Battery management device and method of operation thereof

Abstract

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

Description

[Technical Field]

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

[0002] Electric vehicles generate power by receiving electricity from an external source to charge the battery cells, and then driving the motor with the voltage charged in the battery cells. Battery cells undergo internal deformation and modification due to various charging and discharging processes during production and use, which can change their physicochemical properties and cause internal short circuits, external short circuits, venting due to lithium deposition, or undervoltage, where the battery cell voltage drops below a certain level.

[0003] If a defect occurs inside a battery cell, direct problems may occur in the battery cell, such as a decrease in the performance of the battery cell and an increased risk of fire due to electrolyte leakage. Summary of the Invention [Problem to be solved by the invention]

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

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

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

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

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

[0009] In one embodiment, the controller can calculate the skewness of each of the plurality of batteries by adding the third diagnostic deviation of each of the plurality of batteries to the minimum value of the third diagnostic deviations of each of the plurality of batteries and dividing the result by the third diagnostic deviation.

[0010] In one embodiment, the controller may multiply the third diagnostic deviation of each of the plurality of batteries by the skewness to calculate a fourth diagnostic deviation of each of the plurality of batteries.

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

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

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

[0014] In one embodiment, the step of calculating the second diagnostic deviation of each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant may involve setting the maximum value of the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant as the reference value, and excluding first diagnostic deviations among the first diagnostic deviations of each of the plurality of batteries that are less than the reference value, thereby calculating the second diagnostic deviation of each of the plurality of batteries.

[0015] In one embodiment, the step of calculating the second diagnostic deviation of each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of the first diagnostic deviation of each of the plurality of batteries by a threshold constant can normalize the second diagnostic deviation of each of the plurality of batteries by dividing it by the maximum value of the value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant, thereby calculating the third diagnostic deviation of each of the plurality of batteries.

[0016] In one embodiment, the step of calculating the second diagnostic deviation of each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations of each of the plurality of batteries by a threshold constant can calculate the skewness of each of the plurality of batteries by dividing a value obtained by adding the third diagnostic deviation of each of the plurality of batteries to the minimum value of the third diagnostic deviations of each of the plurality of batteries by the third diagnostic deviation.

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

[0018] In one embodiment, the step of diagnosing whether or not at least one of the plurality of batteries has an abnormality based on the second diagnostic deviation of each of the plurality of batteries can diagnose whether or not at least one of the plurality of batteries has an abnormality based on whether or not a fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold value.

[0019] In one embodiment, the step of diagnosing whether or not at least one of the plurality of batteries has an abnormality based on the second diagnostic deviation of each of the plurality of batteries includes calculating the first deviation and the second deviation per unit time, calculating a fourth diagnostic deviation of each of the plurality of batteries, and diagnosing whether or not there is an abnormality in the at least one battery if the fourth diagnostic deviation of at least one of the plurality of batteries exceeds a threshold value.

[0020] A controller according to one embodiment disclosed herein may include a memory and a processor coupled to the memory and configured to execute a method for operating the battery management unit.

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

[0022] A non-transitory computer-readable storage medium according to one embodiment disclosed herein may store a program for executing the following steps: measuring the voltage of each of a plurality of batteries at predetermined time intervals using a voltmeter; calculating, for each of the plurality of batteries, a first deviation which is the deviation between a long-term moving average value and a short-term moving average value of the battery voltage; calculating, for each of the plurality of batteries, 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, for each of the plurality of batteries, a first diagnostic deviation which is the difference between the first deviation and the second deviation; calculating, for each of the plurality of batteries, a second diagnostic deviation which is the difference between the first deviation and the second deviation; calculating, for each of the plurality of batteries, a second diagnostic deviation which is the difference between the first deviation and the second deviation; and diagnosing whether at least one of the plurality of batteries has an abnormality based on the second diagnostic deviation which is the first diagnostic deviation of each of the plurality of batteries.

[0023] In one embodiment, the step of calculating a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation of the first diagnostic deviations for each of the plurality of batteries by a threshold constant includes the steps of: setting the maximum value of the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant as the reference value; excluding first diagnostic deviations of each of the plurality of batteries that are equal to or less than the reference value; and normalizing the second diagnostic deviation for each of the plurality of batteries by dividing the second diagnostic deviation for each of the plurality of batteries by the maximum value of the value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant, thereby calculating a third diagnostic deviation for each of the plurality of batteries. the step of calculating a skewness for each of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries, the step of dividing the value obtained by adding the minimum value of the third diagnostic deviations for each of the plurality of batteries to the third diagnostic deviation by the third diagnostic deviation, and the step of multiplying the third diagnostic deviation of each of the plurality of batteries by the skewness to calculate a fourth diagnostic deviation for each of the plurality of batteries, wherein the step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries may include the step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on whether or not the fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold. [Effects of the Invention]

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

[0025] [Figure 1] FIG. 1 illustrates a battery pack according to one embodiment disclosed herein. [Figure 2] 1 is a block diagram showing the configuration of a battery management device according to an embodiment disclosed in this document. [Figure 3]1 is a flowchart illustrating a method of operating a battery management device according to one embodiment disclosed herein. [Figure 4] 1 is a graph illustrating the voltage of a battery cell according to one embodiment disclosed herein. [Figure 5a] 1 is a graph illustrating a first diagnostic deviation of a battery cell according to one embodiment disclosed herein. [Figure 5b] 10 is a graph illustrating a third diagnostic deviation of a battery cell according to an embodiment disclosed herein. [Figure 5c] 10 is a graph illustrating the skewness of a third diagnostic deviation of a battery cell according to one embodiment disclosed herein. [Figure 5d] 10 is a graph illustrating a fourth diagnostic deviation of a battery cell according to one embodiment disclosed herein. [Figure 6] 10 is a flowchart illustrating a method of operating a battery management device according to another embodiment disclosed herein. [Figure 7] 10 is a flowchart illustrating a method for operating a battery management device and a method for diagnosing an abnormal battery cell according to still another embodiment disclosed herein. [Figure 8a] 1 is a graph illustrating a first voltage during discharge and a rest period after discharge of a battery cell according to an embodiment disclosed herein. [Figure 8b] 10 is a graph showing a first voltage during charging and a rest period after charging of a battery cell according to another embodiment disclosed herein. [Figure 9a] 1 is a graph showing long-term (solid line) and short-term (dotted line) moving averages of the voltage during discharge and rest periods after discharge of a battery cell according to one embodiment disclosed herein. [Figure 9b] 10 is a graph showing long-term (solid line) and short-term (dotted line) moving averages of the voltage during charging and rest periods after charging of a battery cell according to another embodiment disclosed herein. [Figure 10a] 1 is a graph illustrating a first deviation of a first voltage (dV) during discharge and a rest period after discharge of a battery cell according to an embodiment disclosed herein. [Figure 10b]10 is a graph illustrating a first deviation of a first voltage (dV) during charging and a rest period after charging of a battery cell according to another embodiment disclosed herein. [Figure 11a] 1 is a graph showing a first diagnostic deviation (D1) during discharge and a rest period after discharge of a battery cell according to an embodiment disclosed herein. [Figure 11b] 10 is a graph showing a first diagnostic deviation (D1) during charging and rest periods after charging of a battery cell according to another embodiment disclosed herein. [Figure 12] 10 is a flowchart illustrating a method for operating a battery management device and a method for diagnosing an abnormal battery cell according to still another embodiment disclosed herein. [Figure 13] FIG. 1 is a block diagram showing the hardware configuration of a computing system that implements an operation method of a battery management device according to an embodiment disclosed herein. DETAILED DESCRIPTION OF THE INVENTION

[0026] Some embodiments disclosed herein will be described in detail below with reference to exemplary drawings. When assigning reference numerals to components in each drawing, it should be noted that the same reference numerals are assigned to the same components as long as possible when they appear in other drawings. Furthermore, when describing the embodiments disclosed herein, if a detailed description of related known structures or functions is deemed to hinder understanding of the embodiments disclosed herein, such detailed description will be omitted.

