Battery management device and operating method thereof

Abstract

According to one embodiment disclosed in the present document, a battery management device includes: a voltage measurement unit that measures a voltage of each of a plurality of batteries; and a controller communicable with the voltage measuring unit, in which the controller may be configured to: control the voltage measuring unit to measure a voltage of each battery in the plurality of battery packs at a predetermined time interval; calculating a first deviation which is a pitch between a long-term fluctuation average value and a short-term fluctuation average value of a voltage of each of the plurality of batteries; calculating a second deviation which is a pitch between a long-term fluctuation average value and a short-term fluctuation average value of an average voltage of the plurality of batteries; calculating a first diagnostic deviation of each of the plurality of batteries, which is a difference between the first deviation and the second deviation; calculating a second diagnostic deviation of each of the plurality of batteries based on a reference value of the first diagnostic deviation of each of the plurality of batteries obtained by multiplying a threshold constant by a second deviation; and diagnosing an abnormality of at least one battery among the plurality of batteries based on the second diagnosis deviation of each of the plurality of batteries.

Description

Technical Field

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on and claims the benefit of priority from Korean Patent Applications No. 10-2022-0120366, 10-2023-0058253, and 10-2023-0126472 filed in the Korean Intellectual Property Office on September 22, 2022, May 4, 2023, and September 21, 2023, respectively, which are hereby incorporated by reference in their entirety. Technical Field

[0004] The present disclosure relates to a battery management system and a method of operating the same. Background Art

[0005] Electric vehicles draw energy from an external power source to recharge their battery cells, filling them with the energy needed to propel them. These battery cells deform and degrade internally as they undergo charge and discharge cycles throughout their manufacturing and use phases. As a result, this process can change the physical and chemical properties of the battery cells, potentially leading to defects such as internal shorts, external shorts, outgassing caused by lithium deposition, and undervoltage failures where the battery cell voltage drops below a certain threshold.

[0006] Internal failures of the battery cell may result in direct adverse effects on the battery cell, including degradation of the battery cell performance and electrolyte leakage leading to potential fire. Summary of the invention

[0007] Technical issues

[0008] An object of an embodiment of the present disclosure is to provide a battery management system and an operating method thereof, which can accurately diagnose abnormal battery cells by removing noise of deviations between long-term moving average values ​​and short-term moving average values ​​of voltages of battery cells.

[0009] The objects of the embodiments of the present disclosure are not limited to the above objects, and other objects not described herein can be clearly understood by those skilled in the art from the following description.

[0010] Technical Solution

[0011] According to an embodiment of the present disclosure, a battery management system includes: a voltage measuring unit that measures the voltage of each battery among a plurality of batteries; and a controller that communicates with the voltage measuring unit. The controller: controls the voltage measuring unit to measure the voltage of each battery among the plurality of batteries at predetermined time intervals; calculates a first deviation, which is a deviation between a long-term moving average value and a short-term moving average value of the voltage of each battery among the plurality of batteries; calculates a second deviation, which is a deviation between a long-term moving average value and a short-term moving average value of the average voltage of the plurality of batteries; calculates a first diagnostic deviation of each battery among the plurality of batteries, which is a difference between the first diagnostic deviation and the second deviation; calculates a second diagnostic deviation of each battery among the plurality of batteries by removing the noise of the first diagnostic deviation of each battery among the plurality of batteries based on a reference value of the first diagnostic deviation of each battery among the plurality of batteries obtained by multiplying the second deviation by a threshold constant; and diagnoses an abnormality of at least one battery among the plurality of batteries based on the second diagnostic deviation of each battery among the plurality of batteries.

[0012] In an embodiment, the controller may set a reference value to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and the second threshold constant, and calculate a second diagnostic deviation for each of the multiple batteries by excluding first diagnostic deviations of each of the multiple batteries that are equal to or less than the reference value.

[0013] In an embodiment, the controller may calculate a third diagnostic deviation of each of the plurality of batteries by normalizing the second diagnostic deviation of each of the plurality of batteries, wherein the second diagnostic deviation is divided by a maximum value between a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant.

[0014] In an embodiment, the controller may calculate the skewness of each of the plurality of batteries by adding a minimum value of the third diagnostic deviations of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the obtained value by the third diagnostic deviation.

[0015] In an embodiment, the controller may calculate a fourth diagnostic deviation for each of the plurality of batteries by multiplying the third diagnostic deviation for each of the plurality of batteries by the skewness.

[0016] In an embodiment, the controller may diagnose abnormality of at least one battery among the plurality of batteries based on whether a fourth diagnosis deviation of each of the plurality of batteries exceeds a threshold value.

[0017] In an embodiment, the controller may calculate a plurality of first deviations and a plurality of second deviations at unit time intervals to calculate a fourth diagnostic deviation of each of the plurality of batteries, and when the fourth diagnostic deviation of at least one of the plurality of batteries exceeds a threshold value, the controller diagnoses the at least one battery as an abnormal battery.

[0018] According to another embodiment of the present disclosure, a method for operating a battery management system includes: causing a voltage measuring unit to measure the voltage of each battery among a plurality of batteries at predetermined time intervals; calculating a first deviation, which is the deviation between a long-term moving average value and a short-term moving average value of the voltage of each battery among the plurality of batteries; calculating a second deviation, which is the deviation between a long-term moving average value and a short-term moving average value of the average voltage of the plurality of batteries; calculating a first diagnostic deviation of each battery among the plurality of batteries, which is the difference between the first deviation and the second deviation; calculating a second diagnostic deviation of each battery among the plurality of batteries based on a reference value of the first diagnostic deviation of each battery among the plurality of batteries obtained by multiplying the second deviation by a threshold constant; and diagnosing an abnormality of at least one battery among the plurality of batteries based on the second diagnostic deviation of each battery among the plurality of batteries.

[0019] In an embodiment, calculating the second diagnostic deviation may include setting a reference value to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and the second threshold constant, and calculating the second diagnostic deviation of each of the multiple batteries by excluding first diagnostic deviations of each of the multiple batteries that are equal to or less than the reference value among the first diagnostic deviations of each of the multiple batteries.

[0020] In an embodiment, calculating the second diagnostic deviation may include calculating a third diagnostic deviation for each of the plurality of batteries by normalizing the second diagnostic deviation for each of the plurality of batteries, wherein the second diagnostic deviation is divided by a maximum value between a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant.

[0021] In an embodiment, calculating the second diagnostic deviation may include calculating the skewness of each of the plurality of batteries by adding a 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 obtained value by the third diagnostic deviation.

[0022] In an embodiment, calculating the second diagnostic deviation may include calculating a fourth diagnostic deviation for each of the plurality of batteries by multiplying the third diagnostic deviation for each of the plurality of batteries by the skewness.

[0023] In an embodiment, diagnosing the abnormality of at least one battery may include diagnosing the abnormality of at least one battery among the plurality of batteries based on whether a fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold value.

[0024] In an embodiment, diagnosing an abnormality of at least one battery may include: calculating a plurality of first deviations and a plurality of second deviations at unit time intervals to calculate a fourth diagnostic deviation of each battery among the plurality of batteries, and when the fourth diagnostic deviation of at least one battery among the plurality of batteries exceeds a threshold value, diagnosing the at least one battery as an abnormal battery.

[0025] According to an embodiment of the present disclosure, a controller includes: a memory, and a processor connected to the memory and configured to execute the operating method of the battery management device according to claim 8 .

[0026] In an embodiment, in an operating method of a battery management device, calculating a second diagnostic deviation may include: setting a reference value to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and the second threshold constant; calculating a second diagnostic deviation of each of the plurality of batteries by excluding first diagnostic deviations of each of the plurality of batteries that are equal to or less than the reference value among the first diagnostic deviations of each of the plurality of batteries; calculating a third diagnostic deviation of each of the plurality of batteries by normalizing the second diagnostic deviations of each of the plurality of batteries, wherein the second diagnostic deviation is divided by a value obtained by multiplying the second deviation by the third threshold constant. a maximum value between a value obtained by calculating a third diagnostic deviation constant and a fourth threshold constant; calculating the skewness of each of the multiple batteries by adding a 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 obtained value by the third diagnostic deviation; and calculating the fourth diagnostic deviation of each of the multiple batteries by multiplying the third diagnostic deviation of each of the multiple batteries by the skewness; diagnosing an abnormality of at least one battery including: diagnosing an abnormality of at least one battery among the multiple batteries based on whether the fourth diagnostic deviation of each of the multiple batteries exceeds a threshold.

[0027] According to an embodiment of the present disclosure, a non-temporary computer-readable storage medium storing a program causes an information processing device to perform the following process: using a voltage measuring unit to measure the voltage of each battery among a plurality of batteries at predetermined time intervals; calculating a first deviation, which is the deviation between a long-term moving average value and a short-term moving average value of the voltage of each battery among the plurality of batteries; calculating a second deviation, which is the deviation between a long-term moving average value and a short-term moving average value of the average voltage of the plurality of batteries; calculating a first diagnostic deviation of each battery among the plurality of batteries, which is the difference between the first deviation and the second deviation; calculating a second diagnostic deviation of each battery among the plurality of batteries based on a reference value of the first diagnostic deviation of each battery among the plurality of batteries obtained by multiplying the second deviation by a threshold constant; and diagnosing an abnormality of at least one battery among the plurality of batteries based on the second diagnostic deviation of each battery among the plurality of batteries.

[0028] In an embodiment, calculating the second diagnostic deviation may include: setting a reference value to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and the second threshold constant; calculating the second diagnostic deviation of each of the plurality of batteries by excluding the first diagnostic deviations of each of the plurality of batteries that are equal to or less than the reference value; calculating a third diagnostic deviation of each of the plurality of batteries by normalizing the second diagnostic deviation of each of the plurality of batteries, wherein the second diagnostic deviation is divided by a maximum value between a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant; calculating the skewness of each of the plurality of batteries by adding a 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 obtained 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 skewness; diagnosing an abnormality of at least one battery includes: diagnosing an abnormality of at least one of the plurality of batteries based on whether the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold.

[0029] Effects of the Invention

[0030] According to the battery management system and the operating method thereof according to the embodiment of the present disclosure, it is possible to accurately diagnose abnormal battery cells by removing noise of deviations between long-term moving average values ​​and short-term moving average values ​​of voltages of battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a diagram showing a battery pack according to an embodiment of the present disclosure.

[0032] Figure 2 is a block diagram illustrating a configuration of a battery management system according to an embodiment of the present disclosure.

[0033] Figure 3 is a flowchart illustrating an operating method of a battery management system according to an embodiment of the present disclosure.

[0034] Figure 4 is a graph showing voltage changes of respective battery cells according to an embodiment of the present disclosure.

[0035] Figure 5a is a graph illustrating first diagnosis deviations of respective battery cells according to an embodiment of the present disclosure.

[0036] Figure 5b is a graph illustrating a third diagnosis deviation of each battery cell according to an embodiment of the present disclosure.

[0037] Figure 5c is a graph illustrating skewness of a third diagnosis deviation of a battery cell according to an embodiment of the present disclosure.

[0038] Figure 5d is a graph illustrating a fourth diagnostic deviation of a battery cell according to an embodiment of the present disclosure.

[0039] Figure 6 is a flowchart illustrating an operating method of a battery management system according to another embodiment of the present disclosure.

[0040] Figure 7 is a flowchart illustrating an operating method of a battery management system and a method of diagnosing abnormal battery cells according to yet another embodiment of the present disclosure.

