Battery management device and its operating method

Abstract

A battery management device according to one embodiment disclosed in this document includes a voltage measurement unit that measures the voltage of each of a plurality of batteries, and a controller capable of communicating with the voltage measurement unit. The controller can be configured to control the voltage measurement unit to measure the voltage of each of the plurality of battery banks at predetermined time intervals, calculate a first deviation for each of the plurality of batteries, which is the deviation between a long-term moving average value and a short-term moving average value of battery voltages, calculate a second deviation for each of the plurality of batteries, which is the deviation between a long-term moving average value and a short-term moving average value of average voltages of the plurality of batteries, calculate a first diagnostic deviation for each of the plurality of batteries, which is the difference between the first deviation and the second deviation, calculate a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of the first diagnostic deviation for each of the plurality of batteries by a threshold constant, and diagnose whether or not at least one of the plurality of batteries has an abnormality based on the second diagnostic deviation for each of the plurality of batteries.

Description

Technical Field

[0001] The embodiments disclosed in this document claim the benefit of priority based on Korean Patent Application No. 10-2022-0120366 filed on September 22, 2022, Korean Patent Application No. 10-2023-0058253 filed on May 4, 2023, and Korean Patent Application No. 10-2023-0126472 filed on September 21, 2023, and all the contents disclosed in the documents of the Korean patent applications are incorporated herein by reference in their entirety. The embodiments disclosed in this document relate to a battery management device and an operating method thereof.

Background Art

[0002] An electric vehicle receives electrical supply from the outside to charge a battery cell, and then obtains power by driving a motor with the voltage charged in the battery cell. The battery cell undergoes internal deformation and denaturation due to various charge and discharge processes during production and use, resulting in changes in its physicochemical properties, and may cause internal short circuit, external short circuit, venting due to lithium precipitation, or under-voltage failure where the voltage of the battery cell decreases below a certain level.

[0003] When a defect occurs inside the battery cell, the performance of the battery cell may deteriorate, and direct problems may occur in the battery cell, such as an increase in the possibility of ignition due to leakage of the electrolyte.

Summary of the Invention

Problems to be Solved by the Invention

[0004] One object of the embodiments disclosed in this document is to provide a battery management device and an operating method thereof that can remove noise from the deviation between the long-term moving average value and the short-term moving average value of the battery voltage and accurately diagnose abnormal battery cells.

[0005] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0006] A battery management device according to one embodiment disclosed herein includes a voltage measuring unit for measuring the voltage of each of a plurality of batteries, and a controller capable of communicating with the voltage measuring unit. The controller controls the voltage measuring unit to measure the voltage of each of the plurality of batteries at predetermined time intervals, calculates a first deviation for each of the plurality of batteries which is the deviation between the long-term moving average and the short-term moving average of the battery voltages, calculates a second deviation which is the deviation between the long-term moving average and the short-term moving average of the average voltage of the plurality of batteries, calculates a first diagnostic deviation for each of the plurality of batteries which is the difference between the first deviation and the second deviation, calculates a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation by a threshold constant, and diagnoses whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries.

[0007] In one embodiment, the controller sets the maximum value between the value obtained by multiplying the second deviation by a first threshold constant and the second threshold constant as the reference value, excludes first diagnostic deviations of each of the multiple batteries that are less than or equal to the reference value, and calculates the second diagnostic deviation of each of the multiple batteries.

[0008] In one embodiment, the controller can normalize the second diagnostic deviation of each of the plurality of batteries by dividing the value obtained by multiplying the second deviation by a third threshold constant by the maximum value of the fourth threshold constant, and calculate the third diagnostic deviation of each of the plurality of batteries.

[0009] In one embodiment, the controller can calculate the distortion of each of the multiple batteries by adding the minimum value of the third diagnostic deviation of each of the multiple batteries to the third diagnostic deviation of each of the multiple batteries and dividing the resulting value by the third diagnostic deviation.

[0010] In one embodiment, the controller can calculate a fourth diagnostic deviation for each of the plurality of batteries by multiplying the third diagnostic deviation of each of the plurality of batteries by the degree of distortion.

[0011] In one embodiment, the controller can diagnose whether or not at least one of the plurality of batteries is abnormal based on whether or not the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

[0012] In one embodiment, the controller calculates the first and second deviations for each unit time, calculates the fourth diagnostic deviation for each of the plurality of batteries, and can diagnose whether or not there is an abnormality in at least one of the plurality of batteries if the fourth diagnostic deviation of at least one of the batteries exceeds a threshold.

[0013] The operation method of a battery diagnostic device according to one embodiment disclosed herein may include the steps of: measuring the voltage of each of a plurality of batteries at predetermined time intervals; calculating a first deviation for each of the plurality of batteries, which is the deviation between the long-term moving average value and the short-term moving average value of the battery voltage; calculating a second deviation for each of the plurality of batteries, which is the deviation between the long-term moving average value and the short-term moving average value of the average voltage of the plurality of batteries; calculating a first diagnostic deviation for each of the plurality of batteries, which is the difference between the first deviation and the second deviation; calculating a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of each of the plurality of batteries by a threshold constant; and diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries.

[0014] In one embodiment, the step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation by a threshold constant among the first diagnostic deviations of each of the plurality of batteries is to set the maximum value of the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant as the reference value, and to calculate the second diagnostic deviation for each of the plurality of batteries by excluding the first diagnostic deviation of each of the plurality of batteries that is less than or equal to the reference value.

[0015] In one embodiment, the step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of each of the plurality of batteries by a threshold constant can be normalized by dividing the second diagnostic deviation of each of the plurality of batteries by the maximum value among the value obtained by multiplying the second deviation by a third threshold constant and the fourth threshold constant, and then calculating the third diagnostic deviation for each of the plurality of batteries.

[0016] In one embodiment, the step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of each of the plurality of batteries by a threshold constant can be performed by adding the minimum value of the third diagnostic deviation for each of the plurality of batteries to the third diagnostic deviation for each of the plurality of batteries and dividing the resulting value by the third diagnostic deviation to calculate the distortion of each of the plurality of batteries.

[0017] In one embodiment, the step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of each of the plurality of batteries by a threshold constant can be replaced by multiplying the third diagnostic deviation for each of the plurality of batteries by the degree of distortion to calculate the fourth diagnostic deviation for each of the plurality of batteries.

[0018] In one embodiment, the step of diagnosing whether at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries can be used to diagnose whether at least one of the plurality of batteries is abnormal based on whether the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

[0019] In one embodiment, the step of diagnosing whether at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries is to calculate the first and second deviations for each unit time, calculate the fourth diagnostic deviation of each of the plurality of batteries, and if the fourth diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the presence or absence of abnormality in that at least one battery can be diagnosed.

[0020] A controller according to one embodiment disclosed herein may include a memory and a processor coupled to the memory and configured to perform the operation method of the battery management device.

[0021] In one embodiment, in the operation method of the battery management device, the steps of calculating the second diagnostic deviation of each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of each of the plurality of batteries by a threshold constant include setting the maximum value of the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant as the reference value, excluding the first diagnostic deviation of each of the plurality of batteries that is less than or equal to the reference value, and calculating the second diagnostic deviation of each of the plurality of batteries, and normalizing the second diagnostic deviation of each of the plurality of batteries by dividing the value obtained by multiplying the second deviation by a third threshold constant and the maximum value of the fourth threshold constant, and each of the plurality of batteries The steps include: calculating a third diagnostic deviation; calculating the distortion of each of the plurality of batteries by adding the minimum value of the third diagnostic deviation of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the resulting value by the third diagnostic deviation; and calculating a fourth diagnostic deviation of each of the plurality of batteries by multiplying the third diagnostic deviation of each of the plurality of batteries by the distortion, wherein the step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries may include the step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on whether or not the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

[0022] A non-temporary computer-readable storage medium according to one embodiment disclosed herein can store a program for performing the following steps: measuring the voltage of each of a plurality of batteries at predetermined time intervals using a voltmeter; calculating a first deviation for each of the plurality of batteries, which is the deviation between a long-term moving average and a short-term moving average of the battery voltages; calculating a second deviation for each of the plurality of batteries, which is the deviation between a long-term moving average and a short-term moving average of the average voltage of the plurality of batteries; calculating a first diagnostic deviation for each of the plurality of batteries, which is the difference between the first deviation and the second deviation; calculating a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of each of the plurality of batteries by a threshold constant; and diagnosing whether or not at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries.

[0023] In one embodiment, the step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of each of the plurality of batteries by a threshold constant is to set the reference value to the maximum value of the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant; to exclude the first diagnostic deviations of each of the plurality of batteries that are less than or equal to the reference value and calculate the second diagnostic deviation for each of the plurality of batteries; to normalize the second diagnostic deviation for each of the plurality of batteries by dividing the value obtained by multiplying the second deviation by a third threshold constant and the maximum value of the fourth threshold constant and calculate the third diagnostic deviation for each of the plurality of batteries. The steps include: generating a value; calculating the distortion of each of the plurality of batteries by adding the minimum value of the third diagnostic deviation of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the resulting value by the third diagnostic deviation; and calculating the fourth diagnostic deviation of each of the plurality of batteries by multiplying the third diagnostic deviation of each of the plurality of batteries by the distortion, wherein the step of diagnosing whether or not at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries may include a step of diagnosing whether or not at least one of the plurality of batteries is abnormal based on whether or not the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold. [Effects of the Invention]

[0024] According to one embodiment of the battery management device and its operating method disclosed in this document, noise from the deviation between the long-term moving average value and the short-term moving average value of the battery voltage can be removed, and abnormal battery cells can be accurately diagnosed. [Brief explanation of the drawing]

