Battery management device and its operating method

Abstract

A battery management device according to one embodiment disclosed herein may include: a voltage measuring unit that measures the voltage of each of a plurality of batteries; a controller that calculates a first deviation for each of the plurality of batteries, which is the difference between the long-term moving average and short-term moving average of the battery voltages; calculates a second deviation for each of the plurality of batteries, which is the difference between the long-term moving average and short-term moving average of the average voltage of the plurality of batteries; calculates a first diagnostic deviation for each of the plurality of batteries, which is the difference between the first deviation and the second deviation; calculates a second diagnostic deviation for each of the plurality of batteries based on a reference value obtained by multiplying the second deviation by a threshold constant; and diagnoses at least one of the plurality of batteries based on the second diagnostic deviation for each of the plurality of batteries.

Description

Technical Field

[0001] This application claims the benefit of priority based on Korean Patent Application No. 10-2023-0068627, filed on May 26, 2023, and all the contents disclosed in the document of the patent application are incorporated herein by reference in their entirety. The embodiments disclosed in this document relate to a battery management device and an operating method thereof.

Background Art

[0002] An electric vehicle receives power supply from the outside to charge battery cells, and then drives a motor with the voltage charged in the battery cells to obtain power. Battery cells are subject to internal deformation and denaturation due to various charge and discharge cycles during production and use, resulting in changes in their physicochemical properties, which may lead to internal short circuits, external short circuits, venting due to lithium precipitation, or under voltage failures where the voltage of the battery cell drops below a certain level.

[0003] When a defect occurs inside a battery cell, the performance of the battery cell deteriorates, and there may be direct problems with the battery cell, such as an increased risk of ignition due to electrolyte leakage. Therefore, a technology for determining the presence or absence of abnormalities in battery cells is required.

[0004] Conventional battery management devices have diagnosed voltage abnormalities in battery cells using the deviation of the voltage of individual battery cells from the average voltage of the battery cells. However, such a method is vulnerable to noise and cannot adjust the threshold, which serves as the diagnostic criterion for abnormal battery cells, below a certain level, and has the limitation that it cannot detect abnormal voltages of battery cells caused by fine disconnections occurring in electric vehicles.

Summary of the Invention

Problems to be Solved by the Invention

[0005] One objective of the embodiments disclosed in this document is to provide a battery management device and a method of operating the same 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.

[0006] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other technical problems not mentioned can be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0007] A battery management device according to one embodiment disclosed herein may include: a voltage measuring unit that measures the voltage of each of a plurality of batteries; a controller that calculates a first deviation for each of the plurality of batteries, which is the difference between the long-term moving average and the short-term moving average of the battery voltages; calculates a second deviation for each of the plurality of batteries, which is the difference between the long-term moving average and the short-term moving average of the average voltage of the plurality of batteries; calculates a first diagnostic deviation for each of the plurality of batteries, which is the difference between the first deviation and the second deviation; and, if the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, accumulates the first diagnostic deviations to calculate an accumulated deviation, and diagnoses at least one of the plurality of batteries as an abnormal battery based on the accumulated deviation.

[0008] According to 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 a reference value, and calculates the second diagnostic deviation for each of the multiple batteries by excluding the first diagnostic deviation of each of the multiple batteries that is less than or equal to the reference value.

[0009] According to one embodiment, the controller can calculate the third diagnostic deviation for each of the multiple batteries by normalizing the second diagnostic deviation of each of the multiple batteries by dividing the value obtained by multiplying the second deviation by a third threshold constant by the maximum value of the fourth threshold constant.

[0010] According to one embodiment, the controller can calculate the distortion of each of the plurality of batteries by adding the minimum value of the third diagnostic deviation to the third diagnostic deviation of each of the plurality of batteries and dividing the resulting value by the third diagnostic deviation.

[0011] According to one embodiment, the controller can calculate the fourth diagnostic deviation for each of the plurality of batteries by multiplying the third diagnostic deviation for each of the plurality of batteries by the degree of distortion.

[0012] According to one embodiment, if the fourth diagnostic deviation of at least one of the plurality of batteries exceeds an upper threshold, the controller can calculate the cumulative deviation by accumulating the first diagnostic deviation of the at least one battery.

[0013] According to one embodiment, the cumulative deviation can be calculated by accumulating the first diagnostic deviation of at least one battery from the point in time when the fourth diagnostic deviation of at least one of the plurality of batteries exceeds the upper limit threshold until the point in time when the first diagnostic deviation is below the lower limit threshold.

[0014] According to one embodiment, the controller can diagnose at least one of the plurality of batteries as an abnormal battery if the cumulative deviation of at least one battery exceeds a threshold deviation.

[0015] According to one embodiment, the controller can determine that at least one of the plurality of batteries is a noise battery if the cumulative deviation of at least one of the batteries is less than or equal to a threshold deviation.

[0016] The operation method of a battery management device according to one embodiment disclosed herein may include the steps of: measuring the voltage of each of a plurality of batteries; calculating a first deviation for each of the plurality of batteries, which is the difference between the long-term moving average value and the short-term moving average value of the battery voltage; calculating a second deviation for each of the plurality of batteries, which is the difference between the long-term moving average value and the short-term moving average value of the average voltage of the plurality of batteries; calculating a first diagnostic deviation for each of the plurality of batteries, which is the difference between the first deviation and the second deviation; determining whether the first diagnostic deviation of each of the plurality of batteries exceeds a threshold; if the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, accumulating the first diagnostic deviations to calculate an accumulated deviation; and diagnosing at least one of the plurality of batteries as an abnormal battery based on the accumulated deviation.

[0017] According to one embodiment, the step of calculating the first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries is to set the maximum value of the value obtained by multiplying the second deviation by a first threshold constant and the second threshold constant as a reference value, and then calculate the second diagnostic deviation for each of the plurality of batteries by excluding the first diagnostic deviations for each of the plurality of batteries that are less than or equal to the reference value.

[0018] According to one embodiment, the step of calculating the first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries can be normalized by dividing the second diagnostic deviation of each of the plurality of batteries by the maximum value among the value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant, thereby calculating the third diagnostic deviation of each of the plurality of batteries.

[0019] According to one embodiment, the step of calculating the first diagnostic deviation, which is the difference between the first deviation and the second deviation for each of the plurality of batteries, can be performed by adding the minimum value of the third diagnostic deviation to the third diagnostic deviation of each of the plurality of batteries and dividing the resulting value by the third diagnostic deviation to calculate the distortion of each of the plurality of batteries.

[0020] According to one embodiment, the step of calculating the first diagnostic deviation, which is the difference between the first deviation and the second deviation for each of the plurality of batteries, can be performed by multiplying the third diagnostic deviation of each of the plurality of batteries by the degree of distortion to calculate the fourth diagnostic deviation of each of the plurality of batteries.

[0021] According to one embodiment, if the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the step of accumulating the first diagnostic deviations and calculating the cumulative deviation can be replaced with the step of accumulating the first diagnostic deviations of at least one of the plurality of batteries and calculating the cumulative deviation if the fourth diagnostic deviation of at least one of the plurality of batteries exceeds an upper threshold.

[0022] According to one embodiment, if the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the step of accumulating the first diagnostic deviations and calculating the cumulative deviation can be performed by accumulating the first diagnostic deviations of at least one battery from the point in time when the fourth diagnostic deviation of at least one of the plurality of batteries exceeds the upper threshold until the point in time when the first diagnostic deviation is below the lower threshold, and then calculating the cumulative deviation.