[0041] Figure 8a is a graph showing a first voltage of each battery cell during discharge and a relaxation time after discharge according to another embodiment of the present disclosure.

[0042] Figure 8b is a graph showing a first voltage of each battery cell during charging and a relaxation time after charging according to another embodiment of the present disclosure.

[0043] Figure 9a is a graph showing a long-term moving average (solid line) and a short-term moving average (dashed line) of a first voltage of each battery cell during discharge and a relaxation time after discharge according to another embodiment of the present disclosure.

[0044] Figure 9bis a graph showing a long-term moving average (solid line) and a short-term moving average (dashed line) of a first voltage of each battery cell during charging and a relaxation time after charging according to another embodiment of the present disclosure.

[0045] Fig.10a is a graph showing a first deviation (dV) of a first voltage of each battery cell during discharge and a relaxation time after discharge according to an embodiment of the present disclosure.

[0046] Fig.10b is a graph illustrating a first deviation (dV) of a first voltage of each battery cell during charging and a relaxation time after charging according to another embodiment of the present disclosure.

[0047] Fig.11a is a graph illustrating a first diagnostic deviation ( D1 ) of each battery cell during discharge and relaxation time after discharge according to another embodiment of the present disclosure.

[0048] Fig.11b is a graph illustrating a first diagnostic deviation ( D1 ) of each battery cell during charging and a relaxation time after charging according to another embodiment of the present disclosure.

[0049] Fig.12 is a flowchart illustrating an operating method of a battery management system and a method of diagnosing abnormal battery cells according to yet another embodiment of the present disclosure.

[0050] Fig.13 is a block diagram illustrating a hardware configuration of a computing system that implements an operating method of a battery management system according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] Hereinafter, non-limiting examples of embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Note that the same components will be represented by the same reference numerals even if they are shown in different figures. When describing embodiments herein, detailed descriptions of related known configurations or their functions may be omitted if it is determined to hinder understanding of the embodiments.

[0052] To describe the components of the embodiments, for example, terms such as "first", "second", "A", "B", "(a)", and "(b)" may be used. These terms are intended to simply distinguish one component from another without limiting the nature, order, or sequence of the components. In addition, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by technicians in the field to which the embodiments of the present disclosure belong. Unless explicitly defined herein, terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and should not be interpreted in an idealized or overly formal sense.

[0053] Figure 1 is a view showing a battery pack according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, a battery pack 1000 may include a battery module 100, a battery management system 200 (hereinafter referred to as "BMS200"), and a relay 300. According to various embodiments, the battery module 100 may be a battery cell. For example, the battery pack 1000 may have a cell-to-pack structure in which the battery cells are directly assembled into a battery pack without being divided into separate modules. This is different from existing batteries in which modules each consisting of a battery cell are encapsulated.

[0054] Although Figure 1 One battery module 100 is shown, but according to an embodiment, the battery pack 1000 may be configured with a plurality of battery modules 100 and may have a stacked structure of the plurality of battery modules 100. The battery module 100 may include a plurality of battery cells 110, 120, 130, and 140 (hereinafter referred to as "battery cells 110 to 140"). Figure 1 Four battery cells are shown, but the number of battery cells is not limited thereto. The battery module 100 may include "n" battery cells ("n" is a natural number equal to or greater than 1). In addition, each of the battery cells 110 to 140 may be configured with two or more battery cells connected in parallel to form a battery pack.

[0055] The battery module 100 may supply power to a target device (not shown). To this end, the battery module 100 may be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack 1000 including the battery cells 110 to 140. For example, the target device may be an electric vehicle (EV) or an energy storage system (ESS), but is not limited thereto.

[0056] Each of the battery cells 110 to 140 is a basic unit of a battery that can be used by charging / discharging electric energy, and may be, for example, but not limited to, a lithium ion (Li-ion) cell, a lithium ion polymer (Li-ion polymer) cell, a nickel cadmium (Ni-Cd) cell, or a nickel metal hydride (Ni-MH) cell. Figure 1 One battery module 100 is shown, but a plurality of battery modules 100 may be provided according to an embodiment.

[0057] The BMS 200 may manage and / or control the state and / or operation of the battery module 100. For example, the BMS 200 may manage and / or control the state and / or operation of the battery cells 110 to 140 included in the battery module 100, and may manage charging and / or discharging of the battery module 100.

[0058] In addition, the BMS 200 may control the operation of the relay 300. For example, the BMS 200 may short-circuit the relay 300 to supply power to a target device, and may also short-circuit the relay 300 when a charger is connected to the battery pack 1000.

[0059] The BMS 200 may monitor, for example, the voltage, current, and temperature of the battery module 100 and / or the individual battery cells 110 to 140 included in the battery module 100. For monitoring by the BMS 200, a sensor (not shown) or various measurement modules (not shown) may be additionally installed, for example, at any position in the battery module 100 or the charging / discharging path. The BMS 200 may calculate parameters indicating the state of the battery module 100, such as the state of charge (SOC) or the state of health (SOH), based on the measured values ​​of the monitored voltage, current, temperature, etc.

[0060] The battery cells 110 to 140 generally experience changes in various factors over time and through repeated use, such as a decrease in capacity and an increase in internal resistance, resulting in abnormalities in the battery. Therefore, a technique for diagnosing abnormalities in the battery cells is needed. Based on data of various factors that change as the battery cells age, the BMS 200 is used to diagnose internal abnormalities in the battery cells 110 to 140.

[0061] Battery cell failures may occur due to various reasons, such as defects in the manufacturing stage, internal deformation and degradation through repeated charging / discharging, or external impacts, for example. In battery cells with these faults, faster and more significant voltage changes occur compared to normal battery cells. When utilizing the phenomenon that battery cells with internal faults exhibit faster and more significant voltage changes than normal battery cells during relaxation time, BMS200 can compare the voltage data of each battery cell 110 to 140 during the relaxation time with the statistical normal voltage data of the normal battery cell during the relaxation time, and diagnose the abnormal battery cells among the battery cells 110 to 140. The relaxation time of a battery cell or module refers to a state in which the battery cell or module is not currently charged or discharged or is not electrically connected to a load. For example, BMS200 can monitor the voltage value of the cell or the charge / discharge current value of the battery module to determine whether the battery cell or module is in a relaxed state. Here, the diagnosis of abnormal battery cells during relaxation time will be described. However, the method of diagnosing an abnormal battery cell according to an embodiment of the present disclosure is not limited to the diagnosis during the relaxation time, and may also be performed in other periods such as charging or discharging of a battery cell or module.

[0062] Compared to normal battery cells, the voltage of abnormal battery cells decreases, for example, during the relaxation time after charging, which causes the voltage behavior of abnormal battery cells to deviate significantly from the voltage behavior of normal battery cells. Therefore, the voltage behavior of abnormal battery cells is skewed to one side and has a relatively large skewness (skewness or asymmetry). When utilizing the characteristic that abnormal battery cells have a relatively large deviation and skewness in voltage behavior compared to normal battery cells, the BMS 200 according to an embodiment of the present disclosure can determine that there is an abnormal battery cell among the battery cells 110 to 140.

[0063] The BMS 200 may calculate a deviation dV between an average value of the voltages of the battery cells 110 to 140 and a voltage of each of the battery cells 110 to 140 at a predetermined time. Based on the deviation between the average value of the voltages of the battery cells 110 to 140 and a voltage of each of the battery cells 110 to 140, the BMS 200 may determine abnormal voltage behavior of at least one battery cell among the battery cells 110 to 140 and diagnose abnormality of the battery cell.

[0064] In addition, the BMS 200 may diagnose abnormal battery cells using the voltage deviation data of the respective battery cells 110 to 140 while excluding potential noise data of the voltage deviation data of the respective battery cells 110 to 140. After excluding the noise data of the voltage deviation data of the respective battery cells 110 to 140, the BMS 200 may amplify the voltage deviation data of the respective battery cells 110 to 140. Using the amplified voltage deviation data of the respective battery cells 110 to 140, the BMS 200 may detect and diagnose abnormal battery cells suspected of having abnormal voltages.

[0065] The operation of the BMS 200 described below may be performed in various devices, such as a server, a cloud, a charger, or a charger-discharger connected to the BMS 200 or a vehicle equipped with the BMS 200 through a wired or wireless signal.

[0066] Figure 2 2 is a block diagram showing a configuration of a BMS 200 according to an embodiment of the present disclosure. The configuration of the BMS 200 may vary according to a use environment and application of a battery pack 1000 including a battery module 100, and may include various different operating components.

[0067] Reference Figure 2 , the BMS 200 may include a voltage measuring unit 210 and a controller 220. In an embodiment, the controller 220 may include an arithmetic operation unit 230, a diagnostic unit 240, and a control unit 250. In another embodiment, in addition to the voltage measuring unit 210, the BMS 200 may further include a current measuring unit and / or a temperature measuring unit, for example.

[0068] The voltage measuring unit 210 is configured with a device such as a voltmeter capable of measuring the voltage of a battery pack and / or a cell, and can measure the voltage of each battery cell 110 to 140 at a predetermined time interval or at a unit time interval to obtain time series voltage data of each battery cell 110 to 140. According to an embodiment, the voltage measuring unit 210 can continuously measure and obtain data of voltage rise / fall and long-term relaxation of each battery cell 110 to 140 during charging, relaxation time after charging, discharging, and relaxation time after discharging. As needed, the acquired continuous voltage data can be used to diagnose abnormal battery cells in a specific period among the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging.

[0069] In an embodiment, the arithmetic operation unit 230 performs various arithmetic operations by using the voltage data acquired in the voltage measuring unit 210 for diagnosing an abnormal battery cell to be described below among the battery cells 110 to 140. The diagnosis unit 240 confirms a diagnosis condition to be described later, for example, by using the result of the arithmetic operation, and diagnoses the abnormal battery cell. Based on the diagnosis result, the control unit 250 can take appropriate measures for the abnormal battery cell, such as monitoring the abnormal battery cell or reporting the abnormality to the user.

[0070] Figure 3 is a flowchart showing an operation method of the BMS 200 according to an embodiment of the present disclosure. Figure 3 The operating method of the BMS 200 is described in a step-based manner.

[0071] In S102, the voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 at predetermined time intervals, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140. The voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 in the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging to continuously acquire data of voltage rise / fall and long-term relaxation, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140 using the measured voltage data. As needed, the voltage measuring unit 210 may continuously measure and acquire data of voltage rise / fall and long-term relaxation of each of the battery cells 110 to 140 in a specific period among the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140 using the acquired voltage data.

[0072] Figure 4 is a graph showing voltage changes of each battery cell according to an embodiment of the present disclosure. As an embodiment, Figure 4 The embodiment of represents changes in the voltages of the respective battery cells 110 to 140 measured at intervals of 200 seconds during a specific period (e.g., from the time after 10600 seconds elapsed from the start of charging to 11600 seconds (e.g., relaxation time after charging)) among the voltages measured by the voltage measuring unit 210. Figure 4 , the graph "ab1" represents the voltage change of the battery cell 110 among the battery cells 110 to 140 during the relaxation time after charging. In this graph, for example, a reverse peak where the voltage drops relatively greatly is observed in the circle portion. This means that the battery cell 110 exhibits abnormal voltage behavior in the corresponding period.