[0025] [Figure 1] This figure shows a battery pack according to one embodiment disclosed in this document. [Figure 2] This is a block diagram showing the configuration of a battery management device according to one embodiment disclosed in this document. [Figure 3]This is a flowchart showing the operation method of a battery management device according to one embodiment disclosed in this document. [Figure 4] This is a graph showing the voltage of a battery cell according to one embodiment disclosed in this document. [Figure 5a] This is a graph showing the first diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 5b] This is a graph showing the third diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 5c] This is a graph showing the skewness of the third diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 5d] This is a graph showing the fourth diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 6] This is a flowchart showing the operation method of a battery management device according to another embodiment disclosed in this document. [Figure 7] This flowchart shows the operation method of a battery management device and a method for diagnosing abnormal battery cells according to other embodiments disclosed in this document. [Figure 8a] This is a graph showing the first voltage during the discharge and rest period after discharge of a battery cell according to one embodiment disclosed in this document. [Figure 8b] This graph shows the first voltage during charging and the rest period after charging of a battery cell according to another embodiment disclosed in this document. [Figure 9a] This graph shows the long-term (solid line) and short-term (dotted line) moving averages of the voltage during discharge and the rest period after discharge of a battery cell according to one embodiment disclosed in this document. [Figure 9b] This graph shows the long-term (solid line) and short-term (dotted line) moving averages of the voltage during charging and the rest period after charging of a battery cell according to another embodiment disclosed in this document. [Figure 10a] This graph shows the first deviation of the first voltage (dV) during the discharge and rest period after discharge of a battery cell according to one embodiment disclosed in this document. [Figure 10b]This graph shows the first deviation of the first voltage (dV) during charging and the rest period after charging of a battery cell according to another embodiment disclosed in this document. [Figure 11a] This graph shows the first diagnostic deviation (D1) during the discharge and rest period after discharge of a battery cell according to one embodiment disclosed in this document. [Figure 11b] This graph shows the first diagnostic deviation (D1) during charging and the rest period after charging of a battery cell according to another embodiment disclosed in this document. [Figure 12] This flowchart shows the operation method of a battery management device and a method for diagnosing abnormal battery cells according to other embodiments disclosed in this document. [Figure 13] This is a block diagram showing the hardware configuration of a computing system that implements the operating method of a battery management device according to one embodiment disclosed in this document. [Modes for carrying out the invention]

[0026] Some embodiments disclosed in this document will be described in detail below with reference to illustrative drawings. It should be noted that, when assigning reference numerals to components in each drawing, the same reference numerals will be used for the same components whenever possible when they appear in other drawings. Furthermore, when describing the embodiments disclosed in this document, if a specific description of a related known configuration or function is deemed to hinder understanding of the embodiments disclosed in this document, such detailed description will be omitted.

[0027] In describing the components of the embodiments disclosed herein, terms such as First, Second, A, B, (a), (b), etc., may be used. Such terms are merely for distinguishing a component from other components and do not limit the nature, order, or sequence of the component. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as those generally understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an ideal or overly formal sense unless explicitly defined herein.

[0028] Figure 1 shows a battery pack according to one embodiment disclosed in this document. The battery pack 1000 according to one embodiment disclosed in this document may include a battery module 100, a battery management device 200, and a relay 300. According to various embodiments, the battery module 100 may be a battery cell, in which case the battery pack 1000 may have a CTP (cell to pack) structure in which the modules are directly assembled into the pack, unlike conventional batteries in which multiple cells constitute a module and the module constitutes the package, by omitting the module.

[0029] Although Figure 1 shows a single battery module 100, according to the embodiment, the battery module 100 may consist of multiple modules, and the battery pack 1000 may have multiple battery modules forming a stacked structure. The battery module 100 can include multiple battery cells 110, 120, 130, and 140. Although Figure 1 shows four battery cells, the battery module 100 is not limited to this and can consist of n (where n is a natural number of 1 or more) battery cells. Furthermore, each of the multiple battery cells 110, 120, 130, and 140 can form a cell group or battery bank in which at least two or more battery cells are connected in parallel.

[0030] The battery module 100 can supply power to a target device (not shown). For this purpose, the battery module 100 can be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates on power supplied from a battery pack 1000 containing a plurality of battery cells 110, 120, 130, 140, for example, an electric vehicle (EV) or an energy storage system (ESS), but is not limited to these.

[0031] Each of the multiple battery cells 110, 120, 130, and 140 is a basic unit of a battery that can be used by charging and discharging electrical energy, and may be, but is not limited to, lithium-ion (Li-ion) batteries, lithium-ion polymer (Li-ion polymer) batteries, nickel-cadmium (Ni-Cd) batteries, nickel-metal hydride (Ni-MH) batteries, etc. On the other hand, although Figure 1 shows that there is one battery module 100, according to the embodiment, the battery module 100 may be composed of multiple units.

[0032] The Battery Management System (BMS) 200 can manage and / or control the state and / or operation of the battery module 100. For example, the Battery Management System 200 can manage and / or control the state and / or operation of multiple battery cells 110, 120, 130, 140 contained in the battery module 100, and can also manage the charging and / or discharging of the battery module 100.

[0033] The battery management device 200 can also control the operation of the relay 300. For example, the battery management device 200 can short-circuit the relay 300 to supply power to the target device, and can also short-circuit the relay 300 when a charging device is connected to the battery pack 1000.

[0034] Furthermore, the battery management device 200 can monitor the voltage, current, temperature, etc., of the battery module 100 and / or the multiple battery cells 110, 120, 130, and 140 contained within the battery module 100. In addition, for monitoring via the battery management device 200, sensors and various measurement modules (not shown) can be further installed at arbitrary locations such as the battery module 100 and the charge / discharge path. Based on the measured values ​​of voltage, current, temperature, etc., the battery management device 200 can calculate parameters indicating the state of the battery module 100, such as SOC (State of Charge) or SOH (State of Health).

[0035] As the usage period or number of uses increases, various factors in the multiple battery cells 110, 120, 130, and 140 change, such as a decrease in capacity and an increase in internal resistance, which can lead to abnormal battery phenomena. Therefore, a technology is needed to determine whether or not there are abnormalities in the battery cells. The battery management device 200 can diagnose abnormal phenomena inside the multiple battery cells 110, 120, 130, and 140 based on data of various factors that change as the battery cells deteriorate in this way.

[0036] Battery cells can experience faster and larger voltage changes compared to normal battery cells if they become defective due to various reasons such as defects during the production stage, internal deformation and alteration due to multiple charge / discharge cycles, or external impact. The battery management device 200 utilizes the phenomenon that battery cells with internal defects experience faster and larger voltage changes during the dormant period compared to normal battery cells. By comparing the voltage data of each of the multiple battery cells 110, 120, 130, and 140 during their dormant period with the statistical normal voltage data of normal battery cells during their dormant period, the device can diagnose abnormal battery cells among the multiple battery cells 110, 120, 130, and 140. The dormant period of a battery cell or module means a state in which the battery cell or module is neither charging nor discharging, or is not electrically connected to a load. For example, the battery management device 200 can monitor the voltage value of a cell or the charge / discharge current value of a battery module to detect whether a battery module or cell is in a dormant state. This embodiment describes the diagnosis of abnormal battery cells during the idle period, but the battery cell diagnosis method according to the embodiment disclosed in this document is not limited thereto, and abnormal battery cells can also be diagnosed in other sections of the battery module or cell, for example, during charging or discharging.

[0037] Specifically, in the case of an abnormal battery cell, for example, a phenomenon occurs in which the voltage drops during the rest period after charging compared to a normal battery cell, resulting in a large deviation in voltage behavior compared to the voltage behavior of a normal battery cell, and a phenomenon in which the voltage behavior is biased to one side, resulting in a large degree of distortion (asymmetry, skewness). The battery management device 200 according to one embodiment disclosed in this document can determine the presence or absence of an abnormal battery cell among a plurality of battery cells 110, 120, 130, and 140 by utilizing the characteristics of an abnormal battery cell, which have a large deviation and large degree of distortion compared to the voltage behavior of a normal battery cell.

[0038] The battery management device 200 can calculate the average voltage of multiple battery cells 110, 120, 130, and 140 at a specific point in time, and the deviation (dV) between the average voltage of each of the multiple battery cells 110, 120, 130, and 140 and the voltage of each of the multiple battery cells 110, 120, 130, and 140. Using the voltage deviation relative to the average voltage of each of the multiple battery cells 110, 120, 130, and 140, the battery management device 200 can determine abnormal voltage behavior in at least one of the multiple battery cells 110, 120, 130, and 140 and diagnose whether or not that battery cell is abnormal.

[0039] Furthermore, the battery management device 200 can diagnose battery cells using the voltage deviation data of multiple battery cells 110, 120, 130, and 140, after removing noise voltage data suspected to be noise data from the voltage deviations of each of the multiple battery cells 110, 120, 130, and 140. The battery management device 200 can amplify the voltage deviation data of multiple battery cells 110, 120, 130, and 140 after removing noise voltage data from the voltage deviations of each of the multiple battery cells 110, 120, 130, and 140. Using the amplified voltage deviation data of multiple battery cells 110, 120, 130, and 140, the battery management device 200 can detect and diagnose abnormal battery cells suspected of having abnormal voltages.

[0040] Furthermore, the operation of the battery management device 200 can be performed via wired or wireless signals in various devices such as servers, clouds, chargers, or chargers / dischargers connected to the battery management device 200 or the vehicle on which the battery management device 200 is installed.

[0041] Figure 2 is a block diagram showing the configuration of a battery management device according to one embodiment disclosed in this document. The configuration of the battery management device 200 varies depending on the operating environment and purpose of the battery pack 1000, which includes the battery module 100, and can include a variety of different operating components.

[0042] Referring to Figure 2, the battery management device 200 may include a voltage measuring unit 210 and a controller 220. In one embodiment, the controller 220 may include a calculation unit 230, a diagnostic unit 240, and a control unit 250. In other embodiments, the battery management device 200 may further include a current measuring unit and / or a temperature measuring unit, in addition to the voltage measuring unit 210.

[0043] The voltage measurement unit 210 includes a voltmeter and is composed of a device capable of measuring the voltage of the battery bank and / or cells. It can measure the voltage of each of the multiple battery cells 110, 120, 130, and 140 at regular time intervals or per unit time, and acquire time-series data of the voltage of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the voltage measurement unit 210 can continuously measure and acquire voltage rise and fall during charging, the post-charging rest period, the discharge, and the post-discharge rest period of the multiple battery cells 110, 120, 130, and 140, as well as long-term stabilization (relaxation) data. The continuous voltage data thus acquired can be used, if necessary, to diagnose abnormal battery cells in specific sections, such as the charging section, the post-charging rest period, the discharge section, and the post-discharge rest period.