[0023] According to one embodiment, the step of diagnosing at least one of the plurality of batteries as an abnormal battery based on the cumulative deviation can be performed if the cumulative deviation of at least one of the plurality of batteries exceeds a threshold deviation.

[0024] According to one embodiment, the step of diagnosing at least one of the plurality of batteries as an abnormal battery based on the cumulative deviation can be used to determine that at least one battery is a noise battery if the cumulative deviation of at least one of the plurality of batteries is less than or equal to a threshold deviation. [Effects of the Invention]

[0025] According to the battery management device and its operation method according to an embodiment disclosed in this document, it is possible to 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.

Brief Description of Drawings

[0026] [Figure 1] It is a diagram showing a battery pack according to an embodiment disclosed in this document. [Figure 2] It is a block diagram showing the configuration of a battery management device according to an embodiment disclosed in this document. [Figure 3] It is a graph showing the voltage of a battery cell according to an embodiment disclosed in this document. [Figure 4] It is a flowchart showing a method for calculating the diagnostic deviation of a battery cell of a controller according to an embodiment disclosed in this document. [Figure 5a] It is a graph showing the first diagnostic deviation of a battery cell according to an embodiment disclosed in this document. [Figure 5b] It is a graph showing the third diagnostic deviation of a battery cell according to an embodiment disclosed in this document. [Figure 5c] It is a graph showing the skewness of the third diagnostic deviation of a battery cell according to an embodiment disclosed in this document. [Figure 5d] It is a graph showing the fourth diagnostic deviation of a battery cell according to an embodiment disclosed in this document. [Figure 6] It is a flowchart showing a method for diagnosing an abnormal battery cell of a controller according to an embodiment disclosed in this document. [Figure 7a] It is a graph showing the voltage of an abnormal battery cell according to an embodiment disclosed in this document. [Figure 7b] It is a graph showing the voltage of a noisy battery cell according to an embodiment disclosed in this document. [Figure 8a] It is a graph showing the first diagnostic deviation of an abnormal battery cell according to an embodiment disclosed in this document. [Figure 8b]This is a graph showing the first diagnostic deviation of a noise battery cell according to one embodiment disclosed in this document. [Figure 9] This is a flowchart showing the operation method of a battery management device according to one embodiment disclosed in this document. [Figure 10] This flowchart shows a method for calculating the diagnostic deviation of battery cells in a battery management device according to one embodiment disclosed in this document. [Figure 11] This flowchart shows a method for diagnosing an abnormal battery cell in a battery management device according to one embodiment disclosed in this document. [Figure 12] This is a block diagram showing the hardware configuration of a computing system that implements the operation method of a battery management device according to one embodiment disclosed in this document. [Modes for carrying out the invention]

[0027] Some embodiments disclosed in this document will be described in detail below with reference to illustrative drawings. It should be noted that, when assigning reference numerals to components in each drawing, the same reference numerals will be used for the same components whenever possible when they appear in other drawings. Furthermore, when describing the embodiments disclosed in this document, if a specific description of a related known configuration or function is deemed to hinder understanding of the embodiments disclosed in this document, such detailed description will be omitted.

[0028] In describing the components of the embodiments disclosed herein, terms such as First, Second, A, B, (a), (b), etc., may be used. Such terms are merely for distinguishing a component from other components and do not limit the nature, order, or sequence of the component. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as those generally understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an ideal or overly formal sense unless explicitly defined herein.

[0029] Figure 1 shows a battery pack according to one embodiment disclosed in this document. Referring to Figure 1, a battery pack 1000 according to one embodiment disclosed herein may include a battery module 100, a battery management device 200, and a relay 300. According to various embodiments, the battery module 100 may be a battery cell, in which case the battery pack 1000 may have a cell-to-pack structure.

[0030] Although Figure 1 shows a single battery module 100, according to the embodiment, the battery module 100 may consist of multiple modules, and the battery pack 1000 may have multiple battery modules forming a stacked structure. The battery module 100 can include multiple battery cells 110, 120, 130, and 140. Although Figure 1 shows a configuration with four battery cells, the battery module 100 is not limited to this and can consist of n (where n is a natural number greater than or equal to 2) battery cells. Furthermore, each of the multiple battery cells 110, 120, 130, and 140 can form a cell group or battery bank in which at least two or more battery cells are connected in parallel.

[0031] The battery module 100 can supply power to a target device (not shown). For this purpose, the battery module 100 can be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates on power supplied from a battery pack 1000 including a plurality of battery cells 110, 120, 130, 140, for example, an electric vehicle (EV) or an energy storage system (ESS).

[0032] The multiple battery cells 110, 120, 130, and 140 are the basic units of a battery that can be used by charging and discharging electrical energy, and may be, but are not limited to, lithium-ion (Li-ion) batteries, lithium-ion polymer (Li-ion polymer) batteries, nickel-cadmium (Ni-Cd) batteries, nickel-metal hydride (Ni-MH) batteries, etc. On the other hand, although Figure 1 shows that there is one battery module 100, according to the embodiment, the battery module 100 may be composed of multiple units.

[0033] The Battery Management System (BMS) 200 can manage and / or control the state and / or operation of the battery module 100. For example, the Battery Management System 200 can manage and / or control the state and / or operation of multiple battery cells 110, 120, 130, and 140 contained in the battery module 100. The Battery Management System 200 can manage the charging and / or discharging of the battery module 100.

[0034] The battery management device 200 can control the operation of the relay 300. For example, the battery management device 200 can short-circuit the relay 300 to supply power to the target device. The battery management device 200 can also short-circuit the relay 300 when a charging device is connected to the battery pack 1000.

[0035] Furthermore, the battery management device 200 can monitor the voltage, current, temperature, etc., of the battery module 100 and / or the multiple battery cells 110, 120, 130, and 140 contained within the battery module 100. In addition, for monitoring via the battery management device 200, sensors and various measuring modules (not shown) can be further installed in the battery module 100, the charge / discharge path, or at any other location on the battery module 100. Based on the measured values ​​of voltage, current, temperature, etc., the battery management device 200 can calculate parameters indicating the state of the battery module 100, such as SOC (State of Charge) or SOH (State of Health).

[0036] Multiple battery cells 110, 120, 130, and 140 may experience various changes in their capacity and internal resistance as their usage period or number of uses increases. The battery management device 200 can diagnose abnormal phenomena inside the multiple battery cells 110, 120, 130, and 140 based on data of various factors that change as the battery cells degrade.

[0037] Battery cells can experience faster and larger voltage changes compared to normal battery cells if defects occur due to various reasons such as production defects, internal deformation and modification due to multiple charge / discharge cycles, or external shocks. The battery management device 200 utilizes the phenomenon that battery cells with internal defects experience faster and larger voltage changes during the rest period compared to normal battery cells. By comparing the voltage data of multiple battery cells 110, 120, 130, and 140 during their rest period with the statistically normal voltage data of normal battery cells during their rest period, the device can diagnose abnormal battery cells among the multiple battery cells 110, 120, 130, and 140.

[0038] In the case of an abnormal battery cell, a phenomenon occurs where the voltage drops during the rest period after charging compared to a normal battery cell, resulting in a large deviation in voltage behavior compared to the voltage behavior of a normal battery cell, and a phenomenon where the voltage behavior is biased to one side, resulting in a large degree of distortion. The battery management device 200 can use the characteristics of an abnormal battery cell, which are that the deviation in voltage behavior is large and the distortion is large compared to that of a normal battery cell, to determine whether or not there is an abnormal battery cell among the multiple battery cells 110, 120, 130, and 140.