[0073] In S104, the controller 220 may calculate a moving average of the measured voltages of the respective battery cells 110 to 140. The moving average refers to the average of a portion of data extracted by moving a window of a predetermined size in the overall data. The window is a reference period for determining the data to be extracted and used in the overall data. The window, for example, starts at a time before the reference period from the current time and ends at the current time. For example, when the window is a week, the controller 220 may extract voltage data acquired in the most recent week period ending at the current time from the overall voltage data.

[0074] The controller 220 may calculate a moving average value of the voltages of the respective battery cells 110 to 140 using voltage data extracted by moving a window among the overall voltage data of the respective battery cells 110 to 140. The controller 220 may continuously calculate a moving average value of the voltages of the respective battery cells 110 to 140 using voltage data continuously extracted by moving a window among the overall voltage data of the respective battery cells 110 to 140. The controller 220 may apply any one of a simple moving average (SMA), a weighted moving average (WMA), and an exponential moving average (EMA) to the overall voltage data of the respective battery cells 110 to 140 to calculate the moving average value of the voltages of the respective battery cells 110 to 140.

[0075] According to an embodiment, the controller 220 may apply an exponential moving average (EMA) to the overall voltage data of the respective battery cells 110 to 140 to calculate the exponential moving average of the voltages of the respective battery cells 110 to 140. The exponential moving average is a weighted moving average that uses data over all past time periods while placing a greater weight on the most recent data.

[0076] The controller 220 may calculate a plurality of moving averages by applying windows having different sizes to the voltage data of each battery cell 110 to 140. In an embodiment, the controller 220 may continuously calculate a long-term moving average and a short-term moving average at a unit time interval (e.g., 200 seconds) by applying a relatively long window and a relatively short window to the overall voltage data of each battery cell 110 to 140, respectively. For example, the size of the window for the long-term moving average may include 100 seconds, and the size of the window for the short-term moving average may include 10 seconds. For example, among the voltage data of each battery cell 110 to 140, the controller 220 may calculate the long-term moving average of the voltage of each battery cell 110 to 140 by using the voltage data acquired every second within the last 100 seconds before the calculation time, and calculate the short-term moving average of the voltage of each battery cell 110 to 140 by using the voltage data acquired every second within the last 10 seconds before the calculation time.

[0077] Using the continuous long-term moving average value V_LMA and the short-term moving average value V_SMA of each battery cell 110 to 140, the controller 220 can analyze the long-term voltage change trend and the short-term voltage change trend of each battery cell 110 to 140. In addition, the controller 220 can use the long-term moving average value V_LMA and the short-term moving average value V_SMA of the voltage of each battery cell 110 to 140 to diagnose whether there is an abnormality in each battery cell 110 to 140.

[0078] In S106, the controller 220 may calculate a plurality of first deviations V_LMA-V_SMA at a unit time interval (e.g., 200 seconds), wherein each first deviation is a deviation between a long-term moving average value V_LMA and a short-term moving average value V_SMA of the voltage of each battery cell 110 to 140. For example, the controller 220 may continuously calculate the plurality of first deviations V_LMA-V_SMA at a unit time interval (e.g., 200 seconds), and these first deviations are calculated at each unit time interval for each battery cell 110 to 140. In S106, the controller 220 may continuously calculate the deviation between the long-term behavior and the short-term behavior of the voltage of each battery cell 110 to 140.

[0079] In S108, the controller 220 may calculate a long-term moving average value Vavg_LMA and a short-term moving average value Vavg_SMA of the average voltage V_avg of the battery cells 110 to 140 at a unit time interval (e.g., 200 seconds). Here, the average voltage V_avg of the battery cells 110 to 140 at each unit time interval (e.g., 200 seconds) may include a mean value, a median value, or a minimum value of the voltage of the battery cells 110 to 140.

[0080] The controller 220 may continuously calculate the average voltage V_avg of the battery cells 110 to 140 at a unit time interval (e.g., 200 seconds), and calculate a long-term moving average value Vavg_LMA and a short-term moving average value Vavg_SMA of the average voltage V_avg of the battery cells 110 to 140 by using the calculated average voltage V_avg of the battery cells 110 to 140. Here, the size of the window of the long-term moving average value Vavg_LMA of the average voltage V_avg of the battery cells 110 to 140 may be the same as the size of the window of the long-term moving average value V_LMA of the voltage of each battery cell 110 to 140 (e.g., 100 seconds). In addition, the size of the window of the short-term moving average value Vavg_SMA of the average voltage V_avg of the battery cells 110 to 140 may be the same as the size of the window of the short-term moving average value V_SMA of the voltage of each battery cell 110 to 140 (e.g., 10 seconds).

[0081] In S110, the controller 220 may continuously calculate a second deviation Vavg_LMA-Vavg_SMA at a unit time interval (e.g., 200 seconds), the second deviation being a deviation between a long-term moving average value Vavg_LMA and a short-term moving average value Vavg_SMA of the average voltage V_avg of the battery cells 110 to 140. In S110, the controller 220 may calculate a deviation between a long-term behavior and a short-term behavior of the average voltage V_avg of the battery cells 110 to 140.

[0082] In S112, the controller 220 may calculate the first diagnostic deviation D1 of each battery cell 110 to 140, which is the deviation between each of the plurality of first deviations V_LMA-V_SMA and the second deviation Vavg_LMA-Vavg_SMA. Specifically, the controller 220 may calculate the first diagnostic deviation D1 of each battery cell 110 to 140 based on the following formula 1.

[0083] [Formula 1]

[0084] First diagnostic deviation (D1) = first deviation - second deviation

[0085] =(V_LMA-V_SMA)-(V avg _LMA-V avg _SMA)

[0086] Referring to Formula 1, the controller 220 may calculate a deviation between each of the plurality of first deviations V_LMA-V_SMA and the second deviation Vavg_LMA-Vavg_SMA as a first diagnosis deviation D1 for each of the battery cells 110 to 140 .

[0087] Figure 5a is a graph showing a first diagnosis deviation D1 of each battery cell according to an embodiment of the present disclosure. Figure 5a , the controller 220 may continuously calculate the first diagnostic deviations D1 of the respective battery cells 110 to 140 at unit time intervals during a specific period, and generate a graph representing changes in the first diagnostic deviations D1 of the respective battery cells 110 to 140 .

[0088] The controller 220 may continuously calculate the first diagnostic deviation D1 of each battery cell 110 to 140 at a unit time interval (e.g., 200 seconds) to compare the deviation between the long-term behavior and the short-term behavior of the voltage of each battery cell 110 to 140 and the deviation between the long-term behavior and the short-term behavior of the average voltage V_avg of the battery cells 110 to 140. As an embodiment, Figure 5a The curve graph "ab2" in FIG. 1 may represent a change in the first diagnostic deviation D1 of the battery cell 110. Figure 4 As shown in the curve diagram, compared with other battery cells, Figure 5a The first diagnostic deviation D1 of the battery cell 110 in FIG. 1 also shows an unusual behavior and exceeds a reference value to be described below within a specific range.

[0089] In S114 , the controller 220 may calculate the second diagnostic deviation D2 of each of the battery cells 110 to 140 by removing noise data of the first diagnostic deviation D1 of each of the battery cells 110 to 140 .

[0090] For example, the controller 220 may set a reference value for determining whether the first diagnosis deviation D1 of each of the battery cells 110 to 140 is noise based on the following Equation 2.

[0091] [Formula 2]

[0092] Reference value = Max[V avg _LMA-V avg _SMA|*C1,C2]

[0093] The controller 220 may set the reference value for each of the battery cells 110 to 140 to a maximum value Max between a value (|Vavg_LMA-Vavg_SMA|*C1) obtained by multiplying the absolute value of the second deviation (|Vavg_LMA-Vavg_SMA|) by the first threshold constant C1 and the second threshold constant C2. In an embodiment, the first threshold constant C1 may include "0.1", and the second threshold constant C2 may include "0.4". The first threshold constant C1 and the second threshold constant C2 may be changed according to the size and characteristics of the voltage data of each of the battery cells 110 to 140.

[0094] As an example of noise removal, when the first diagnostic deviation D1 of any one of the battery cells 110 to 140 is equal to or less than the reference value, the controller 220 may determine the corresponding first diagnostic deviation D1 as noise data. The controller 220 may calculate the second diagnostic deviation D2 of each of the battery cells 110 to 140 by excluding the first diagnostic deviation D1 of each of the battery cells 110 to 140 that is equal to or less than the reference value.

[0095] In S116 , the controller 220 may calculate the third diagnostic deviation D3 of each of the battery cells 110 to 140 by normalizing the second diagnostic deviation D2 of each of the battery cells 110 to 140 .

[0096] Specifically, the controller 220 may normalize the second diagnostic deviations D2 of the respective battery cells 110 to 140 based on the following Equation 3 to calculate the third diagnostic deviations D3 of the respective battery cells 110 to 140 .

[0097] [Formula 3]

[0098]

[0099] The controller 220 may calculate a maximum value Max between a value (|Vavg_LMA-Vavg_SMA|*C3) obtained by multiplying the absolute value of the second deviation (|Vavg_LMA-Vavg_SMA|) by the third threshold constant C3 and a fourth threshold constant C4. Then, the controller 220 may normalize the second diagnostic deviation D2 of each battery cell 110 to 140 by dividing the second diagnostic deviation D2 by a maximum value Max (Max[|Vavg_LMA-Vavg_SMA|*C3, C4]) between a value obtained by multiplying the absolute value of the second deviation representing the behavior of the average voltage V_avg of the battery cells 110 to 140 by the third threshold constant C3 and the fourth threshold constant C4 to calculate a third diagnostic deviation D3. Here, the third threshold constant C3 may include "0.1", and the fourth threshold constant C4 may include "0.1". The third threshold constant C3 and the fourth threshold constant C4 may be changed according to the size and characteristics of the voltage data of each battery cell 110 to 140.

[0100] As another example of normalization, in an embodiment, the controller 220 may use a logarithmic (log) operation to normalize the second diagnostic deviation D2 of each battery cell 110 to 140. The controller 220 may calculate a value obtained by normalizing the second diagnostic deviation D2 of each battery cell 110 to 140 using a logarithmic operation as a third diagnostic deviation D3 of each battery cell 110 to 140.

[0101] As another example of normalization, in an embodiment, the controller 220 may set the average value (D2_avg) of the second diagnostic deviations D2 of the battery cells 110 to 140 as a normalization reference value. When the average value (D2_avg) of the second diagnostic deviations D2 is used as the normalization reference value, in S116, the controller 220 may normalize the second diagnostic deviations D2 of the respective battery cells 110 to 140 by dividing the second diagnostic deviations D2 by the average value (D2_avg) of the second diagnostic deviations D2. The controller 220 may calculate a value normalized by dividing the second diagnostic deviations D2 of the respective battery cells 110 to 140 by the average value (D2_avg) of the second diagnostic deviations D2 as the third diagnostic deviations D3 of the respective battery cells 110 to 140.

[0102] Figure 5b is a graph showing a third diagnostic deviation D3 of each battery cell according to an embodiment of the present disclosure. Figure 5b , the controller 220 may normalize the second diagnostic deviations D2 of the respective battery cells 110 to 140 to calculate the third diagnostic deviations D3 of the respective battery cells 110 to 140 according to various embodiments.