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

[0028] 1 is a diagram illustrating a battery pack according to one embodiment disclosed herein. The battery pack 1000 according to one embodiment disclosed herein may include a battery module 100, a battery management unit 200, and a relay 300. According to various embodiments, the battery module 100 may be a battery cell. In this case, the battery pack 1000 may have a cell-to-pack (CTP) structure in which the modules are directly assembled into a pack, omitting the modules, unlike conventional batteries in which multiple cells form modules and the modules form a package.

[0029] Although FIG. 1 illustrates one battery module 100, according to an embodiment, a plurality of battery modules 100 may be configured, and the battery pack 1000 may include a plurality of battery modules forming a stacked structure. The battery module 100 may include a plurality of battery cells 110, 120, 130, and 140. Although FIG. 1 illustrates four battery cells, the number of battery cells is not limited thereto, and the battery module 100 may include n battery cells (n is a natural number greater than or equal to 1). Furthermore, each of the plurality of battery cells 110, 120, 130, and 140 may be a cell group or a battery bank in which at least two battery cells are connected in parallel.

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

[0031] Each of the plurality of battery cells 110, 120, 130, 140 is a basic unit of a battery that can be used by charging and discharging electrical energy, and may be, but is not limited to, a lithium ion (Li-ion) battery, a lithium ion polymer (Li-ion polymer) battery, a nickel cadmium (Ni-Cd) battery, a nickel metal hydride (Ni-MH) battery, etc. Meanwhile, although FIG. 1 shows one battery module 100, according to an embodiment, the battery module 100 may be made up of a plurality of battery modules 100.

[0032] The battery management system (BMS) 200 can manage and / or control the state and / or operation of the battery module 100. For example, the battery management system 200 can manage and / or control the state and / or operation of the plurality of battery cells 110, 120, 130, 140 included in the battery module 100, and can also manage the charging and / or discharging of the battery module 100.

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

[0034] The battery management unit 200 can also monitor the voltage, current, temperature, etc. of the battery module 100 and / or each of the plurality of battery cells 110, 120, 130, and 140 included in the battery module 100. For monitoring via the battery management unit 200, sensors and various measurement modules (not shown) can be further provided at any positions in the battery module 100 or in the charge / discharge path. The battery management unit 200 can calculate parameters indicating the state of the battery module 100, such as SOC (State of Charge) or SOH (State of Health), based on the measured values ​​of the monitored voltage, current, temperature, etc.

[0035] As the number of battery cells 110, 120, 130, and 140 increases in use period or number of times, various factors such as a decrease in capacity and an increase in internal resistance change, which can cause abnormalities in the battery. Therefore, a technology for determining whether or not there is an abnormality in a battery cell is required. The battery management device 200 can diagnose abnormalities in the battery cells 110, 120, 130, and 140 based on data on the various factors that change as the battery cells deteriorate.

[0036] When a battery cell is defective due to various reasons, such as defects during production, internal deformation and denaturation caused by repeated charging and discharging, or external impact, the battery cell may experience a faster and larger voltage change than a normal battery cell. The battery management device 200 uses the phenomenon that a battery cell with an internal defect experiences a faster and larger voltage change during a resting period than a normal battery cell to diagnose an abnormal battery cell among the plurality of battery cells 110, 120, 130, and 140 by comparing the resting period voltage data of each of the plurality of battery cells 110, 120, 130, and 140 with statistically normal resting period voltage data of normal battery cells. The resting period of a battery cell or module refers to a state in which the battery cell or module is not charging or discharging, or is not electrically connected to a load. For example, the battery management device 200 can detect whether a battery module or cell is in a resting state by monitoring the cell voltage value or the charge / discharge current value of a battery module. In this embodiment, the diagnosis of abnormal battery cells during a resting period is described, but the battery cell diagnosis method according to the embodiment disclosed in this document is not limited to this, and abnormal battery cells can also be diagnosed in other sections of the battery module or cell, for example, during charging or discharging.

[0037] Specifically, in the case of an abnormal battery cell, for example, a phenomenon occurs in which the voltage drops during a rest period after charging compared to a normal battery cell, and a large deviation occurs in the voltage behavior compared to the voltage behavior of a normal battery cell, resulting in a phenomenon in which the voltage behavior is biased to one side, resulting in a large skewness.The battery management device 200 according to an embodiment disclosed herein can determine whether or not there is an abnormal battery cell among the plurality of battery cells 110, 120, 130, and 140 using the characteristic of an abnormal battery cell having a large deviation and large skewness compared to the voltage behavior of a normal battery cell.

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

[0039] Furthermore, the battery management unit 200 can diagnose the battery cells using voltage deviation data of each of the plurality of battery cells 110, 120, 130, and 140, which excludes noise voltage data suspected to be noise data from among the voltage deviations of each of the plurality of battery cells 110, 120, 130, and 140. The battery management unit 200 can amplify the voltage deviation data of each of the plurality of battery cells 110, 120, 130, and 140 after eliminating the noise voltage data from among the voltage deviations of each of the plurality of battery cells 110, 120, 130, and 140. The battery management unit 200 can detect and diagnose an abnormal battery cell suspected of having an abnormal voltage using the amplified voltage deviation data of each of the plurality of battery cells 110, 120, 130, and 140.

[0040] In addition, the following operation of the battery management device 200 may be performed via wired or wireless signals in various devices such as a server, cloud, charger, or charger / discharger connected to the battery management device 200 or a vehicle in which the battery management device 200 is installed.

[0041] 2 is a block diagram showing the configuration of a battery management device according to one embodiment disclosed herein. The configuration of the battery management device 200 varies depending on the usage environment and purpose of the battery pack 1000 including the battery module 100, and may include various different operating components.

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

[0043] The voltage measurement unit 210 is configured with a device, such as a voltmeter, capable of measuring the voltage of a battery bank and / or cells. The voltage measurement unit 210 measures the voltage of each of the battery cells 110, 120, 130, and 140 at regular time intervals or for each unit time to acquire time-series data of the voltage of each of the battery cells 110, 120, 130, and 140. According to one embodiment, the voltage measurement unit 210 continuously measures and acquires data on the rise and fall of the voltage of each of the battery cells 110, 120, 130, and 140 during charging, a rest period after charging, discharging, and a rest period after discharging, as well as long-term relaxation data. The acquired continuous voltage data can be used to diagnose abnormal battery cells in specific sections, such as the charging section, the rest period after charging, the discharging section, and the rest period after discharging, as needed.

