Battery diagnosis device and operation method thereof

Abstract

A battery diagnosis method according to one embodiment disclosed in the present document may comprise the operations of: providing a battery device including a plurality of battery cells; measuring the voltage of each of the plurality of battery cells; providing a composite voltage by processing the plurality of voltages; determining first specific battery cells of which the voltage reduction rates in a first period are greater than the reduction rate of the composite voltage; determining second specific battery cells of which the voltage reduction rates in a second period are greater than the reduction rate of the composite voltage; and generating a notification if at least some of the first specific battery cells and the second specific battery cells are identified.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2024-0045175, filed April 3, 2024, Korean Patent Application No. 10-2024-0059709, filed May 7, 2024, and Korean Patent Application No. 10-2025-0042886, filed April 2, 2025, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery diagnostic device and an operating method thereof.

[0005] Recently, research and development on secondary batteries has been actively conducted. Here, secondary batteries are rechargeable and include both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them suitable for use as power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Additionally, secondary batteries can be utilized as battery packs, which typically include battery modules in which multiple battery cells are connected in series and / or parallel. Furthermore, secondary batteries can be utilized as battery racks, which include multiple battery modules and a rack frame that accommodates these battery modules.

[0007] Battery cells, battery modules, battery packs, or battery racks like these can be utilized in a variety of devices. For example, batteries can be used in mobile devices such as cell phones, laptops, smartphones, and tablets, as well as in electric vehicles (EVs, HEVs, PHEVs) and large-capacity energy storage systems (ESS).

[0008] These batteries can have their status and operation managed and controlled by a battery management system (BMS). The BMS can be included with the batteries in a single device.

[0009] Additionally, the battery management system can manage and control the battery while being separated from the device containing the battery. For example, the battery management system can be implemented as a separate server device. In this case, the battery management system can collect battery data and vehicle data from vehicles and other devices, and utilize the collected data to manage and control the battery.

[0010] If a short circuit or other type of failure occurs within a battery, the risk of damage to devices containing the battery (e.g., EVs, ESS) may increase. Therefore, a method is needed to detect abnormal battery conditions and reduce the risk of damage to devices containing the battery.

[0011] Traditionally, battery cell diagnosis was performed using a calculation method that combined information such as State of Charge (SOC), current, capacity, and Open Circuit Voltage (OCV). Because this diagnostic method required numerous factors, it was difficult to diagnose if certain pieces of information were missing. This could be problematic for battery management systems implemented as server devices that collect data from vehicles, and could result in excessively high memory usage. Consequently, there is a pressing need to streamline the data required for battery diagnosis.

[0012] The embodiments disclosed in this document can provide a battery diagnostic device and an operating method thereof that can diagnose a battery abnormality using only battery OCV data information.

[0013] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0014] According to one embodiment of the present invention, a battery diagnosis device may include an interface for acquiring OCV (Open Circuit Voltage) data of a battery cell; and one or more processors for calculating a plurality of OCV deviations representing differences between an average OCV corresponding to a plurality of time points and the OCV of the battery cell for each of a plurality of battery cells included in a specific battery unit based on the OCV data, acquiring a plurality of OCV deviation changes representing a degree of change in the plurality of OCV deviations for each of the plurality of time points, applying a weighted moving average to the plurality of OCV deviation changes to acquire an OCV moving average, and diagnosing an abnormality of the battery cell based on the OCV moving average.

[0015] In the battery diagnosis device according to one embodiment disclosed in the present document, the one or more processors may, when a k-th OCV deviation change amount corresponding to a k-th time point among the plurality of time points is less than a first threshold OCV deviation change amount, and a (k-1)-th OCV deviation corresponding to a (k-1)-th time point before the k-th time point is equal to or greater than the threshold OCV deviation, change the k-th OCV deviation change amount to a specific OCV deviation change amount, and apply the weighted moving average to the plurality of OCV deviation changes including the specific OCV deviation change amount to obtain the OCV moving average.

