Battery management device and its operating method

Abstract

A battery management device according to one embodiment disclosed in this document may include: a data management unit that calculates the open-circuit voltage (OCV) of each of a plurality of batteries; a controller that calculates the average open-circuit voltage of the plurality of batteries, calculates a diagnostic deviation which is the deviation between the open-circuit voltage and the average open-circuit voltage for each of the plurality of batteries, categorizes the diagnostic deviations of each of the plurality of batteries based on the range of the average open-circuit voltage, generates diagnostic deviations for each range of the average open-circuit voltage, and diagnoses at least one of the plurality of batteries based on the diagnostic deviations for each range of the average open-circuit voltage of each of the plurality of batteries.

Description

Technical Field

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2023-0061140 filed on May 11, 2023, and all the contents disclosed in the document of the Korean 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 electrical supply from the outside to charge a battery cell, and then obtains power by driving a motor with the voltage charged in the battery cell. The battery cell of the electric vehicle is manufactured by accommodating an electrode assembly in a battery case and injecting an electrolyte into the inside of the battery case.

[0003] During production, the battery cell may generate abnormal battery cells including under-voltage (UV) faults where the voltage of the battery cell drops below a certain level due to various causes such as foreign matter, folding of the separator, or internal short circuit, or low-capacity faults where the capacity of the battery cell is smaller than that of a normal battery cell. After being installed in a vehicle, abnormal battery cells may cause voltage changes due to the use of abnormal battery cells in the vehicle, making it difficult to separately measure the abnormal voltage behavior of the abnormal battery cells themselves and diagnose the abnormal battery cells.

Summary of the Invention

Problems to be Solved by the Invention

[0004] One object of the embodiments disclosed in this document is to provide a battery management device and an operating method thereof that can early diagnose abnormal battery cells by using the deviation of the open-circuit voltage of the battery cell and ensure the safety and reliability of battery energy.

[0005] 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]

[0006] A battery management device according to one embodiment disclosed in this document may include: a data management unit that calculates the open-circuit voltage (OCV) of each of a plurality of batteries; a controller that calculates the average open-circuit voltage of the plurality of batteries, calculates a diagnostic deviation which is the deviation between the open-circuit voltage and the average open-circuit voltage for each of the plurality of batteries, categorizes the diagnostic deviations of each of the plurality of batteries based on the range of the average open-circuit voltage, generates diagnostic deviations for each range of the average open-circuit voltage, and diagnoses at least one of the plurality of batteries based on the diagnostic deviations for each range of the average open-circuit voltage of each of the plurality of batteries.

[0007] According to one embodiment, the controller calculates the mean or median of the open-circuit voltages of the plurality of batteries as the mean open-circuit voltage, and classifies the diagnostic deviations of each of the plurality of batteries into first diagnostic deviation, second diagnostic deviation, and third diagnostic deviation in order of increasing range of the mean open-circuit voltage.

[0008] According to one embodiment, the controller can diagnose at least one of the plurality of batteries based on whether the first diagnostic deviation and the third diagnostic deviation of each of the plurality of batteries are within a first threshold range.

[0009] According to one embodiment, the controller can diagnose a battery as a low-voltage battery if the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are outside the first threshold range.

[0010] According to one embodiment, the controller can diagnose a battery based on its cumulative balancing time if the first and third diagnostic deviations of at least one of the plurality of batteries are within the first threshold range, and the first and third diagnostic deviations of the battery are outside the second threshold range.

[0011] According to one embodiment, the controller can calculate the cumulative balancing time for each of the plurality of batteries, calculate a reference time based on the average or median of the cumulative balancing times of the plurality of batteries, and diagnose the battery based on whether or not its cumulative balancing time is less than the reference time.

[0012] According to one embodiment, the controller can diagnose a battery as a low-voltage battery if the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are within the first threshold range and outside the second threshold range, and the cumulative balancing time of the battery is less than the reference time.

[0013] According to one embodiment, the controller calculates a fourth diagnostic deviation, which is the difference between the average value of the first diagnostic deviation and the average value of the third diagnostic deviation for each of the plurality of batteries, and can diagnose at least one of the plurality of batteries based on whether the fourth diagnostic deviation for each of the plurality of batteries is within the third threshold range.

[0014] According to one embodiment, the controller can diagnose a battery as a low-capacity battery if the fourth diagnostic deviation of at least one of the plurality of batteries is outside the third threshold range.

[0015] A method for operating a battery management device according to one embodiment disclosed herein may include the steps of: calculating the open-circuit voltage (OCV) of each of a plurality of batteries; calculating the average open-circuit voltage of the plurality of batteries; calculating a diagnostic deviation for each of the plurality of batteries, which is the deviation between the open-circuit voltage and the average open-circuit voltage; classifying the diagnostic deviations of each of the plurality of batteries based on the range of the average open-circuit voltage and generating diagnostic deviations for each range of the average open-circuit voltage; and diagnosing at least one of the plurality of batteries based on the diagnostic deviations for each range of the average open-circuit voltage of each of the plurality of batteries.

[0016] According to one embodiment, the step of calculating the average open-circuit voltage of the plurality of batteries is to calculate the average or median value of the open-circuit voltages of the plurality of batteries as the average open-circuit voltage, and the step of classifying the diagnostic deviation of each of the plurality of batteries based on the range of the average open-circuit voltage and generating diagnostic deviations for each range of the average open-circuit voltage is to classify the diagnostic deviation of each of the plurality of batteries into a first diagnostic deviation, a second diagnostic deviation, and a third diagnostic deviation in order from the lowest average open-circuit voltage range.

[0017] According to one embodiment, the step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries can be performed to diagnose at least one of the plurality of batteries based on whether the first diagnostic deviation and the third diagnostic deviation of each of the plurality of batteries are within a first threshold range.

[0018] According to one embodiment, the step of diagnosing at least one of the plurality of batteries based on the diagnostic deviations for each range of the average open-circuit voltage of each of the plurality of batteries allows the battery to be diagnosed as a low-voltage battery if the first and third diagnostic deviations of at least one of the plurality of batteries are outside the first threshold range.

[0019] According to one embodiment, the step of diagnosing at least one of the plurality of batteries based on the diagnostic deviations for each range of the average open-circuit voltage of each of the plurality of batteries is as follows: If the first and third diagnostic deviations of at least one of the plurality of batteries are within the first threshold range, and the first and third diagnostic deviations of the battery are outside the second threshold range, the battery can be diagnosed based on the cumulative balancing time of the battery.

[0020] According to one embodiment, the step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries can be performed by calculating the cumulative balancing time of each of the plurality of batteries, calculating a reference time based on the average or median of the cumulative balancing times of the plurality of batteries, and diagnosing the battery based on whether or not the cumulative balancing time of the battery is less than the reference time.

[0021] According to one embodiment, the step of diagnosing at least one of the plurality of batteries based on the diagnostic deviations for each range of the average open-circuit voltage of each of the plurality of batteries is that if the first and third diagnostic deviations of at least one of the plurality of batteries are within the first threshold range and outside the second threshold range, and the cumulative balancing time of the battery is less than the reference time, the battery can be diagnosed as a low-voltage battery.