[0039] Specifically, the battery management device 200 can calculate the average voltage of multiple battery cells 110, 120, 130, and 140, and the deviation (dV) between the average voltage of each of the multiple battery cells 110, 120, 130, and 140 and the individual voltage of each of the multiple battery cells 110, 120, 130, and 140. Using the voltage deviations of each of the multiple battery cells 110, 120, 130, and 140, the battery management device 200 can determine abnormal voltage behavior in at least one of the multiple battery cells 110, 120, 130, and 140.

[0040] The battery management device 200 can calculate the voltage deviation data for each of the multiple battery cells 110, 120, 130, and 140, after removing noise voltage data suspected to be noise data from the voltage deviations of each of the multiple battery cells 110, 120, 130, and 140. After removing the noise voltage data from the voltage deviations of each of the multiple battery cells 110, 120, 130, and 140, the battery management device 200 can amplify the voltage deviation data for each of the multiple battery cells 110, 120, 130, and 140.

[0041] The battery management device 200 can determine which battery cells are suspected of having abnormal voltages using the amplified voltage deviation data of each of the multiple battery cells 110, 120, 130, and 140, and then diagnose a battery cell as abnormal using the cumulative value of the voltage deviation data of that battery cell. When diagnosing abnormal battery cells using the distortion of the voltage deviation data of a battery cell, even in the case of a battery cell with large voltage measurement noise, the voltage deviation data signal is measured as a large value, and it is not possible to prevent over-detection. Therefore, the battery management device 200 can use the cumulative value of the voltage deviation data of a battery cell to reduce the over-detection rate of the method of diagnosing abnormal battery cells using the distortion of the voltage deviation data.

[0042] Furthermore, the operation of the battery management device 200 can be performed by various devices such as a server, cloud, charger, or charger / discharger connected to the battery management device 200 or a vehicle equipped with the battery management device 200.

[0043] Figure 2 is a block diagram showing the configuration of a battery management device according to one embodiment disclosed in this document. The configuration of the battery management device 200 will be described in detail below with reference to Figure 2.

[0044] Referring to Figure 2, the battery management device 200 may include a voltage measuring unit 210 and a controller 220. The voltage measurement unit 210 can calculate the voltage of each of the multiple battery cells 110, 120, 130, and 140. The voltage measurement unit 210 can calculate the voltage of each of the multiple battery cells 110, 120, 130, and 140 for each unit of time and can calculate time-series data of the voltage of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the voltage measurement unit 210 can continuously calculate the rise and fall of voltage during charging, the rest period after charging, discharging, and the rest period after discharging of the multiple battery cells 110, 120, 130, and 140, as well as long-term stabilization (relaxation) data.

[0045] Figure 3 is a graph showing the voltage of a battery cell according to one embodiment disclosed in this document. Referring to Figure 3, the voltage measurement unit 210 measures the voltage of multiple battery cells 110, 120, 130, and 140 during charging, the rest period after charging, discharging, and the rest period after discharging, and can calculate time-series data of the voltage of each of the multiple battery cells 110, 120, 130, and 140. The voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 at unit time intervals and can generate a graph showing the voltage changes of each of the multiple battery cells 110, 120, 130, and 140.

[0046] The controller 220 can calculate the moving average of the voltages of each of the multiple battery cells 110, 120, 130, and 140. Here, the moving average is the average of a portion of the total data extracted while moving through a window of a specific size. Here, the window is a reference interval from which a portion of the total data can be extracted to determine which data to use. The start time of the window is a reference time from the current time, and the end time of the window is the current time. For example, if the window is one week, the controller 220 can extract data from the total data acquired from the current time to the most recent week.

[0047] The controller 220 can calculate the moving average voltage of each of the multiple battery cells 110, 120, 130, and 140 by using voltage data extracted from the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 while moving through the window. The controller 220 can also calculate the continuous moving average voltage of each of the multiple battery cells 110, 120, 130, and 140 by using voltage data continuously extracted from the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 while moving through the window. For example, the controller 220 can apply one of the following methods to the total voltage data of each of the multiple battery cells 110, 120, 130, and 140: Simple Moving Average, Weighted Moving Average, or Exponential Moving Average (EMA), and calculate the moving average voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0048] According to one embodiment, the controller 220 can apply an exponential moving average (EMA) to the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 to calculate the exponential moving average value of the voltages of each of the multiple battery cells 110, 120, 130, and 140. The exponential moving average is a type of weighted moving average method that uses data from the entire past period and gives more weight to recent data.

[0049] The controller 220 can calculate multiple moving average values ​​with different window sizes using the voltage data of each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 can calculate a long moving average with a relatively long window length and a short moving average with a relatively short window length using the total voltage data of each of the multiple battery cells 110, 120, 130, and 140. For example, the window size of the long moving average may include 100 seconds, and the window size of the short moving average may include 10 seconds. For example, the controller 220 can use the voltage data of multiple battery cells 110, 120, 130, and 140 to calculate the long-term moving average of each of the multiple battery cells 110, 120, 130, and 140 using the voltage data acquired in the most recent 100 seconds from the calculation point, and can also calculate the short-term moving average of each of the multiple battery cells 110, 120, 130, and 140 using the voltage data acquired in the most recent 10 seconds from the calculation point.

[0050] The controller 220 can analyze the long-term voltage change trend and short-term voltage change trend of multiple battery cells 110, 120, 130, and 140 using the continuous long-term moving average (V_LMA) and short-term moving average (V_SMA) of each of the multiple battery cells 110, 120, 130, and 140. The controller 220 can diagnose whether there are any abnormalities in the voltage of each of the multiple battery cells using the long-term moving average (V_LMA) and short-term moving average (V_SMA) of the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0051] Figure 4 is a flowchart showing a method for calculating the diagnostic deviation of a battery cell in a controller according to one embodiment disclosed in this document. The method for calculating the diagnostic deviation of the controller's battery cells will be explained in detail below, with reference to Figure 4.

[0052] In step S101, the controller 220 can calculate multiple first deviations (V_LMA-V_SMA), which are the deviations between the long-term moving average value (V_LMA) and the short-term moving average value (V_SMA) of the voltages of each of the multiple battery cells 110, 120, 130, and 140. In step S101, the controller 220 can continuously calculate the first deviations (V_LMA-V_SMA) of each of the multiple battery cells 110, 120, 130, and 140 that were calculated during a unit time. In step S101, that is, the controller 220 can continuously calculate the deviations between the long-term and short-term behaviors of the voltages of each of the multiple battery cells 110, 120, 130, and 140.

[0053] In step S102, the controller 220 can calculate the long-term moving average (V_avg_LMA) and short-term moving average (V_avg_SMA) of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140. Here, the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 may include the mean, median, or minimum voltages of the multiple battery cells 110, 120, 130, and 140.

[0054] In step S102, the controller 220 continuously calculates the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 at unit time intervals, and uses the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 to calculate the long-term moving average value (V_avg_LMA) and short-term moving average value (V_avg_SMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140. Here, the size of the window for the long-term moving average value (V_avg_LMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 may be the same as the size of the window for the long-term moving average value (V_LMA) of the voltage of each of the multiple battery cells 110, 120, 130, and 140. Furthermore, the window size of the short-term moving average (V_avg_SMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 may be the same as the window size of the short-term moving average (V_SMA) of each of the multiple battery cells 110, 120, 130, and 140.