[0044] In one embodiment, the calculation unit 230 of the controller 220 uses the voltage data measured by the voltage measurement unit 210 to perform various calculations for diagnosing each of the plurality of battery cells 110, 120, 130, and 140 (described later). The diagnosis unit 240 uses the calculation results to check the diagnostic conditions (described later) and diagnoses whether or not there is an abnormality in the battery bank. The control unit 250 uses the diagnosis results to monitor the abnormal battery bank or take appropriate measures for the battery bank, such as notifying the user of the abnormality.

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

[0046] In S102, the voltage measurement unit 210 measures the voltage of each of the plurality of battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing the voltage change of each of the plurality of battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage in all sections of the charge section, the rest section after charge, the discharge section, and the rest section after discharge of each of the plurality of battery cells 110, 120, 130, and 140 to continuously calculate the rise and fall of the voltage and long-term relaxation data, and the controller 220 can generate a graph showing the change of each voltage using the measured voltage data. Alternatively, the voltage measurement unit 210 may continuously calculate the voltage rise and fall and long-term stabilization data in specific sections of the charge, rest section after charge, discharge, and rest section after discharge of each of the multiple battery cells 110, 120, 130, and 140 as needed, and the controller 220 may use this to generate a graph showing each voltage change.

[0047] FIG. 4 is a graph showing voltage changes of battery cells according to an embodiment disclosed herein. The example in FIG. 4 shows, as an embodiment, voltage changes measured by the voltage measurement unit 210 at 200-second intervals during a specific period, for example, a period (e.g., a pause period) from 10,600 seconds after the start of charging the battery cells 110, 120, 130, and 140 to 11,600 seconds after the start of charging the battery cells 110, 120, 130, and 140. In particular, the graph labeled "ab1" in FIG. 4 shows the voltage change during the pause period after charging the battery cell 110 among the battery cells 110, 120, 139, and 140. A kind of reverse peak, where the voltage drops significantly relative to the circular portion, is observed. This suggests that the voltage of the battery cell 110 is exhibiting abnormal behavior during that period.

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

[0049] The controller 220 can calculate a moving average value of the voltages of each of the plurality of battery cells 110, 120, 130, and 140 by using voltage data extracted while moving a window from among all the voltage data of each of the plurality of battery cells 110, 120, 130, and 140. The controller 220 can calculate a continuous moving average value of the voltages of each of the plurality of battery cells 110, 120, 130, and 140 by using voltage data extracted continuously while moving a window from among all the voltage data of each of the plurality of battery cells 110, 120, 130, and 140. When calculating the moving average value, the controller 220 can calculate the moving average value of the voltages of each of the plurality of battery cells 110, 120, 130, and 140 by applying any one of a simple moving average, a weighted moving average, and an exponential moving average (EMA) to the all the voltage data of each of the plurality of battery cells 110, 120, 130, and 140.

[0050] In one embodiment, the controller 220 may apply an exponential moving average (EMA) to all the voltage data for each of the plurality of battery cells 110, 120, 130, and 140 to calculate an exponential moving average value for the voltage of each of the plurality of battery cells 110, 120, 130, and 140. The exponential moving average is a type of weighted moving average that uses data from the entire past period and assigns more weight to recent data.

[0051] The controller 220 may calculate multiple moving average values ​​with different window sizes using the voltage data of each of the battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 may continuously calculate a long-term moving average with a relatively long window length and a short-term moving average with a relatively short window length for each unit time (e.g., 200 seconds) using all the voltage data of each of the battery cells 110, 120, 130, and 140. For example, the window size of the long-term moving average may include 100 seconds, and the window size of the short-term moving average may include 10 seconds. For example, the controller 220 can calculate a long-term moving average value for each of the multiple battery cells 110, 120, 130, and 140 using voltage data for each of the multiple battery cells 110, 120, 130, and 140 obtained every second for the most recent 100 seconds from the time of calculation, and can calculate a short-term moving average value for each of the multiple battery cells 110, 120, 130, and 140 using voltage data obtained every second for the most recent 10 seconds from the time of calculation.

[0052] The controller 220 can analyze the long-term voltage change trend (Trend) and short-term voltage change trend of each of the plurality of battery cells 110, 120, 130, and 140 using the continuous long-term moving average value (V_LMA) and short-term moving average value (V_SMA) of each of the plurality of battery cells 110, 120, 130, and 140. The controller 220 can diagnose whether or not there is an abnormality in the voltage of each of the plurality of battery cells using the long-term moving average value (V_LMA) and short-term moving average value (V_SMA) of the voltage of each of the plurality of battery cells 110, 120, 130, and 140.

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

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

[0055] The controller 220 continuously calculates the average voltages (V_avg) of the plurality of battery cells 110, 120, 130, and 140 for each unit time (for example, 200 seconds), and calculates a long-term moving average value (V_avg) of the average voltages (V_avg) of the plurality of battery cells 110, 120, 130, and 140 using the average voltages (V_avg) of the plurality of battery cells 110, 120, 130, and 140. avg_LMA ) and short-term moving average value (V avg_SMA ) can be calculated. Here, the long-term moving average value (V avg_LMA The window size of the long-term moving average (V_LMA) of the voltages of the battery cells 110, 120, 130, and 140 may be the same as the window size (for example, 100 seconds) of the long-term moving average (V_LMA) of the voltages of the battery cells 110, 120, 130, and 140. avg_SMA The window size of the short-term moving average value (V_SMA) of each of the battery cells 110, 120, 130, and 140 may be the same as the window size (for example, 10 seconds) of the short-term moving average value (V_SMA).

[0056] In S110, the controller 220 calculates a long-term moving average value (V_avg) of the average voltages (V_avg) of the plurality of battery cells 110, 120, 130, and 140 for each unit time (for example, 200 seconds). avg_LMA ) and short-term moving average value (V avg_SMA ) deviation (V avg_LMA -V avg_SMA In step S110, the controller 220 can calculate the deviation between the long-term behavior and the short-term behavior of the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, and 140.

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

[0058] [Formula 1]

number

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

[0060] 5A is a graph showing a first diagnostic deviation of a battery cell according to an embodiment disclosed herein. Referring to FIG. 5A, the controller 220 continuously calculates the first diagnostic deviation (D1) of each of the plurality of battery cells 110, 120, 130, and 140 for each unit time in the section, and generates a graph showing the change in the first diagnostic deviation (D1) of each of the plurality of battery cells 110, 120, 130, and 140.

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

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

[0063] Specifically, the controller 220 can set a reference value that can determine whether or not there is noise in the first diagnostic deviation (D1) of each of the plurality of battery cells 110, 120, 130, 140, based on the following [Equation 2].

[0064] [Formula 2]

number

[0065] The controller 220 determines the second deviation (V avg_LMA -V avg_SMA ) by the first threshold constant (C1) (|V avg_LMA -V avg_SMAThe maximum value (Max) of the first threshold constant (C1) and the second threshold constant (C2) may be set as the reference value for each of the plurality of battery cells 110, 120, 130, and 140. In one embodiment, the first threshold constant (C1) may include 0.1, and the second threshold constant (C2) may include 0.4. In addition, 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 plurality of battery cells 110, 120, 130, and 140.