[0016] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can diagnose an abnormality in the battery cell based on a result of comparing the OCV moving average and the threshold moving average.

[0017] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can increase the value of a diagnostic count by a first increment when the OCV moving average is less than the threshold moving average, and diagnose an abnormality of the battery cell based on a result of comparing the diagnostic count and the threshold count.

[0018] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors may calculate a second increment based on the degree to which the OCV moving average is less than the threshold moving average, and further increase the value of the diagnostic count by the second increment.

[0019] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors may decrease the value of the diagnostic count when the OCV moving average is greater than or equal to the threshold moving average.

[0020] In a battery diagnosis device according to one embodiment disclosed in the present document, the one or more processors may change some OCV deviation change amounts that are less than a second threshold OCV deviation change amount among the plurality of OCV deviation change amounts to the second threshold OCV deviation change amount, and apply the weighted moving average to the plurality of OCV deviation change amounts including the some OCV deviation change amounts to obtain the OCV moving average.

[0021] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can obtain the OCV moving average by applying an exponentially weighted moving average to the plurality of OCV deviation changes.

[0022] In a battery diagnostic device according to one embodiment disclosed in this document, the OCV data may be a result of compensating OCV data before compensation of at least some of the batteries using a balancing capacity resulting from a balancing process performed on at least some of the battery cells.

[0023] In a battery diagnostic device according to one embodiment disclosed in this document, the balancing capacity may be a result of accumulating the discharge capacity of the battery cell by the balancing process.

[0024] A battery diagnosis method according to an embodiment disclosed in the present document may include: an operation of acquiring OCV (Open Circuit Voltage) data of a battery cell; an operation of calculating a plurality of OCV deviations representing differences between an average OCV corresponding to a plurality of points in time and the OCV of the battery cell for each of a plurality of battery cells included in a specific battery unit based on the OCV data; an operation of acquiring a plurality of OCV deviation changes based on the plurality of OCV deviations; an operation of acquiring an OCV moving average by applying a weighted moving average to the plurality of OCV deviation changes; and an operation of diagnosing an abnormality of the battery cell based on the OCV moving average.

[0025] In a battery diagnosis method according to an embodiment disclosed in the present document, the operation of obtaining the OCV moving average may include an operation of changing the k-th OCV deviation change amount corresponding to a k-th point in time among the plurality of points in time to a first threshold OCV deviation change amount, and applying the weighted moving average to the plurality of OCV deviation change amounts including the specific OCV deviation change amount to obtain the OCV moving average, when the k-th OCV deviation change amount corresponding to a k-th point in time among the plurality of points in time is less than a first threshold OCV deviation change amount, and the (k-1)-th OCV deviation corresponding to a (k-1)-th point in time before the k-th point in time is equal to or greater than the threshold OCV deviation.

[0026] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing an abnormality of the battery cell may include an operation of diagnosing an abnormality of the battery cell based on a result of comparing the OCV moving average and the threshold moving average.

[0027] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing an abnormality of the battery cell may include an operation of increasing the value of the diagnosis count by a first increment when the OCV moving average is less than the threshold moving average, and diagnosing an abnormality of the battery cell based on a result of comparing the diagnosis count and the threshold count.

[0028] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing an abnormality of the battery cell may include an operation of calculating a second increment based on the degree to which the OCV moving average is less than the threshold moving average, and further increasing the value of the diagnosis count by the second increment.

[0029] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing an abnormality of the battery cell may include an operation of decreasing the value of the diagnosis count when the OCV moving average is greater than or equal to the threshold moving average.

[0030] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of obtaining the OCV moving average may include an operation of changing some OCV deviation change amounts that are less than a second threshold OCV deviation change amount among the plurality of OCV deviation change amounts to the second threshold OCV deviation change amount, and applying the weighted moving average to the plurality of OCV deviation change amounts including the some OCV deviation change amounts to obtain the OCV moving average.