[0103] The controller 220 may continuously calculate the third diagnostic deviation D3 of each of the battery cells 110 to 140 at unit time intervals, and generate a graph indicating changes in the third diagnostic deviation D3 of each of the battery cells 110 to 140. As an embodiment, Figure 5b The curve graph "ab3" in FIG. 1 may represent the change of the third diagnostic deviation D3 of the battery cell 110. Figure 5b As can be seen from FIG. 1 , the third diagnostic deviation D3 of the battery cell 110 exhibits a value equal to or greater than 0 (zero) within a specific range.

[0104] In S118, the controller 220 may calculate the skewness of the third diagnostic deviation D3 of each of the battery cells 110 to 140. Specifically, the controller 220 may calculate the skewness of the third diagnostic deviation D3 of each of the battery cells 110 to 140 based on Equation 4 below.

[0105] [Formula 4]

[0106]

[0107] Referring to Formula 4, the controller 220 can calculate the skewness of the third diagnostic deviation D3 of each battery cell 110 to 140 by adding the minimum value (Min[third diagnostic deviation D3]) of the third diagnostic deviation D3 of the battery cells 110 to 140 to the third diagnostic deviation D3 of each battery cell 110 to 140, and dividing the obtained value by the third diagnostic deviation D3.

[0108] Figure 5c is a graph showing skewness of a third diagnostic deviation D3 of each battery cell according to an embodiment of the present disclosure.

[0109] Reference Figure 5c , the controller 220 may continuously calculate the skewness of the third diagnostic deviation D3 of each battery cell 110 to 140 at a unit time interval of 200 seconds during a specific period between 10600 seconds and 11600 seconds, and generate a graph representing changes in the skewness of the third diagnostic deviation D3 of each battery cell 110 to 140. As an embodiment, Figure 5c The curve graph of may represent the change in the skewness of the third diagnostic deviation D3 of the battery cell 110. Figure 5b Compared with the third diagnostic deviation D3, Figure 5c The skewness of the third diagnostic deviation D3 in FIG. 1 shows improved clarity, which will lead to improved diagnostic results.

[0110] In S120, the controller 220 may calculate the fourth diagnostic deviation D4 of each battery cell 110 to 140 by reflecting the skewness of the third diagnostic deviation D3 of each battery cell 110 to 140. Specifically, the controller 220 may calculate the fourth diagnostic deviation D4 of each battery cell 110 to 140 based on Equation 5 below.

[0111] [Formula 5]

[0112] Fourth diagnostic deviation (D4) = Third diagnostic deviation (D3) * skewness

[0113] The controller 220 may calculate a fourth diagnostic deviation D4 of each of the battery cells 110 to 140 by multiplying the third diagnostic deviation D3 of each of the battery cells 110 to 140 by the skewness at a unit time interval (eg, 200 seconds).

[0114] Figure 5d is a graph showing a fourth diagnostic deviation D4 of a battery cell according to an embodiment of the present disclosure. Figure 5d , the controller 220 may continuously calculate the fourth diagnostic deviation D4 of each battery cell 110 to 140 at a unit time interval (eg, 200 seconds), and generate a graph indicating changes in the fourth diagnostic deviation D4 of each battery cell 110 to 140. As an embodiment, Figure 5d The curve graph of can represent the change of the fourth diagnostic deviation D4 of the battery cell 110. Figure 5d It can be seen that, compared with the third diagnostic deviation D3 without applying the skewness, the fourth diagnostic deviation D4 with the skewness applied shows a more stable and accurate diagnostic deviation, in which the noise is reduced and the abnormal voltage behavior signal is amplified.

[0115] In S122, the controller 220 may determine whether the fourth diagnostic deviation D4 of each battery cell 110 to 140 exceeds a threshold. The threshold may be defined as a reference value for determining an inappropriate result as "abnormal". The threshold may be defined as a standard for presenting how much data deviates from a specific statistical model. When the fourth diagnostic deviation D4 of any one of the battery cells 110 to 140 exceeds the threshold, the controller 220 may determine that the corresponding battery cell is a battery cell with abnormal voltage behavior. Here, the threshold is determined in consideration of the state of each battery cell, the sensitivity of the measurement system, and the measurement environment, and may vary according to, for example, the type of battery cell and / or the vehicle to which the battery cell is applied. Figure 5d In the example of FIG. 2 , for example, assuming that the threshold is set to 0.4 V, the controller 220 may determine that the battery cell 110 is a battery cell having abnormal voltage behavior because the fourth diagnostic deviation D4 of the battery cell 110 exceeds 0.4 V during a specific period.

[0116] At this time, when it is determined in S122 that the fourth diagnostic deviation D4 does not exceed the threshold, the process returns to S102 to repeat the measurement, calculation and diagnosis process. In another embodiment, the process may not return to S102, but may return to any step before S116 to repeat the process.

[0117] In S124, based on the result of determining whether the fourth diagnostic deviation D4 of each of the battery cells 110 to 140 exceeds the threshold value, the controller 220 may diagnose at least one battery cell among the battery cells 110 to 140 as an abnormal battery cell. When the fourth diagnostic deviation D4 of at least one battery cell among the battery cells 110 to 140 exceeds the threshold value, the controller 220 may diagnose the at least one battery cell as a battery cell having abnormal voltage behavior.

[0118] In S124, according to an embodiment, when the fourth diagnostic deviation D4 of at least one battery cell among the battery cells 110 to 140 exceeds the threshold value, the controller 220 may increase the diagnostic count value of the at least one battery cell. In an embodiment, when the fourth diagnostic deviation D4 of at least one battery cell among the battery cells 110 to 140 exceeds the threshold value for the first time, the controller 220 may not immediately diagnose the at least one battery cell as an abnormal battery cell. In contrast, when the diagnostic count value of the at least one battery cell is equal to or greater than the threshold count value, for example, when a preset time (e.g., threshold count) passes after the fourth diagnostic deviation D4 of the at least one battery cell exceeds the threshold value, the controller 220 may diagnose the at least one battery cell as an abnormal battery cell. Therefore, for a battery cell whose fourth diagnostic deviation D4 exceeds the threshold value for an instant and then quickly drops to the threshold value or lower, the diagnosis of the abnormal battery cell may not be performed. Therefore, the reliability of the diagnosis of the abnormal battery cell may be improved.

[0119] After diagnosing an abnormality in at least one battery cell among the battery cells 110 to 140 , the controller 220 may track and monitor whether a defect, such as an internal short circuit, an external short circuit, and lithium deposition, occurs in the at least one battery cell.

[0120] When it is determined that a defect occurs in the at least one battery cell, the controller 220 may provide information about the corresponding battery cell to a user of the battery cell. For example, the controller 220 may provide information about the battery cell having an internal short circuit to a user terminal through a communication unit (not shown) or a display (not shown) installed in, for example, a vehicle or a charger.

[0121] As described above, according to an embodiment of the present disclosure, the BMS 200 can accurately diagnose abnormal battery cells by removing the noise of the deviation between the long-term moving average and the short-term moving average of the voltage of each battery cell. The BMS 200 can minimize the distortion of the voltage of each battery cell and remove noise data by using the deviation between the long-term moving average and the short-term moving average of the voltage of each battery cell, and amplify the voltage behavior of the potential abnormal battery cell by reflecting the skewness of the voltage of each battery cell, which can improve the accuracy of diagnosis.

[0122] In addition, the BMS 200 can diagnose battery cells with abnormal voltage behavior at an earlier stage by using the deviation between the long-term moving average and the short-term moving average of the voltage of each battery cell, which ensures the safety and reliability of battery energy. Moreover, the BMS 200 can diagnose battery cells with abnormal voltage behavior even in a state where the battery is installed in the vehicle, so that the abnormal battery cells can be easily and quickly diagnosed without being separated from the vehicle.

[0123] Figure 6 is a flowchart illustrating an operating method of a battery management system according to another embodiment of the present disclosure.

[0124] In the referenced Figure 3 In the described operation method, the diagnosis of abnormal battery cells is performed by comparing the fourth diagnostic deviation D4 with the threshold value in S122. However, the present disclosure is not limited thereto. For example, the diagnosis of abnormal battery cells may be performed at any step before S122. For example, the diagnosis of abnormal battery cells may be performed after calculating the second diagnostic deviation D2 in S114. Figure 6 The embodiment is described in a step-based manner. In order to avoid overlapping of descriptions, descriptions substantially the same as the above descriptions will be omitted.

[0125] Reference Figure 6 In S202, the voltage measuring unit 210 may measure the voltage of each of the plurality of battery cells 110 to 140 at predetermined time intervals, and the controller 220 may generate a graph representing the voltage change of each of the battery cells 110 to 140. The voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 during the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging to continuously acquire data of voltage rise / fall and long-term relaxation, and the controller 220 may generate a graph representing the voltage change of each of the battery cells 110 to 140 using the measured voltage data.

[0126] In S204, the controller 220 may calculate a moving average of the measured voltages of the respective battery cells 110 to 140. According to an embodiment, the controller 220 may calculate the exponential moving average of the voltages of the respective battery cells 110 to 140 by applying an exponential moving average (EMA) to the overall data of the voltages of the respective battery cells 110 to 140.

[0127] The controller 220 may continuously calculate a long-term moving average and a short-term moving average at unit time intervals (eg, 200 seconds) by applying a relatively long window and a relatively short window to overall data of voltages of the respective battery cells 110 to 140 , respectively.

[0128] In S206 , the controller 220 may calculate a plurality of first deviations V_LMA-V_SMA at unit time intervals (eg, 200 seconds), wherein each first deviation is a deviation between a long-term moving average value V_LMA and a short-term moving average value V_SMA of the voltages of each of the battery cells 110 to 140 .

[0129] In S208, the controller 220 may calculate a long-term moving average value Vavg_LMA and a short-term moving average value Vavg_SMA of the average voltage V_avg of the battery cells 110 to 140 at a unit time interval (e.g., 200 seconds). Here, the average voltage V_avg of the battery cells 110 to 140 at each unit time interval (e.g., 200 seconds) may include a mean value, a median value, or a minimum value of the voltage of the battery cells 110 to 140.

[0130] In S210 , the controller 220 may continuously calculate a second deviation Vavg_LMA−Vavg_SMA, which is a deviation between a long-term moving average value Vavg_LMA and a short-term moving average value Vavg_SMA of the average voltage V_avg of the battery cells 110 to 140 , at unit time intervals (eg, 200 seconds).

[0131] In S212, the controller 220 may calculate the first diagnostic deviation D1 of each battery cell 110 to 140, which is the deviation between each of the plurality of first deviations V_LMA-V_SMA and the second deviation Vavg_LMA-Vavg_SMA. Specifically, the controller 220 may calculate the first diagnostic deviation D1 of each battery cell 110 to 140 based on the above formula 1.

[0132] In S214, the controller 220 may calculate the second diagnostic deviation D2 of each of the battery cells 110 to 140 by removing the noise data of the first diagnostic deviation D1 of each of the battery cells 110 to 140. Specifically, the controller 220 may set a reference value for determining whether the first diagnostic deviation D1 of each of the battery cells 110 to 140 is noise based on the above-mentioned Formula 2. The controller 220 may calculate the second diagnostic deviation D2 of each of the battery cells 110 to 140 by excluding the first diagnostic deviation D1 of each of the battery cells 110 to 140 that is equal to or less than the reference value.

[0133] In S216, the controller 220 may determine whether the second diagnostic deviation D2 of each battery cell 110 to 140 exceeds the threshold value. When it is determined in S216 that the second diagnostic deviation D2 does not exceed the threshold value, the process returns to S202 to repeat the measurement, calculation and diagnosis process. In another embodiment, the process may not return to S202, but may return to any step before S214 to repeat the process.