[0066] As an example of noise removal, the controller 220 can determine as noise data the first diagnostic deviation (D1) that is equal to or less than a reference value among the first diagnostic deviations (D1) of the respective battery cells 110, 120, 130, and 140. The controller 220 can exclude the first diagnostic deviations (D1) that are equal to or less than the reference value among the first diagnostic deviations (D1) of the respective battery cells 110, 120, 130, and 140, and calculate the second diagnostic deviation (D2) of each of the respective battery cells 110, 120, 130, and 140.

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

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

[0069] [Formula 3]

number

[0070] The controller 220 calculates the absolute value of the second deviation (|V avg_LMA -V avg_SMA |) is multiplied by the third threshold constant (C3) to obtain the value (|Vavg_LMA -V avg_SMA Then, the controller 220 calculates the maximum value (Max) of the previously obtained second diagnostic deviation, i.e., the value obtained by multiplying the absolute value of the second deviation, which indicates the behavior of the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, and 140, by the third threshold constant and the maximum value (Max[|V avg_LMA -V avg_SMA The second diagnostic deviation (D2) of each of the plurality of battery cells 110, 120, 130, and 140 can be normalized by dividing by [|·C3, C4] to calculate a third diagnostic deviation (D3). Here, the third threshold constant (C3) can include "0.1" and the fourth threshold constant (C4) can include "0.1", and the third threshold constant (C3) and the fourth threshold constant (C4) can be changed according to the size and characteristics of the voltage data of each of the plurality of battery cells 110, 120, 130, and 140.

[0071] As another example of normalization, according to one embodiment, the controller 220 may normalize the second diagnostic deviation (D2) of each of the plurality of battery cells 110, 120, 130, and 140 by logarithmic calculation. The controller 220 may calculate the value obtained by normalizing the second diagnostic deviation (D2) of each of the plurality of battery cells 110, 120, 130, and 140 by logarithmic calculation as the third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140.

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

[0073] 5b is a graph illustrating a third diagnostic deviation (D3) of the battery cells according to one embodiment disclosed herein. Referring to FIG. 5b, according to various embodiments, the controller 220 can normalize the second diagnostic deviation (D2) of each of the plurality of battery cells 110, 120, 130, and 140 to calculate a third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140.

[0074] The controller 220 may continuously calculate the third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140 per unit time and generate a graph showing changes in the third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140. Exemplarily, the graph (ab3) may be a graph showing changes in the third diagnostic deviation (D3) of the battery cell 110. As shown in FIG. 5b, the third diagnostic deviation (D3) of the battery cell 110 exhibits a value greater than or equal to 0 in a specific section.

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

[0076] [Formula 4] Skewness = (Third Diagnostic Deviation (D3) + Min[Third Diagnostic Deviation (D3)]) / Third Diagnostic Deviation (D3)

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

[0078] FIG. 5c is a graph illustrating the skewness of the third diagnostic deviation of a battery cell according to an embodiment disclosed herein. 5c, the controller 220 continuously calculates the skewness of the third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140 every 200 seconds in the 10,600 second and 11,600 second intervals, and generates a graph showing changes in the skewness of the third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140. Exemplarily, the graph shown in FIG. 5c may be a graph showing changes in the skewness of the third diagnostic deviation (D3) of the battery cell 110. Compared to the third diagnostic deviation (D3) of FIG. 5b, the skewness of FIG. 5c shows improved clarity, which leads to improved diagnostic results.

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

[0080] [Formula 5] Fourth diagnostic deviation (D4) = Third diagnostic deviation (D3) * Skewness

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

[0082] 5d is a graph showing a fourth diagnostic deviation of a battery cell according to an embodiment disclosed herein. Referring to FIG. 5d, the controller 220 may continuously calculate the fourth diagnostic deviation (D4) of each of the battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds) and generate a graph showing changes in the fourth diagnostic deviation (D4) of each of the battery cells 110, 120, 130, and 140. For example, the graph shown in FIG. 5d may be a graph showing changes in the fourth diagnostic deviation (D4) of the battery cell 110. As can be seen from FIG. 5d, the fourth diagnostic deviation (D4) to which the skewness is applied has reduced noise compared to the third diagnostic deviation (D3) to which the skewness is not applied, and the abnormal battery cell voltage behavior signal is amplified, more stabilized, and more accurate.

[0083] In S122, the controller 220 determines whether the fourth diagnostic deviation (D4) of each of the plurality of battery cells 110, 120, 130, and 140 exceeds a threshold value. Here, the threshold value may be defined as a reference value at which an extreme result is obtained and can be determined as "abnormal." The threshold value may be defined as a criterion indicating how much the data contradicts a specific statistical model. If the fourth diagnostic deviation (D4) of any of the plurality of battery cells 110, 120, 130, and 140 exceeds the threshold value, the controller 220 may determine that the battery cell is experiencing abnormal voltage behavior. Here, the threshold value is a value determined taking into consideration the state of the battery cell, the sensitivity of the measurement system, and the measurement environment, and may vary depending on, for example, the type of battery cell and / or the vehicle to which the battery cell is applied. In the example of Figure 5d, for example, if the threshold is set to 0.4 volts, the controller 220 can determine that the battery cell 110 is a battery cell in which abnormal behavior has occurred because the fourth diagnostic deviation (D4) of the battery cell 110 exceeds 0.4 V in a specific section.

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

[0085] In S124, the controller 220 can diagnose at least one of the plurality of battery cells 110, 120, 130, 140 as an abnormal battery cell based on whether or not the fourth diagnostic deviation (D4) of each of the plurality of battery cells 110, 120, 130, 140 exceeds a threshold. That is, when the fourth diagnostic deviation (D4) of at least one of the plurality of battery cells 110, 120, 130, 140 exceeds a threshold, the controller 220 can diagnose that battery cell as a battery cell in which abnormal behavior has occurred.

[0086] Meanwhile, in S124, according to one embodiment, the controller 220 may increase the diagnostic count value of at least one battery cell when the fourth diagnostic deviation (D4) of at least one battery cell among the plurality of battery cells 110, 120, 130, and 140 exceeds the threshold value. That is, according to one embodiment, the controller 220 does not immediately diagnose the battery cell as an abnormal cell when the fourth diagnostic deviation (D4) of at least one battery cell among the plurality of battery cells 110, 120, 130, and 140 exceeds the threshold value for the first time, but diagnoses the battery cell as an abnormal cell only when the diagnostic count value is equal to or greater than the threshold count value, for example, when the state of the battery cell exceeding the threshold value is maintained for a predetermined time (e.g., the threshold count). As a result, an abnormality diagnosis is not performed on a battery cell whose fourth diagnostic deviation (D4) falls below the threshold value quickly after momentarily exceeding the threshold value, thereby improving the reliability of the diagnosis of an abnormal battery cell.

[0087] After diagnosing an abnormality, the controller 220 diagnoses at least one of the plurality of battery cells 110, 120, 130, and 140, and then tracks and monitors whether or not there is a defect such as an internal short circuit within the battery cell, an external short circuit, or lithium deposition.

[0088] If the diagnosis reveals that an internal defect has occurred in a battery cell, the controller 220 can provide information about the battery cell to a user of the battery. For example, the controller 220 can provide information about the battery cell in which an internal short circuit has occurred to a user terminal via a communication unit (not shown), and can also provide information about the battery cell via a display provided in the vehicle or a charger.