[0031] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of obtaining the OCV moving average may include an operation of obtaining the OCV moving average by applying an exponentially weighted moving average to the plurality of OCV deviation changes.

[0032] In a battery diagnosis method according to an embodiment disclosed in this document, the OCV data may be a result of compensating the pre-compensation OCV data of at least some of the batteries using a balancing capacity obtained by a balancing process performed on at least some of the battery cells.

[0033] In a battery diagnosis method according to an embodiment disclosed in this document, the balancing capacity may be a result of accumulating the discharge capacity of the battery cell by the balancing process.

[0034] A battery diagnosis method according to one embodiment disclosed in the present document comprises the steps of: providing a battery device including a plurality of battery cells; measuring voltages of each of the plurality of battery cells in a single measurement time interval and providing a plurality of voltages of the plurality of battery cells measured in the single measurement time interval; processing the plurality of voltages of the plurality of battery cells measured in the single measurement time interval and providing a composite voltage of the plurality of battery cells in the single measurement time interval; providing a plurality of sets of voltages of the plurality of battery cells by repeating voltage measurements a plurality of times and providing a plurality of voltages of each of the battery cells measured in the plurality of measurement time intervals so that each set of voltages is a voltage of the plurality of battery cells measured in one of the plurality of measurement time intervals; providing a plurality of composite voltages of the plurality of battery cells by repeating processing of each set of voltages so that each of the plurality of composite voltages is a composite voltage of the plurality of battery cells in one of the plurality of measurement time intervals; determining a first specific battery cell among the plurality of battery cells, wherein a voltage decrease rate in a first period is greater than a first threshold; An operation of determining a second specific battery cell among the plurality of battery cells, wherein the voltage decrease rate in the second period is greater than a second threshold; and an operation of generating a notification when at least some of the first specific battery cell and the second specific battery cell are identified; wherein the first threshold is smaller than the second threshold, the first period is longer than the second period, and the operation of determining the first specific battery cell can identify a battery cell that is not identified through the operation of determining the second specific battery cell because the voltage decrease rate is slower than that of the second specific battery cell.

[0035] In a battery diagnosis method according to one embodiment disclosed in this document, the measured voltage may be an open circuit voltage (OCV), and the synthesized voltage may be an average voltage.

[0036] In a battery diagnosis method according to an embodiment disclosed in the present document, if the third period is shorter than the second period and the voltage decrease rates of the first specific battery cell and the second specific battery cell are within a critical variation range of the voltage decrease rates of the individual battery cells in the third period, the notification may not be generated even if the voltage decrease rates of the first specific battery cell and the second specific battery cell in the third period are greater than the decrease rate of the composite voltage in the third period by the first threshold.

[0037] In the battery diagnosis method according to one embodiment disclosed in the present document, the third period may be within a range formed by two elements selected from the group including 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 10 hours, 11 hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 8 days, 9 days, and 10 days.

[0038] In a battery diagnosis method according to one embodiment disclosed in the present document, the one or more processors may be configured such that the threshold variation range is within a range formed by two elements selected from a group comprising 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 mV / t.

[0039] In a battery diagnosis method according to one embodiment disclosed in this document, the notification may include at least some of information suggesting consultation on the battery status, information suggesting a service related to the battery device, and information for replacing at least a part of the battery device.

[0040] In a battery diagnosis method according to one embodiment disclosed in the present document, the method further includes: an operation of determining a third specific battery cell, among the plurality of battery cells, in which a voltage decrease rate in a third period is greater than a decrease rate of the composite voltage in the third period by a third threshold; and an operation of generating a notification when at least one of the first specific battery cell, the second specific battery cell, and the third specific battery cell is identified, wherein the second threshold may be smaller than the third threshold, and the third period may be shorter than the second period.