[0134] In S218 , when the second diagnosis deviation D2 of any one of the battery cells 110 to 140 exceeds the threshold, the controller 220 may diagnose the corresponding battery cell as an abnormal battery cell.

[0135] According to another embodiment of the present disclosure, the BMS 200 may diagnose an abnormal battery cell among the battery cells 110 to 140 by using a deviation dV between an average voltage V_avg of the battery cells 110 to 140 and a voltage of each battery cell 110 to 140 .

[0136] First, the voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 in a manner similar to that in the above-described embodiment. The voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 at unit time intervals to obtain time series data of a first voltage dV of each of the battery cells 110 to 140. In an embodiment, the voltage measuring unit 210 may continuously calculate data of voltage rise / fall and long-term relaxation of each of the battery cells 110 to 140 during charging, relaxation time after charging, discharging, and relaxation time after discharging, and the controller 220 may generate a graph representing voltage changes of each of the battery cells 110 to 140.

[0137] At this time, the controller 220 may calculate the average voltage V_avg of the battery cells 110 to 140 at a unit time interval (e.g., 200 seconds) using the voltage data measured as described above. In an embodiment, the controller 220 may calculate the mean, median, or minimum value of the voltages of the battery cells 110 to 140 as the average voltage V_avg of the battery cells 110 to 140 at each unit time interval (e.g., 200 seconds). Then, the controller 220 may calculate the deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140 at a unit time interval (e.g., 200 seconds) for each battery cell 110 to 140. According to an embodiment, the controller 220 may calculate the deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140 as the first voltage dV.

[0138] Hereinafter, it will be described assuming that the first voltage dV of each battery cell 110 to 140 is a deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140. However, the present disclosure is not limited thereto. For example, according to an embodiment, the controller 220 may calculate the voltage of each battery cell 110 to 140 as the first voltage dV of each battery cell 110 to 140.

[0139] Figure 7 is a flowchart showing an operation method of the BMS 200 and a method of diagnosing abnormal battery cells according to another embodiment of the present disclosure. Figure 7 The operating method of the BMS 200 is described in a step-based manner.

[0140] In S302, the voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 at predetermined time intervals, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140. The voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 in the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging to continuously acquire data of voltage rise / fall and long-term relaxation, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140 using the measured voltage data. As needed, the voltage measuring unit 210 may continuously measure and acquire data of voltage rise / fall and long-term relaxation of each of the battery cells 110 to 140 in a specific period among the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140 using the acquired voltage data.

[0141] In S304, by using the voltage data of the respective battery cells 110 to 140 measured in the previous step, the controller 220 may calculate the deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of the respective battery cells 110 to 140 at predetermined time intervals as the first voltage dV. For example, as a method of calculating the deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of the respective battery cells 110 to 140 as the first voltage dV, the controller 220 may first add up all voltage values ​​of the battery cells 110 to 140 at a specific time t1, and divide the obtained value by 4, and calculate the obtained average value (mean) as the average voltage V_avg of the battery cells 110 to 140 at time t1. In another embodiment, instead of calculating the average value (mean), a median value or a minimum value of the voltage values ​​of the battery cells 110 to 140 at a specific time t1 may be calculated as the average voltage V_avg of the battery cells 110 to 140 .

[0142] Then, the controller 220 may calculate the deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140 as the first voltage dV for each battery cell 110 to 140 at a predetermined time interval. For example, the difference between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140 at a specific time t1 is calculated as the first voltage dV of each battery cell 110 to 140 at time t1. For each battery cell 110 to 140, the calculation process may be repeated at a predetermined time interval to continuously calculate the first voltage dV.

[0143] For example, suppose in Figure 4 The voltages of the battery cells 110 to 140 at the time of 10800 seconds are 3.92V, 3.9175V, 3.9150V and 3.9125V, respectively, and the average voltage of the battery cells 110 to 140 at the time of 10800 seconds is 3.91625V. In this case, the deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140 is 0.00375V (battery cell 110), 0.00125V (battery cell 120), 0.00125V (battery cell 130) and 0.00375V (battery cell 140). These values ​​are each the first voltage dV at the time of 10800 seconds. The controller 220 repeats the arithmetic operation at unit time intervals to continuously calculate the first voltage dV in a specific period of time, for example, from 10600 seconds to 11600 seconds.

[0144] At this time, in an embodiment, the controller 220 may calculate the voltage of each battery cell 110 to 140 measured in the previous step as the first voltage dV instead of the deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140 .

[0145] Figure 8a is a graph showing a first voltage dV of each battery cell according to an embodiment of the present disclosure. Figure 8a The controller 220 may measure the voltage of each of the battery cells 110 to 140 during discharge and a relaxation time after discharge (eg, a period of 0 seconds to 3500 seconds) to obtain time series data of a first voltage dV of each of the battery cells 110 to 140.

[0146] Figure 8b is a graph showing a first voltage dV of each battery cell according to another embodiment of the present disclosure. Figure 8b The controller 220 may measure the voltage of each of the battery cells 110 to 140 during charging and a relaxation time after charging (eg, a period of 0 seconds to 3500 seconds) to acquire time series data of a first voltage dV of each of the battery cells 110 to 140.

[0147] In S306, the controller 220 may calculate a long-term moving average and a short-term moving average of the first voltage dV of each battery cell 110 to 140. The moving average refers to the average value of some data extracted by moving a window of a predetermined size in the overall data. The window is a reference period for determining the data to be extracted and used in the overall data. The window starts at a time before the reference period from the current time and ends at the current time. For example, when the window is a week, the controller 220 may extract voltage data acquired in the most recent week period ending at the current time from the overall voltage data.

[0148] The controller 220 may continuously calculate a moving average value of the first voltage dV of each battery cell 110 to 140 using data of the first voltage dV continuously extracted by moving a window among the overall data of the first voltage dV of each battery cell 110 to 140. For example, the controller 220 may calculate a moving average value of a deviation dV between an average voltage V_avg of the battery cells 110 to 140 and a voltage of each battery cell 110 to 140, or a moving average value of the voltage of each battery cell 110 to 140, by applying any one of a simple moving average (SMA), a weighted moving average (WMA), and an exponential moving average (EMA) to the overall data of the first voltage dV of each battery cell 110 to 140.

[0149] According to an embodiment, the controller 220 may apply an exponential moving average (EMA) to the overall data of the first voltage dV of each battery cell 110 to 140 to calculate the exponential moving average of the first voltage dV of each battery cell 110 to 140. The exponential moving average is a weighted moving average that uses data over all past time periods while placing a greater weight on the most recent data.

[0150] Specifically, the controller 220 may calculate a plurality of moving averages by applying windows having different sizes to the data of the first voltage dV of each battery cell 110 to 140. In an embodiment, the controller 220 may calculate a long-term moving average and a short-term moving average by applying a relatively long window and a relatively short window to the overall data of the first voltage dV of each battery cell 110 to 140, respectively. For example, the size of the window for the long-term moving average may include 100 seconds, and the size of the window for the short-term moving average may include 10 seconds. For example, using the data of the first voltage dV of each battery cell 110 to 140, the controller 220 may calculate the long-term moving average of the first voltage dV of each battery cell 110 to 140 by using the data of the first voltage dV acquired in the last 100 seconds before the calculation time, and calculate the short-term moving average of the first voltage dV of each battery cell 110 to 140 by using the data of the first voltage dV acquired in the last 10 seconds before the calculation time.

[0151] Figure 9a is a graph showing a long-term moving average and a short-term moving average of a first voltage dV of each battery cell during discharge and a relaxation time after discharge according to an embodiment of the present disclosure. Figure 9b is a graph showing a long-term moving average value and a short-term moving average value of a first voltage dV of each battery cell during charging and a relaxation time after charging according to another embodiment of the present disclosure.

[0152] In an embodiment, Figure 9a and Figure 9b In FIG. 1 , the dotted line represents the change of the short-term moving average value dV_SMA of each battery cell, and the solid line represents the change of the long-term moving average value dV_LMA of each battery cell.

[0153] Reference Figure 9a , the controller 220 may measure the voltage of each of the battery cells 110 to 140 during discharge and relaxation time after discharge to obtain time series data of a long-term moving average dV_LMA and a short-term moving average dV_SMA of the first voltage dV of each of the battery cells 110 to 140 .

[0154] Reference Figure 9b, the controller 220 may measure the voltage of each of the battery cells 110 to 140 during charging and the relaxation time after charging to obtain time series data of a long-term moving average dV_LMA and a short-term moving average dV_SMA of the first voltage dV of each of the battery cells 110 to 140 .

[0155] Using the continuous long-term moving average values ​​dV_LMA and short-term moving average values ​​dV_SMA of the first voltage dV of each battery cell 110 to 140, the controller 220 may analyze the long-term voltage change trend and the short-term voltage change trend of the first voltage dV of each battery cell 110 to 140. The controller 220 may diagnose abnormalities in each battery cell 110 to 140 using the long-term moving average value dV_LMA and the short-term moving average value dV_SMA of the deviation dV between the voltage of each battery cell 110 to 140 and the average voltage V_avg of the battery cells 110 to 140, or the long-term moving average value and the short-term moving average value of the voltage of each battery cell 110 to 140.

[0156] In S308, by using the calculated long-term moving average value dV_LMA and short-term moving average value dV_SMA, the controller 220 may calculate, at predetermined time intervals, a first deviation dV_LMA-dV_SMA, the first deviation being a deviation between the long-term moving average value dV_LMA and the short-term moving average value dV_SMA of the first voltage dV of each of the battery cells 110 to 140. The controller 220 may continuously calculate the deviation between the long-term behavior and the short-term behavior of the deviation dV between the voltage of each of the battery cells 110 to 140 and the average voltage V_avg of the battery cells 110 to 140, or the deviation between the long-term behavior and the short-term behavior of the voltage of each of the battery cells 110 to 140.

[0157] In an embodiment, Fig.10a is a graph showing a first deviation between a long-term moving average dV_LMA and a short-term moving average dV_SMA of a first voltage dV during discharge and a relaxation time after the discharge. Fig.10b is a graph showing a first deviation between a long-term moving average dV_LMA and a short-term moving average dV_SMA of a first voltage dV during charging and a relaxation time after charging. Fig.10a and Fig.10b , it is assumed that the graph C1 represents the first deviation of the battery cell 110. The controller 220 may continuously calculate the first deviations dV_LMA-dV_SMA of the respective battery cells 110 to 140 at unit time intervals.

[0158] According to an embodiment, the deviation between the long-term moving average value dV_LMA and the short-term moving average value dV_SMA of each of the battery cells 110 to 140 may depend on the short-term variation history and the long-term variation history of the cell voltage.

[0159] The temperature or state of health (SOH) of each battery cell 110 to 140 constantly affects the voltage of each battery cell 110 to 140 over a long period as well as a short period. Therefore, unless an abnormality occurs in the voltage of each battery cell 110 to 140, the deviation between the long-term moving average value dV_LMA and the short-term moving average value dV_SMA of each battery cell 110 to 140 may not be significantly different from other battery cells. At this time, when a voltage abnormality suddenly occurs in a specific battery cell (e.g., the battery cell 110), for example, due to an internal short circuit and / or an external short circuit, the abnormality may affect the short-term moving average value instead of the long-term moving average value. As a result, the corresponding battery cell (e.g., the battery cell 110) may show a relatively significant difference in the deviation between the long-term moving average value dV_LMA and the short-term moving average value dV_SMA compared to other battery cells without any voltage abnormality.