[0022] According to one embodiment, the step of diagnosing at least one of the plurality of batteries based on the diagnostic deviations for each range of average open-circuit voltage of each of the plurality of batteries involves calculating a fourth diagnostic deviation, which is the difference between the average value of the first diagnostic deviation and the average value of the third diagnostic deviation for each of the plurality of batteries, and diagnosing at least one of the plurality of batteries based on whether or not the fourth diagnostic deviation for each of the plurality of batteries is within the third threshold range.

[0023] According to one embodiment, based on the diagnostic deviation for each range of the average open circuit voltage of each of the plurality of batteries, the step of diagnosing at least one battery among the plurality of batteries can diagnose the battery as a low-capacity battery when the fourth diagnostic deviation of at least one battery among the plurality of batteries is outside the third threshold range.

Advantages of the Invention

[0024] According to the battery management device and its operation method according to one embodiment disclosed in this document, by using the deviation of the open circuit voltage of the battery cell, abnormal battery cells can be diagnosed early, and the safety and reliability of battery energy can be ensured.

Brief Description of the Drawings

[0025] [Figure 1] It is a diagram showing a battery pack according to one embodiment disclosed in this document. [Figure 2] It is a block diagram showing the configuration of a battery management device according to one embodiment disclosed in this document. [Figure 3] It is a graph showing the change in voltage of a battery cell according to one embodiment disclosed in this document. [Figure 4] It is a graph showing the change in open circuit voltage of a battery cell according to one embodiment disclosed in this document. [Figure 5] It is a graph showing the change in diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 6a] It is a graph showing the change in the first diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 6b] It is a graph showing the change in the third diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 7] It is a graph showing the cumulative balancing time of a battery cell according to one embodiment disclosed in this document. [Figure 8a] It is a graph showing the change in the first diagnostic deviation of a battery cell according to another embodiment disclosed in this document. [Figure 8b]This graph shows the change in the third diagnostic deviation of a battery cell according to another embodiment disclosed in this document. [Figure 9a] This graph shows the change in the first diagnostic deviation of a battery cell according to another embodiment disclosed in this document. [Figure 9b] This graph shows the change in the third diagnostic deviation of a battery cell according to another embodiment disclosed in this document. [Figure 10] This is a graph showing the fourth diagnostic deviation of a battery cell according to one embodiment disclosed in this document. [Figure 11] This is a flowchart showing the operation method of a battery management device according to one embodiment disclosed in this document. [Figure 12] This is a flowchart showing a diagnostic method for a low-voltage battery cell according to one embodiment disclosed in this document. [Figure 13] This is a flowchart showing a diagnostic method for low-capacity battery cells according to one embodiment disclosed in this document. [Figure 14] 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]

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

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

[0028] Figure 1 shows a battery pack according to one embodiment disclosed in this document. 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.

[0029] Although Figure 1 shows only one battery module 100, the battery pack 1000 may be composed of multiple battery modules forming a stacked structure. The battery module 100 may include multiple battery cells 110, 120, 130, and 140. Although Figure 1 shows a configuration with four battery cells, the battery module 100 may be composed of n (where n is a natural number greater than or equal to 2) battery cells.

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

[0031] 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.

[0032] The battery management device 200 can manage and / or control the state and / or operation of the battery module 100. For example, the battery management device 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 device 200 can manage the charging and / or discharging of the battery module 100.

[0033] 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. 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. 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 within the battery module 100. Based on the measured values ​​of the monitored voltage, current, temperature, etc., the battery management device 200 can calculate parameters indicating the state of the battery module 100, such as the State of Charge (SOC).

[0034] The battery management device 200 may include a balancing circuit (not provided). Here, the balancing circuit refers to a circuit consisting of resistors and switching elements connected to both ends of each of the multiple battery cells 110, 120, 130, and 140. The battery management device 200 can transmit control signals to the balancing circuit and turn the switching elements ON / OFF. By controlling the ON / OFF state of the switching elements in the balancing circuit, the battery management device 200 can control the connection of resistors, thereby consuming the balancing current of the battery cells and lowering the voltage, thus adjusting the voltage of each battery cell to be equal.

[0035] Specifically, the battery management device 200 can balance the voltages of multiple battery cells 110, 120, 130, and 140 using a balancing circuit when the voltage deviation between multiple battery cells 110, 120, 130, and 140, i.e., the SOC deviation, is above a certain level. For example, if the difference between the maximum and minimum voltages among the multiple battery cells 110, 120, 130, and 140 is above a pre-set threshold level, the battery management device 200 can balance the battery cell corresponding to the maximum voltage. The battery management device 200 can set a target voltage for the battery cell for balancing, and when the voltage of the battery cell reaches the target voltage, it can transmit a control signal to the balancing circuit and terminate the balancing.

[0036] The battery management device 200 can calculate the balancing time for each of the multiple battery cells 110, 120, 130, and 140. Here, the balancing time is the time required to balance the battery cells. For example, the battery management device 200 can calculate the balancing time based on the state of charge (SOC), battery capacity, and balancing efficiency of each of the multiple battery cells 110, 120, 130, and 140.

[0037] The battery management device 200 can analyze the open-circuit voltage (OCV) of a battery cell. Here, the open-circuit voltage (OCV) refers to the voltage measured when no current flows through the battery. In other words, the open-circuit voltage is the voltage between the anode and cathode of the battery when they are not electrically connected. According to Ohm's law, as the resistance of the battery increases infinitely, the current approaches "0", allowing for accurate measurement of the battery voltage. Therefore, the battery management device 200 can measure and analyze the OCV of a battery cell for accurate electrochemical analysis of the battery cell.

[0038] According to one embodiment, the battery management device 200 can diagnose whether at least one of the multiple battery cells 110, 120, 130, and 140 is under voltage by using the deviation (dV) of the open-circuit voltage of each of the multiple battery cells 110, 120, 130, and 140 and the cumulative balancing time of each of the multiple battery cells 110, 120, 130, and 140. In the case of undervoltage battery cells, a state of charge (SOC) deviation from normal battery cells occurs over time due to the self-discharge phenomenon. Therefore, a battery pack containing undervoltage battery cells will perform balancing of the battery cells using a balancing circuit to eliminate the SOC deviation between battery cells, and the cumulative balancing time of the battery pack will increase. In this case, the balancing circuit will perform balancing on the battery cell with the highest SOC rather than the battery cell with the lowest SOC, and the balancing time of the undervoltage battery cells included in the battery pack will be recorded as relatively shorter compared to the other normal battery cells. Therefore, the battery management device 200 can diagnose low-voltage battery cells using the deviation of open-circuit voltages and balancing time of multiple battery cells 110, 120, 130, and 140.

