 14, in, The step (e) comprises: (e1) generating time series data of the battery cell diagnostic deviation for each battery cell; and (e2) detecting a voltage abnormality of the battery cell based on a time period during which the cell diagnosis deviation exceeds a diagnosis threshold or an amount of data of the cell diagnosis deviation exceeding the diagnosis threshold.

16. The battery diagnosis method according to claim 13, in, The step (e) comprises: (e1) determining, for each battery cell, short-term and long-term average differences corresponding to 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 of the short-term and long-term mean differences of the battery cell from an average of the short-term and long-term mean differences of all battery cells; (e3) determining a statistical variable threshold value that depends on the standard deviation of the cell diagnostic deviation for all battery cells; (e4) generating time series data of filtered diagnostic values ​​for each battery cell by filtering the time series data of the cell diagnostic deviation for each battery cell based on the statistical variable threshold; and (e5) detecting a voltage abnormality of the battery cell based on a time period during which the filtered diagnostic value exceeds a diagnostic threshold or an amount of data of the filtered diagnostic value exceeding the diagnostic threshold.

17. The battery diagnosis method according to claim 13, in, The step (e) comprises: (e1) determining, for each battery cell, short-term and long-term average differences corresponding to a difference between the first average cell voltage and the second average cell voltage; (e2) for each battery cell, determining a normalized value of the difference between the short-term and long-term average values ​​as a normalized cell diagnostic deviation; (e3) determining a statistical variable threshold value that depends on the standard deviation of the normalized cell diagnostic deviation for all battery cells; (e4) generating time series data of filtered diagnostic values ​​by filtering the time series data of the normalized cell diagnostic deviation for each battery cell based on the statistical variable threshold; and (e5) detecting a voltage abnormality of the battery cell based on a time period during which the filtered diagnostic value exceeds a diagnostic threshold or an amount of data of the filtered diagnostic value exceeding the diagnostic threshold.

18. The battery diagnosis method according to claim 17, in, The step (e2) is a step of normalizing the short-term and long-term average value difference of each battery cell by dividing the short-term and long-term average value difference of each battery cell by the average value of the short-term and long-term average value differences of all battery cells.

19. The battery diagnosis method according to claim 17, in, The step (e2) is a step of normalizing the difference between the short-term and long-term average values ​​of each battery cell by calculating the logarithm of the difference between the short-term and long-term average values.

20. The battery diagnosis method according to claim 13, in, The step (a) is a step of generating time series data indicating a change in the cell voltage of each battery cell over time using a voltage difference between an average value of the cell voltages of all battery cells measured per unit time and the cell voltage of each battery cell.

21. The battery diagnosis method according to claim 13, in, The step (e) comprises: (e1) determining, for each battery cell, short-term and long-term average differences corresponding to a difference between the first average cell voltage and the second average cell voltage; (e2) for each battery cell, determining a normalized value of the difference between the short-term and long-term average values ​​as a normalized cell diagnostic deviation; (e3) generating time series data of normalized cell diagnostic deviation for each battery cell; (e4) generating time series data of normalized cell diagnostic deviation for each battery cell by recursively repeating the following steps (i) to (iv) at least once: (i) determining a first moving average value and a second moving average value of time series data of normalized cell diagnostic deviation for each battery cell, wherein the first moving average value is a short-term moving average value and the second moving average value is a long-term moving average value, (ii) determining, for each battery cell, a short-term and long-term average value difference corresponding to a difference between the first moving average value and the second moving average value, (iii) determining, for each battery cell, a normalized value of the short-term and long-term average value difference as a normalized cell diagnostic deviation, and (iv) generating time series data of normalized cell diagnostic deviation for each battery cell, (e5) determining a statistical variable threshold value that depends on the standard deviation of the normalized cell diagnostic deviations of all battery cells; (e6) generating time series data of filtered diagnostic values ​​by filtering the time series data of the cell diagnostic deviation of each battery cell based on the statistical variable threshold; and (e7) detecting a voltage abnormality of the battery cell based on a time period during which the filtered diagnostic value exceeds a diagnostic threshold or an amount of data of the filtered diagnostic value exceeding the diagnostic threshold.

22. The battery diagnosis method according to claim 13, in, The curve of the time series data of the cell voltage of each battery cell includes voltage data equal to or less than a preset diagnosis start voltage and includes an inflection point after a time point at which the voltage data is measured.

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