[0160] In S310, the controller 220 may calculate a second deviation (dVLMA-dVSMA) AVG, which is an average value of the first deviations dV_LMA-dV_SMA of the battery cells 110 to 140, at each predetermined unit time interval. Although the overall average value may be calculated as the average value of the first deviations dV_LMA-dV_SMA, the controller 220 may calculate a median value or a minimum value of the first deviations dV_LMA-dV_SMA of the battery cells 110 to 140 as the second deviation.

[0161] For example, the controller 220 may continuously calculate the first deviations dV_LMA-dV_SMA of each of the battery cells 110 to 140 at unit time intervals. Then, by using the calculated first deviations dV_LMA-dV_SMA of the battery cells 110 to 140, the controller 220 may continuously calculate the average, median, or minimum value of the first deviations dV_LMA-dV_SMA of the battery cells 110 to 140 as the second deviation (dVLMA-dVSMA) AVG at each unit time interval. The controller 220 may calculate the average value of the deviations between the long-term behavior and the short-term behavior of the first voltage dV of the battery cells 110 to 140.

[0162] In S312 , the controller 220 may calculate a first diagnostic deviation D1 of each of the battery cells 110 to 140 at each unit time interval, the first diagnostic deviation being a difference between a first deviation dV_LMA−dV_SMA and a second deviation (dVLMA−dVSMA) AVG of each of the battery cells 110 to 140 .

[0163] Specifically, the controller 220 may calculate the first diagnosis deviation D1 of each of the battery cells 110 to 140 at each unit time interval based on the following Equation 6.

[0164] [Formula 6]

[0165] First diagnostic deviation (D1) = second deviation - first deviation

[0166] =((dV LMA -dV SMA ) AVG )-(dV_LMA-dV_SMA)

[0167] Referring to Formula 6, the controller 220 may calculate a difference between each of the plurality of first deviations dV_LMA-dV_SMA and the second deviation (dVLMA-dVSMA) AVG as a first diagnosis deviation D1 for each of the battery cells 110 to 140 .

[0168] Fig.11a is a graph showing a first diagnosis deviation D1 of each battery cell during discharge and relaxation time after discharge according to an embodiment of the present disclosure. Fig.11b is a graph showing the first diagnostic deviation D1 of each battery cell during charging and relaxation time after charging according to another embodiment of the present disclosure. For ease of understanding of the description, it is assumed that Fig.11a and Fig.11b The graph “ C1 ” in shows the first diagnosis deviation D1 of the battery cell 110 among the battery cells 110 to 140 .

[0169] Reference Fig.11a The voltage measuring unit 210 can measure the voltage of each battery cell 110 to 140 at unit time intervals during discharge and relaxation time after discharge to obtain time series data of the first diagnostic deviation D1 of each battery cell 110 to 140, which is the difference between the first deviation dV_LMA-dV_SMA and the second deviation (dVLMA-dVSMA) AVG of each battery cell 110 to 140.

[0170] Reference Fig.11bThe voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 at unit time intervals during charging and the relaxation time after charging to obtain time series data of a first diagnostic deviation D1 of each of the battery cells 110 to 140 which is a difference between a first deviation dV_LMA-dV_SMA and a second deviation (dVLMA-dVSMA) AVG of each of the battery cells 110 to 140.

[0171] The controller 220 may calculate the first diagnostic deviation D1 of each battery cell 110 to 140 to compare the deviation between the long-term and short-term behaviors of the first voltage dV of each battery cell 110 to 140 with an average value of the deviation between the long-term and short-term behaviors of the first voltage dV of the battery cells 110 to 140 .

[0172] At this time, the controller 220 may calibrate the first voltage dV of each battery cell 110 to 140 using the first diagnostic deviation D1 of each battery cell 110 to 140. For example, in S312, the controller 220 may input the first diagnostic deviation D1 of each battery cell 110 to 140 calculated through S302 to S312 as the first voltage dV of each battery cell 110 to 140 in S304 to repeat S304 to S312 and recalculate the first diagnostic deviation D1. According to an embodiment, the controller 220 may input the first diagnostic deviation D1 calculated in S312 as the calibrated first voltage dV' of each battery cell 110 to 140 to repeat S304 to S312 one or more times and recalculate the first diagnostic deviation D1 of each battery cell 110 to 140.

[0173] The controller 220 may calculate a moving average of the calibrated first voltage dV' of each battery cell 110 to 140 using the calibrated first voltage dV' (eg, the calibrated first voltage dV' of each battery cell 110 to 140) extracted by moving a window among the time series data of the first diagnostic deviation D1.

[0174] According to an embodiment, the controller 220 may apply an exponential moving average (EMA) to the calibrated first voltage dV′ of each of the battery cells 110 to 140 to calculate an exponential moving average of the calibrated first voltage dV′ of each of the battery cells 110 to 140 .

[0175] The controller 220 may calculate time series data of a long-term moving average dV'_LMA and a short-term moving average dV'_SMA of the calibrated first voltage dV' of each battery cell 110 to 140. The controller 220 may continuously calculate the long-term moving average dV'_LMA and the short-term moving average dV'_SMA of the calibrated first voltage dV' for each battery cell 110 to 140 at unit time intervals.

[0176] By using the continuous long-term moving average dV′_LMA and short-term moving average dV′_SMA of the calibrated first voltage dV′ of each battery cell 110 to 140 , the controller 220 may analyze the long-term voltage variation trend and the short-term voltage variation trend of the calibrated first voltage dV′ of each battery cell 110 to 140 .

[0177] According to an embodiment, the controller 220 may input the first diagnostic deviation D1 calculated from the voltages of the battery cells 110 to 140 during the relaxation time during discharge and after discharge as the first voltage dV to recalculate the first diagnostic deviation D1 of the respective battery cells 110 to 140. According to another embodiment, the controller 220 may input the first diagnostic deviation D1 calculated from the voltages of the battery cells 110 to 140 during the relaxation time during charge and after charge as the first voltage dV to recalculate the first diagnostic deviation D1 of the respective battery cells 110 to 140.

[0178] Then, the controller 220 may calculate a calibrated first deviation dV′_LMA−dV′_SMA of each battery cell 110 to 140 , which is a deviation between a long-term moving average dV′_LMA and a short-term moving average dV′_SMA of the first diagnostic deviation D1 , for example, a calibrated first voltage dV′ of each battery cell 110 to 140 .

[0179] The controller 220 may continuously calculate the calibrated first deviations dV′_LMA-dV′_SMA of the respective battery cells 110 to 140 at unit time intervals.

[0180] Subsequently, the controller 220 may calculate a calibrated second deviation (dV'LMA-dV'SMA) AVG, which is an average value of the calibrated first deviations dV'LMA-dV'SMA of the battery cells 110 to 140. Here, the controller 220 may calculate the average, median, or minimum value of the calibrated first deviations dV'_LMA-dV'_SMA of the battery cells 110 to 140 as the calibrated second deviation.

[0181] Specifically, the controller 220 may continuously calculate the calibrated first deviations dV'_LMA-dV'_SMA of each battery cell 110 to 140 at each unit time interval. Then, the controller 220 may calculate the average, median, or minimum value of the calibrated first deviations dV'_LMA-dV'_SMA of the battery cells 110 to 140 as a calibrated second deviation (dV'LMA-dV'SMA)AVG, which is the average value of the calibrated first deviations dV'_LMA-dV'_SMA of the battery cells 110 to 140. The controller 220 may continuously calculate the calibrated second deviations (dV'LMA-dV'SMA)AVG of the battery cells 110 to 140 at each unit time interval.

[0182] Then, the controller 220 may calculate a calibrated first diagnostic deviation D1 which is a difference between the calibrated first deviation dV′_LMA−dV′_SMA and the calibrated second deviation (dV′LMA−dV′SMA) AVG of each of the battery cells 110 to 140 .

[0183] Return to reference Figure 7 In S314, the controller 220 may remove the noise of the first diagnostic deviation D1 of each battery cell 110 to 140 based on the following Formula 7 to calculate the second diagnostic deviation D2. To this end, the controller 220 may first set a reference value for determining whether the first diagnostic deviation D1 of each battery cell 110 to 140 is noise based on the following Formula 7.

[0184] [Formula 7]

[0185] Reference value = Max[|dV LMΛ -dV SMA ) AVG |*C1,C2]

[0186] The controller 220 may set the reference value for each of the battery cells 110 to 140 to a maximum value Max between a value (|(dVLMA-dVSMA)AVG|*C1) obtained by multiplying the absolute value of the second deviation (|(dVLMA-dVSMA)AVG|) by the first threshold constant C1 and a second threshold constant C2. The first threshold constant C1 may include "0.1", and the second threshold constant C2 may include "0.4". The first threshold constant C1 and the second threshold constant C2 may vary according to the magnitude and characteristics of the first voltage dV of each of the battery cells 110 to 140.

[0187] Then, when the first diagnostic deviation D1 of any battery cell among the battery cells 110 to 140 is equal to or less than the reference value, the controller 220 may determine the corresponding first diagnostic deviation D1 as noise data. In S314, the controller 220 may calculate the second diagnostic deviation D2 of each of the battery cells 110 to 140 by excluding the first diagnostic deviation D1 that is equal to or less than the reference value among the first diagnostic deviations D1 of the battery cells 110 to 140.

[0188] In S316 , the controller 220 may normalize the second diagnostic deviations D2 of the respective battery cells 110 to 140 to calculate a third diagnostic deviation D3 .

[0189] For example, the controller 220 may normalize the second diagnostic deviations D2 of the respective battery cells 110 to 140 based on the following Equation 8 to calculate the third diagnostic deviations D3 of the respective battery cells 110 to 140 .

[0190] [Formula 8]

[0191]

[0192] The controller 220 may calculate a maximum value Max between a value (|(dVLMA-dVSMA)AVG|*C3) obtained by multiplying the absolute value of the second deviation (|(dVLMA-dVSMA)AVG|)) by the third threshold constant C3 and a fourth threshold constant C4. The controller 220 may calculate a third diagnostic deviation D3 by normalizing the second diagnostic deviation D2 of each battery cell 110 to 140 by dividing the second diagnostic deviation D2 by a maximum value Max (Max[|(dVLMA-dVSMA)AVG|*C3,C4]) between a value obtained by multiplying the absolute value of the second deviation representing the behavior of the average voltage V_avg of the battery cells 110 to 140 by the third threshold constant C3. Here, the third threshold constant C3 may include "0.1", and the fourth threshold constant C4 may include "0.1". The third threshold constant C3 and the fourth threshold constant C4 may be changed according to the amplitude and characteristics of the first voltage dV of each battery cell 110 to 140.

[0193] In S316 , the controller 220 may normalize the second diagnostic deviations D2 of the respective battery cells 110 to 140 to calculate third diagnostic deviations D3 of the respective battery cells 110 to 140 according to various embodiments.

[0194] As another example of normalization, in an embodiment, the controller 220 may use a logarithmic (log) operation to normalize the second diagnostic deviation D2 of each battery cell 110 to 140. The controller 220 may calculate a value obtained by normalizing the second diagnostic deviation D2 of each battery cell 110 to 140 using a logarithmic operation as a third diagnostic deviation D3 of each battery cell 110 to 140.