[0039] According to one embodiment, the battery management device 200 can diagnose low-capacity battery cells among the multiple battery cells 110, 120, 130, and 140, including battery cells in which a wire break has occurred in the electrode tab, or battery cells in which both a wire break in the electrode tab and lithium deposition have occurred, using the deviation (dV) of the open-circuit voltage of the multiple battery cells 110, 120, 130, and 140. Here, lithium deposition is a phenomenon in which lithium ions released from the positive electrode during charging of the battery cell cannot chemically bond with the negative electrode, and the lithium ions exist on the surface of the negative electrode in the form of metal. In the case of a normal battery cell, lithium ions released from the positive electrode of the battery cell during charging are reduced into the negative electrode, but in the case of a low-capacity battery cell, some lithium ions may be deposited on the surface of the negative electrode in the form of lithium metal. If the lithium deposition phenomenon is repeated and lithium byproducts grow, they may come into contact with the positive electrode or the positive electrode current collector, and an internal short circuit (Inner Short) may occur between the negative electrode and the positive electrode of the battery cell. In the case of a battery cell with an internal short circuit, a deviation in the open-circuit voltage compared to a normal battery cell may occur over time due to self-discharge. Therefore, the battery management device 200 can diagnose low-capacity battery cells using the deviation in the open-circuit voltage of multiple battery cells 110, 120, 130, and 140.

[0040] 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.

[0041] Figure 2 is a diagram for specifically illustrating 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.

[0042] Referring to Figure 2, the battery management device 200 may include a data management unit 210 and a controller 220. The data management unit 210 can calculate the voltage of each of the multiple battery cells 110, 120, 130, and 140. The data management 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.

[0043] Figure 3 is a graph showing the voltage change of a battery cell according to one embodiment disclosed in this document. Referring to Figure 3, the data management unit 210 can continuously calculate the voltage rise and fall during charging, the post-charging rest period, the discharge, and the post-discharge rest period for multiple battery cells 110, 120, 130, and 140, as well as long-term stabilization (relaxation) data.

[0044] According to one embodiment, the data management unit 210 can calculate time-series data of the voltages of multiple battery cells 110, 120, 130, and 140 for each battery module. For example, if the battery pack 1000 includes a total of eight battery modules, and each battery module includes 16 battery cells, the data management unit 210 can calculate time-series data of the voltages of the 16 battery cells for each battery module.

[0045] The data management unit 210 can calculate the open-circuit voltage (OCV) of each of the multiple battery cells 110, 120, 130, and 140, which are the voltage values ​​measured when the current value of the battery cell approaches "0" from the voltage data of each of the multiple battery cells 110, 120, 130, and 140. The data management unit 210 can calculate the open-circuit voltage of each of the multiple battery cells 110, 120, 130, and 140 for accurate electrochemical analysis of each of the multiple battery cells 110, 120, 130, and 140.

[0046] Figure 4 is a graph showing the change in the open-circuit voltage of a battery cell according to one embodiment disclosed in this document. Referring to Figure 4, the data management unit 210 can continuously calculate the open-circuit voltage of each of the multiple battery cells 110, 120, 130, and 140, and calculate time-series data of the open-circuit voltages of each of the multiple battery cells 110, 120, 130, and 140.

[0047] According to one embodiment, the data management unit 210 can calculate time-series data of the open-circuit voltages of multiple battery cells 110, 120, 130, and 140 for each battery module.

[0048] According to one embodiment, the controller 220 can diagnose whether at least one of the battery cells 110, 120, 130, and 140 is under voltage by using the deviation (dV) of the open-circuit voltage of each of the battery cells 110, 120, 130, and 140 and the cumulative balancing time of each of the battery cells 110, 120, 130, and 140.

[0049] First, the controller 220 can calculate the average open-circuit voltage (V_avg) of multiple battery cells 110, 120, 130, and 140. For example, the controller 220 can calculate the average value (Mean), median, or minimum value (Min) of the open-circuit voltages of multiple battery cells 110, 120, 130, and 140 as the average open-circuit voltage (V_avg) of multiple battery cells 110, 120, 130, and 140.

[0050] According to one embodiment, the controller 220 can calculate the average open-circuit voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 for each battery module. For example, if the battery pack 1000 includes a total of eight battery modules, and each battery module includes 16 battery cells, the controller 220 can calculate the average open-circuit voltage (V_avg), which is the average of the open-circuit voltages of the 16 battery cells for each battery module.

[0051] The controller 220 can calculate the voltage difference (dV) between the open-circuit voltage and the average open-circuit voltage (V_avg) for each of the multiple battery cells 110, 120, 130, and 140.

[0052] Specifically, the controller 220 can calculate the diagnostic deviation (dV) for each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 1].

[0053] [Formula 1] Diagnostic deviation (dV) = Open-circuit voltage (OCV) - Average open-circuit voltage (V_avg)

[0054] Referring to [Equation 1], the controller 220 can calculate the deviation between the open-circuit voltage of each of the multiple battery cells 110, 120, 130, and 140 and the average open-circuit voltage (V_avg) as the diagnostic deviation (dV).

[0055] Figure 5 is a graph showing the change in diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Referring to Figure 5, the controller 220 can calculate the deviation between the open-circuit voltage (OCV) of each of the multiple battery cells 110, 120, 130, and 140 and the average open-circuit voltage (V_avg) for each battery module. For example, if the battery pack 1000 contains a total of 8 battery modules, and each battery module contains 16 battery cells, the controller 220 can calculate the average open-circuit voltage (V_avg), which is the average of the open-circuit voltages of the 16 battery cells for each battery module, and can calculate the diagnostic deviation (dV), which is the deviation of the open-circuit voltage of each battery cell relative to the average open-circuit voltage (V_avg) for each battery module. Battery cells contained in the same battery module tend to exhibit the same characteristics such as scale error, offset error, and noise on a battery module basis. Therefore, in order to normalize the different characteristics of each battery module, the controller 220 can calculate the diagnostic deviation (dV), which is the deviation of each battery cell relative to the average open-circuit voltage (V_avg) of the battery cells contained in the battery module.

[0056] The controller 220 can calculate the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 for each unit of time, and calculate time-series data of the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0057] The controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 based on the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140. For example, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 contained in each battery module based on the range of the average open-circuit voltage (V_avg) for each battery module.

[0058] Specifically, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 based on the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140, or the range of the State of Charge (SOC) of the battery pack 1000 including the multiple battery cells 110, 120, 130, and 140.

[0059] The controller 220 can divide the range of average open-circuit voltages (V_avg) of the multiple battery cells 110, 120, 130, and 140 into three sections, the first, second, and third, in descending order of average open-circuit voltage (V_avg) or SOC of the battery pack 1000. For example, the first section can be defined as the section where the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is around 3500mv, or the SOC of the battery pack 1000 is 20% to 30%. Similarly, the second section can be defined as the section where the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is around 3800mv, or the SOC of the battery pack 1000 is 45% to 55%. Furthermore, for example, the third interval can be defined as the interval in which the average open-circuit voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 is around 4100mV, or the SOC of battery pack 1000 is between 85% and 99%.

[0060] The controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a first diagnostic deviation (dV1), a second diagnostic deviation (dV2), and a third diagnostic deviation (dV3) based on the range of the average open-circuit voltage (V_avg). Specifically, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a first diagnostic deviation (dV1) if the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 falls within a first interval. Furthermore, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a second diagnostic deviation (dV2) if the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 falls within a second interval. Additionally, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a third diagnostic deviation (dV3) if the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 falls within a third interval.