[0044] In one embodiment, the calculation unit 230 of the controller 220 uses the voltage data measured by the voltage measurement unit 210 to perform various calculations for diagnosing each of the multiple battery cells 110, 120, 130, and 140, which will be described later. The diagnosis unit 240 uses the calculation results to check the diagnosis conditions, which will be described later, and diagnoses whether there is an abnormality in the battery bank. The control unit 250 uses the diagnosis results to monitor the abnormal battery bank or to take appropriate measures for the battery bank, such as notifying the user of the presence or absence of an abnormality.

[0045] Figure 3 is a flowchart showing the operation method of a battery management device 200 according to one embodiment disclosed in this document. The operation of the device and the method for diagnosing abnormal battery cells in each step will be described below with reference to Figure 3.

[0046] In S102, the voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing the voltage changes of each of the multiple battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 throughout the entire charging period, the rest period after charging, the discharge period, and the rest period after discharge, and continuously calculates the voltage rise and fall and long-term stabilization (relaxation) data, and the controller 220 can generate a graph showing the voltage changes of each using the measured voltage data. Alternatively, the voltage measurement unit 210 may, as needed, continuously calculate the voltage rise and fall and long-term stabilization (relaxation) data for each of the multiple battery cells 110, 120, 130, and 140 during specific periods such as charging, the rest period after charging, discharging, and the rest period after discharging, and the controller 220 may use this data to generate a graph showing the voltage changes for each.

[0047] Figure 4 is a graph showing the voltage change of a battery cell according to one embodiment disclosed in this document. In the example of Figure 4, as one embodiment, the voltage measured by the voltage measuring unit 210 shows the voltage change of each of the multiple battery cells 110, 120, 130, and 140 measured at 200-second intervals during a specific time, for example, from 10,600 seconds to 11,600 seconds after the start of charging of the multiple battery cells 110, 120, 130, and 140 (for example, a rest period). In particular, the graph labeled "ab1" in Figure 4 shows the voltage change of battery cell 110 during the rest period after charging, and a kind of inverse peak portion is observed where the voltage drops significantly relative to the circular portion. This suggests that the voltage of battery cell 110 is exhibiting abnormal behavior in that period.

[0048] In S104, the controller 220 can calculate the moving average of the measured voltages of each of the multiple battery cells 110, 120, 130, and 140. Here, the moving average is the average of a portion of the data extracted from the total data while moving through a window of a specific size. Here, the window is a reference interval from which a portion of the total data can be extracted to determine which data to use. The start time of the window is a reference time before the current time, and the end time of the window is the current time. For example, if the window is one week, the controller 220 can extract voltage data from the current time to the most recent week from the total voltage data.

[0049] The controller 220 can calculate the moving average of the voltages of multiple battery cells 110, 120, 130, and 140 by using voltage data extracted from the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 while moving through the window. The controller 220 can also calculate the continuous moving average of the voltages of multiple battery cells 110, 120, 130, and 140 by using voltage data continuously extracted from the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 while moving through the window. In calculating the moving average, the controller 220 can apply one of the following methods to the total voltage data of each of the multiple battery cells 110, 120, 130, and 140: Simple Moving Average, Weighted Moving Average, or Exponential Moving Average (EMA).

[0050] According to one embodiment, the controller 220 can apply an exponential moving average (EMA) to the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 to calculate the exponential moving average value of the voltages of each of the multiple battery cells 110, 120, 130, and 140. The exponential moving average is a type of weighted moving average method that uses data from the entire past period and gives more weight to recent data.

[0051] The controller 220 can calculate multiple moving averages with different window sizes using the voltage data of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 can continuously calculate a Long Moving Average with a relatively long window length and a Short Moving Average with a relatively short window length for each unit time (e.g., 200 seconds) using the total voltage data of each of the multiple battery cells 110, 120, 130, and 140. For example, the window size of the Long Moving Average may include 100 seconds, and the window size of the Short Moving Average may include 10 seconds. For example, the controller 220 can use the voltage data of multiple battery cells 110, 120, 130, and 140 to calculate the long-term moving average of each of the multiple battery cells 110, 120, 130, and 140 using the voltage data acquired every second for the last 100 seconds from the calculation point, and can also calculate the short-term moving average of each of the multiple battery cells 110, 120, 130, and 140 using the voltage data acquired every second for the last 10 seconds from the calculation point.

[0052] The controller 220 can analyze the long-term voltage change trend and short-term voltage change trend of multiple battery cells 110, 120, 130, and 140 using the continuous long-term moving average (V_LMA) and short-term moving average (V_SMA) of each of the multiple battery cells 110, 120, 130, and 140. The controller 220 can diagnose whether there are any abnormalities in the voltage of each of the multiple battery cells using the long-term moving average (V_LMA) and short-term moving average (V_SMA) of the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0053] In S106, the controller 220 can calculate multiple first deviations (V_LMA-V_SMA), which are the deviations between the long-term moving average (V_LMA) and short-term moving average (V_SMA) of the voltages of multiple battery cells 110, 120, 130, and 140, for each unit time (e.g., 200 seconds). For example, the controller 220 can continuously calculate the first deviations (V_LMA-V_SMA) for each of the multiple battery cells 110, 120, 130, and 140, which have been calculated for each unit time (e.g., 200 seconds). In S106, the controller 220 can continuously calculate the deviations between the long-term and short-term behaviors of the voltages of multiple battery cells 110, 120, 130, and 140.

[0054] In S108, the controller 220 calculates the long-term moving average (V_avg) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds). avg_LMA ) and short-term moving average (V avg_SMA The average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds) can include the mean, median, or minimum voltage of the multiple battery cells 110, 120, 130, and 140.

[0055] The controller 220 continuously calculates the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, 140 every respective unit time (e.g., 200 seconds), and uses the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, 140 to calculate the long-term moving average value (V avg_LMA ) and the short-term moving average value (V avg_SMA ). Here, the window size of the long-term moving average value (V avg_LMA ) of the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, 140 may be the same as the window size (e.g., 100 seconds) of the long-term moving average value (V_LMA) of the voltage of each of the plurality of battery cells 110, 120, 130, 140. Also, the window size of the short-term moving average value (V avg_SMA ) of the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, 140 may be the same as the window size (e.g., 10 seconds) of the short-term moving average value (V_SMA) of each of the plurality of battery cells 110, 120, 130, 140.

[0056] In S110, the controller 220 can continuously calculate the second deviation (V avg_LMA - V avg_SMA ), which is the deviation between the long-term moving average value (V avg_LMA ) and the short-term moving average value (V avg_SMA ) of the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, 140 every respective unit time (e.g., 200 seconds). In the S110 step, the controller 220 can calculate the deviation between the long-term behavior and the short-term behavior of the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, 140.

[0057] In S112, the controller 220 calculates the plurality of first deviations (V_LMA - V_SMA) and the second deviation (V avg_LMA - V avg_SMAThe controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, which is the deviation of ). Specifically, the controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140 based on [Equation 1] described below.

[0058] [Formula 1]

number

[0059] Referring to [Equation 1], the controller 220 calculates multiple first deviations (V_LMA-V_SMA) and second deviations (V avg_LMA -V avg_SMA The deviation of ) can be calculated as the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140.

[0060] Figure 5a is a graph showing the first diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Referring to Figure 5a, the controller 220 can continuously calculate the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 for each unit time in the given interval, and generate a graph showing the change in the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140.

[0061] The controller 220 continuously calculates the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140 in each unit time (e.g., 200 seconds), and can compare the deviations between the long-term and short-term behavior of the voltage of each of the multiple battery cells 110, 120, 130, and 140 with the deviations between the long-term and short-term behavior of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140. Exemplarily, graph (ab2) may be a graph showing the change in the first diagnostic deviation (D1) of battery cell 110. As shown in the voltage measurement result graph of Figure 4 above, the first diagnostic deviation (D1) of battery cell 110 shows a unique pattern compared to other normal battery cells, and in particular, it shows that it exceeds the reference value described later in a specific interval compared to other battery cells.

[0062] In S114, the controller 220 can remove noise data from the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140.

[0063] Specifically, the controller 220 can set a reference value that allows it to determine the presence or absence of noise in the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, based on the following [Equation 2].

[0064] [Formula 2]

number

[0065] Controller 220 controls the second deviation (V avg_LMA -V avg_SMA The value obtained by multiplying the absolute value of (|V) by the first threshold constant (C1) is (|V avg_LMA -V avg_SMAThe maximum value (Max) of the first threshold constant (C1) and the second threshold constant (C2) can be set to the reference value for each of the multiple battery cells 110, 120, 130, and 140. In one embodiment, the first threshold constant (C1) may include "0.1" and the second threshold constant (C2) may include "0.4". Furthermore, the first threshold constant (C1) and the second threshold constant (C2) may be changed according to the size and characteristics of the voltage data for each of the multiple battery cells 110, 120, 130, and 140.

[0066] As an example of noise reduction, the controller 220 can determine that the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below a reference value is noise data. The controller 220 can exclude the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below a reference value and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140.

[0067] In S116, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 and calculate the third diagnostic deviation (D3).

[0068] Specifically, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 3], and calculate the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0069] [Formula 3]

number

[0070] Controller 220 calculates the absolute value of the second deviation (|V avg_LMA -V avg_SMA The value obtained by multiplying |) by the third threshold constant (C3) is (|Vavg_LMA -V avg_SMA The controller 220 can then calculate the maximum value (Max) of the second diagnostic deviation obtained earlier, that is, the value obtained by multiplying the absolute value of the second deviation, which shows the behavior of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140, by the third threshold constant, and the maximum value (Max[|V avg_LMA -V avg_SMA The second diagnostic deviation (D2) for each of the multiple battery cells 110, 120, 130, and 140 can be normalized by dividing by [·C3,C4]) to calculate the third diagnostic deviation (D3). Here, the third threshold constant (C3) can include "0.1", and the fourth threshold constant (C4) can also include "0.1", and the third threshold constant (C3) and the fourth threshold constant (C4) can be changed according to the size and characteristics of the voltage data for each of the multiple battery cells 110, 120, 130, and 140.