[0089] As described above, the battery management device 200 according to an embodiment disclosed herein can accurately diagnose an abnormal battery cell by removing noise from the deviation between the long-term moving average value and the short-term moving average value of the voltage of the battery cell. The battery management device 200 according to an embodiment disclosed herein can minimize distortion in the voltage of the battery cell by using the deviation between the long-term moving average value and the short-term moving average value of the voltage of each battery cell, remove noise data, and reflect the distortion of the voltage of the battery cell to amplify the voltage behavior of the abnormal battery cell, thereby improving the accuracy of diagnosis.

[0090] In addition, the battery management unit 200 can ensure the safety and reliability of battery energy by early diagnosing battery cells that have experienced abnormal voltage behavior using the deviation between the long-term moving average value and the short-term moving average value of the battery cell voltage. Furthermore, the battery management unit 200 can diagnose battery cells that have experienced abnormal voltage behavior while the battery is installed in the vehicle, eliminating the need to separately separate the battery and enabling quick and easy diagnosis of the battery cells.

[0091] FIG. 6 is a flowchart illustrating a method of operating a battery management device according to another embodiment disclosed herein. In the operation method described with reference to FIG. 3, the diagnosis of an abnormal battery cell is described as comparing the fourth diagnostic deviation (D4) with a threshold value in step S122, but this is not limited to this. For example, the diagnosis of an abnormal battery cell may be performed in any step before step S122. For example, the diagnosis of an abnormal battery cell may be performed after calculating the second diagnostic deviation (D2) in step S114. Such an embodiment will be described below with reference to FIG. 6. To avoid redundancy, substantially similar descriptions will be omitted.

[0092] 6, in S202, the voltage measurement unit 210 measures the voltage of each of the plurality of battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing a change in voltage of each of the plurality of battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage in all sections of the charge section, the rest section after charge, the discharge section, and the rest section after discharge of each of the plurality of battery cells 110, 120, 130, and 140 to continuously calculate the rise and fall of the voltage and long-term relaxation data, and the controller 220 can generate a graph showing each voltage change using the measured voltage data.

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

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

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

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

[0097] In S210, the controller 220 calculates a long-term moving average value (V_avg) of the average voltages (V_avg) of the plurality of battery cells 110, 120, 130, and 140 for each unit time (for example, 200 seconds). avg_LMA ) and short-term moving average value (V avg_SMA ) deviation (V avg_LMA -V avg_SMA ) can be calculated continuously.

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

[0099] In S214, the controller 220 can remove noise data from the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 can set a reference value that can determine whether or not there is noise in the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, based on the above-mentioned [Equation 2]. The controller 220 can remove first diagnostic deviations (D1) that are equal to or smaller than the reference value from the first diagnostic deviations (D1) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140.

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

[0101] In S218, if the second diagnostic deviation (D2) of any of the plurality of battery cells 110, 120, 130, 140 exceeds the threshold value, the controller 220 can diagnose that battery cell as one in which a voltage abnormality has occurred.

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

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

[0104] Meanwhile, the controller 220 can calculate the average voltage (V_avg) of the plurality of battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds) using the voltage data measured above. According to one embodiment, the controller 220 can calculate the mean, median, or minimum of the voltages of the plurality of battery cells 110, 120, 130, and 140 for each unit time (e.g., 200 seconds) as the average voltage of the plurality of battery cells 110, 120, 130, and 140. Next, the controller 220 can calculate the deviation (dV) between the average voltage (V_avg) and the voltage for each unit time (e.g., 200 seconds) for each of the plurality of battery cells 110, 120, 130, and 140. According to an embodiment, the controller 220 may calculate the deviation (dV) of the voltage from the average voltage (V_avg) of each of the plurality of battery cells 110, 120, 130, and 140 as the first voltage (dV).

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

[0106] 7 is a flowchart showing a method for diagnosing a battery cell of a controller according to another embodiment disclosed herein. Hereinafter, the operation of the device and the method for diagnosing an abnormal battery cell in each step according to another embodiment disclosed herein will be described with reference to FIG.

[0107] In S302, the voltage measurement unit 210 measures the voltage of each of the plurality of battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing the voltage change of each of the plurality of battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage in all sections of the charge section, the rest section after charge, the discharge section, and the rest section after discharge of each of the plurality of battery cells 110, 120, 130, and 140 to continuously calculate the rise and fall of the voltage and long-term relaxation data, and the controller 220 can use the data to generate a graph showing the change of each voltage. Alternatively, the voltage measurement unit 210 may continuously calculate the voltage rise and fall and long-term stabilization data in specific sections of the charge, rest section after charge, discharge, and rest section after discharge of each of the multiple battery cells 110, 120, 130, and 140 as needed, and the controller 220 may use this to generate a graph showing each voltage change.

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

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

[0110] 4, if the voltages of the battery cells 110, 120, 130, and 140 at 10,800 seconds are 3.92 V, 3.9175 V, 3.9150 V, and 3.9125 V, respectively, the average voltage of the battery cells 110, 120, 130, and 140 at 10,800 seconds is 3.91475 V. In this case, the deviations (dV) between the average voltage (V_avg) and the voltages of the battery cells 110, 120, 130, and 140 are 0.00525 V (battery cell 110), 0.00275 V (battery cell 120), 0.00025 V (battery cell 130), and 0.00225 V, and each of these values ​​becomes the first voltage (dV) value at 10,800 seconds. The controller 220 repeats this calculation for each unit time, and calculates the first voltage (dV) continuously between the required intervals, for example, the 10,600 second interval and the 11,600 second interval.

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

[0112] 8A is a graph showing the first voltage (dV) of the battery cells according to one embodiment of the present disclosure. Referring to FIG. 8A, the controller 220 measures the voltages of the battery cells 110, 120, 130, and 140 during discharge and during a rest period after discharge, for example, between 0 and 3,500 seconds, and calculates time-series data of the first voltage (dV) of each of the battery cells 110, 120, 130, and 140.

[0113] 8b is a graph showing the first voltage (dV) of the battery cells according to another embodiment disclosed herein. Referring to FIG. 8b, the controller 220 measures the voltages of the battery cells 110, 120, 130, and 140 during charging and rest periods after charging, for example, between 0 and 3,500 seconds, and calculates time-series data of the first voltage (dV) of each of the battery cells 110, 120, 130, and 140.

[0114] In S306, the controller 220 may calculate a long moving average and a short moving average of the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140. Here, the moving average is an average of a portion of data extracted while moving through a window of a specific size from all data. Here, the window is a reference interval from which a portion of all data can be extracted and used data can be determined. The start point of the window is a reference time before the current time, and the end point of the window is the current time. For example, if the window is one week, the controller 220 may extract data acquired within the most recent week from all data.

[0115] The controller 220 may calculate a continuous moving average value of the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140 by using first voltage (dV) data continuously extracted while moving a window among all first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140. For example, the controller 220 may calculate a voltage deviation (dV) from an average voltage (V_avg) or a moving average value of voltage of each of the plurality of battery cells 110, 120, 130, and 140 by applying any one of a simple moving average, a weighted moving average, and an exponential moving average (EMA) to all first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140.