[0061] Figure 6a is a graph showing the change in the first diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Figure 6b is a graph showing the change in the third diagnostic deviation of a battery cell according to one embodiment disclosed in this document.

[0062] Referring to Figures 6a and 6b, the controller 220 can calculate time-series data of the first diagnostic deviation (dV1) and the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140. Based on the time-series data of the first diagnostic deviation (dV1) and the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140.

[0063] Specifically, the controller 220 analyzes the time-series data of the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140. If the time-series data of the first diagnostic deviation (dV1) for at least one of the multiple battery cells 110, 120, 130, and 140 is continuously outside the threshold range, and the time-series data of the third diagnostic deviation (dV3) for that battery cell is continuously outside the threshold range, the controller 220 can diagnose that battery cell as a low-voltage battery cell.

[0064] The controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140 based on whether the time-series data of the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of each of the multiple battery cells 110, 120, 130, and 140 are within a first threshold range. Specifically, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140 as a low-voltage battery cell if the first diagnostic deviation (dV1) of that battery cell is outside the first threshold range and the third diagnostic deviation (dV3) of that battery cell is also outside the first threshold range. Here, the first threshold range is the criterion for determining that a battery cell is a low-voltage battery cell. That is, the first threshold range is a criterion that shows how much the first diagnostic deviation (dV1) of a battery cell differs from the deviation of the open-circuit voltage of a normal battery cell. For example, the first threshold range may be (-)20mV to (+)20mV.

[0065] The controller 220 can determine whether or not a battery cell is a low-voltage battery cell based on the cumulative balancing time of at least one of the multiple battery cells 110, 120, 130, and 140, if the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of that battery cell are within the first threshold range and outside the second threshold range. Here, the second threshold range is the range of threshold voltages that the balancing circuit of the battery management device 200 can correct through balancing operation.

[0066] Specifically, the controller 220 can determine whether or not a battery cell is a low-voltage battery cell based on its cumulative balancing time, if the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of at least one of the multiple battery cells 110, 120, 130, and 140 do not exceed the first threshold range, which is the reference range for determining whether a battery cell is a low-voltage battery cell, but are outside the second threshold range.

[0067] Figure 7 is a graph showing the cumulative balancing time of a battery cell according to one embodiment disclosed in this document. Referring to Figure 7, the controller 220 compares the cumulative balancing time of each of the multiple battery cells 110, 120, 130, and 140 with a reference time, and can diagnose at least one of the multiple battery cells 110, 120, 130, and 140.

[0068] The controller 220 can calculate the balancing time for each of the multiple battery cells 110, 120, 130, and 140 based on their respective SOC, battery capacity, and balancing efficiency. For example, the controller 220 can measure the time it takes for the switching elements of the balancing circuits connected to each of the battery cells 110, 120, 130, and 140 to turn ON and for balancing to be performed for each battery cell, and calculate the balancing time for each of the multiple battery cells 110, 120, 130, and 140.

[0069] The controller 220 can repeatedly measure the balancing time of each of the multiple battery cells 110, 120, 130, and 140 at a preset period and record it in memory (not provided). For example, the controller 220 can store the balancing times of each of the multiple battery cells 110, 120, 130, and 140 separately, or store them separately for each battery module 100 that contains the multiple battery cells 110, 120, 130, and 140.

[0070] The controller 220 can accumulate the balancing times of each of the multiple battery cells 110, 120, 130, and 140 and record them in memory. In other words, the controller 220 can calculate the cumulative balancing time of each of the multiple battery cells 110, 120, 130, and 140.

[0071] According to one embodiment, the controller 220 can manage the cumulative balancing time separately for each of the multiple battery cells 110, 120, 130, and 140 based on the battery cell number of each of the multiple battery cells 110, 120, 130, and 140. According to another embodiment, the controller 220 can manage the cumulative balancing time separately for each battery module based on the battery module number of the battery module 100 that contains each of the multiple battery cells 110, 120, 130, and 140. For example, if the battery pack 1000 contains a total of 8 battery modules, and each battery module contains 16 battery cells, the data management unit 210 can manage the cumulative balancing time of the 128 battery cells separately based on the battery cell number of each of the 128 battery cells and the battery module number of the battery module that contains each battery cell.

[0072] The controller 220 can sort multiple battery cells 110, 120, 130, and 140 based on their cumulative balancing time, and extract battery cells within a threshold rank from among the multiple battery cells 110, 120, 130, and 140. For example, the controller 220 can sort multiple battery cells 110, 120, 130, and 140 based on their cumulative balancing time, and extract the remaining battery cells 110, 120, 130, and 140 that fall into the top 10% and bottom 10%.

[0073] The controller 220 can calculate the average (Mean) or median (Median) of the cumulative balancing times of multiple battery cells 110, 120, 130, and 140 as the average balancing time of the multiple battery cells 110, 120, 130, and 140. Based on the average balancing time of the multiple battery cells 110, 120, 130, and 140, the controller 220 can calculate a reference time. Here, the reference time is a criterion for diagnosing a battery cell as a low-voltage battery cell. In other words, the reference time is a criterion that shows how much the balancing time of a battery cell deviates from the balancing time of a normal battery cell.

[0074] The controller 220 can generate a reference time based on the following [Equation 2].

[0075] [Formula 2] Reference time = Average balancing time - Standard deviation (σ) × Threshold constant

[0076] Referring to [Equation 2], the controller 220 can calculate the standard deviation (σ) of the cumulative balancing time for each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 can calculate the standard deviation (σ) of the cumulative balancing time for each of the battery cells within a threshold rank. For example, the controller 220 can rank the multiple battery cells 110, 120, 130, and 140 based on their cumulative balancing time, and calculate the standard deviation (σ) of the cumulative balancing time for the remaining multiple battery cells 110, 120, 130, and 140, excluding the top 10% and bottom 10% of the battery cells 110, 120, 130, and 140.

[0077] The controller 220 can calculate a reference time by subtracting a value obtained by multiplying the standard deviation (σ) by a threshold constant from the average balancing time of multiple battery cells 110, 120, 130, and 140.

[0078] The controller 220 can diagnose a battery cell as a low-voltage battery cell if at least one of the multiple battery cells 110, 120, 130, and 140 has a first diagnostic deviation (dV1) and a third diagnostic deviation (dV3) within a first threshold range and outside a second threshold range, and the cumulative balancing time of that battery cell is less than a reference time. Specifically, the controller 220 can diagnose a battery cell as a low-voltage battery cell if at least one of the multiple battery cells 110, 120, 130, and 140 has a first diagnostic deviation (dV1) and a third diagnostic deviation (dV3) within a first threshold range and outside a second threshold range, and the cumulative balancing time of that battery cell is less than a reference time.