[0071] As another example of normalization, according to one embodiment, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by logarithm operation. The controller 220 can calculate the values ​​normalized by logarithm operation of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0072] As another example of normalization, according to one embodiment, the controller 220 can set the average value (D2_avg) of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the normalization criterion. In S116, the controller 220 can use the average value (D2_avg) of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the normalization criterion and normalize by dividing the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by the average value (D2_avg) of the second diagnostic deviation (D2). The controller 220 can calculate the normalized value obtained by dividing the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by the average value (D2_avg) of the second diagnostic deviation as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0073] Figure 5b is a graph showing the third diagnostic deviation (D3) of a battery cell according to one embodiment disclosed in this document. Referring to Figure 5b, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 according to various embodiments, and calculate the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0074] The controller 220 can continuously calculate the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 at each unit time interval and generate a graph showing the change in the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140. Exemplarily, graph (ab3) may be a graph showing the change in the third diagnostic deviation (D3) of battery cell 110. As shown in Figure 5b, the third diagnostic deviation (D3) of battery cell 110 shows a value of 0 or greater in a specific interval.

[0075] In S118, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 4].

[0076] [Formula 4] Skewness = (Third diagnostic deviation (D3) + Min[Third diagnostic deviation (D3)]) / Third diagnostic deviation (D3)

[0077] Referring to [Equation 4], the controller 220 can calculate the distortion of each of the battery cells 110, 120, 130, and 140 by adding the minimum value (Min[Third Diagnostic Deviation (D3)]) of the third diagnostic deviation (D3) of each of the battery cells 110, 120, 130, and 140 to the third diagnostic deviation (D3) of each of the battery cells 110, 120, 130, and 140 and dividing the result by the third diagnostic deviation (D3).

[0078] Figure 5c is a graph showing the skewness of the third diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Referring to Figure 5c, the controller 220 can continuously calculate the distortion of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 every 200 seconds in 10,600-second and 11,600-second intervals, and generate a graph showing the change in the distortion of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140. Exemplarily, the graph shown in Figure 5c may be a graph showing the change in the distortion of the third diagnostic deviation (D3) for battery cell 110. Compared with the third diagnostic deviation (D3) in Figure 5b, the distortion in Figure 5c shows an improvement in clarity, which leads to improved diagnostic results.

[0079] In S120, the controller 220 can reflect the skewness in the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 can calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 5].

[0080] [Formula 5] 4th diagnostic deviation (D4) = 3rd diagnostic deviation (D3) * skewness

[0081] The controller 220 can calculate the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140 by multiplying the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140 by the skewness for each unit of time (for example, 200 seconds).

[0082] Figure 5d is a graph showing the fourth diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Referring to Figure 5d, the controller 220 can continuously calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds) and generate a graph showing the change in the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140. Exemplarily, the graph shown in Figure 5d may be a graph showing the change in the fourth diagnostic deviation (D4) of battery cell 110. As can be seen from Figure 5d, the fourth diagnostic deviation (D4) with distortion applied has reduced noise, the voltage behavior signal of the abnormal battery cell is amplified and further stabilized, and shows an accurate diagnostic deviation compared to the third diagnostic deviation (D3) without distortion applied.

[0083] In S122, the controller 220 can determine whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold. Here, the threshold can be defined as a reference value that indicates an extreme result and can be judged as "abnormal." The threshold can also be defined as a standard that shows how much the data deviates from a particular statistical model. If, among the multiple battery cells 110, 120, 130, and 140, the controller 220 can determine that a battery cell has exhibited abnormal voltage behavior if its fourth diagnostic deviation (D4) exceeds the threshold. Here, the threshold is a value determined considering the state of the battery cell, the sensitivity of the measurement system, and the measurement environment, and may differ, for example, depending on the type of battery cell and / or the vehicle to which the battery cell is applied. In the example shown in Figure 5d, if we consider that the threshold is set to 0.4 volts, the controller 220 can determine that the battery cell 110 is exhibiting abnormal behavior because its fourth diagnostic deviation (D4) exceeds 0.4V in a specific interval.

[0084] On the other hand, if it is determined in S122 that the fourth diagnostic deviation (D4) does not exceed the threshold, the process returns to S102 and repeats the measurement, calculation, and diagnostic process. In other embodiments, instead of returning to S102, the process may be repeated by returning to any one of the steps prior to S116, as needed.

[0085] In S124, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140 as an abnormal battery cell based on whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold. In other words, if the fourth diagnostic deviation (D4) of at least one of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold, the controller 220 can diagnose that battery cell as a battery cell that has exhibited abnormal behavior.

[0086] On the other hand, in S124, according to one embodiment, the controller 220 can increase the diagnostic count value of at least one battery cell if the fourth diagnostic deviation (D4) of at least one of the plurality of battery cells 110, 120, 130, 140 exceeds a threshold. That is, according to one embodiment, when the fourth diagnostic deviation (D4) of at least one of the plurality of battery cells 110, 120, 130, 140 first exceeds a threshold, the controller 220 does not immediately diagnose that battery cell as an abnormal battery cell. Instead, it diagnoses that battery cell as an abnormal battery cell only if the diagnostic count value is equal to or greater than the threshold count value, for example, if the state in which the battery cell exceeds the threshold is maintained for a preset time (e.g., threshold count). As a result, abnormality diagnosis is not performed on battery cells whose fourth diagnostic deviation (D4) momentarily exceeds the threshold and then falls below the threshold within a short time, thereby improving the reliability of the diagnosis of abnormal battery cells.

[0087] After diagnosing an abnormality, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140, and then track and monitor for defects such as internal short circuits, external short circuits, and lithium deposition within that battery cell.

[0088] Furthermore, if the controller 220 confirms, as a result of the diagnosis, that a defect has occurred inside the battery cell, it can provide information about the battery cell to the battery user. For example, the controller 220 can provide information about the battery cell that has experienced an internal short circuit to the user terminal via a communication unit (not shown), and it can also provide information about the battery cell via a display provided in the vehicle or charger, etc.

[0089] As described above, the battery management device 200 according to one embodiment disclosed in this document can remove noise from the deviation between the long-term moving average and short-term moving average values ​​of the battery cell voltage, and accurately diagnose abnormal battery cells. The battery management device 200 according to one embodiment disclosed in this document can minimize the voltage distortion of the battery cells by using the deviation between the long-term moving average and short-term moving average values ​​of the voltage of each battery cell, remove noise data, amplify the voltage behavior of abnormal battery cells to reflect the degree of voltage distortion of the battery cells, and improve the accuracy of the diagnosis.

[0090] Furthermore, the battery management device 200 can diagnose battery cells exhibiting abnormal voltage behavior early by using the deviation between the long-term moving average and short-term moving average values ​​of the battery cell voltage, thereby ensuring the safety and reliability of the battery energy. In addition, since the battery management device 200 diagnoses battery cells exhibiting abnormal voltage behavior while the battery is installed in the vehicle, it does not require separate separation of the battery, allowing for quick and easy diagnosis of the battery cells.

[0091] Figure 6 is a flowchart showing the operation method of a battery management device according to another embodiment disclosed in this document. The operation described with reference to Figure 3 involves comparing the fourth diagnostic deviation (D4) with a threshold in step S122 to diagnose an abnormal battery cell, but is not limited thereto. For example, the diagnosis of an abnormal battery cell may be performed in any step prior to step S122. For example, the diagnosis of an abnormal battery cell may be performed after calculating the second diagnostic deviation (D2) in step S114. Such embodiments will be described below with reference to Figure 6. To avoid duplication, substantially similar descriptions will be omitted.

[0092] Referring to Figure 6, in S202, the voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing the voltage changes of each of the multiple battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 throughout the entire charging period, the rest period after charging, the discharge period, and the rest period after discharge, and continuously calculates the voltage rise and fall and long-term stabilization (relaxation) data, and the controller 220 can generate a graph showing the voltage changes of each using the measured voltage data.

[0093] In S204, the controller 220 can calculate the moving average of the measured voltages of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 can apply an exponential moving average (EMA) to all the voltage data of each of the multiple battery cells 110, 120, 130, and 140 to calculate the exponential moving average of the voltages of each of the multiple battery cells 110, 120, 130, and 140.

[0094] The controller 220 can continuously calculate a Long Moving Average (with a relatively long window) and a Short Moving Average (with a relatively short window) for each unit of time (e.g., 200 seconds) using the total voltage data of each of the multiple battery cells 110, 120, 130, and 140.

[0095] In S206, the controller 220 can calculate multiple first deviations (V_LMA-V_SMA), which are the deviations between the long-term moving average value (V_LMA) and the short-term moving average value (V_SMA) of the voltages of multiple battery cells 110, 120, 130, and 140 for each unit time (for example, 200 seconds).

[0096] In S208, the controller 220 calculates the long-term moving average (V_avg) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds). avg_LMA ) and short-term moving average (V avg_SMA The average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds) can include the mean, median, or minimum voltage of the multiple battery cells 110, 120, 130, and 140.

[0097] In S210, the controller 220 calculates the long-term moving average (V_avg) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds). avg_LMA ) and short-term moving average (V avg_SMA The second deviation (V) is the deviation of ). avg_LMA -V avg_SMA ) can be calculated continuously.

[0098] In S212, the controller 220 calculates multiple first deviations (V_LMA-V_SMA) and second deviations (V avg_LMA -V avg_SMA The controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, which are deviations. Specifically, the controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140 based on the above-mentioned [Equation 1].

[0099] In S214, the controller 220 can remove noise data from the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 can set a reference value that allows it to determine the presence or absence of noise in the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 based on the above-mentioned [Equation 2]. The controller 220 can exclude the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below the reference value, and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140.

[0100] In S216, the controller 220 can determine whether the second diagnostic deviation (D2) of each of the battery cells 110, 120, 130, and 140 exceeds the threshold. On the other hand, if it is determined in S216 that the second diagnostic deviation (D2) does not exceed the threshold, the process returns to step S202 and the measurement and diagnostic process is repeated. In other embodiments, instead of returning to S202, the process may be repeated by returning to any one of the steps prior to step S214, as needed.

[0101] In S218, the controller 220 can diagnose a battery cell as having a voltage anomaly if, among the multiple battery cells 110, 120, 130, and 140, the second diagnostic deviation (D2) of that battery cell exceeds a threshold.