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

[0117] Specifically, the controller 220 may calculate a plurality of moving average values ​​having different window sizes using the first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 may calculate a long-term moving average value having a relatively long window length (Long) and a short-term moving average value having a relatively short window length (Short) using all the first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140. For example, the window size of the long-term moving average value may include 100 seconds, and the window size of the short-term moving average value may include 10 seconds. For example, the controller 220 can calculate a long-term moving average value for each of the multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140 obtained in the most recent 100 seconds from the time of calculation, and can calculate a short-term moving average value for each of the multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data obtained in the most recent 10 seconds from the time of calculation.

[0118] 9a is a graph showing a long-term moving average and a short-term moving average of a first voltage during a rest period after discharging of a battery cell according to one embodiment disclosed herein, and FIG. 9b is a graph showing a long-term moving average and a short-term moving average of a first voltage during a rest period after charging of a battery cell according to another embodiment disclosed herein.

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

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

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

[0122] The controller 220 can analyze the long-term voltage change trend (Trend) and short-term voltage change trend of the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140 using the continuous long-term moving average value (dV_LMA) and short-term moving average value (dV_SMA) of the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140. The controller 220 can diagnose whether or not there is an abnormality in the voltage of each of the plurality of battery cells using the voltage deviation (dV) from the average voltage (V_avg) of each of the plurality of battery cells 110, 120, 130, and 140 or the long-term moving average value (dV_LMA) and short-term moving average value (dV_SMA) of the voltage of each of the plurality of battery cells 110, 120, 130, and 140.

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

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

[0125] According to an embodiment, the deviation between the long-term moving average (dV_LMA) and the short-term moving average (dV_SMA) of each of the plurality of battery cells 110, 120, 130, and 140 may depend on the short-term and long-term change histories of the cell voltages.

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

[0127] In S310, the controller 220 calculates the second deviation ((dV LMA -dV SMA ) AVG Here, the controller 220 may calculate a median or minimum value of the first deviations (dV_LMA-dV_SMA) of the plurality of battery cells 110, 120, 130, and 140 as the second deviation, in addition to a general average value as the average value of the first deviations (dV_LMA-dV_SMA).

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

[0129] In S312, the controller 220 calculates the first deviation (dV_LMA-dV_SMA) and the second deviation (dV LMA -dV SMA ) AVG ) and calculates a first diagnostic deviation (D1) for each of the plurality of battery cells 110, 120, 130, and 140.

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

[0131] [Formula 6]

number

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

[0133] 11a is a graph showing a first diagnostic deviation during discharge of a battery cell according to one embodiment disclosed herein and a rest period after discharge. FIG. 11b is a graph showing a first diagnostic deviation during charge of a battery cell according to another embodiment disclosed herein and a rest period after charge. For ease of understanding, in FIGS. 11a and 11b, it is assumed that the graph (C1) shows the first diagnostic deviation (D1) of the battery cell 110 among the plurality of battery cells 110, 120, 130, and 140.

[0134] Referring to FIG. 11a, the voltage measurement unit 210 measures the voltages of the plurality of battery cells 110, 120, 130, and 140 in the discharge and rest periods after discharge for each unit time, and calculates a first deviation (dV_LMA-dV_SMA) and a second deviation (dV LMA -dV SMA ) AVG ) and calculates time-series data of the first diagnostic deviation (D1) for each of the plurality of battery cells 110, 120, 130, and 140.

[0135] Referring to FIG. 11b, the controller 220 calculates a first deviation (dV_LMA-dV_SMA) and a second deviation (dV LMA -dV SMA ) AVG ) and calculates time-series data of the first diagnostic deviation (D1) for each of the plurality of battery cells 110, 120, 130, and 140.

[0136] The controller 220 can calculate a first diagnostic deviation (D1) for each of the plurality of battery cells 110, 120, 130, 140 and compare the deviation of the long-term and short-term behavior of the first voltage (dV) for each of the plurality of battery cells 110, 120, 130, 140 against the deviation of the average long-term and short-term behavior of the plurality of battery cells 110, 120, 130, 140.

[0137] Meanwhile, the controller 220 can correct the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140 to a first diagnostic deviation (D1) of each of the plurality of battery cells 110, 120, 130, and 140. In S312, specifically, the controller 220 can input the first diagnostic deviation (D1) of each of the plurality of battery cells 110, 120, 130, and 140 calculated in S302 to S312 again as the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140 in S304, and repeat S304 to S312 to recalculate the first diagnostic deviation (D1). According to one embodiment, the controller 220 can input the first diagnostic deviation (D1) calculated by S312 as the corrected first voltage (dV') of each of the multiple battery cells 110, 120, 130, and 140, and repeat steps S304, S306, S308, S310, and S312 one or more times to recalculate the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140.

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

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

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

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

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

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

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

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

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

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

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

[0149] [Formula 7]

number

[0150] That is, the controller 220 calculates the second deviation ((dV LMA -dV SMA ) AVG ) by the first threshold constant (C1) (|(dV LMA -dV SMA ) AVG The maximum value (Max) of the first threshold constant (C1) and the second threshold constant (C2) may be set as the reference value for each of the plurality of battery cells 110, 120, 130, and 140. Here, the first threshold constant (C1) may include 0.1, and the second threshold constant (C2) may include 0.4. In addition, the first threshold constant (C1) and the second threshold constant (C2) may be changed according to the size and characteristics of the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140.

[0151] Next, the controller 220 can determine as noise data the first diagnostic deviations (D1) that are equal to or less than the reference value among the first diagnostic deviations (D1) of the respective battery cells 110, 120, 130, and 140. That is, in S314, the controller 220 can exclude the first diagnostic deviations (D1) that are equal to or less than the reference value among the first diagnostic deviations (D1) of the respective battery cells 110, 120, 130, and 140, and calculate the second diagnostic deviations (D2) of the respective battery cells 110, 120, 130, and 140.

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

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

[0154] [Formula 8]

number

[0155] The controller 220 calculates the absolute value of the second deviation (|(dV LMA -dV SMA ) AVG |) by the third threshold constant (C3) (|(dV LMA -dV SMA ) AVG Then, the controller 220 calculates the maximum value (Max) of the previously obtained second diagnostic deviation, i.e., the maximum value (Max[|(dV LMA -dVSMA ) AVG The second diagnostic deviation (D2) of each of the plurality of battery cells 110, 120, 130, and 140 can be normalized by dividing by [|·C3, C4] to calculate a third diagnostic deviation (D3). Here, the third threshold constant (C3) can include "0.1" and the fourth threshold constant (C4) can include "0.1", and the third threshold constant (C3) and the fourth threshold constant (C4) can be changed according to the size and characteristics of the first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140.

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

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

[0158] According to another embodiment, the controller 220 may set the average value (D2_avg) of the second diagnostic deviations (D2) of each of the plurality of battery cells 110, 120, 130, and 140 as the normalization reference value. In S316, the controller 220 may use the average value (D2_avg) of the second diagnostic deviations as the normalization reference value and normalize the second diagnostic deviations (D2) of each of the plurality of battery cells 110, 120, 130, and 140 by the average value (D2_avg) of the second diagnostic deviations (D2). The controller 220 may calculate the normalized value by dividing the second diagnostic deviations (D2) of each of the plurality of battery cells 110, 120, 130, and 140 by the average value (D2_avg) of the second diagnostic deviations as the third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140.