[0055] In step S102, the controller 220 can calculate the second deviation (V_avg_LMA-V_avg_SMA), which is the difference between the long-term moving average (V_avg_LMA) and the short-term moving average (V_avg_SMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140. In step S102, the controller 220 can continuously calculate the second deviation (V_avg_LMA-V_avg_SMA) of multiple battery cells 110, 120, 130, and 140 for each unit time. In other words, the controller 220 can calculate the deviation between the long-term and short-term behavior of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140.

[0056] In step S103, the controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, which are the deviations of multiple first deviations (V_LMA-V_SMA) and second deviations (V_avg_LMA-V_avg_SMA).

[0057] In step S103, specifically, the controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140 based on [Equation 1].

[0058] [Formula 1] First diagnostic deviation (D1) = Second deviation - First deviation = (V_avg_LMA - V_avg_SMA) - (V_LMA - V_SMA)

[0059] Referring to [Equation 1], the controller 220 can calculate the deviations of multiple first deviations (V_LMA-V_SMA) and second deviations (V_avg_LMA-V_avg_SMA) as the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140.

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

[0061] In other words, the controller 220 calculates the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, and can compare the deviations between the long-term and short-term behavior of the voltage of each of the multiple battery cells 110, 120, 130, and 140 with the deviations between the long-term and short-term behavior of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140.

[0062] Referring again to Figure 4, in step S104, the controller 220 can remove noise data from the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140.

[0063] In step S104, specifically, the controller 220 can set a reference value that allows it to determine the presence or absence of noise in the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, based on the following [Equation 2].

[0064] [Formula 2] Reference value = Max[|V_avg_LMA-V_avg_SMA|*C1, C2]

[0065] In step S104, the controller 220 can set the maximum value of the second threshold constant (C2) and the value obtained by multiplying the absolute value of the second deviation (V_avg_LMA-V_avg_SMA) by the first threshold constant (C1) (|V_avg_LMA-V_avg_SMA|*C1) as the reference value for each of the multiple battery cells 110, 120, 130, and 140. Here, the first threshold constant (C1) can include "0.1", and the second threshold constant (C2) can include "0.4". Furthermore, the first threshold constant (C1) and the second threshold constant (C2) can be changed according to the magnitude and characteristics of the voltage data for each of the multiple battery cells 110, 120, 130, and 140.

[0066] In step S104, the controller 220 can determine that the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below the reference value is noise data. In step S104, the controller 220 can calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by excluding the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below the reference value.

[0067] In step S105, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 and calculate the third diagnostic deviation (D3).

[0068] In step S105, 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] 3rd diagnostic deviation = 2nd diagnostic deviation / Max[|V_avg_LMA-V_avg_SMA|*C3, C4]

[0070] In step S105, the controller 220 can calculate the maximum value (Max) of the fourth threshold constant (C4) obtained by multiplying the absolute value of the second deviation (|V_avg_LMA-V_avg_SMA|) by the third threshold constant (C3) (|V_avg_LMA-V_avg_SMA|*C3) and the fourth threshold constant (C4). The controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 using the value obtained by multiplying the absolute value of the second deviation by the third threshold constant and the maximum value of the fourth threshold constant (C4) (Max[|V_avg_LMA-V_avg_SMA|*C3, C4]). Here, the third threshold constant (C3) can include "0.1", and the fourth threshold constant (C4) can also include "0.1", and the third threshold constant (C3) and the fourth threshold constant (C4) can be changed according to the magnitude and characteristics of the voltage data of each of the multiple battery cells 110, 120, 130, and 140. In step S105, the controller 220 can use the second deviation (V_avg_LMA-V_avg_SMA) which shows the behavior of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 to calculate the normalized value of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0071] In step S105, according to one embodiment, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by logarithm calculation. That is, the controller 220 can calculate the value obtained by normalizing the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by logarithm calculation as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0072] In step S105, according to one embodiment, the controller 220 can set the average value (D2_avg) of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the normalization criterion. In step S105, the controller 220 can use the average value (D2_avg) of the second diagnostic deviation as the normalization criterion and normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by dividing it by the average value (D2_avg) of the second diagnostic deviation (D2). That is, the controller 220 can calculate the normalized value obtained by dividing the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by the average value (D2_avg) of the second diagnostic deviation as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

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

[0074] The controller 220 can continuously calculate the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 at each unit time interval, and generate a graph showing the change in the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140.

[0075] For example, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 using the second deviation (V_avg_LMA-V_avg_SMA) which shows the behavior of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140.

[0076] Referring again to Figure 4, in step S106, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140. Specifically, in step S106, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 4].

[0077] [Formula 4] Skewness = (Third diagnostic deviation (D3) + Min[Third diagnostic deviation (D3)]) / Third diagnostic deviation (D3)

[0078] In step S106, referring to [Equation 4], the controller 220 can calculate the distortion of each of the multiple battery cells 110, 120, 130, and 140 by adding the minimum value (Min[Third Diagnostic Deviation (D3)]) of the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140 to the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140, and dividing the resulting value by the third diagnostic deviation (D3).

[0079] Figure 5c is a graph showing the skewness of the third diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Referring to Figure 5c, the controller 220 can continuously calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 for each unit time, and generate a graph showing the change in the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140.

[0080] Referring again to Figure 4, in step S106, the controller 220 can reflect the skewness in the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140, and calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 can calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 5].

[0081] [Formula 5] 4th diagnostic deviation (D4) = 3rd diagnostic deviation (D3) * skewness

[0082] In step S106, the controller 220 can calculate the fourth diagnostic deviation (D4) for each of the battery cells 110, 120, 130, and 140 by multiplying the third diagnostic deviation (D3) by the skewness.

[0083] Figure 5d is a graph showing the fourth diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Referring to Figure 5d, the controller 220 can continuously calculate the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140 at each unit time interval, and generate a graph showing the change in the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140.

[0084] The controller 220 can determine whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds the threshold. If any of the multiple battery cells 110, 120, 130, and 140 has a fourth diagnostic deviation (D4) that exceeds the threshold, the controller 220 can determine that the battery cell is suspected of having abnormal voltage behavior.

[0085] The controller 220 can calculate the cumulative deviation by accumulating the values ​​of the first diagnostic deviation (D1) of battery cells among the multiple battery cells 110, 120, 130, and 140 whose fourth diagnostic deviation (D4) exceeds a threshold. Based on the cumulative deviation of the battery cells among the multiple battery cells 110, 120, 130, and 140 whose fourth diagnostic deviation (D4) exceeds a threshold, the controller 220 can classify those battery cells as abnormal battery cells or noisy battery cells.

[0086] Figure 6 is a flowchart showing a method for diagnosing an abnormal battery cell in a controller according to one embodiment disclosed in this document. The following section will specifically explain how to diagnose abnormal battery cells in the controller, referring to Figure 6.

[0087] In step S201, the controller 220 can determine whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds the upper threshold (UT). Here, the upper threshold (UT) can be defined as a threshold value that can be judged as "abnormal" if an extreme result occurs. In other words, the upper threshold (UT) can be defined as a criterion that shows how much the data deviates from a particular statistical model. In step S201, the controller 220 determines whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds the upper threshold (UT), and can identify the battery cells among the multiple battery cells 110, 120, 130, and 140 that exhibit abnormal voltage behavior and are suspected to be abnormal.