[0079] According to one embodiment, the controller 220 can diagnose whether at least one of the multiple battery cells 110, 120, 130, and 140 has low capacity, based on the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of each of the multiple battery cells 110, 120, 130, and 140. Battery cells may experience disconnection of the positive or negative electrode tab due to various causes such as defects during the production stage, internal deformation and modification due to multiple charge and discharge cycles, or external impact. In this case, if both lithium deposition and the problem of disconnection of the electrode tab occur in the battery cell, the electrode of the disconnected battery cell and the electrode of a normal battery cell may become connected to each other by lithium deposits. If the negative electrode of the disconnected battery cell has a higher state of charge (SOC) than the negative electrode of a normal battery, the negative electrodes of the two battery cells may come into contact by lithium deposits, and charging may occur from the negative electrode of the disconnected battery cell to the negative electrode of the normal battery. Therefore, low-capacity battery cells in which both lithium deposition and electrode tab disconnection problems occur may experience faster changes in open-circuit voltage compared to normal battery cells.

[0080] Therefore, the controller 220 can diagnose low-capacity battery cells that have experienced both electrode tab breakage and lithium deposition, by comparing the open-circuit voltage data of a battery cell with the statistically normal open-circuit voltage data of a normal battery cell, using the phenomenon that low-capacity battery cells that have experienced both electrode tab breakage and lithium deposition exhibit a faster and larger change in open-circuit voltage compared to normal battery cells.

[0081] Figure 8a is a graph showing the change in the first diagnostic deviation of a battery cell according to another embodiment disclosed in this document. Figure 8b is a graph showing the change in the third diagnostic deviation of a battery cell according to another embodiment disclosed in this document.

[0082] Referring to Figure 8a, the controller 220 can calculate the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140 for each unit of time, and calculate time-series data of the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140.

[0083] Referring to Figure 8b, the controller 220 can calculate the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140 for each unit of time, and calculate time-series data of the third diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140.

[0084] The controller 220 can calculate the mean or median of the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140 over a unit of time. The controller 220 can calculate the mean or median of the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140 over a unit of time as the average of the first diagnostic deviations (dV1_avg).

[0085] Furthermore, the controller 220 can calculate the mean or median of the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140 over a unit of time. The controller 220 can calculate the mean or median of the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140 over a unit of time as the mean of the third diagnostic deviation (dV3_avg).

[0086] Figure 9a is a graph showing the change in the first diagnostic deviation of a battery cell according to another embodiment disclosed in this document. Figure 9b is a graph showing the change in the third diagnostic deviation of a battery cell according to another embodiment disclosed in this document.

[0087] Referring to Figure 9a, the controller 220 can calculate time-series data of the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140.

[0088] Referring to Figure 9b, the controller 220 can calculate time-series data of the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140.

[0089] The controller 220 can diagnose low-capacity battery cells by calculating the difference between the time-series data of the first diagnostic deviation and the time-series data of the third diagnostic deviation for each of the multiple battery cells 110, 120, 130, and 140. Specifically, the controller 220 calculates the difference between the average value (dV1_avg) of the first diagnostic deviation for each of the multiple battery cells 110, 120, 130, and 140 corresponding to the first interval in which the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is relatively low, and the average value (dV3_avg) of the third diagnostic deviation for each of the multiple battery cells 110, 120, 130, and 140 corresponding to the third interval in which the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is relatively high, thereby diagnosing low-capacity battery cells in which the open-circuit voltage changes are faster and larger compared to normal battery cells.

[0090] Specifically, the controller 220 can calculate the difference between the average value of the first diagnostic deviation (dV1_avg) and the average value of the third diagnostic deviation (dV3_avg) for each of the multiple battery cells 110, 120, 130, and 140 as the fourth diagnostic deviation (dV4) for each of the multiple battery cells 110, 120, 130, and 140.

[0091] The controller 220 can calculate the fourth diagnostic deviation (dV4) for each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 3].

[0092] [Formula 3] The fourth diagnostic deviation (dV4) = Average value of the first diagnostic deviation (dV1_avg) - Average value of the third diagnostic deviation (dV3_avg)

[0093] The controller 220 can calculate the fourth diagnostic deviation (dV4) for each of the multiple battery cells 110, 120, 130, and 140 by calculating the deviation between the average value of the first diagnostic deviation (dV1_avg) and the average value of the third diagnostic deviation (dV3_avg). Using the fourth diagnostic deviation (dV4) for each of the multiple battery cells 110, 120, 130, and 140, the controller 220 can diagnose whether at least one of the multiple battery cells 110, 120, 130, and 140 is low capacity.

[0094] Figure 10 is a graph showing the fourth diagnostic deviation of a battery cell according to one embodiment disclosed in this document. Referring to Figure 10, the controller 220 can diagnose a battery cell as a low-capacity battery cell if the fourth diagnostic deviation (dV4) of at least one of the multiple battery cells 110, 120, 130, and 140 is outside the third threshold range. Here, the third threshold range is the criterion for determining that a battery cell is a low-capacity battery cell. For example, the third threshold range may be, for example, (-)15mV to (+)5mV.

[0095] Furthermore, if the diagnosis confirms that a low-voltage or low-capacity battery cell has occurred, the controller 220 can provide information about the battery cell to the user. For example, the controller 220 can provide information about the low-voltage or low-capacity battery cell 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.

[0096] As described above, according to the battery management device according to one embodiment disclosed in this document, by using the deviation of the open-circuit voltage of the battery cell, it is possible to diagnose abnormal battery cells early and ensure the safety and reliability of the battery energy.

[0097] Furthermore, the battery management device 200 diagnoses abnormal battery cells using the open-circuit voltage deviation of the battery cells while the battery cells are installed in the vehicle, eliminating the need for separate isolation and allowing for quick and easy diagnosis of abnormal battery cells.

[0098] Figure 11 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 10.

[0099] Since the battery management device 200 is substantially the same as the battery management device 200 described with reference to Figures 1 to 10, a brief description will be given below to avoid repetition.

[0100] Referring to Figure 11, the operation method of the battery management device may include the steps of: calculating the open-circuit voltage (OCV) of each of the multiple batteries (S101); calculating the average open-circuit voltage of the multiple batteries (S102); calculating the diagnostic deviation, which is the deviation between the open-circuit voltage and the average open-circuit voltage, for each of the multiple batteries (S103); classifying the diagnostic deviations of each of the multiple batteries based on the range of the average open-circuit voltage and generating diagnostic deviations for each range of the average open-circuit voltage (S104); and diagnosing at least one of the multiple batteries based on the diagnostic deviations for each range of the average open-circuit voltage of each of the multiple batteries (S105).

[0101] The following provides a detailed explanation of steps S101 through S105. In step S101, the data management unit 210 can calculate the voltage of each of the multiple battery cells 110, 120, 130, and 140. The data management 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.

[0102] In step S101, the data management unit 210 can continuously calculate the voltage rise and fall during charging, the post-charging rest period, the discharge, and the post-discharge rest period for multiple battery cells 110, 120, 130, and 140, as well as long-term stabilization (relaxation) data.

[0103] In step S101, according to one embodiment, the data management unit 210 can calculate time-series data of the voltages of multiple battery cells 110, 120, 130, and 140 for each battery module. For example, if the battery pack 1000 includes a total of eight battery modules, and each battery module includes 16 battery cells, the data management unit 210 can calculate time-series data of the voltages of 16 battery cells for each battery module.