[0195] As another example of normalization, in an embodiment, the controller 220 may set the average value (D2_avg) of the second diagnostic deviations D2 of the battery cells 110 to 140 as a normalization reference value. When the average value (D2_avg) of the second diagnostic deviations D2 is used as the normalization reference value, in S316, the controller 220 may normalize the second diagnostic deviations D2 of the respective battery cells 110 to 140 by dividing the second diagnostic deviations D2 by the average value (D2_avg) of the second diagnostic deviations D2. The controller 220 may calculate a value normalized by dividing the second diagnostic deviations D2 of the respective battery cells 110 to 140 by the average value (D2_avg) of the second diagnostic deviations D2 as the third diagnostic deviations D3 of the respective battery cells 110 to 140.

[0196] In S316 , the controller 220 may continuously calculate the third diagnostic deviation D3 of each of the battery cells 110 to 140 at unit time intervals and generate a graph indicating changes in the third diagnostic deviation D3 of each of the battery cells 110 to 140 .

[0197] In S318, the controller 220 may calculate the skewness of the third diagnostic deviation D3 of each of the battery cells 110 to 140. Specifically, the controller 220 may calculate the skewness of the third diagnostic deviation D3 of each of the battery cells 110 to 140 based on Equation 9 below.

[0198] [Formula 9]

[0199]

[0200] Referring to Formula 9, the controller 220 may calculate the skewness of the third diagnostic deviation D3 of each of the battery cells 110 to 140 by adding the minimum value (Min[third diagnostic deviation D3]) of the third diagnostic deviation D3 of the battery cells 110 to 140 to the third diagnostic deviation D3 of each of the battery cells 110 to 140, and dividing the obtained value by the third diagnostic deviation D3.

[0201] The controller 220 may continuously calculate the skewness of the third diagnostic deviation D3 of each of the battery cells 110 to 140 at unit time intervals and generate a graph indicating changes in the skewness of the third diagnostic deviation D3 of each of the battery cells 110 to 140 .

[0202] In S320, the controller 220 may calculate the fourth diagnostic deviation D4 of each battery cell 110 to 140 by reflecting the skewness of the third diagnostic deviation D3 of each battery cell 110 to 140. Specifically, the controller 220 may calculate the fourth diagnostic deviation D4 of each battery cell 110 to 140 based on Equation 10 below.

[0203] [Formula 10]

[0204] Fourth diagnostic deviation (D4) = Third diagnostic deviation (D3) * skewness

[0205] The controller 220 may calculate the fourth diagnostic deviation D4 of each of the battery cells 110 to 140 by multiplying the third diagnostic deviation D3 of each of the battery cells 110 to 140 by the skewness at a unit time interval (e.g., 200 seconds). As described above, the fourth diagnostic deviation D4 to which the skewness is applied represents a more stable and accurate diagnostic deviation in which noise is reduced and an abnormal voltage behavior signal is amplified, compared to the third diagnostic deviation D3 to which the skewness is not applied.

[0206] The controller 220 may continuously calculate the fourth diagnostic deviation D4 of the respective battery cells 110 to 140 at unit time intervals and generate a graph indicating changes in the fourth diagnostic deviation D4 of the respective battery cells 110 to 140 .

[0207] In S322, the controller 220 may determine whether the fourth diagnostic deviation D4 of each battery cell 110 to 140 exceeds a threshold value. The threshold value may be defined as a reference value for determining an inappropriate result as "abnormal". The threshold value may be defined as a standard for presenting how much data deviates from a specific statistical model. When the fourth diagnostic deviation D4 of any battery cell among the battery cells 110 to 140 exceeds the threshold value, the controller 220 may determine that the corresponding battery cell is a battery cell with abnormal voltage behavior. Here, the threshold value is determined in consideration of the state of each battery cell, the sensitivity of the measurement system, and the measurement environment, and may be changed according to, for example, the type of battery cell and / or the vehicle to which the battery cell is applied.

[0208] At this time, when it is determined in S322 that the fourth diagnostic deviation D4 does not exceed the threshold, the process returns to S302 to repeat the measurement, calculation and diagnosis process. In another embodiment, the process may not return to S302, but may return to any step before S322 to repeat the process.

[0209] In S322, based on the result of determining whether the fourth diagnostic deviation D4 of each of the battery cells 110 to 140 exceeds the threshold value, the controller 220 may diagnose at least one battery cell among the battery cells 110 to 140 as an abnormal battery cell. When the fourth diagnostic deviation D4 of at least one battery cell among the battery cells 110 to 140 exceeds the threshold value, the controller 220 may diagnose the at least one battery cell as a battery cell having abnormal voltage behavior.

[0210] In S324 , according to an embodiment, when the fourth diagnostic deviation D4 of at least one battery cell among the battery cells 110 to 140 exceeds a threshold value, the controller 220 may increase a diagnostic count value of the at least one battery cell.

[0211] According to an embodiment, when a diagnosis count value of at least one battery cell among the battery cells 110 and 140 is equal to or greater than a threshold count value, the controller 220 may diagnose abnormality of the at least one battery cell.

[0212] In an embodiment, when the fourth diagnostic deviation D4 of at least one battery cell among the battery cells 110 to 140 exceeds the threshold value for the first time, the controller 220 may not immediately diagnose the at least one battery cell as an abnormal battery cell. In contrast, when the diagnostic count value of the at least one battery cell is equal to or greater than the threshold count value, for example, when a preset time (e.g., threshold count) has passed after the fourth diagnostic deviation D4 of the at least one battery cell exceeds the threshold value, the controller 220 may diagnose the at least one battery cell as an abnormal battery cell. Therefore, for a battery cell in which the fourth diagnostic deviation D4 exceeds the threshold value for an instant and thereafter quickly drops to the threshold value or lower, the diagnosis of the abnormal battery cell may not be performed. Therefore, the reliability of the diagnosis of the abnormal battery cell may be improved.

[0213] In the diagnostic method described above, the diagnosis of the abnormal battery cell is performed by comparing the fourth diagnostic deviation D4 and the threshold value in S322. However, the present disclosure is not limited thereto. For example, the diagnosis of the abnormal battery cell may be performed at any step before S322. For example, the diagnosis of the abnormal battery cell may be performed after calculating the second diagnostic deviation D2 in S314.

[0214] After diagnosing an abnormality in at least one battery cell among the battery cells 110 to 140 , the controller 220 may track and monitor whether a defect, such as an internal short circuit, an external short circuit, and lithium deposition, occurs in the at least one battery cell.

[0215] When it is determined that a defect occurs in the at least one battery cell, the controller 220 may provide information about the corresponding battery cell to the user. For example, the controller 220 may provide information about the battery cell having an internal short circuit to the user terminal through a communication unit (not shown) or a display (not shown) installed in, for example, a vehicle or a charger.

[0216] As described above, according to another embodiment of the present disclosure, BMS200 can accurately diagnose abnormal battery cells by removing noise of the deviation between the long-term moving average and the short-term moving average of the voltage deviation between the voltage of each battery cell in the battery cells and the average voltage of the battery cells.

[0217] The BMS200 according to the present disclosure can minimize the distortion of the voltage of each battery cell and remove noise data by using the deviation between the long-term moving average and the short-term moving average of the voltage deviation of each battery cell, and amplify the voltage behavior of abnormal battery cells by reflecting the skewness of the voltage of the battery cells, which can improve the accuracy of diagnosis.

[0218] In addition, the BMS 200 according to the present disclosure can diagnose battery cells with abnormal voltage behavior at an earlier time by using the deviation between the long-term moving average value and the short-term moving average value of the voltage deviation of each battery cell, which ensures the safety and reliability of battery energy. Moreover, even in a state where the battery is installed in a vehicle, the BMS 200 can diagnose battery cells with abnormal voltage behavior so that abnormal battery cells can be easily and quickly diagnosed without being separated from the vehicle.

[0219] Fig.12 is a flowchart showing an operation method of the BMS 200 according to another embodiment of the present disclosure. Fig.12 The operating method of the BMS 200 is described in a step-based manner.

[0220] In S402, the voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 at predetermined time intervals, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140. The voltage measuring unit 210 may measure the voltage of each of the battery cells 110 to 140 in the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging to continuously acquire data of voltage rise / fall and long-term relaxation, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140 using the measured voltage data. As needed, the voltage measuring unit 210 may continuously measure and acquire data of voltage rise / fall and long-term relaxation of each of the battery cells 110 to 140 in a specific period among the periods of charging, relaxation time after charging, discharging, and relaxation time after discharging, and the controller 220 may generate a graph indicating the voltage change of each of the battery cells 110 to 140 using the acquired voltage data.

[0221] In S404 , the controller 220 may calculate a deviation dV between the average voltage V_avg of the battery cells 110 to 140 and the voltage of each battery cell 110 to 140 , or the measured voltage itself of each battery cell 110 to 140 , as a first voltage dV at each predetermined time interval.

[0222] In S406, the controller 220 may calculate a moving average of the first voltage dV of each battery cell 110 to 140. The moving average refers to the average of a portion of data extracted by moving a window of a predetermined size in the overall data. The window is a reference period for determining the data to be extracted and used in the overall data. The window starts, for example, at a time before the reference period from the current time and ends at the current time. For example, when the window is a week, the controller 220 may extract voltage data acquired in the most recent week period ending at the current time from the overall voltage data.

[0223] The controller 220 may continuously calculate a moving average value of the first voltage dV of each of the battery cells 110 to 140 using the first voltage dV continuously extracted by moving a window among the time series data of the first voltage dV of each of the battery cells 110 to 140. For example, the controller 220 may calculate the moving average value of the first voltage dV of each of the battery cells 110 to 140 by applying any one of a simple moving average (SMA), a weighted moving average (WMA), and an exponential moving average (EMA) to the overall data of the first voltage dV of each of the battery cells 110 to 140.

[0224] According to an embodiment, the controller 220 may apply an exponential moving average (EMA) to the overall data of the first voltage dV of each battery cell 110 to 140 to calculate the exponential moving average of the first voltage dV of each battery cell 110 to 140. The exponential moving average is a weighted moving average that uses data over all past time periods while placing a greater weight on the most recent data.

[0225] The controller 220 may calculate a plurality of moving averages by applying windows having different sizes to the data of the first voltage dV of each battery cell 110 to 140. In an embodiment, the controller 220 may calculate a long-term moving average and a short-term moving average by applying a relatively long window and a relatively short window to the overall data of the first voltage dV of each battery cell 110 to 140, respectively. For example, the size of the window for the long-term moving average may include 100 seconds, and the size of the window for the short-term moving average may include 10 seconds. For example, using the data of the first voltage dV of each battery cell 110 to 140, the controller 220 may calculate the long-term moving average of the first voltage dV of each battery cell 110 to 140 by using the first voltage dV acquired in the last 100 seconds before the calculation time, and calculate the short-term moving average of the first voltage dV of each battery cell 110 to 140 by using the first voltage dV acquired in the last 10 seconds before the calculation time.

[0226] In S408 , the controller 220 may calculate a first deviation dV_LMA−dV_SMA, which is a deviation between a long-term moving average dV_LMA and a short-term moving average dV_SMA of the first voltage dV of each of the battery cells 110 to 140 .