[0088] In step S202, the controller 220 determines that abnormal voltage behavior has occurred if any of the battery cells 110, 120, 130, and 140 have a fourth diagnostic deviation (D4) that exceeds the upper threshold (UT), and can calculate the cumulative deviation of the battery cell. Specifically, the controller 220 can calculate the first diagnostic deviation (D1) for each of the battery cells 110, 120, 130, and 140 per unit time, and can continuously calculate the first diagnostic deviation (D1) for each of the battery cells 110, 120, 130, and 140.

[0089] In step S202, the controller 220 can calculate the cumulative deviation of each of the multiple battery cells 110, 120, 130, and 140 based on [Equation 6].

[0090] [Formula 6]

number

[0091] Referring to [Equation 6], the controller 220 can calculate the cumulative deviation by accumulating the first diagnostic deviations (D1) of any of the multiple battery cells 110, 120, 130, and 140 where the fourth diagnostic deviation (D4) exceeds the upper threshold (UT) for that battery cell, from the time when the fourth diagnostic deviation (D4) of that battery cell exceeds the upper threshold (UT) (t=a) to the time when the first diagnostic deviation (D1) of that battery cell is less than or equal to the lower threshold (LT) (t=b).

[0092] Here, the lower threshold (LT) of the first diagnostic deviation (D1) can include, for example, "0V". That is, the controller 220 can accumulate the first diagnostic deviation (D1) from the point in time when the fourth diagnostic deviation (D4) of the battery cell exceeds the upper threshold (UT) (t=a) to the point in time when the first diagnostic deviation (D1) becomes "0" (t=b), and calculate the cumulative deviation of the battery cell.

[0093] In step S203, the controller 220 can determine whether the cumulative deviation of each of the multiple battery cells 110, 120, 130, and 140 exceeds the threshold deviation. In step S203, based on whether the cumulative deviation of each of the multiple battery cells 110, 120, 130, and 140 exceeds the threshold deviation, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140 as an abnormal battery cell or as a noisy battery cell.

[0094] Figure 7a is a graph showing the voltage of an abnormal battery cell according to one embodiment disclosed in this document. Figure 7b is a graph showing the voltage of an abnormal battery cell according to one embodiment disclosed in this document. Figure 8a is a graph showing the first diagnostic deviation of an abnormal battery cell according to one embodiment disclosed in this document. Figure 8b is a graph showing the first diagnostic deviation of a noisy battery cell according to one embodiment disclosed in this document.

[0095] Referring to Figures 7a to 8b, a phenomenon occurs where a noisy battery cell with a relatively large measured noise value has a relatively smaller cumulative deviation value compared to an abnormal battery cell. Therefore, the controller 220 can classify a battery cell as either an abnormal battery cell or a noisy battery cell based on a threshold deviation, which is a reference value that allows the battery cell to be diagnosed as either an abnormal battery cell or a noisy battery cell.

[0096] Referring again to Figure 6, in step S204, the controller 220 can diagnose a battery cell as abnormal if the cumulative deviation of at least one of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold deviation.

[0097] In step S205, the controller 220 can determine that at least one of the multiple battery cells 110, 120, 130, and 140 is a noisy battery cell if its cumulative deviation is less than or equal to a threshold deviation.

[0098] After diagnosing at least one of the multiple battery cells 110, 120, 130, and 140, the controller 220 can track and monitor whether there are any defects in that battery cell, such as whether an internal short circuit has occurred, whether an external short circuit has occurred, or whether lithium deposition has occurred.

[0099] Furthermore, if the diagnosis confirms that a defect has occurred inside 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 that has experienced an internal short circuit to the 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 charger.

[0100] As described above, according to the battery management device 200 according to one embodiment disclosed in this document, abnormal battery cells can be accurately diagnosed using the cumulative value of the deviation between the long-term moving average value and the short-term moving average value of the battery cell voltage.

[0101] Conventional battery management devices use the deviation of the voltage of each battery cell relative to the average voltage of the battery cells, which distorts the abnormal voltage behavior signals of each battery cell and increases the possibility of misdiagnosis due to noise data. However, the battery management device 200 according to one embodiment disclosed in this document uses the deviation between the long-term moving average value and the short-term moving average value of the voltage of each battery cell, thereby minimizing the distortion of the voltage of the battery cells, removing noise data, and amplifying the voltage behavior of abnormal battery cells in accordance with the degree of voltage distortion, thereby improving the accuracy of diagnosis. Furthermore, the battery management device 200 can use the cumulative value of the voltage deviation data of a battery cell suspected of exhibiting abnormal voltage behavior to diagnose the battery cell as an abnormal battery cell or determine it to be a noisy battery cell.

[0102] The battery management device 200 uses the deviation between the long-term moving average and short-term moving average voltages of the battery cells to diagnose battery cells exhibiting abnormal voltage behavior at an early stage, thereby ensuring the safety and reliability of the battery energy. Furthermore, because the battery management device 200 diagnoses battery cells exhibiting abnormal voltage behavior while the battery is installed in the vehicle, it eliminates the need for separate battery separation, allowing for quick and easy diagnosis of battery cells.

[0103] Figure 9 is a flowchart showing the operation method of a battery management device according to one embodiment disclosed in this document. The operation method of the battery management device 200 will be explained in detail below with reference to Figures 1 to 8b.

[0104] Since the battery management device 200 is substantially the same as the battery management device 200 described with reference to Figures 1 to 8b, a brief description will be given below to avoid repetition.

[0105] Referring to Figure 9, the operation method of the battery management device is as follows: step (S301) measure the voltage of each of the multiple batteries 110, 120, 130, and 140; step (S302) calculate multiple first deviations, which are the deviations between the long-term moving average and short-term moving average of the voltages of each of the multiple batteries 110, 120, 130, and 140; step (S303) calculate the second deviation, which is the deviation between the long-term moving average and short-term moving average of the average values ​​of the multiple batteries 110, 120, 130, and 140; and step (S303) calculate the deviations between the first deviation and the second deviation of each of the multiple batteries 110, 120, 130, and 140. The procedure may include the steps of: calculating the first diagnostic deviation for each of the 40 batteries (S304); determining whether the first diagnostic deviation for each of the multiple batteries 110, 120, 130, and 140 exceeds a threshold (S305); if the first diagnostic deviation for at least one of the multiple batteries 110, 120, 130, and 140 exceeds a threshold, accumulating the first diagnostic deviations to calculate the cumulative deviation (S306); and diagnosing at least one of the multiple batteries based on the cumulative deviation for each of the multiple batteries 110, 120, 130, and 140 (S307).

[0106] The following provides a detailed explanation of steps S301 through S307. In step S301, the voltage measurement unit 210 can calculate the voltage of each of the multiple battery cells 110, 120, 130, and 140. The voltage measurement unit 210 can calculate the voltage of each of the multiple battery cells 110, 120, 130, and 140 for each unit of time and calculate time-series data of the voltage of each of the multiple battery cells 110, 120, 130, and 140. In step S301, according to one embodiment, the voltage measurement unit 210 can continuously calculate the rise and fall of voltage during charging, the rest period after charging, the discharge, and the rest period after discharge of the multiple battery cells 110, 120, 130, and 140, as well as long-term stabilization (relaxation) data.

[0107] In step S301, the voltage measurement unit 210 measures the voltage of multiple battery cells 110, 120, 130, and 140 during charging, the rest period after charging, discharging, and the rest period after discharging, and can calculate time-series data of the voltage of each of the multiple battery cells 110, 120, 130, and 140. In step S301, the voltage measurement unit 210 measures the voltage of each of the multiple battery cells 110, 120, 130, and 140 at unit time intervals and can generate a graph showing the voltage changes of each of the multiple battery cells 110, 120, 130, and 140.