[0102] Other embodiments will be described below. The battery management device 200 according to other embodiments disclosed herein can diagnose multiple battery cells 110, 120, 130, and 140 using the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 and the deviation (dV) between the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0103] To this end, the voltage measurement unit 210 can first measure the voltage of each of the multiple battery cells 110, 120, 130, and 140 in the same manner as in the embodiment described above. The voltage measurement unit 210 can calculate the voltage of each of the multiple battery cells 110, 120, 130, and 140 for each unit of time and calculate time-series data of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the voltage measurement unit 210 can continuously calculate the voltage rise and fall during charging, the rest period after charging, the discharge, and the rest period after discharge of the multiple battery cells 110, 120, 130, and 140, as well as long-term stabilization (relaxation) data, and the controller 220 can generate a graph showing the voltage changes for each.

[0104] On the other hand, the controller 220 can use the voltage data measured above to calculate the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 at intervals of a unit of time (e.g., 200 seconds). According to one embodiment, the controller 220 can calculate the mean, median, or minimum voltage of the multiple battery cells 110, 120, 130, and 140 at intervals of a unit of time (e.g., 200 seconds) as the average voltage of the multiple battery cells 110, 120, 130, and 140. Next, the controller 220 can calculate the deviation (dV) between the average voltage (V_avg) and the voltage for each of the multiple battery cells 110, 120, 130, and 140 at intervals of a unit of time (e.g., 200 seconds). According to one embodiment, the controller 220 can calculate the first voltage (dV) by determining the difference (dV) between the average voltage (V_avg) and the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0105] In the following explanation, the first voltage of each of the multiple battery cells 110, 120, 130, and 140 will be described using the deviation (dV) between the voltage of each of the multiple battery cells 110, 120, 130, and 140 and the average voltage (V_avg) as an example, but it is not limited to this. For example, according to one embodiment, the controller 220 may calculate the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140 as the voltage of each of the multiple battery cells 110, 120, 130, and 140, rather than the deviation (dV) between the average voltage (V_avg) and the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0106] Figure 7 is a flowchart showing a method for diagnosing battery cells in a controller according to other embodiments disclosed in this document. The operation of the apparatus and the method for diagnosing abnormal battery cells in each step according to other embodiments disclosed in this document will be described below with reference to Figure 7.

[0107] In S302, the voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing the voltage changes of each of the multiple battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 throughout the entire charging period, the rest period after charging, the discharge period, and the rest period after discharge, and continuously calculates the voltage rise and fall and long-term stabilization (relaxation) data, and the controller 220 can use this to generate a graph showing the voltage changes of each. Alternatively, the voltage measurement unit 210 may, as needed, continuously calculate the voltage rise and fall and long-term stabilization (relaxation) data for each of the multiple battery cells 110, 120, 130, and 140 during specific periods such as charging, the rest period after charging, discharging, and the rest period after discharging, and the controller 220 may use this data to generate a graph showing the voltage changes for each.

[0108] In S304, the controller 220 can use the voltage data for the multiple battery cells 110, 120, 130, and 140 measured in the previous step to calculate the first voltage (dV) at regular time intervals, which is the difference (dV) between the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 and the voltage of each of the multiple battery cells 110, 120, 130, and 140. Specifically, as a method for determining the first voltage (dV), which is the deviation (dV) between the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 and the individual voltages of each of the multiple battery cells 110, 120, 130, and 140, the controller 220 first adds up the voltage values ​​of all the multiple battery cells 110, 120, 130, and 140 at a specific time (t1), divides by 4, and calculates the average value (Mean) as the average voltage (V_avg) at time t1. In other embodiments, instead of calculating the average value (Mean) in this way, the median or minimum value of the voltage values ​​of the multiple battery cells 110 to 140 at a specific time (t1) may be used to calculate the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140.

[0109] Next, the controller 220 can calculate the first voltage (dV) for each of the multiple battery cells 110, 120, 130, and 140 as the deviation (dV) between the average voltage (V_avg) and the voltage of each of the multiple battery cells 110, 120, 130, and 140 at regular time intervals. For example, the difference between the average voltage (V_avg) calculated at a specific time point (t1) and the measured voltage of each of the multiple battery cells 110 to 140 is calculated as the first voltage (dV) at time t1 for each of the multiple battery cells 110, 120, 130, and 140. This calculation process can be repeated at predetermined time points for each of the multiple battery cells 110, 120, 130, and 140 to continuously calculate the first voltage (dV).

[0110] For example, in Figure 4, assuming that the voltages of multiple battery cells 110, 120, 130, and 140 at 10,800 seconds are 3.92V, 3.9175V, 3.9150V, and 3.9125V, respectively, the average voltage of multiple battery cells 110, 120, 130, and 140 at 10,800 seconds will be 3.91475V. In this case, the deviation (dV) between the average voltage (V_avg) and the voltage of each of the multiple battery cells 110, 120, 130, and 140 will be 0.00525V (battery cell 110), 0.00275V (battery cell 120), 0.00025V (battery cell 130), and 0.00225V, and each of these values ​​will be the first voltage (dV) value at 10,800 seconds. The controller 220 repeats this calculation for each unit of time, continuously calculating the first voltage (dV) between the required intervals, for example, between a 10,600-second interval and an 11,600-second interval.

[0111] On the other hand, in this embodiment, instead of using the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 as the first voltage (dV), the voltages of each of the multiple battery cells 110, 120, 130, and 140 measured in a previous step may be used to calculate the first voltage (dV).

[0112] Figure 8a is a graph showing the first voltage (dV) of a battery cell according to one embodiment disclosed in this document. Referring to Figure 8a, the controller 220 can measure the voltage of multiple battery cells 110, 120, 130, and 140 during the discharge and post-discharge rest periods, for example, between 0 seconds and 3,500 seconds, and calculate time-series data of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0113] Figure 8b is a graph showing the first voltage (dV) of a battery cell according to another embodiment disclosed in this document. Referring to Figure 8b, the controller 220 can measure the voltage of multiple battery cells 110, 120, 130, and 140 during charging and the rest period after charging, for example, between 0 seconds and 3,500 seconds, and calculate time-series data of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0114] In S306, the controller 220 can calculate the long-term moving average and short-term moving average of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140. Here, the moving average is the average of a portion of the data extracted from the total data while moving through a window of a specific size. Here, the window is a reference interval from which a portion of the total data can be extracted to determine which data to use. The start time of the window is a reference time earlier than the current time, and the end time of the window is the current time. For example, if the window is one week, the controller 220 can extract data from the total data acquired from the current time to the most recent week.

[0115] The controller 220 can calculate a continuous moving average of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140 by using first voltage (dV) data continuously extracted from all first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140 while moving the window. For example, the controller 220 can apply one of the following methods to all first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140: Simple Moving Average, Weighted Moving Average, or Exponential Moving Average (EMA), and calculate the voltage deviation (dV) or moving average of the voltage relative to the average voltage (V_avg) of each of the multiple battery cells 110, 120, 130, and 140.

[0116] According to one embodiment, the controller 220 can apply an exponential moving average (EMA) to all first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140 to calculate the exponential moving average value of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140. The exponential moving average is a type of weighted moving average method that uses data from the entire past period and gives more weight to recent data.

[0117] Specifically, the controller 220 can calculate multiple moving averages with different window sizes using the first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 can calculate a long moving average with a relatively long window length and a short moving average with a relatively short window length using all the first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140. For example, the window size of the long moving average may include 100 seconds, and the window size of the short moving average may include 10 seconds. For example, the controller 220 can calculate the long-term moving average of multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data acquired in the last 100 seconds from the calculation time, and can also calculate the short-term moving average of multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data acquired in the last 10 seconds from the calculation time.

[0118] Figure 9a is a graph showing the long-term moving average and short-term moving average of the first voltage during the discharge and rest period after discharge of a battery cell according to one embodiment disclosed in this document. Figure 9b is a graph showing the long-term moving average and short-term moving average of the first voltage during the charge and rest period after charge of a battery cell according to another embodiment disclosed in this document.

[0119] According to the embodiment, in the graphs shown in Figures 9a and 9b, the dotted line graph shows the change in the short-term moving average (dV_SMA) of each of the multiple battery cells, and the solid line graph shows the change in the long-term moving average (dV_LMA) of each of the multiple battery cells.

[0120] Referring to Figure 9a, the controller 220 can measure the voltage of multiple battery cells 110, 120, 130, and 140 during discharge and the rest period after discharge, and calculate time-series data of the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0121] Referring to Figure 9b, the controller 220 can measure the voltage of multiple battery cells 110, 120, 130, and 140 during charging and the rest period after charging, and calculate time-series data of the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0122] The controller 220 can analyze the long-term voltage change trend (Trend) and short-term voltage change trend of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140 using the continuous long-term moving average value (dV_LMA) and short-term moving average value (dV_SMA) of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140. The controller 220 can diagnose whether there is an abnormality in the voltage of each of the multiple battery cells using the voltage deviation (dV) relative to the average voltage (V_avg) of each of the multiple battery cells 110, 120, 130, and 140, or the long-term moving average value (dV_LMA) and short-term moving average value (dV_SMA) of the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0123] In S308, the controller 220 can use the obtained long-term moving average (dV_LMA) and short-term moving average (dV_SMA) to calculate the first deviation (dV_LMA-dV_SMA) for each of the multiple battery cells 110, 120, 130, and 140 at predetermined time intervals, which is the deviation between the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140. The controller 220 can continuously calculate the voltage deviation (dV) with respect to the average voltage (V_avg) of each of the multiple battery cells 110, 120, 130, and 140, or the deviation between the long-term and short-term behavior of the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0124] In one embodiment, Figure 10a shows the first deviation, which is the deviation between the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the first voltage (dV) during the discharge and post-discharge rest periods, and Figure 10b is a graph showing the first deviation, which is the deviation between the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the first voltage (dV) during the charge and post-charge rest periods. In Figures 10a and 10b, it is assumed that graph (C1) is a graph showing the first deviation of battery cell 110. The controller 220 can continuously calculate the first deviation (dV_LMA-dV_SMA) of each of the multiple battery cells 110, 120, 130, and 140 calculated during a unit time.

[0125] According to the embodiment, the deviations of the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of each of the multiple battery cells 110, 120, 130, and 140 can depend on the short-term and long-term change history of the cell voltage.