[0104] In step S101, the data management unit 210 can calculate the open-circuit voltage (OCV) of each of the multiple battery cells 110, 120, 130, and 140, which are the voltage values ​​measured when the current value of the battery cell approaches "0" from the voltage data of each of the multiple battery cells 110, 120, 130, and 140.

[0105] In step S101, the data management unit 210 can continuously calculate the open-circuit voltage of each of the multiple battery cells 110, 120, 130, and 140, and calculate time-series data of the open-circuit voltages of each of the multiple battery cells 110, 120, 130, and 140.

[0106] In step S101, according to one embodiment, the data management unit 210 can calculate time-series data of the open-circuit voltages of each of the multiple battery cells 110, 120, 130, and 140 for each battery module.

[0107] In step S102, the controller 220 can calculate the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140. For example, the controller 220 can calculate the average value (Mean), median, or minimum value (Min) of the open-circuit voltages of the multiple battery cells 110, 120, 130, and 140 as the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140.

[0108] In step S102, the controller 220 can calculate the average open-circuit voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 for each battery module. For example, if the battery pack 1000 contains a total of 8 battery modules, and each battery module contains 16 battery cells, the controller 220 can calculate the average open-circuit voltage (V_avg), which is the average of the open-circuit voltages of the 16 battery cells for each battery module.

[0109] In step S103, the controller 220 can calculate the voltage difference (dV) between the open-circuit voltage and the average open-circuit voltage (V_avg) for each of the multiple battery cells 110, 120, 130, and 140.

[0110] In step S103, the controller 220 can specifically calculate the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 4].

[0111] [Formula 4] Diagnostic deviation (dV) = Open-circuit voltage (OCV) - Average open-circuit voltage (V_avg)

[0112] Referring to [Equation 4], the controller 220 can calculate the deviation (dV) between the open-circuit voltage of each of the multiple battery cells 110, 120, 130, and 140 and the average open-circuit voltage (V_avg) as the diagnostic deviation (dV).

[0113] In step S103, the controller 220 can calculate the deviation between the open-circuit voltage (OCV) of each of the multiple battery cells 110, 120, 130, and 140 and the average open-circuit voltage (V_avg) for each battery module. For example, if the battery pack 1000 contains a total of 8 battery modules, and each battery module contains 16 battery cells, the controller 220 can calculate the average open-circuit voltage (V_avg), which is the average of the open-circuit voltages of the 16 battery cells for each battery module, and can calculate the diagnostic deviation (dV), which is the deviation of the open-circuit voltage of each battery cell relative to the average open-circuit voltage (V_avg) for each battery module.

[0114] In step S103, the controller 220 calculates the diagnostic deviation (dV) 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 diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140.

[0115] In step S104, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 based on the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140. For example, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 contained in each battery module based on the range of the average open-circuit voltage (V_avg) for each battery module.

[0116] In step S104, specifically, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 based on the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140, or the range of the State of Charge (SOC) of the battery pack 1000 including the multiple battery cells 110, 120, 130, and 140.

[0117] In step S104, the controller 220 can divide the range of average open-circuit voltages (V_avg) of the multiple battery cells 110, 120, 130, and 140 into a first, second, and third section in order of decreasing average open-circuit voltage (V_avg) or SOC of the battery pack 1000. For example, the first section can be defined as the section where the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is around 3500mv, or the SOC of the battery pack 1000 is 20% to 30%. Similarly, the second section can be defined as the section where the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is around 3800mv, or the SOC of the battery pack 1000 is 45% to 55%. Furthermore, for example, the third interval can be defined as the interval in which the average open-circuit voltage (V_avg) of multiple battery cells 110, 120, 130, and 140 is around 4100mV, or the SOC of battery pack 1000 is between 85% and 99%.

[0118] In step S104, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a first diagnostic deviation (dV1), a second diagnostic deviation (dV2), and a third diagnostic deviation (dV3) based on the range of the average open-circuit voltage (V_avg). Specifically, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a first diagnostic deviation (dV1) if the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 falls within a first interval. Furthermore, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a second diagnostic deviation (dV2) if the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 falls within a second interval. Additionally, the controller 220 can classify the diagnostic deviation (dV) of each of the multiple battery cells 110, 120, 130, and 140 into a third diagnostic deviation (dV3) if the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 falls within a third interval.

[0119] In step S105, the controller 220 can calculate time-series data of the first diagnostic deviation (dV1) and the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140. In step S104, the controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130, and 140 based on the time-series data of the first diagnostic deviation (dV1) and the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140.

[0120] In step S105, the controller 220 analyzes the time-series data of the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140. If the time-series data of the first diagnostic deviation (dV1) for at least one of the multiple battery cells 110, 120, 130, and 140 is continuously outside the threshold range, and the time-series data of the third diagnostic deviation (dV3) for that battery cell is continuously outside the threshold range, the controller 220 can diagnose that battery cell as a low-voltage battery cell.

[0121] Figure 12 is a flowchart showing a diagnostic method for a low-voltage battery cell according to one embodiment disclosed in this document. The following describes in detail how the controller 220 diagnoses at least one of the multiple battery cells 110, 120, 130, and 140 based on whether the time-series data of the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140 are within the first threshold range, with reference to Figure 12.

[0122] In step S201, the controller 220 can calculate time-series data of the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140.

[0123] In step S202, the controller 220 can determine whether the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of each of the multiple battery cells 110, 120, 130, and 140 are within the first threshold range. Here, the first threshold range is the criterion for determining that a battery cell is a low-voltage battery cell. That is, the first threshold range is a criterion that shows how much the first diagnostic deviation (dV1) of a battery cell differs from the deviation of the open-circuit voltage of a normal battery cell. For example, the first threshold range may be (-)20mV to (+)20mV.

[0124] In step S203, the controller 220 can determine whether the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of each of the multiple battery cells 110, 120, 130, and 140 are within the second threshold range. Here, the second threshold range is the range of threshold voltages that the balancing circuit of the battery management device 200 can correct through balancing operation.

[0125] In step S204, the controller 220 can calculate the cumulative balancing time of at least one of the multiple battery cells 110, 120, 130, and 140 if its first diagnostic deviation (dV1) and third diagnostic deviation (dV3) are within the first threshold range and outside the second threshold range. Specifically in step S204, the controller 220 can calculate the cumulative balancing time of at least one of the multiple battery cells 110, 120, 130, and 140 if its first diagnostic deviation (dV1) and third diagnostic deviation (dV3) do not exceed the first threshold range, which is the reference range for determining that it is a low-voltage battery cell, but are outside the second threshold range.

[0126] In step S204, the controller 220 can calculate the balancing time for each of the multiple battery cells 110, 120, 130, and 140 based on their respective SOC, battery capacity, and balancing efficiency. For example, the controller 220 can measure the time it takes for the switching elements of the balancing circuits connected to each of the battery cells 110, 120, 130, and 140 to turn ON and for balancing to be performed for each battery cell, and calculate the balancing time for each of the multiple battery cells 110, 120, 130, and 140.

[0127] In step S204, the controller 220 can repeatedly measure the balancing time of each of the multiple battery cells 110, 120, 130, and 140 at a preset period and record it in memory (not provided). For example, the controller 220 can store the balancing times of each of the multiple battery cells 110, 120, 130, and 140 separately, or store them separately for each battery module 100 that contains the multiple battery cells 110, 120, 130, and 140.