[0108] In step S302, the controller 220 can calculate the moving average of the voltages of each of the multiple battery cells 110, 120, 130, and 140. Here, the moving average is the average of a portion of the data extracted from the total data while moving through a window of a specific size.

[0109] In step S302, the controller 220 can calculate a moving average of the voltages of multiple battery cells 110, 120, 130, and 140 by using voltage data extracted from the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 while moving through the window. In step S302, the controller 220 can calculate a continuous moving average of the voltages of multiple battery cells 110, 120, 130, and 140 by using voltage data extracted continuously from the total voltage data of each of the multiple battery cells 110, 120, 130, and 140 while moving through the window. In step S302, for example, the controller 220 can apply one of the following to the total voltage data of each of the multiple battery cells 110, 120, 130, and 140: a simple moving average, a weighted moving average, or an exponential moving average, to calculate a moving average of the voltages of multiple battery cells 110, 120, 130, and 140. In step S302, according to one embodiment, the controller 220 can apply an exponential moving average (EMA) to all voltage data of each of the multiple battery cells 110, 120, 130, and 140 to calculate the exponential moving average value of the voltages of each of the multiple battery cells 110, 120, 130, and 140.

[0110] In step S302, the controller 220 can calculate multiple moving average values ​​with different window sizes using the voltage data of each of the multiple battery cells 110, 120, 130, and 140. In step S302, according to one embodiment, the controller 220 can calculate a long-term moving average with a relatively long window length and a short-term moving average with a relatively short window length using the total voltage data of each of the multiple battery cells 110, 120, 130, and 140.

[0111] In step S302, the controller 220 can diagnose whether there are any abnormalities in the voltage of each of the multiple battery cells 110, 120, 130, and 140 by using the long-term moving average (V_LMA) and short-term moving average (V_SMA) of the voltage of each of the multiple battery cells 110, 120, 130, and 140.

[0112] In step S302, the controller 220 can calculate the first deviation (V_LMA-V_SMA), which is the deviation 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 multiple battery cells 110, 120, 130, and 140. In step S302, the controller 220 can continuously calculate the first deviation (V_LMA-V_SMA) for each of the multiple battery cells 110, 120, 130, and 140 that was calculated during a unit time. In step S302, that is, the controller 220 can calculate the deviation between the long-term behavior and short-term behavior of the voltages of each of the multiple battery cells 110, 120, 130, and 140.

[0113] In step S303, the controller 220 can calculate the long-term moving average (V_avg_LMA) and short-term moving average (V_avg_SMA) of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140. Here, the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 may include the mean, median, or minimum voltages of the multiple battery cells 110, 120, 130, and 140.

[0114] In step S303, the controller 220 continuously calculates the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 at unit time intervals, and uses the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 to calculate the long-term moving average value (V_avg_LMA) and short-term moving average value (V_avg_SMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140. Here, the window size of the long-term moving average value (V_avg_LMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 may be the same as the window size of the long-term moving average value (V_LMA) of the voltage of each of the multiple battery cells 110, 120, 130, and 140. Furthermore, the window size of the short-term moving average (V_avg_SMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 may be the same as the window size of the short-term moving average (V_SMA) of each of the multiple battery cells 110, 120, 130, and 140.

[0115] In step S303, the controller 220 can calculate the second deviation (V_avg_LMA-V_avg_SMA), which is the difference between the long-term moving average (V_avg_LMA) and the short-term moving average (V_avg_SMA) of the average voltage (V_avg) of multiple battery cells 110, 120, 130, and 140. In step S303, the controller 220 can continuously calculate the second deviation (V_avg_LMA-V_avg_SMA) of multiple battery cells 110, 120, 130, and 140 for each unit time.

[0116] In step S304, the controller 220 can calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140, which are the deviations of multiple first deviations (V_LMA-V_SMA) and second deviations (V_avg_LMA-V_avg_SMA).

[0117] In step S304, the controller 220 can specifically calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140 based on [Equation 7].

[0118] [Formula 7] First diagnostic deviation (D1) = Second deviation - First deviation = (V_avg_LMA - V_avg_SMA) - (V_LMA - V_SMA)

[0119] Referring to [Equation 7], the controller 220 can calculate the deviations of multiple first deviations (V_LMA-V_SMA) and second deviations (V_avg_LMA-V_avg_SMA) as the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140.

[0120] In step S304, the controller 220 can continuously calculate the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140 at unit time intervals, and generate a graph showing the change in the first diagnostic deviation (D1) for each of the multiple battery cells 110, 120, 130, and 140.

[0121] In step S305, the controller 220 can determine whether the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold.

[0122] In step S306, if the first diagnostic deviation (D1) of at least one of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold, the controller 220 can accumulate the first diagnostic deviations (D1) to calculate the cumulative deviation.

[0123] In step S307, the controller 220 can diagnose at least one of the battery cells 110, 120, 130, and 140 as an abnormal battery cell based on the cumulative deviation of each of the battery cells 110, 120, 130, and 140.

[0124] Figure 10 is a flowchart showing a method for calculating the diagnostic deviation of battery cells in a battery management device according to one embodiment disclosed in this document. The following will specifically explain how the controller 220 of the battery management device 200 calculates the diagnostic deviation for each of the multiple battery cells 110, 120, 130, and 140, with reference to Figure 10.

[0125] In step S401, 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.

[0126] In step S401, specifically, the controller 220 can set a reference value that allows it to determine the presence or absence of noise in the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140, based on the following [Equation 8].

[0127] [Formula 8] Reference value = Max[|V_avg_LMA-V_avg_SMA|*C1, C2]

[0128] In step S401, the controller 220 can set the maximum value (Max) of the two values ​​obtained by multiplying the absolute value of the second deviation (V_avg_LMA-V_avg_SMA) by the first threshold constant (C1) (|V_avg_LMA-V_avg_SMA|*C1) and the second threshold constant (C2) as the reference value for each of the multiple battery cells 110, 120, 130, and 140.

[0129] In step S401, the controller 220 can determine that the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below the reference value is noise data. In step S401, the controller 220 can exclude the first diagnostic deviation (D1) of each of the multiple battery cells 110, 120, 130, and 140 that is below the reference value, and calculate the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140.

[0130] In step S401, the controller 220 can diagnose at least one of the battery cells 110, 120, 130, and 140 using the second diagnostic deviation (D2) of each of the battery cells 110, 120, 130, and 140.

[0131] Referring to Figure 10, the operation method of the battery management device may include the steps of: normalizing the second diagnostic deviation of each of the multiple batteries 110, 120, 130, and 140 and calculating the third diagnostic deviation of each of the multiple batteries 110, 120, 130, and 140 (S402); calculating the skewness of each of the multiple batteries 110, 120, 130, and 140's third diagnostic deviation (S403); multiplying each of the multiple batteries 110, 120, 130, and 140's third diagnostic deviation by the skewness and calculating the fourth diagnostic deviation of each of the multiple batteries 110, 120, 130, and 140 (S404); and diagnosing the batteries by determining whether each of the multiple batteries 110, 120, 130, and 140's fourth diagnostic deviation exceeds a threshold (S404).

[0132] The following provides a detailed explanation of steps S402 through S404. In step S402, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 and calculate the third diagnostic deviation (D3). Specifically, in step S401, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 and calculate the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 9].