[0126] The temperature and State of Health (SOH) of each of the multiple battery cells 110, 120, 130, and 140 affect the cell voltage of each of the multiple battery cells 110, 120, 130, and 140, not only in the short term but also sustainably in the long term. Therefore, if there are no abnormalities in the voltage of each of the multiple battery cells 110, 120, 130, and 140, the deviations of the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of each of the multiple battery cells 110, 120, 130, and 140 will not differ significantly from each other. In contrast, a sudden voltage anomaly in a specific battery cell (e.g., battery cell 110) due to an internal and / or external short circuit may have a greater impact on the short-term moving average than on the long-term moving average. As a result, the deviation between the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the battery cell in question (for example, battery cell 110) may show a relatively large difference compared to the deviation between the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the remaining battery cells that do not have voltage abnormalities.

[0127] In S310, the controller 220 calculates the second deviation ((dV_LMA-dV_SMA), which is the average of the first deviations (dV_LMA-dV_SMA) of each of the multiple battery cells 110, 120, 130, and 140 at predetermined unit time intervals. LMA -dV SMA ) AVG The controller 220 can calculate the following. Here, in addition to the general average value of the first deviation (dV_LMA-dV_SMA), the controller 220 may also calculate the median or minimum value of the first deviation (dV_LMA-dV_SMA) for each of the multiple battery cells 110, 120, 130, and 140 as the second deviation.

[0128] For example, the controller 220 continuously calculates the first deviation (dV_LMA-dV_SMA) for each of the multiple battery cells 110, 120, 130, and 140 per unit time, and uses the first deviation (dV_LMA-dV_SMA) of the multiple battery cells 110, 120, 130, and 140 to calculate the second deviation ((dV_LMA-dV_SMA) for each unit time, using the mean, median, or minimum value of the first deviation (dV_LMA-dV_SMA) of the multiple battery cells 110, 120, 130, and 140. LMA -dVSMA ) AVG The voltage deviation (dV) can be calculated continuously. The controller 220 can calculate the average value of the long-term and short-term deviations of the voltage deviation (dV) of multiple battery cells 110, 120, 130, and 140.

[0129] In S312, the controller 220 calculates the first deviation (dV_LMA - dV_SMA) and the second deviation ((dV_LMA)) of each of the multiple battery cells 110, 120, 130, and 140 for each unit time. LMA -dV SMA ) AVG The first diagnostic deviation (D1) can be calculated for each of the multiple battery cells 110, 120, 130, and 140, which is the difference from ).

[0130] Specifically, the controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140 for each unit of time based on [Equation 6].

[0131] [Formula 6]

number

[0132] Referring to [Equation 6], the controller 220 calculates multiple first deviations (dV_LMA-dV_SMA) and second deviations ((dV LMA -dV SMA ) AVG The difference from ) can be calculated as the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140.

[0133] Figure 11a is a graph showing the first diagnostic deviation during discharge and the post-discharge rest period of a battery cell according to one embodiment disclosed in this document. Figure 11b is a graph showing the first diagnostic deviation during charging and the post-charge rest period of a battery cell according to another embodiment disclosed in this document. For the sake of understanding, in Figures 11a and 11b, let us assume that graph (C1) is a graph showing the first diagnostic deviation (D1) of battery cell 110 among a plurality of battery cells 110, 120, 130, and 140.

[0134] Referring to Figure 11a, the voltage measurement unit 210 measures the voltage of multiple battery cells 110, 120, 130, and 140 during discharge and the rest period after discharge at unit time intervals, and calculates the first deviation (dV_LMA-dV_SMA) and second deviation ((dV_LMA)) for each of the multiple battery cells 110, 120, 130, and 140. LMA -dV SMA ) AVG It is possible to calculate time-series data of the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, which is the difference from ).

[0135] Referring to Figure 11b, using the voltages of multiple battery cells 110, 120, 130, and 140 measured at each unit time during charging and the rest period after charging, the controller 220 calculates the first deviation (dV_LMA-dV_SMA) and the second deviation ((dV_LMA) for each of the multiple battery cells 110, 120, 130, and 140. LMA -dV SMA ) AVG It is possible to calculate time-series data of the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, which is the difference from ).

[0136] The controller 220 calculates the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, and can compare the deviations between the long-term and short-term behavior of the first voltage (dV) for each of the multiple battery cells 110, 120, 130, and 140 with the deviations between the average long-term and short-term behaviors of the multiple battery cells 110, 120, 130, and 140.

[0137] On the other hand, the controller 220 can correct the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140 to the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140. Specifically in S312, the controller 220 inputs the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 calculated in S302 to S312 as the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140 again in S304, and repeats S304 to S312 to recalculate the first diagnostic deviation (D1). According to one embodiment, the controller 220 inputs the first diagnostic deviation (D1) calculated in S312 as the corrected first voltage (dV') for each of the multiple battery cells 110, 120, 130, and 140, and repeats steps S304, S306, S308, S310, and S312 once or more times to recalculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140.

[0138] Specifically, the controller 220 can calculate the moving average of the corrected first voltage (dV') for each of the multiple battery cells 110, 120, 130, and 140 by using the corrected first voltage (dV') extracted from the time-series data of the first diagnostic deviation (D1) and corrected first voltage (dV') for each of the multiple battery cells 110, 120, 130, and 140 while moving the window.

[0139] According to one embodiment, the controller 220 can apply an exponential moving average (EMA) to the corrected first voltage (dV') of each of the multiple battery cells 110, 120, 130, and 140, and calculate the exponential moving average value of the corrected first voltage (dV') of each of the multiple battery cells 110, 120, 130, and 140.

[0140] The controller 220 can calculate time-series data of the long-term moving average (dV'_LMA) and short-term moving average (dV'_SMA) of the corrected first voltage (dV') for each of the multiple battery cells 110, 120, 130, and 140. The controller 220 can continuously calculate the long-term moving average (dV'_LMA) and short-term moving average (dV'_SMA) of the corrected first voltage (dV') for each of the multiple battery cells 110, 120, 130, and 140 calculated over a unit of time.

[0141] The controller 220 can analyze the long-term voltage change trend and short-term voltage change trend of the corrected first voltage (dV') of each of the multiple battery cells 110, 120, 130, and 140, using the continuous long-term moving average value (dV'_LMA) and short-term moving average value (dV'_SMA) of the corrected first voltage (dV') of each of the multiple battery cells 110, 120, 130, and 140.

[0142] According to one embodiment, the controller 220 can input a first diagnostic deviation (D1) calculated from the voltages of the multiple battery cells 110, 120, 130, and 140 during discharge and the rest period after discharge as a first voltage (dV), and recalculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140. According to another embodiment, the controller 220 can input a first diagnostic deviation (D1) calculated from the voltages of the multiple battery cells 110, 120, 130, and 140 during charging and the rest period after charging as a first voltage (dV), and recalculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140.

[0143] Specifically, the controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, and the corrected first deviation (dV'_LMA-dV'_SMA) for each of the multiple battery cells 110, 120, 130, and 140, which is the deviation between the long-term moving average (dV'_LMA) and short-term moving average (dV'_SMA) of the corrected first voltage (dV').

[0144] The controller 220 can continuously calculate the corrected first deviation (dV'_LMA-dV'_SMA) for each of the multiple battery cells 110, 120, 130, and 140 calculated during a unit of time.

[0145] Next, the controller 220 calculates the second deviation ((dV'_LMA-dV'_SMA), which is the average of the corrected first deviation (dV'_LMA-dV'_SMA) of the multiple battery cells 110, 120, 130, and 140. LMA -dV' SMA ) AVG The controller 220 can calculate the mean, median, or minimum of the first deviation (dV'_LMA-dV'_SMA) of multiple battery cells 110, 120, 130, and 140 as the second deviation.

[0146] The controller 220 continuously calculates the corrected first deviation (dV'_LMA-dV'_SMA) for each of the multiple battery cells 110, 120, 130, and 140 at each unit time interval, and uses the corrected first deviation (dV'_LMA-dV'_SMA) of the multiple battery cells 110, 120, 130, and 140 to calculate the mean, median, or minimum of the corrected second deviation ((dV'_LMA-dV'_SMA) as the average of the corrected first deviation (dV'_LMA-dV'_SMA) of the multiple battery cells 110, 120, 130, and 140. LMA -dV' SMA ) AVG The controller 220 calculates the corrected second deviation ((dV') of multiple battery cells 110, 120, 130, and 140 per unit time. LMA -dV' SMA ) AVG ) can be calculated continuously.

[0147] Next, the controller 220 calculates the corrected first deviation (dV'_LMA-dV'_SMA) and the corrected second deviation ((dV' LMA -dV' SMA ) AVGThe corrected first diagnostic deviation (D1) can be calculated for each of the multiple battery cells 110, 120, 130, and 140, which is the difference from ).

[0148] Referring again to Figure 7, in S314, the controller 220 can remove noise from the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 and calculate the second diagnostic deviation (D2) based on the following [Equation 7]. To this end, the controller 220 can first set a reference value that allows it to determine whether or not there is noise in the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, based on the following [Equation 7].

[0149] [Formula 7]

number

[0150] In other words, the controller 220 determines the second deviation ((dV LMA -dV SMA ) AVG The value obtained by multiplying the absolute value of by the first threshold constant (C1) is (|(dV LMA -dV SMA ) AVG The maximum value (Max) of the first threshold constant (C1) and the second threshold constant (C2) can be set to the reference value for each of the multiple battery cells 110, 120, 130, and 140. Here, the first threshold constant (C1) can include "0.1", and the second threshold constant (C2) can include "0.4". Furthermore, the first threshold constant (C1) and the second threshold constant (C2) can be changed according to the size and characteristics of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0151] Next, the controller 220 can determine that the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below the reference value is noise data. That is, in S314, the controller 220 can exclude the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below the reference value and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140.

[0152] In S316, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 to calculate the third diagnostic deviation (D3).

[0153] Specifically, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 8], and calculate the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0154] [Formula 8]

number

[0155] Controller 220 calculates the absolute value of the second deviation (|(dV LMA -dV SMA ) AVG The value obtained by multiplying |) by the third threshold constant (C3) is (|(dV LMA -dV SMA ) AVG The controller 220 can then calculate the maximum value (Max) of the second diagnostic deviation obtained earlier, that is, the value obtained by multiplying the absolute value of the second deviation, which shows the behavior of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140, by the third threshold constant and the maximum value (Max[|(dV LMA -dVSMA ) AVG The second diagnostic deviation (D2) for each of the multiple battery cells 110, 120, 130, and 140 can be normalized by dividing by [·C3,C4]) to calculate the third diagnostic deviation (D3). Here, the third threshold constant (C3) may include "0.1", and the fourth threshold constant (C4) may also include "0.1", and the third threshold constant (C3) and the fourth threshold constant (C4) may be changed depending on the size and characteristics of the first voltage (dV) data for each of the multiple battery cells 110, 120, 130, and 140.