[0041] In a battery diagnosis method according to an embodiment disclosed in the present document, if the voltage decrease rates of the first specific battery cell, the second specific battery cell, and the third specific battery cell are within a critical variation range of the voltage decrease rates of the individual battery cells in the third period, the notification may not be generated even if the voltage decrease rates of the first specific battery cell, the second specific battery cell, and the third specific battery cell in the third period are greater by a first threshold than the decrease rate of the composite voltage in the third period.

[0042] In a battery diagnosis method according to one embodiment disclosed in this document, measuring the voltage of each of the plurality of battery cells in the single measurement time period may be performed simultaneously or continuously for the plurality of battery cells so that the measurement is completed within the same measurement time period.

[0043] In a battery diagnosis method according to an embodiment disclosed in the present document, in an operation of determining the first specific battery cell, for each of the plurality of battery cells, a deviation between the voltage of each of the battery cells and the composite voltage in a first measurement time period among the plurality of measurement time periods is calculated, so that a first value set for the deviation in the first measurement time period of the plurality of battery cells is provided such that a first value set includes a value for the deviation of each of the plurality of battery cells in the first measurement time period, and an operation of calculating a deviation in an additional measurement time period among the plurality of measurement time periods is repeated such that an additional value set includes a value for the deviation for each of the plurality of battery cells in the additional measurement time period, and for each of the plurality of battery cells, a rate of change of the deviation in the first period is calculated using at least a part of the first value set and the additional value set, and for each of the plurality of battery cells, using the calculated rate of change, a voltage decrease rate in the first period is determined for the first specific battery cell that is greater than the first threshold. It can determine whether a battery cell is present or not.

[0044] In a battery diagnosis method according to an embodiment disclosed in the present document, in the operation of calculating the rate of change of the deviation in the first period, the rate of change of the deviation in the first period starting from the first measurement time interval is calculated, and in the operation of determining the first specific battery cell, the operation of calculating the rate of change of the deviation in the first period starting from the one or more additional measurement time intervals for each of the plurality of battery cells is repeated, so that the rate of change of the deviation for each of the plurality of battery cells becomes the rate of change of the deviation for each of the plurality of battery cells in the one or more additional measurement time intervals, and using at least a part of the rate of change of the deviation calculated for each of the plurality of battery cells, the first specific battery cell in which the rate of decrease in voltage in the first period is greater than the first threshold in the first period starting from the one or more additional measurement time intervals can be determined.

[0045] In a battery diagnosis method according to one embodiment disclosed in this document, if a voltage decrease rate of the first specific battery cell among the plurality of battery cells is greater than the first threshold in the first period starting from at least one of the measurement time periods, the first specific battery cell may be identified as a battery cell that generates the notification.

[0046] In a battery diagnosis method according to an embodiment disclosed in this document, in the operation of determining the first specific battery cell, the number of times a voltage decrease rate in the first period is greater than the first threshold is counted for each of the plurality of battery cells, and it is determined for each of the plurality of battery cells whether the number of times reaches the counting threshold.

[0047] In a battery diagnosis method according to one embodiment disclosed in this document, when the number of times for the first specific battery cell among the plurality of battery cells reaches the counting threshold, the first specific battery cell can be identified as a battery cell that generates the notification.

[0048] In a battery diagnosis method according to an embodiment disclosed in this document, in the operation of determining the second specific battery cell, for each of the plurality of battery cells, a rate of change of the deviation in the second period is calculated using at least some of the first value set and the additional value set, and, using the calculated rate of change for each of the plurality of battery cells, it is possible to determine whether there is a second specific battery cell in which a voltage decrease rate in the second period is greater than the second threshold.