[0133] [Formula 9] 3rd diagnostic deviation = 2nd diagnostic deviation / Max[|V_avg_LMA-V_avg_SMA|*C3, C4]

[0134] In step S402, the controller 220 can calculate the maximum value (Max) of the fourth threshold constant (C4) obtained by multiplying the absolute value of the second deviation (|V_avg_LMA-V_avg_SMA|) by the third threshold constant (C3) (|V_avg_LMA-V_avg_SMA|*C3) and the fourth threshold constant (C4). The controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 using the value obtained by multiplying the absolute value of the second deviation by the third threshold constant and the maximum value of the fourth threshold constant (C4) (Max[|V_avg_LMA-V_avg_SMA|*C3, C4]). Here, the third threshold constant (C3) can include "0.1", and the fourth threshold constant (C4) can also include "0.1", and the third threshold constant (C3) and the fourth threshold constant (C4) can be changed according to the magnitude and characteristics of the voltage data of each of the multiple battery cells 110, 120, 130, and 140. In step S402, the controller 220 can use the second deviation (V_avg_LMA-V_avg_SMA) which shows the behavior of the average voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 to calculate the normalized value of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0135] In step S402, according to one embodiment, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by logarithm calculation. That is, the controller 220 can calculate the value obtained by normalizing the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by logarithm calculation as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0136] In step S402, according to one embodiment, the controller 220 can set the average value (D2_avg) of the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 as the normalization criterion. In step S402, the controller 220 can use the average value (D2_avg) of the second diagnostic deviation as the normalization criterion and normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by dividing it by the average value (D2_avg) of the second diagnostic deviation (D2). That is, the controller 220 can calculate the normalized value obtained by dividing the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 by the average value (D2_avg) of the second diagnostic deviation as the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140. In step S402, the controller 220 can normalize the second diagnostic deviation (D2) of each of the multiple battery cells 110, 120, 130, and 140 according to various embodiments, and calculate the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140.

[0137] In step S403, the controller 220 can continuously calculate the third diagnostic deviation (D3) 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 third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140. In step S403, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140. Specifically in step S403, the controller 220 can calculate the skewness of the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 10].

[0138] [Formula 10] Skewness = (Third diagnostic deviation (D3) + Min[Third diagnostic deviation (D3)]) / Third diagnostic deviation (D3)

[0139] In step S403, referring to [Equation 10], the controller 220 can calculate the distortion of each of the multiple battery cells 110, 120, 130, and 140 by adding the minimum value (Min[Third Diagnostic Deviation (D3)]) of the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140 to the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140, and dividing the resulting value by the third diagnostic deviation (D3).

[0140] In step S403, the controller 220 can continuously calculate the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140 at unit time intervals, and generate a graph showing the change in the skewness of the third diagnostic deviation (D3) for each of the multiple battery cells 110, 120, 130, and 140.

[0141] In step S404, the controller 220 can reflect the skewness in the third diagnostic deviation (D3) of each of the multiple battery cells 110, 120, 130, and 140 and calculate the fourth diagnostic deviation (D4). Specifically in step S404, the controller 220 can calculate the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 11].

[0142] [Formula 11] 4th diagnostic deviation (D4) = 3rd diagnostic deviation (D3) * skewness

[0143] In step S404, the controller 220 can calculate the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140 by multiplying the third diagnostic deviation (D3) by the skewness. In step S404, the controller 220 can continuously calculate the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140 at unit time intervals and generate a graph showing the change in the fourth diagnostic deviation (D4) for each of the multiple battery cells 110, 120, 130, and 140.

[0144] Figure 11 is a flowchart showing a method for diagnosing an abnormal battery cell in a battery management device according to one embodiment disclosed in this document. The following will specifically explain, with reference to Figure 11, how the controller 220 of the battery management device 200 diagnoses abnormal battery cells using the diagnostic deviations of each of the multiple battery cells 110, 120, 130, and 140.

[0145] In step S501, the controller 220 can determine whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds the upper threshold (UT). Here, the upper threshold (UT) can be defined as a threshold value that can be judged as "abnormal" if an extreme result occurs. In other words, the upper threshold (UT) can be defined as a criterion that shows how much the data deviates from a particular statistical model. In step S501, the controller 220 determines whether the fourth diagnostic deviation (D4) of each of the multiple battery cells 110, 120, 130, and 140 exceeds the upper threshold (UT), and can identify the battery cells among the multiple battery cells 110, 120, 130, and 140 that exhibit abnormal voltage behavior and are suspected to be abnormal.

[0146] In step S502, the controller 220 determines that abnormal voltage behavior has occurred if any of the battery cells 110, 120, 130, and 140 have a fourth diagnostic deviation (D4) that exceeds the upper threshold (UT), and can calculate the cumulative deviation of the battery cell. Specifically, the controller 220 calculates the first diagnostic deviation (D1) for each of the battery cells 110, 120, 130, and 140 per unit time, and can continuously calculate the first diagnostic deviation (D1) for each of the battery cells 110, 120, 130, and 140.

[0147] In step S502, the controller 220 can calculate the cumulative deviation of each of the multiple battery cells 110, 120, 130, and 140 based on [Equation 12].

[0148] [Formula 12]

number

[0149] Referring to [Equation 12], the controller 220 can calculate the cumulative deviation for any of the battery cells 110, 120, 130, and 140 in which the fourth diagnostic deviation (D4) exceeds the upper threshold (UT) by accumulating the first diagnostic deviation (D1) from the point in time when the fourth diagnostic deviation (D4) of the battery cell exceeds the upper threshold (UT) (t=a) to the point in time when the first diagnostic deviation (D1) of the battery cell is less than or equal to the lower threshold (LT) (t=b). Here, the lower threshold (LT) of the first diagnostic deviation (D1) can include, for example, "0V". In other words, the controller 220 can calculate the cumulative deviation for a battery cell by accumulating the first diagnostic deviation (D1) from the point in time when the fourth diagnostic deviation (D4) of the battery cell exceeds the upper threshold (UT) (t=a) to the point in time when the first diagnostic deviation (D1) becomes "0" (t=b).

[0150] In step S503, the controller 220 can determine whether the cumulative deviation of each of the multiple battery cells 110, 120, 130, and 140 exceeds the threshold deviation. In step S503, based on whether the cumulative deviation of each of the multiple battery cells 110, 120, 130, and 140 exceeds the threshold deviation, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140 as an abnormal battery cell or as a noisy battery cell.

[0151] Noisy battery cells exhibit a phenomenon where the cumulative deviation value is relatively smaller compared to abnormal battery cells. Therefore, the controller 220 can classify a battery cell as either an abnormal or noisy battery cell based on a threshold deviation, which is a reference value that allows it to be determined whether the battery cell is abnormal or a noisy battery cell.

[0152] In step S503, the controller 220 can diagnose a battery cell as abnormal if the cumulative deviation of at least one of the multiple battery cells 110, 120, 130, and 140 exceeds a threshold deviation.

[0153] In step S503, the controller 220 can determine that at least one of the multiple battery cells 110, 120, 130, and 140 is a noisy battery cell if its cumulative deviation is less than or equal to a threshold deviation.

[0154] In step S503, the controller 220 diagnoses at least one of the multiple battery cells 110, 120, 130, and 140 as an abnormal battery cell, and then tracks and monitors whether there are any defects in that battery cell, such as whether an internal short circuit has occurred, whether an external short circuit has occurred, or whether lithium deposition has occurred.

[0155] In step S503, if the diagnosis confirms that a defect has occurred inside the 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 with an internal short circuit to the user terminal via a communication unit (not shown), or it can provide information about the battery cell via a display provided in the vehicle or charger.