[0156] In S316, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 according to various embodiments, and calculate the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0157] According to one embodiment, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by logarithm calculation. That is, the controller 220 can calculate the value normalized by logarithm calculation of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0158] In another embodiment, the controller 220 can set the average value (D2_avg) of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the normalization criterion. In S316, the controller 220 can use the average value of the second diagnostic deviation (D2_avg) as the normalization criterion and normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by dividing it by the average value of the second diagnostic deviation (D2) (D2_avg). The controller 220 can calculate the normalized value obtained by dividing the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by the average value of the second diagnostic deviation (D2_avg) as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0159] In S316, the controller 220 can continuously calculate the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 at each unit time interval, and generate a graph showing the change in the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140.

[0160] In step S318, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 9].

[0161] [Formula 9] Skewness = (Third diagnostic deviation (D3) + Min[Third diagnostic deviation (D3)]) / Third diagnostic deviation (D3)

[0162] Referring to [Equation 9], the controller 220 can calculate the distortion of each of the battery cells 110, 120, 130, and 140 by adding the minimum value (Min[Third Diagnostic Deviation (D3)]) of the third diagnostic deviation (D3) of each of the battery cells 110, 120, 130, and 140 to the third diagnostic deviation (D3) of each of the battery cells 110, 120, 130, and 140 and dividing the result by the third diagnostic deviation (D3).

[0163] The controller 220 can continuously calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 at each unit time interval, and generate a graph showing the change in the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140.

[0164] In S320, the controller 220 can reflect the skewness in the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 can calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 10].

[0165] [Formula 10] 4th diagnostic deviation (D4) = 3rd diagnostic deviation (D3) * skewness

[0166] The controller 220 can calculate the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140 by multiplying the third diagnostic deviation (D3) by the distortion at predetermined unit time intervals (e.g., 200 seconds). As mentioned above, the fourth diagnostic deviation (D4) with distortion applied has reduced noise compared to the third diagnostic deviation (D3) without distortion applied, the voltage behavior signal of abnormal battery cells is amplified and stabilized, and a more accurate diagnostic deviation is shown.

[0167] The controller 220 can continuously calculate the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140 at each unit time interval, and generate a graph showing the change in the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140.

[0168] In S322, the controller 220 can determine whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold. Here, the threshold can be defined as a reference value that indicates an extreme result and can be judged as "abnormal." The threshold can also be defined as a standard that shows how much the data deviates from a particular statistical model. If, among the multiple battery cells 110, 120, 130, and 140, the controller 220 can determine that a battery cell has exhibited abnormal voltage behavior if its fourth diagnostic deviation (D4) exceeds the threshold. Here, the threshold is a value determined considering the state of the battery cell, the sensitivity of the measurement system, and the measurement environment, and may differ, for example, depending on the type of battery cell and / or the vehicle to which the battery cell is applied.

[0169] On the other hand, if it is determined in S322 that the fourth diagnostic deviation (D4) does not exceed the threshold, the process returns to S302 and the measurement and diagnostic process is repeated. In other embodiments, instead of returning to S302, the process may be repeated by returning to any one of the steps prior to S322, as needed.

[0170] In S322, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140 as an abnormal cell based on whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold. If the fourth diagnostic deviation (D4) of at least one of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold, the controller 220 can diagnose that battery cell as a battery cell that has exhibited abnormal behavior.

[0171] In S324, according to one embodiment, the controller 220 can increase the diagnostic count value of at least one battery cell if the fourth diagnostic deviation (D4) of at least one of the plurality of battery cells 110, 120, 130, 140 exceeds a threshold.

[0172] According to one embodiment, the controller 220 can diagnose an abnormality in at least one of the plurality of battery cells 110, 120, 130, and 140 if the diagnostic count value of at least one battery cell is equal to or greater than a threshold count value.

[0173] According to one embodiment, when the fourth diagnostic deviation (D4) of at least one of the multiple battery cells 110, 120, 130, and 140 first exceeds the threshold, the controller 220 does not immediately diagnose that battery cell as abnormal. Instead, it diagnoses the battery cell as abnormal only if the diagnostic count value is greater than or equal to the threshold count value, for example, if the state in which the battery cell exceeds the threshold is maintained for a predetermined time (e.g., the threshold count). As a result, an abnormal diagnosis is not performed on battery cells whose fourth diagnostic deviation (D4) momentarily exceeds the threshold and then falls below the threshold within a short time, thereby improving the reliability of the diagnosis of abnormal battery cells.

[0174] On the other hand, the diagnostic method described above involves comparing the fourth diagnostic deviation (D4) with a threshold in S322, but is not limited to this. For example, the battery cell diagnosis may be performed in any one step prior to S322. According to one embodiment, the battery cell diagnosis may be performed after calculating the second diagnostic deviation (D2) in step S314.

[0175] After diagnosing an abnormality, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140, and then track and monitor for defects such as internal short circuits, external short circuits, and lithium deposition within that battery cell.

[0176] Furthermore, if the controller 220 confirms, as a result of the diagnosis, that a defect has occurred inside the battery cell, it can provide information about the battery cell to the user. For example, the controller 220 can provide information about the battery cell that has experienced an internal short circuit to the user terminal via a communication unit (not shown), and it can also provide information about the battery cell via a display provided in the vehicle or charger, etc.

[0177] As described above, according to the battery management device 200 of other embodiments disclosed in this document, noise in the long-term moving average and short-term moving average values ​​of the voltage deviation, which is the voltage difference with respect to the average voltage of the battery cell, can be removed, and abnormal battery cells can be accurately diagnosed.

[0178] A battery management device 200 according to one embodiment disclosed herein can minimize the voltage distortion of battery cells by using the long-term moving average value and short-term moving average value of the voltage deviation of each battery cell, remove noise data, amplify the voltage behavior of abnormal battery cells to reflect the degree of voltage distortion of the battery cells, and improve the accuracy of diagnosis.

[0179] The battery management device 200 can diagnose battery cells exhibiting abnormal voltage behavior early by using the deviation between the long-term moving average and short-term moving average of the voltage deviation of the battery cells, thereby ensuring the safety and reliability of the battery energy. Furthermore, because the battery management device 200 diagnoses battery cells exhibiting abnormal voltage behavior while the battery is installed in the vehicle, it does not require separate separation of the battery, allowing for quick and easy diagnosis of the battery cells.

[0180] Figure 12 is a flowchart showing the operation of the battery management device 200 according to another embodiment disclosed in this document. The operation of the device and the method for diagnosing abnormal battery cells in each step will be described below with reference to Figure 12.

[0181] In S402, the voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing the voltage changes of each of the multiple battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 throughout the entire charging period, the rest period after charging, the discharge period, and the rest period after discharge, and continuously calculates the voltage rise and fall and long-term stabilization (relaxation) data, and the controller 220 can generate a graph showing the voltage changes of each using the measured voltage data. Alternatively, the voltage measurement unit 210 may, as needed, continuously calculate the voltage rise and fall and long-term stabilization (relaxation) data for each of the multiple battery cells 110, 120, 130, and 140 during specific periods such as charging, the rest period after charging, discharging, and the rest period after discharging, and the controller 220 may use this data to generate a graph showing the voltage changes for each.

[0182] In S404, the controller 220 can calculate the difference (dV) between the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 and the individual voltages of each of the multiple battery cells 110, 120, 130, and 140, or the measured voltage of the multiple battery cells 110, 120, 130, and 140 themselves as the first voltage (dV) at predetermined unit time intervals.

[0183] In S406, the controller 220 can calculate the moving average of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140. Here, the moving average is the average of a portion of the data extracted from the total data while moving through a window of a specific size. Here, the window is a reference interval from which a portion of the total data can be extracted to determine which data to use. The start time of the window is a reference time earlier than the current time, and the end time of the window is the current time. For example, if the window is one week, the controller 220 can extract data from the total data acquired from the current time to the most recent week.

[0184] The controller 220 can calculate a continuous moving average of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140 by using the first voltage (dV) continuously extracted from the time-series data of all first voltages (dV) of each of the multiple battery cells 110, 120, 130, and 140 while moving the window. For example, the controller 220 can apply one of the following methods to the total first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140: Simple Moving Average, Weighted Moving Average, or Exponential Moving Average (EMA), to calculate the moving average of the first voltage (dV1) of each of the multiple battery cells 110, 120, 130, and 140.

[0185] According to one embodiment, the controller 220 can apply an exponential moving average (EMA) to all first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140 to calculate the exponential moving average value of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140. The exponential moving average is a type of weighted moving average method that uses data from the entire past period and gives more weight to recent data.

[0186] Furthermore, the controller 220 can calculate multiple moving averages with different window sizes using the first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 can calculate a long moving average with a relatively long window length and a short moving average with a relatively short window length using all the first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140. For example, the window size of the long moving average may include 100 seconds, and the window size of the short moving average may include 10 seconds. For example, the controller 220 can calculate the long-term moving average of multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data acquired in the last 100 seconds from the calculation time, and can also calculate the short-term moving average of multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data acquired in the last 10 seconds from the calculation time.

[0187] In S408, the controller 220 can calculate the first deviation as the deviation (dV_LMA-dV_SMA) between the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0188] In S410, the controller 220 calculates the average of the long-term moving average (dV_LMA) and short-term moving average (dV_SMA) of the first voltage (dV) of each of the multiple battery cells 110, 120, 130, and 140 for each unit time (D avg ) can be continuously calculated as the second deviation.

[0189] In S412, the controller 220 calculates the first deviation (dV_LMA - dV_SMA) and the second deviation, which is the mean value (D avgThe diagnostic deviation (D) can be calculated using the second deviation (D) for each of the multiple battery cells 110, 120, 130, and 140. According to the embodiment, the controller 220 calculates the average value (D) which is the second deviation for each of the multiple battery cells 110, 120, 130, and 140. avg The difference between ( ) and the first deviation (dV_LMA-dV_SMA) can be calculated as the diagnostic deviation (D) for each of the multiple battery cells 110, 120, 130, and 140.