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

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

[0161] [Formula 9] Skewness = (Third Diagnostic Deviation (D3) + Min[Third Diagnostic Deviation (D3)]) / Third Diagnostic Deviation (D3)

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

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

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

[0165] [Formula 10] Fourth diagnostic deviation (D4) = Third diagnostic deviation (D3) * Skewness

[0166] The controller 220 multiplies the third diagnostic deviation (D3) of each of the plurality of battery cells 110, 120, 130, and 140 by the skewness every predetermined unit time (e.g., 200 seconds) to calculate the fourth diagnostic deviation (D4) of each of the plurality of battery cells 110, 120, 130, and 140. As described above, the fourth diagnostic deviation (D4) to which the skewness is applied has reduced noise compared to the third diagnostic deviation (D3) to which the skewness is not applied, and the voltage behavior signal of the abnormal battery cell is amplified, resulting in a more stable and accurate diagnostic deviation.

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

[0168] In S322, the controller 220 determines whether the fourth diagnostic deviation (D4) of each of the plurality of battery cells 110, 120, 130, and 140 exceeds a threshold value. Here, the threshold value may be defined as a reference value at which an extreme result is obtained and can be determined as "abnormal." The threshold value may be defined as a criterion indicating how much the data contradicts a specific statistical model. If the fourth diagnostic deviation (D4) of any of the plurality of battery cells 110, 120, 130, and 140 exceeds the threshold value, the controller 220 may determine that the battery cell is experiencing abnormal voltage behavior. Here, the threshold value is a value determined taking into consideration the state of the battery cell, the sensitivity of the measurement system, and the measurement environment, and may vary depending on, for example, the type of battery cell and / or the vehicle to which the battery cell is applied.

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

[0170] In S322, the controller 220 can diagnose at least one battery cell among the plurality of battery cells 110, 120, 130, 140 as an abnormal cell based on whether or not the fourth diagnostic deviation (D4) of each of the plurality of battery cells 110, 120, 130, 140 exceeds a threshold. If the fourth diagnostic deviation (D4) of at least one battery cell among the plurality of battery cells 110, 120, 130, 140 exceeds a threshold, the controller 220 can diagnose that battery cell as a battery cell in which abnormal behavior has occurred.

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

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

[0173] According to one embodiment, when the fourth diagnostic deviation (D4) of at least one battery cell among the plurality of battery cells 110, 120, 130, 140 exceeds the threshold value for the first time, the controller 220 does not immediately diagnose the battery cell as an abnormal battery cell, but diagnoses the battery cell as an abnormal battery cell only when the diagnostic count value is equal to or greater than the threshold count value, for example, when the state of the battery cell exceeding the threshold value is maintained for a preset time (for example, the threshold count). As a result, the controller 220 does not diagnose an abnormal battery cell if the fourth diagnostic deviation (D4) falls below the threshold value quickly after momentarily exceeding the threshold value, thereby improving the reliability of the diagnosis of an abnormal battery cell.

[0174] Meanwhile, in the diagnostic method described above, the diagnosis of the battery cells is described as comparing the fourth diagnostic deviation (D4) with the threshold value in S322, but is not limited to this. For example, the diagnosis of the battery cells may be performed in any one of the steps before S322. According to one embodiment, the diagnosis of the battery cells may be performed after calculating the second diagnostic deviation (D2) in step S314.

[0175] After diagnosing an abnormality, the controller 220 diagnoses at least one of the plurality of battery cells 110, 120, 130, and 140, and then tracks and monitors whether or not there is a defect such as an internal short circuit within the battery cell, an external short circuit, or lithium deposition.

[0176] If the diagnosis reveals that an internal defect has occurred in a battery cell, the controller 220 can provide information about the battery cell to the user. For example, the controller 220 can provide information about the battery cell in which an internal short circuit has occurred to a user terminal via a communication unit (not shown), and can also provide information about the battery cell via a display provided in the vehicle or a charger.

[0177] As described above, the battery management device 200 according to another embodiment disclosed in this document can remove noise from the long-term moving average value and short-term moving average value of the voltage deviation, which is the difference between the voltage of the battery cell and the average voltage, and accurately diagnose abnormal battery cells.

[0178] The battery management device 200 according to one embodiment disclosed in this document can minimize the distortion of the voltage of the battery cell 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, remove noise data, and reflect the distortion of the voltage of the battery cell to amplify the voltage behavior of an abnormal battery cell, thereby improving the accuracy of diagnosis.

[0179] The battery management unit 200 can ensure the safety and reliability of battery energy by quickly diagnosing battery cells that have experienced abnormal voltage behavior using the deviation between the long-term moving average value and the short-term moving average value of the voltage deviation of the battery cells. In addition, the battery management unit 200 can diagnose battery cells that have experienced abnormal voltage behavior while the battery is installed in the vehicle, eliminating the need to separately separate the battery and enabling quick and easy diagnosis of the battery cells.

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

[0181] In S402, the voltage measurement unit 210 measures the voltage of each of the plurality of battery cells 110, 120, 130, and 140 at regular time intervals, and the controller 220 can generate a graph showing the voltage change of each of the plurality of battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage in all sections of the charge section, the rest section after charge, the discharge section, and the rest section after discharge of each of the plurality of battery cells 110, 120, 130, and 140 to continuously calculate the rise and fall of the voltage and long-term relaxation data, and the controller 220 can generate a graph showing the change of each voltage using the measured voltage data. Alternatively, the voltage measurement unit 210 may continuously calculate the voltage rise and fall and long-term stabilization data in specific sections of the charge, rest section after charge, discharge, and rest section after discharge of each of the multiple battery cells 110, 120, 130, and 140 as needed, and the controller 220 may use this to generate a graph showing each voltage change.

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

[0183] In S406, the controller 220 may calculate a moving average of the first voltage (dV) of each of the plurality of battery cells 110, 120, 130, and 140. Here, the moving average is an average of a portion of data extracted while moving through a window of a specific size from all data. Here, the window is a reference interval from which a portion of all data can be extracted and used data can be determined. The start point of the window is a reference time before the current time, and the end point of the window is the current time. For example, if the window is one week, the controller 220 may extract data acquired within the most recent week from all data.

[0184] The controller 220 can calculate a continuous moving average value of the first voltages (dV) of each of the plurality of battery cells 110, 120, 130, and 140 by using first voltages (dV) continuously extracted while moving a window among the time-series data of all first voltages (dV) of each of the plurality of battery cells 110, 120, 130, and 140. For example, the controller 220 can calculate a moving average value of the first voltages (dV1) of each of the plurality of battery cells 110, 120, 130, and 140 by applying any one of a simple moving average, a weighted moving average, and an exponential moving average (EMA) to the all first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140.