[0128] In step S204, the controller 220 can accumulate the balancing times of each of the multiple battery cells 110, 120, 130, and 140 and record them in memory. In other words, the controller 220 can calculate the cumulative balancing time of each of the multiple battery cells 110, 120, 130, and 140.

[0129] In step S204, according to one embodiment, the controller 220 can manage the cumulative balancing time separately for each of the multiple battery cells 110, 120, 130, and 140 based on the battery cell number of each of the multiple battery cells 110, 120, 130, and 140. According to another embodiment, the controller 220 can manage the cumulative balancing time separately for each battery module based on the battery module number of the battery module 100 which contains each of the multiple battery cells 110, 120, 130, and 140.

[0130] In step S204, the controller 220 can calculate the average (Mean) or median (Median) of the cumulative balancing time of the multiple battery cells 110, 120, 130, and 140 as the average balancing time of the multiple battery cells 110, 120, 130, and 140.

[0131] In step S205, the controller 220 can calculate a reference time based on the average balancing time of multiple battery cells 110, 120, 130, and 140. Here, the reference time is a criterion for diagnosing a battery cell as a low-voltage battery cell. In other words, the reference time is a criterion that shows how much the balancing time of a battery cell deviates from the balancing time of a normal battery cell. In step S205, the controller 220 can generate a reference time based on the following [Equation 5].

[0132] [Formula 5] Reference time = Average balancing time - Standard deviation (σ) × Threshold constant

[0133] In step S205, referring to [Equation 5], the controller 220 can calculate the standard deviation (σ) of the cumulative balancing time for each of the multiple battery cells 110, 120, 130, and 140. According to one embodiment, the controller 220 can calculate the standard deviation (σ) of the cumulative balancing time for each of the battery cells within a threshold rank. For example, the controller 220 can rank the multiple battery cells 110, 120, 130, and 140 based on their cumulative balancing time, and calculate the standard deviation (σ) of the cumulative balancing time for the remaining multiple battery cells 110, 120, 130, and 140, excluding the top 10% and bottom 10% of the battery cells 110, 120, 130, and 140.

[0134] In step S205, the controller 220 can calculate the reference time by subtracting the value obtained by multiplying the standard deviation (σ) by a threshold constant from the average balancing time of the multiple battery cells 110, 120, 130, and 140.

[0135] In step S206, according to one embodiment, the controller 220 can diagnose a battery cell as a low-voltage battery cell if the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of at least one of the plurality of battery cells 110, 120, 130, and 140 are outside the first threshold range.

[0136] In step S206, according to one embodiment, the controller 220 can diagnose a battery cell as a low-voltage battery cell if the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of at least one of the plurality of battery cells 110, 120, 130, and 140 are within the first threshold range and outside the second threshold range, and the cumulative balancing time of the battery cell is less than the reference time. Specifically in step S206, the controller 220 can diagnose a battery cell as a low-voltage battery cell if the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of at least one of the plurality of battery cells 110, 120, 130, and 140 are within the first threshold range and outside the second threshold range, and the cumulative balancing time of the battery cell is less than the reference time.

[0137] In step S206, if the controller 220 confirms as a result of the diagnosis that a low-voltage battery cell has occurred, it can provide information about the battery cell to the user. For example, the controller 220 can provide information about the low-voltage battery cell 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, etc.

[0138] Figure 13 is a flowchart showing a diagnostic method for low-capacity battery cells according to one embodiment disclosed in this document. The following describes in detail how the controller 220 diagnoses whether at least one of the battery cells 110, 120, 130, and 140 is low capacity, based on the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) of each of the battery cells 110, 120, 130, and 140, with reference to Figure 13. The controller 220 uses the phenomenon that low-capacity battery cells where electrode tab breakage and lithium deposition occur simultaneously experience faster and larger changes in open-circuit voltage compared to normal battery cells to compare the open-circuit voltage data of the battery cell with the statistically normal open-circuit voltage data of a normal battery cell, thereby diagnosing low-capacity battery cells where electrode tab breakage and lithium deposition have occurred.

[0139] In step S301, the controller 220 can calculate time-series data of the first diagnostic deviation (dV1) and third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140.

[0140] In step S301, the controller 220 calculates the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140 for each unit of time, and can calculate time-series data of the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140.

[0141] In step S301, the controller 220 calculates the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140 for each unit of time, and can calculate time-series data of the third diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140.

[0142] In step S302, the controller 220 can calculate the mean or median of the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140 over a unit time. In step S302, the controller 220 can calculate the mean or median of the first diagnostic deviation (dV1) for each of the multiple battery cells 110, 120, 130, and 140 over a unit time as the mean of the first diagnostic deviation (dV1_avg).

[0143] In step S302, the controller 220 can also calculate the mean or median of the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140 over a unit time. In step S302, the controller 220 can calculate the mean or median of the third diagnostic deviation (dV3) for each of the multiple battery cells 110, 120, 130, and 140 over a unit time as the mean of the third diagnostic deviation (dV3_avg).

[0144] In step S303, the controller 220 can calculate the difference between the average value of the first diagnostic deviation (dV1_avg) and the average value of the third diagnostic deviation (dV3_avg) for each of the multiple battery cells 110, 120, 130, and 140. Specifically in step S303, the controller 220 can calculate the difference between the average value of the first diagnostic deviation (dV1_avg) for each of the multiple battery cells 110, 120, 130, and 140 corresponding to the first interval in which the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is relatively low, and the average value of the third diagnostic deviation (dV3_avg) for each of the multiple battery cells 110, 120, 130, and 140 corresponding to the third interval in which the range of the average open-circuit voltage (V_avg) of the multiple battery cells 110, 120, 130, and 140 is relatively high.

[0145] In step S303, specifically, the controller 220 can calculate the fourth diagnostic deviation (dV4) for each of the multiple battery cells 110, 120, 130, and 140, which is the difference between the average value of the first diagnostic deviation (dV1_avg) and the average value of the third diagnostic deviation (dV3_avg).

[0146] In step S303, the controller 220 can calculate the fourth diagnostic deviation (dV4) for each of the multiple battery cells 110, 120, 130, and 140 based on the following [Equation 6].

[0147] [Formula 6] The fourth diagnostic deviation (dV4) = Average value of the first diagnostic deviation (dV1_avg) - Average value of the third diagnostic deviation (dV3_avg)

[0148] In step S303, the controller 220 can calculate the fourth diagnostic deviation (dV4) for each of the battery cells 110, 120, 130, and 140 by calculating the deviation between the average value of the first diagnostic deviation (dV1_avg) and the average value of the third diagnostic deviation (dV3_avg).

[0149] In step S303, the controller 220 can calculate the fourth diagnostic deviation (dV4) for each of the multiple battery cells 110, 120, 130, and 140 for each unit of time.

[0150] In step S304, the controller 220 can determine whether the fourth diagnostic deviation of at least one of the multiple battery cells 110, 120, 130, and 140 is outside the third threshold range.