[0190] In S414, the controller 220 can determine whether the diagnostic deviation (D) of each of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold.

[0191] On the other hand, if it is determined in S414 that the diagnostic deviation (D) does not exceed the threshold, the process returns to S402 and the measurement and diagnostic process is repeated. In other embodiments, instead of returning to S402, the process may be repeated by returning to any one of the steps prior to S414, as needed.

[0192] In S416, the controller 220 can determine that any of the multiple battery cells 110, 120, 130, and 140 has a diagnostic deviation (D) that exceeds a threshold, and that battery cell is the one that has experienced a voltage abnormality.

[0193] The operation of the battery management device 200 and the method for diagnosing battery cells according to the embodiment disclosed in this document have been described above. The battery management device 200 according to the embodiment disclosed in this document can remove noise from the deviation between the long-term moving average value and the short-term moving average value of the battery voltage and apply distortion to accurately diagnose abnormal battery cells.

[0194] Figure 13 is a block diagram showing the hardware configuration of a computing system that implements the operation method of a battery management device according to one embodiment disclosed in this document.

[0195] Referring to Figure 13, the computing system 2000 according to one embodiment disclosed in this document may include an MCU 2100, a memory 2200, an input / output I / F 2300, and a communication I / F 2400.

[0196] The MCU2100 may be a processor that executes various programs stored in the memory 2200 (for example, a battery voltage deviation analysis program), processes various data used in such programs, and performs the functions of the battery management device 200 shown in Figure 1 above.

[0197] The memory 2200 can store various programs related to the operation of the battery management device 200 for diagnosing the battery bank, as well as operating data of the battery management device 200.

[0198] Multiple such memory 2200s may be provided as needed. The memory 2200 may be volatile or non-volatile. As volatile memory, RAM, DRAM, SRAM, etc., can be used. As non-volatile memory, ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc., can be used. The examples of memory 2200 listed above are merely illustrative and the system is not limited to these examples.

[0199] The I / O I / F 2300 can provide an interface that connects input devices (not shown), such as keyboards, mice, and touch panels, with output devices (not shown), such as displays, and the MCU 2100, enabling data transmission and reception.

[0200] The communication interface 2400 is configured to send and receive various data with the server and may be various devices that support wired or wireless communication. For example, programs for voltage measurement and anomaly diagnosis, as well as various data, can be sent and received via wired or wireless connection from a separately provided external server through the communication interface 2400.

[0201] The above description is merely illustrative of the technical concept of this disclosure, and any person with ordinary skill in the art to which this disclosure belongs can make various modifications and variations without departing from the essential characteristics of this disclosure.

[0202] Therefore, the embodiments disclosed herein are for illustrative purposes only, and not to limit the technical concept of the disclosure, and such embodiments do not limit the scope of the technical concept of the disclosure. The scope of protection of this disclosure must be interpreted in accordance with the claims set forth below, and all technical concepts within an equivalent scope should be interpreted as being included in the scope of rights of this disclosure.

Claims

1. A voltage measuring unit that measures the voltage of each of the multiple batteries, A controller capable of communicating with the aforementioned voltage measuring unit, Includes, The aforementioned controller, The voltage measuring unit is controlled to measure the voltage of each of the multiple batteries at predetermined time intervals. For each of the aforementioned multiple batteries, a first deviation is calculated, which is the difference between the long-term moving average and the short-term moving average of the battery voltages. A second deviation is calculated, which is the difference between the long-term moving average and the short-term moving average of the average voltages of the aforementioned multiple batteries. For each of the aforementioned multiple batteries, a first diagnostic deviation is calculated, which is the difference between the first deviation and the second deviation. The second diagnostic deviation for each of the plurality of batteries is calculated based on a reference value obtained by multiplying the second deviation from the first diagnostic deviation of each of the plurality of batteries by a threshold constant. A battery management device configured to diagnose whether or not at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries.

2. The aforementioned controller, The maximum value between the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant is set as the reference value. The battery management device according to claim 1, wherein the first diagnostic deviations of each of the plurality of batteries that are less than or equal to the reference value are excluded from the first diagnostic deviations of each of the plurality of batteries, and the second diagnostic deviation of each of the plurality of batteries is calculated.

3. The battery management device according to claim 2, wherein the controller normalizes the second diagnostic deviation of each of the plurality of batteries by dividing the value obtained by multiplying the second deviation by a third threshold constant by the maximum value of the fourth threshold constant, and calculates the third diagnostic deviation of each of the plurality of batteries.

4. The battery management device according to claim 3, wherein the controller calculates the strain of each of the plurality of batteries by adding the minimum value of the third diagnostic deviation of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the resulting value by the third diagnostic deviation.

5. The battery management device according to claim 4, wherein the controller multiplies the third diagnostic deviation of each of the plurality of batteries by the degree of distortion to calculate the fourth diagnostic deviation of each of the plurality of batteries.

6. The battery management device according to claim 5, wherein the controller diagnoses whether at least one of the plurality of batteries is abnormal based on whether the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

7. The controller calculates the first and second deviations for each unit time, and calculates the fourth diagnostic deviation for each of the plurality of batteries. The battery management device according to claim 6, which diagnoses whether or not there is an abnormality in at least one of the plurality of batteries if the fourth diagnostic deviation of at least one battery exceeds a threshold.

8. The steps include measuring the voltage of each of several batteries at predetermined time intervals, For each of the aforementioned plurality of batteries, the first deviation is calculated, which is the difference between the long-term moving average value and the short-term moving average value of the battery voltage. The steps include: calculating a second deviation, which is the difference between the long-term moving average and the short-term moving average of the average voltages of the plurality of batteries; For each of the plurality of batteries, the first diagnostic deviation is calculated, which is the difference between the first deviation and the second deviation. A step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant, A step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries, A method for operating a battery management device, including the operation of the battery management device.

9. The step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant is: The maximum value between the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant is set as the reference value. A method for operating a battery management device according to claim 8, comprising excluding first diagnostic deviations of each of the plurality of batteries that are below the reference value, and calculating a second diagnostic deviation for each of the plurality of batteries.

10. The step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant is: A method for operating a battery management device according to claim 9, wherein the second diagnostic deviation of each of the plurality of batteries is normalized by dividing the value obtained by multiplying the second deviation by a third threshold constant by the maximum value of the fourth threshold constant, and the third diagnostic deviation of each of the plurality of batteries is calculated.

11. The step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant is: A method for operating a battery management device according to claim 10, comprising adding the minimum value of the third diagnostic deviation of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries, dividing the resulting value by the third diagnostic deviation, and calculating the strain of each of the plurality of batteries.

12. The step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant is: A method for operating a battery management device according to claim 11, comprising multiplying the third diagnostic deviation of each of the plurality of batteries by the degree of distortion to calculate the fourth diagnostic deviation of each of the plurality of batteries.

13. The step of diagnosing whether or not at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries is: A method for operating a battery management device according to claim 12, wherein the presence or absence of an abnormality in at least one of the plurality of batteries is diagnosed based on whether the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

14. The step of diagnosing whether or not at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries is: The first and second deviations are calculated for each unit time, and the fourth diagnostic deviation is calculated for each of the multiple batteries. A method for operating a battery management device according to claim 13, wherein if the fourth diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the device diagnoses whether or not there is an abnormality in the at least one battery.

15. Memory and A processor coupled to the memory and configured to perform the operation method of the battery management device described in any one of claims 8 to 14, A controller, including...

16. In the operation method of the battery management device, The step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant is: The steps include setting the maximum value of the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant as the reference value, A step of excluding the first diagnostic deviations of each of the plurality of batteries that are below the reference value, and calculating the second diagnostic deviation for each of the plurality of batteries, The steps include: Calculating the third diagnostic deviation for each of the plurality of batteries by normalizing the second diagnostic deviation of each of the plurality of batteries by dividing the value obtained by multiplying the second deviation by a third threshold constant by the maximum value of the fourth threshold constant; The steps include: calculating the distortion of each of the multiple batteries by adding the minimum value of the third diagnostic deviation of each of the multiple batteries to the third diagnostic deviation of each of the multiple batteries and dividing the resulting value by the third diagnostic deviation; The step includes multiplying the third diagnostic deviation of each of the plurality of batteries by the skewness to calculate the fourth diagnostic deviation of each of the plurality of batteries, The step of diagnosing whether or not at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries is: The controller according to claim 15, further comprising the step of diagnosing whether at least one of the plurality of batteries is abnormal based on whether the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

17. The steps include: measuring the voltage of each of several batteries at predetermined time intervals using a voltmeter; For each of the aforementioned plurality of batteries, the first deviation is calculated, which is the difference between the long-term moving average value and the short-term moving average value of the battery voltage. The steps include: calculating a second deviation, which is the difference between the long-term moving average and the short-term moving average of the average voltages of the plurality of batteries; For each of the plurality of batteries, the first diagnostic deviation is calculated, which is the difference between the first deviation and the second deviation. A step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant, A step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries, A program to be executed by a controller.

18. The step of calculating the second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant is: The steps include setting the maximum value of the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant as the reference value, A step of excluding the first diagnostic deviations of each of the plurality of batteries that are below the reference value, and calculating the second diagnostic deviation for each of the plurality of batteries, The steps include: Calculating the third diagnostic deviation for each of the plurality of batteries by normalizing the second diagnostic deviation of each of the plurality of batteries by dividing the value obtained by multiplying the second deviation by a third threshold constant by the maximum value of the fourth threshold constant; The steps include: calculating the distortion of each of the multiple batteries by adding the minimum value of the third diagnostic deviation of each of the multiple batteries to the third diagnostic deviation of each of the multiple batteries and dividing the resulting value by the third diagnostic deviation; The step includes multiplying the third diagnostic deviation of each of the plurality of batteries by the skewness to calculate the fourth diagnostic deviation of each of the plurality of batteries, The step of diagnosing whether or not at least one of the plurality of batteries is abnormal based on the second diagnostic deviation of each of the plurality of batteries is: The program according to claim 17, further comprising the step of diagnosing whether at least one of the plurality of batteries is abnormal based on whether the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

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