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

[0186] Furthermore, the controller 220 may calculate a plurality of moving average values ​​having different window sizes using the first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 may calculate a long-term moving average value having a relatively long window length (Long) and a short-term moving average value having a relatively short window length (Short) using all of the first voltage (dV) data of each of the plurality of battery cells 110, 120, 130, and 140. For example, the window size of the long-term moving average value may include 100 seconds, and the window size of the short-term moving average value may include 10 seconds. For example, the controller 220 can calculate a long-term moving average value for each of the multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data of each of the multiple battery cells 110, 120, 130, and 140 obtained in the most recent 100 seconds from the time of calculation, and can calculate a short-term moving average value for each of the multiple battery cells 110, 120, 130, and 140 using the first voltage (dV) data obtained in the most recent 10 seconds from the time of calculation.

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

[0188] In S410, the controller 220 calculates the average value (D avg ) can be calculated continuously as the second deviation.

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

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

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

[0192] In S416, if the diagnostic deviation (D) of any of the plurality of battery cells 110, 120, 130, 140 exceeds the threshold, the controller 220 can determine that this battery cell is the battery cell in which a voltage abnormality has occurred.

[0193] The operation of the battery management device 200 and the battery cell diagnosis method according to the embodiment disclosed herein have been described above. The battery management device 200 according to the embodiment disclosed herein can remove noise from the deviation between the long-term moving average value and the short-term moving average value of the battery voltage and accurately diagnose an abnormal battery cell by applying skewness.

[0194] FIG. 13 is a block diagram showing the hardware configuration of a computing system that implements the method of operating a battery management device according to an embodiment disclosed herein.

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

[0196] The MCU 2100 may be a processor that executes various programs (e.g., a battery voltage deviation analysis program) stored in the memory 2200, processes various data used in such programs, and performs the functions of the battery management device 200 shown in Figure 1 described above.

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

[0198] A plurality of such memories 2200 may be provided as necessary. The memories 2200 may be volatile memories or nonvolatile memories. The volatile memories 2200 may be RAM, DRAM, SRAM, etc. The nonvolatile memories 2200 may be ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the memories 2200 listed above are merely illustrative and are not limited to these examples.

[0199] The input / output I / F 2300 can provide an interface that connects input devices (not shown) such as a keyboard, mouse, or touch panel, and output devices such as a display (not shown), to the MCU 2100, enabling data to be sent and received.

[0200] The communication I / F 2400 is configured to be able to send and receive various data to and from a server, and may be any device that supports wired or wireless communication. For example, programs for voltage measurement and abnormality diagnosis, various data, and the like can be sent and received via wired or wireless communication from a separately provided external server via the communication I / F 2400.

[0201] The above description merely exemplifies the technical concept of the present disclosure, and various modifications and variations are possible by a person having ordinary knowledge in the technical field to which the present disclosure pertains, without departing from the essential characteristics of the present disclosure.

[0202] Therefore, the embodiments disclosed in this disclosure are intended to illustrate, not limit, the technical idea of ​​the disclosure, and the scope of the technical idea of ​​the disclosure is not limited by such embodiments. The scope of protection of the disclosure should be interpreted by the claims below, and all technical ideas within the equivalent range should be interpreted as being included in the scope of rights of the disclosure.

Claims

1. a voltage measurement unit for measuring the voltage of each of the plurality of batteries; a controller capable of communicating with the voltage measurement unit; Including, The controller controlling the voltage measurement unit to measure the voltage of each of the 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 battery voltages, for each of 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 average voltages of the plurality of batteries; and calculating a first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries; calculating a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations for each of the plurality of batteries by a threshold constant; A battery management device configured to diagnose whether or not at least one of the plurality of batteries has an abnormality based on a second diagnostic deviation for each of the plurality of batteries.

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

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

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

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

6. The battery management device according to claim 5 , wherein the controller diagnoses whether or not at least one of the plurality of batteries has an abnormality based on whether or not a fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold value.

7. the controller calculates the first deviation and the second deviation for each unit time, and calculates a fourth diagnostic deviation for each of the plurality of batteries; The battery management device according to claim 6 , wherein when a fourth diagnostic deviation of at least one battery among the plurality of batteries exceeds a threshold value, the at least one battery is diagnosed for abnormality.

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

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

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

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

12. calculating a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations for each of the plurality of batteries by a threshold constant, The method of claim 11 , further comprising multiplying the third diagnostic deviation of each of the plurality of batteries by the skewness to calculate a fourth diagnostic deviation of each of the plurality of batteries.

13. The step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries includes: The method for operating a battery management device according to claim 12 , further comprising diagnosing whether or not at least one of the plurality of batteries has an abnormality based on whether or not a fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold value.

14. The step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries includes: calculating the first deviation and the second deviation for each unit time, and calculating a fourth diagnostic deviation for each of the plurality of batteries; The method of claim 13 , further comprising: diagnosing whether or not there is an abnormality in at least one battery among the plurality of batteries when a fourth diagnostic deviation of the at least one battery exceeds a threshold value.

15. Memory and a processor coupled to the memory and configured to execute the method for operating a battery management device according to any one of claims 8 to 14; Including the controller.

16. In the method for operating the battery management device, calculating a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations for each of the plurality of batteries by a threshold constant, a step of multiplying the second deviation by a first threshold constant and setting the maximum value of the second threshold constant as the reference value; excluding first diagnostic deviations equal to or less than the reference value from the first diagnostic deviations of each of the plurality of batteries, and calculating second diagnostic deviations of each of the plurality of batteries; normalizing the second diagnostic deviation of each of the plurality of batteries by dividing the second diagnostic deviation by the maximum value of a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant, thereby calculating a third diagnostic deviation of each of the plurality of batteries; calculating a skewness of each of the plurality of batteries by dividing a value obtained by adding a third diagnostic deviation of each of the plurality of batteries to a minimum value of the third diagnostic deviations of each of the plurality of batteries by the third diagnostic deviation; multiplying the third diagnostic deviation of each of the plurality of batteries by the skewness to calculate a fourth diagnostic deviation of each of the plurality of batteries; The step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries includes: The controller according to claim 15 , further comprising diagnosing whether or not at least one of the plurality of batteries has an abnormality based on whether or not a fourth diagnostic deviation of each of the plurality of batteries exceeds a threshold value.

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

18. calculating a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation among the first diagnostic deviations for each of the plurality of batteries by a threshold constant, a step of multiplying the second deviation by a first threshold constant and setting the maximum value of the second threshold constant as the reference value; excluding first diagnostic deviations equal to or less than the reference value from the first diagnostic deviations of each of the plurality of batteries, and calculating second diagnostic deviations of each of the plurality of batteries; normalizing the second diagnostic deviation of each of the plurality of batteries by dividing the second diagnostic deviation by the maximum value of a value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant, thereby calculating a third diagnostic deviation of each of the plurality of batteries; calculating a skewness of each of the plurality of batteries by dividing a value obtained by adding a third diagnostic deviation of each of the plurality of batteries to a minimum value of the third diagnostic deviations of each of the plurality of batteries by the third diagnostic deviation; multiplying the third diagnostic deviation of each of the plurality of batteries by the skewness to calculate a fourth diagnostic deviation of each of the plurality of batteries; The step of diagnosing whether or not there is an abnormality in at least one of the plurality of batteries based on the second diagnostic deviation of each of the plurality of batteries includes:

18. The program according to claim 17, further comprising diagnosing whether or not at least one of the plurality of batteries has an abnormality based on whether or not a fourth diagnostic deviation for each of the plurality of batteries exceeds a threshold value.

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