[0151] In step S305, the controller 220 can diagnose a battery cell as a low-capacity battery cell if the fourth diagnostic deviation of at least one of the multiple battery cells 110, 120, 130, and 140 is outside the third threshold range. Here, the third threshold range is the criterion for determining that a battery cell is a low-capacity battery cell. For example, the third threshold range may be, for example, (-)15mV to (+)5mV.

[0152] In step S305, if the controller 220 confirms as a result of the diagnosis that a low-capacity battery cell has occurred, it can provide information about the battery cell to the user. For example, the controller 220 can provide information about the low-capacity battery cell 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, etc.

[0153] Figure 14 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.

[0154] Referring to Figure 14, 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.

[0155] The MCU2100 may be a processor that executes various programs stored in the memory 2200 (for example, a battery voltage change analysis program), processes various data through such programs, and performs the functions of the battery management device 200 shown in Figure 1 above.

[0156] The memory 2200 can store various programs related to the operation of the equipment control device 200. The memory 2200 can also store operating data for the battery management device 200.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Therefore, the embodiments disclosed herein are for illustrative purposes only, and not to limit the technical concept of the disclosure, and such embodiments do not limit the scope of the technical concept of the disclosure. The scope of protection of this disclosure must be interpreted in accordance with the claims set forth below, and all technical concepts within an equivalent scope should be interpreted as being included in the scope of rights of this disclosure.

Claims

1. A data management unit that calculates the open-circuit voltage (OCV) of each of the multiple batteries, The average open-circuit voltage of the aforementioned plurality of batteries is calculated, and for each of the plurality of batteries, a diagnostic deviation is calculated, which is the difference between the open-circuit voltage and the average open-circuit voltage. The diagnostic deviations of each of the aforementioned multiple batteries are classified based on the range of the average open-circuit voltage, and diagnostic deviations are generated for each range of the average open-circuit voltage. A controller that diagnoses at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries, A battery management device, including a battery management device.

2. The controller calculates the average value (Mean) or median value (Medium) of the open-circuit voltages of the plurality of batteries as the average open-circuit voltage. The battery management device according to claim 1, characterized in that the diagnostic deviations of each of the plurality of batteries are classified into a first diagnostic deviation, a second diagnostic deviation, and a third diagnostic deviation in order of increasing average open-circuit voltage range.

3. The battery management device according to claim 2, characterized in that the controller diagnoses at least one of the plurality of batteries based on whether the first diagnostic deviation and the third diagnostic deviation of each of the plurality of batteries are within a first threshold range.

4. The battery management device according to claim 3, characterized in that the controller diagnoses a battery as a low-voltage battery when the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are outside the first threshold range.

5. The battery management device according to claim 3, characterized in that the controller diagnoses the battery based on the cumulative balancing time of the battery if the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are within the first threshold range, and the first diagnostic deviation and the third diagnostic deviation of the battery are outside the second threshold range.

6. The controller calculates the cumulative balancing time for each of the multiple batteries, A reference time is calculated based on the average or median of the cumulative balancing times of the aforementioned multiple batteries. The battery management device according to claim 5, characterized in that it diagnoses the battery based on whether or not the cumulative balancing time of the battery is less than the reference time.

7. The battery management device according to claim 6, characterized in that the controller diagnoses a battery as a low-voltage battery if the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are within the first threshold range and outside the second threshold range, and the cumulative balancing time of the battery is less than the reference time.

8. The controller calculates a fourth diagnostic deviation, which is the difference between the average value of the first diagnostic deviation and the average value of the third diagnostic deviation for each of the plurality of batteries. The battery management device according to claim 2, characterized in that it diagnoses at least one of the plurality of batteries based on whether the fourth diagnostic deviation of each of the plurality of batteries is within the third threshold range.

9. The battery management device according to claim 8, characterized in that the controller diagnoses a battery as a low-capacity battery when the fourth diagnostic deviation of at least one of the plurality of batteries is outside the third threshold range.

10. The steps include calculating the open-circuit voltage (OCV) of each of the multiple batteries, The steps include: calculating the average open-circuit voltage of the plurality of batteries; The steps include: calculating a diagnostic deviation, which is the difference between the open-circuit voltage and the average open-circuit voltage, for each of the aforementioned multiple batteries; The steps include: classifying the diagnostic deviations of each of the aforementioned multiple batteries based on the range of the average open-circuit voltage, and generating diagnostic deviations for each range of the average open-circuit voltage; A step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries, A method for operating a battery management device, including the operation of the battery management device.

11. The step of calculating the average open-circuit voltage of the aforementioned plurality of batteries is: The average or median value of the open-circuit voltages of the aforementioned multiple batteries is calculated as the average open-circuit voltage. The step of classifying the diagnostic deviations of each of the aforementioned multiple batteries based on the range of the average open-circuit voltage and generating diagnostic deviations for each range of the average open-circuit voltage is as follows: The method for operating a battery management device according to claim 10, characterized in that the diagnostic deviations of each of the plurality of batteries are classified into a first diagnostic deviation, a second diagnostic deviation, and a third diagnostic deviation in order of increasing average open-circuit voltage range.

12. The step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries is: A method for operating a battery management device according to claim 11, characterized in that at least one of the plurality of batteries is diagnosed based on whether the first diagnostic deviation and the third diagnostic deviation of each of the plurality of batteries are within a first threshold range.

13. The step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries is: The method for operating a battery management device according to claim 12, characterized in that if the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are outside the first threshold range, the battery is diagnosed as a low-voltage battery.

14. The step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries is: A method for operating a battery management device according to claim 12, characterized in that, if the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are within the first threshold range, and the first diagnostic deviation and the third diagnostic deviation of the battery are outside the second threshold range, the battery is diagnosed based on the cumulative balancing time of the battery.

15. The step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries is: The cumulative balancing time for each of the aforementioned multiple batteries is calculated, A reference time is calculated based on the average or median of the cumulative balancing times of the aforementioned multiple batteries. A method for operating a battery management device according to claim 14, characterized in that the battery is diagnosed based on whether or not the cumulative balancing time of the battery is less than the reference time.

16. The step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries is: The method for operating a battery management device according to claim 15, characterized in that if the first diagnostic deviation and the third diagnostic deviation of at least one of the plurality of batteries are within the first threshold range and outside the second threshold range, and the cumulative balancing time of the battery is less than the reference time, the battery is diagnosed as a low-voltage battery.

17. The step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries is: The fourth diagnostic deviation is calculated as the difference between the average value of the first diagnostic deviation and the average value of the third diagnostic deviation for each of the aforementioned multiple batteries. A method for operating a battery management device according to claim 11, characterized in that at least one of the plurality of batteries is diagnosed based on whether the fourth diagnostic deviation of each of the plurality of batteries is within the third threshold range.

18. The step of diagnosing at least one of the plurality of batteries based on the diagnostic deviation for each range of the average open-circuit voltage of each of the plurality of batteries is: The method for operating a battery management device according to claim 17, characterized in that if the fourth diagnostic deviation of at least one of the plurality of batteries is outside the third threshold range, the battery is diagnosed as a low-capacity battery.

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