[0227] In S410 , the controller 220 may continuously calculate a second deviation, which is an average value Davg of a long-term moving average value dV_LMA and a short-term moving average value dV_SMA of the first voltage dV of each of the battery cells 110 to 140 , at unit time intervals.

[0228] In S412 , the controller 220 may calculate the diagnosis deviation D by using the first deviation dV_LMA-dV_SMA and the average value Davg as the second deviation. According to an embodiment, the controller 220 may calculate the difference between the first deviation dV_LMA-dV_SMA and the average value Davg as the second deviation as the diagnosis deviation D of each of the battery cells 110 to 140 .

[0229] In S414 , the controller 220 may determine whether the diagnosis deviation D of each of the battery cells 110 to 140 exceeds a threshold value.

[0230] When it is determined in S414 that the diagnostic deviation D does not exceed the threshold, the process returns to S402 to repeat the measurement, calculation and diagnosis process. In another embodiment, the process may not return to S402, but may return to any step before S414 to repeat the process as needed.

[0231] In S416 , when the diagnosis deviation D of any one of the battery cells 110 to 140 exceeds the threshold, the controller 220 may determine that the corresponding battery cell is a battery cell having abnormal voltage behavior.

[0232] The operating method of the BMS 200 according to the embodiment of the present disclosure has been described. The BMS 200 of the present disclosure can accurately diagnose abnormal battery cells by removing noise of deviations of the long-term moving average and the short-term moving average of the battery voltage and applying skewness.

[0233] Fig.13 is a block diagram illustrating a hardware configuration of a computing system that implements an operating method of a battery management system according to an embodiment of the present disclosure.

[0234] Reference Fig.13 , the computing system 2000 according to the embodiment may include a main control unit (MCU) 2100 , a memory 2200 , an input / output I / F 2300 , and a communication I / F 2400 .

[0235] The MCU 2100 may be a computer that executes various programs stored in the memory 2200, processes various data used for these programs, and performs Figure 1 The functions of the BMS 200 are shown as a processor.

[0236] The memory 2200 may store various programs and data for implementing operations of the BMS 200 to diagnose abnormal battery cells.

[0237] As required, a plurality of memories 2200 may be provided. The memory 2200 may be a volatile or non-volatile memory. As for the volatile memory, the memory 2200 may be, for example, a RAM, a DRAM, or an SRAM. As for the non-volatile memory, the memory 2200 may be, for example, a ROM, a PROM, an EAROM, an EPROM, an EEPROM, or a flash memory. Examples of the memory 2200 may not be limited to those described above.

[0238] The input / output I / F 2300 may connect the MCU 2100 with an input device (not shown) such as a keyboard, a mouse, or a touch panel and an output device (not shown) such as a display to transmit and receive data.

[0239] The communication I / F 2400 is configured to send / receive various data to / from the server, and may be any of various devices capable of supporting wired or wireless communication. For example, through the communication I / F 2400, various programs or data for voltage measurement and abnormality diagnosis may be sent / received to / from a separate external server via wired communication or wireless communication.

[0240] From the foregoing, it should be apparent that various embodiments of the present disclosure have been described herein for purposes of illustration, and that various modifications may be made without departing from the scope and spirit of the present disclosure.

[0241] Therefore, the embodiments disclosed herein only illustrate the technical concept of the present disclosure and do not limit its scope. The protection scope of the present disclosure should be interpreted based on the scope described in the attached claims, and other technical concepts equivalent to the scope of the claims should be interpreted as included in the protection scope of the present disclosure.

Claims

1. A battery management system, comprising: a voltage measuring unit configured to measure a voltage of each battery among the plurality of batteries; as well as a controller in communication with the voltage measuring unit, Wherein, the controller is configured as: controlling the voltage measuring unit to measure the voltage of each of the plurality of batteries at predetermined time intervals; calculating a first deviation, the first deviation being a deviation between a long-term moving average value and a short-term moving average value of a voltage of each battery in the plurality of batteries; calculating a second deviation, the second deviation being a deviation between a long-term moving average and a short-term moving average of average voltages of the plurality of batteries; calculating a first diagnostic deviation for each battery in the plurality of batteries, the first diagnostic deviation being a 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 of the first diagnostic deviation for each of the plurality of batteries obtained by multiplying the second deviation by a threshold constant; and Based on the second diagnostic deviation of each of the plurality of batteries, an abnormality of at least one battery among the plurality of batteries is diagnosed.

2. The battery management system according to claim 1, wherein: The controller sets the reference value to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and a second threshold constant, and The second diagnostic deviation of each of the plurality of batteries is calculated by excluding first diagnostic deviations that are equal to or smaller than the reference value among the first diagnostic deviations of each of the plurality of batteries.

3. The battery management system according to claim 2, wherein: The controller calculates a third diagnostic deviation of each of the plurality of batteries by normalizing the second diagnostic deviation of each of the plurality of batteries, wherein the second diagnostic deviation is divided by a maximum value between a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant.

4. The battery management system according to claim 3, wherein: The controller calculates the skewness of each of the plurality of batteries by adding a minimum value of the third diagnostic deviations of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the obtained value by the third diagnostic deviation.

5. The battery management system according to claim 4, wherein: The controller calculates a fourth diagnostic deviation for each of the plurality of batteries by multiplying the third diagnostic deviation for each of the plurality of batteries by the skewness.

6. The battery management system according to claim 5, wherein: The controller diagnoses abnormality of at least one battery among the plurality of batteries based on whether the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold value.

7. The battery management system according to claim 6, wherein: The controller calculates a plurality of first deviations and a plurality of second deviations at unit time intervals to calculate the fourth diagnostic deviation of each of the plurality of batteries, and When the fourth diagnosis deviation of at least one battery among the plurality of batteries exceeds the threshold value, the controller diagnoses the at least one battery as an abnormal battery.

8. A method for operating a battery management system, the method comprising the steps of: causing a voltage measuring unit to measure a voltage of each of the plurality of batteries at predetermined time intervals; calculating a first deviation, the first deviation being a deviation between a long-term moving average value and a short-term moving average value of a voltage of each battery in the plurality of batteries; calculating a second deviation, the second deviation being a deviation between a long-term moving average and a short-term moving average of average voltages of the plurality of batteries; calculating a first diagnostic deviation for each battery in the plurality of batteries, the first diagnostic deviation being a 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 of the first diagnostic deviation for each of the plurality of batteries obtained by multiplying the second deviation by a threshold constant; as well as Based on the second diagnostic deviation of each of the plurality of batteries, an abnormality of at least one battery among the plurality of batteries is diagnosed.

9. The method for operating a battery management system according to claim 8, wherein: The step of calculating the second diagnostic deviation comprises: The reference value is set to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and a second threshold constant, and The second diagnostic deviation of each of the plurality of batteries is calculated by excluding first diagnostic deviations that are equal to or smaller than the reference value among the first diagnostic deviations of each of the plurality of batteries.

10. The method for operating a battery management system according to claim 9, wherein: The step of calculating the second diagnostic deviation comprises: A third diagnostic deviation of each of the plurality of batteries is calculated by normalizing the second diagnostic deviation of each of the plurality of batteries, wherein the second diagnostic deviation is divided by a maximum value between a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant.

11. The method for operating a battery management system according to claim 10, wherein: The step of calculating the second diagnostic deviation comprises: The skewness of each of the plurality of batteries is calculated by adding a minimum value of the third diagnostic deviations of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the obtained value by the third diagnostic deviation.

12. The method for operating a battery management system according to claim 11, wherein: The step of calculating the second diagnostic deviation comprises: A fourth diagnostic deviation for each of the plurality of batteries is calculated by multiplying the third diagnostic deviation for each of the plurality of batteries by the skewness.

13. The method for operating a battery management system according to claim 12, wherein: The step of diagnosing an abnormality of at least one battery includes: An abnormality of at least one battery among the plurality of batteries is diagnosed based on whether the fourth diagnosis deviation of each of the plurality of batteries exceeds a threshold value.

14. The method for operating a battery management system according to claim 13, wherein: The step of diagnosing an abnormality of at least one battery includes: calculating a plurality of first deviations and a plurality of second deviations at unit time intervals to calculate the fourth diagnostic deviation of each of the plurality of batteries, and When the fourth diagnosis deviation of at least one battery among the plurality of batteries exceeds the threshold value, the at least one battery is diagnosed as an abnormal battery.

15. A controller for a battery management system, the controller comprising: Memory; as well as A processor is coupled to the memory and is configured to execute the method for operating a battery management system according to claim 8.

16. The controller according to claim 15, wherein: The step of calculating the second diagnostic deviation comprises: setting the reference value to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and a second threshold constant; calculating the second diagnostic deviation of each of the plurality of batteries by excluding first diagnostic deviations that are equal to or smaller than the reference value among the first diagnostic deviations of each of the plurality of batteries; calculating a third diagnostic deviation for each of the plurality of batteries by normalizing the second diagnostic deviation for each of the plurality of batteries, wherein the second diagnostic deviation is divided by a maximum value between a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant; calculating the skewness of each of the plurality of batteries by adding a minimum value of the third diagnostic deviations of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the obtained value by the third diagnostic deviation; and calculating a fourth diagnostic deviation for each of the plurality of batteries by multiplying the third diagnostic deviation for each of the plurality of batteries by the skewness, and The step of diagnosing an abnormality of at least one battery among the plurality of batteries includes: An abnormality of at least one battery among the plurality of batteries is diagnosed based on whether the fourth diagnosis deviation of each of the plurality of batteries exceeds a threshold value.

17. A non-transitory computer-readable storage medium storing a program, the program executing the following process, the process comprising: causing a voltage measuring unit to measure a voltage of each of the plurality of batteries at predetermined time intervals; calculating a first deviation, the first deviation being a deviation between a long-term moving average value and a short-term moving average value of the voltage of each battery in the plurality of batteries; calculating a second deviation, the second deviation being a deviation between a long-term moving average and a short-term moving average of average voltages of the plurality of batteries; calculating a first diagnostic deviation for each battery in the plurality of batteries, the first diagnostic deviation being a 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 of the first diagnostic deviation for each of the plurality of batteries obtained by multiplying the second deviation by a threshold constant; as well as Based on the second diagnostic deviation of each of the plurality of batteries, an abnormality of at least one battery among the plurality of batteries is diagnosed.

18. The non-transitory computer-readable storage medium of claim 17, wherein: The step of calculating the second diagnostic deviation comprises: setting the reference value to a maximum value between a value obtained by multiplying the second deviation by a first threshold constant and a second threshold constant; calculating the second diagnostic deviation of each of the plurality of batteries by excluding first diagnostic deviations that are equal to or smaller than the reference value among the first diagnostic deviations of each of the plurality of batteries; calculating a third diagnostic deviation for each of the plurality of batteries by normalizing the second diagnostic deviation for each of the plurality of batteries, wherein the second diagnostic deviation is divided by a maximum value between a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant; calculating the skewness of each of the plurality of batteries by adding a minimum value of the third diagnostic deviations of each of the plurality of batteries to the third diagnostic deviation of each of the plurality of batteries and dividing the obtained value by the third diagnostic deviation; and calculating a fourth diagnostic deviation for each of the plurality of batteries by multiplying the third diagnostic deviation for each of the plurality of batteries by the skewness, and The step of diagnosing an abnormality of at least one battery among the plurality of batteries includes: An abnormality of at least one battery among the plurality of batteries is diagnosed based on whether the fourth diagnosis deviation of each of the plurality of batteries exceeds a threshold value.

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