[0156] Figure 12 is a block diagram showing the hardware configuration of a computing system that implements the operation method of a battery management device according to one embodiment disclosed in this document.

[0157] Referring to Figure 12, the computing system 2000 according to one embodiment disclosed in this document may include an MCU 2100, a memory 2200, an input / output I / F 2300, and a communication I / F 2400.

[0158] The MCU2100 may be a processor that executes various programs stored in the memory 2200 (for example, a battery voltage deviation analysis program), processes various data through such programs, and performs the functions of the battery management device 200 shown in Figure 1 above.

[0159] The memory 2200 can store various programs related to the operation of the battery management device 200. The memory 2200 can also store the operation data of the battery management device 200.

[0160] Multiple such memory 2200s may be provided as needed. The memory 2200 may be volatile or non-volatile. As volatile memory, RAM, DRAM, SRAM, etc., can be used. As non-volatile memory, ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc., can be used. The examples of memory 2200 listed above are merely illustrative and the system is not limited to these examples.

[0161] The I / O I / F 2300 can provide an interface that connects input devices (not shown), such as keyboards, mice, and touch panels, with output devices (not shown), such as displays, and the MCU 2100, enabling data transmission and reception.

[0162] The communication interface 2400 is configured to send and receive various data with the server and may be various devices that support wired or wireless communication. For example, programs for resistance measurement and anomaly diagnosis, as well as various data, can be sent and received from a separately provided external server via the communication interface 2400.

[0163] The above description is merely illustrative of the technical concept of this disclosure, and any person with ordinary skill in the art to which this disclosure belongs can make various modifications and variations without departing from the essential characteristics of this disclosure.

[0164] Therefore, the embodiments disclosed herein are for illustrative purposes only, and not to limit the technical concept of the disclosure, and such embodiments do not limit the scope of the technical concept of the disclosure. The scope of protection of this disclosure must be interpreted in accordance with the claims set forth below, and all technical concepts within an equivalent scope should be interpreted as being included in the scope of rights of this disclosure. [Explanation of Symbols]

[0165] 1000: Battery pack 100: Battery Module 110: Battery cell 120: Battery cell 130: Battery cell 140: Battery cell 200:Battery management device 210: Voltage measurement section 220: Controller 300: Relay 2000: Computing Systems 2100:MCU 2200: Memory 2300: Input / Output Interface 2400: Communication I / F

Claims

1. A voltage measuring unit that measures the voltage of each of the multiple batteries, For each of the aforementioned multiple batteries, a first deviation is calculated, which is the difference between the long-term moving average and short-term moving average of the battery voltage. A second deviation is calculated, which is the difference between the long-term moving average and short-term moving average of the average voltage of the aforementioned multiple batteries. For each of the aforementioned multiple batteries, a first diagnostic deviation is calculated, which is the difference between the first deviation and the second deviation. If the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the first diagnostic deviations are accumulated to calculate the cumulative deviation. A controller that diagnoses at least one of the plurality of batteries as an abnormal battery based on the cumulative deviation, A battery management device, including a battery management device.

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

3. The battery management device according to claim 2, wherein the controller normalizes the second diagnostic deviation of each of the plurality of batteries by dividing it by the maximum value among 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.

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

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

6. The controller, if the fourth diagnostic deviation of at least one of the plurality of batteries exceeds the upper threshold, The battery management device according to claim 5, which calculates the cumulative deviation by accumulating the first diagnostic deviation of at least one battery.

7. The battery management device according to claim 6, wherein the controller calculates the cumulative deviation by accumulating the first diagnostic deviation of at least one battery from the point in time when the fourth diagnostic deviation of at least one of the plurality of batteries exceeds the upper limit threshold until the point in time when the first diagnostic deviation is below the lower limit threshold.

8. The battery management device according to claim 7, wherein the controller diagnoses at least one of the plurality of batteries as an abnormal battery if the cumulative deviation of at least one battery exceeds a threshold deviation.

9. The battery management device according to claim 7 or 8, wherein the controller determines that at least one of the plurality of batteries is a noise battery if the cumulative deviation of at least one battery is less than or equal to a threshold deviation.

10. The steps include measuring the voltage of each of the multiple batteries, The steps include: calculating a first deviation for each of the aforementioned plurality of batteries, which is the difference between the long-term moving average value and the short-term moving average value of the battery voltage; The steps include: calculating a second deviation, which is the difference between the long-term moving average and short-term moving average of the average voltages of the aforementioned plurality of batteries; A step of calculating a first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries, The steps include determining whether the first diagnostic deviation of each of the aforementioned plurality of batteries exceeds a threshold, If the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the first diagnostic deviation is accumulated to calculate the cumulative deviation. A step of diagnosing at least one of the plurality of batteries as an abnormal battery based on the cumulative deviation, A method for operating a battery management device, including the operation of the battery management device.

11. The step of calculating a first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries is: The maximum value between the value obtained by multiplying the second deviation by the first threshold constant and the second threshold constant is set as the reference value. A method for operating a battery management device according to claim 10, wherein the second diagnostic deviation for each of the plurality of batteries is calculated by excluding the first diagnostic deviation of each of the plurality of batteries that is less than or equal to the reference value.

12. The step of calculating a first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries is: The method for operating the battery management device according to claim 11, wherein the second diagnostic deviation of each of the plurality of batteries is normalized by dividing the second diagnostic deviation of each of the plurality of batteries by the maximum value among the value obtained by multiplying the second deviation by a third threshold constant and a fourth threshold constant, and the third diagnostic deviation of each of the plurality of batteries is calculated.

13. The step of calculating a first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries is: A method for operating a battery management device according to claim 12, wherein the distortion of each of the plurality of batteries is calculated by adding the minimum value of the third diagnostic deviation to the third diagnostic deviation of each of the plurality of batteries and dividing the resulting value by the third diagnostic deviation.

14. The step of calculating a first diagnostic deviation, which is the difference between the first deviation and the second deviation, for each of the plurality of batteries is: A method for operating a battery management device according to claim 13, wherein the fourth diagnostic deviation for each of the plurality of batteries is calculated by multiplying the third diagnostic deviation for each of the plurality of batteries by the degree of distortion.

15. If the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the step of accumulating the first diagnostic deviations and calculating the cumulative deviation is as follows: If the fourth diagnostic deviation of at least one of the plurality of batteries exceeds an upper threshold, the first diagnostic deviation of the at least one battery is accumulated to calculate the accumulated deviation, a method for operating a battery management device according to claim 14.

16. If the first diagnostic deviation of at least one of the plurality of batteries exceeds a threshold, the step of accumulating the first diagnostic deviations and calculating the cumulative deviation is as follows: A method for operating a battery management device according to claim 15, comprising accumulating the first diagnostic deviation of at least one battery from the point in time when the fourth diagnostic deviation of at least one of the plurality of batteries exceeds the upper threshold until the point in time when the first diagnostic deviation is below the lower threshold, thereby calculating the accumulated deviation.

17. The step of diagnosing at least one of the plurality of batteries as an abnormal battery based on the cumulative deviation is: A method for operating a battery management device according to claim 16, wherein if the cumulative deviation of at least one of the plurality of batteries exceeds a threshold deviation, the device diagnoses the at least one battery as an abnormal battery.

18. The step of diagnosing at least one of the plurality of batteries as an abnormal battery based on the cumulative deviation is: A method for operating a battery management device according to claim 16 or 17, wherein if the cumulative deviation of at least one of the plurality of batteries is less than or equal to a threshold deviation, the at least one battery is determined to be a noise battery.

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