[0049] In a battery diagnosis method according to an embodiment disclosed in the present document, in the operation of calculating the rate of change of the deviation in the second period, the rate of change of the deviation in the second period starting from the first measurement time period or the additional measurement time period is calculated, and in the operation of determining the second specific battery cell, the operation of calculating the rate of change of the deviation in the second period starting from the one or more additional measurement time periods is repeated for each of the plurality of battery cells, so that each rate of change of the deviation of each of the plurality of battery cells becomes the rate of change of each of the battery cells in the one or more additional measurement time periods, and using at least a part of the rate of change of the deviation calculated for each of the plurality of battery cells, the second specific battery cell in which the rate of decrease in voltage in the second period is greater than the second threshold in the second period starting from the one or more additional measurement time periods can be determined.

[0050] In a battery diagnosis method according to one embodiment disclosed in this document, if a voltage decrease rate of the second specific battery cell among the plurality of battery cells is greater than the second threshold in the second period starting from at least one of the measurement time periods, the second specific battery cell may be identified as a battery cell that generates the notification.

[0051] In a battery diagnosis method according to one embodiment disclosed in this document, in the operation of determining the second specific battery cell, the number of times a voltage decrease rate in the second period is greater than the second threshold is counted for each of the plurality of battery cells, and it is determined for each of the plurality of battery cells whether the number of times reaches the counting threshold.

[0052] In a battery diagnosis method according to one embodiment disclosed in this document, when the number of times for the second specific battery cell among the plurality of battery cells reaches the counting threshold, the second specific battery cell can be identified as a battery cell that generates the notification.

[0053] In a battery diagnosis method according to an embodiment disclosed in the present document, in the operation of determining the second specific battery cell, for each of the plurality of battery cells, a deviation between the voltage of each battery cell and the composite voltage in a first measurement time period among the plurality of measurement time periods is calculated, so that the first value set includes a value for the deviation of each of the plurality of battery cells in the first measurement time period, and a first value set for the deviation in the first measurement time period of the plurality of battery cells is provided, and an operation of calculating a deviation in an additional measurement time period among the plurality of measurement time periods is repeated, so that the additional value set includes a value for the deviation for each of the plurality of battery cells in the additional measurement time period, and for each of the plurality of battery cells, a rate of change of the deviation during the second period is calculated using at least a part of the first value set and the additional value set, and for each of the plurality of battery cells, the second specific battery cell having a voltage decrease rate in the second period greater than the second threshold is determined using the calculated rate of change. It can determine whether a battery cell is present or not.

[0054] In a battery diagnosis method according to an embodiment disclosed in the present document, in the operation of calculating the rate of change of the deviation in the second period, the rate of change of the deviation in the second period starting from the first measurement time period or another measurement time period is calculated, and in the operation of determining the second specific battery cell, the operation of calculating the rate of change of the deviation in the second period starting from the one or more additional measurement time periods for each of the plurality of battery cells is repeated, so that the rate of change of the deviation for each of the plurality of battery cells becomes the rate of change of the deviation for each of the plurality of battery cells in the one or more additional measurement time periods, and using at least a part of the rate of change of the deviation calculated for each of the plurality of battery cells, the second specific battery cell in which the rate of decrease in voltage in the second period is greater than the second threshold in the second period starting from the one or more additional measurement time periods can be determined.

[0055] In a battery diagnosis method according to one embodiment disclosed in this document, if a voltage decrease rate of the second specific battery cell among the plurality of battery cells is greater than the second threshold in the second period starting from at least one of the measurement time periods, the second specific battery cell may be identified as a battery cell that generates the notification.

[0056] In a battery diagnosis method according to one embodiment disclosed in this document, in the operation of determining the second specific battery cell, the number of times a voltage decrease rate in the second period is greater than the second threshold is counted for each of the plurality of battery cells, and it is determined for each of the plurality of battery cells whether the number of times reaches the counting threshold.

[0057] In a battery diagnosis method according to one embodiment disclosed in this document, when the number of times for the second specific battery cell among the plurality of battery cells reaches the counting threshold, the second specific battery cell can be identified as a battery cell that generates the notification.

[0058] A non-transitory computer-readable medium according to one embodiment disclosed in this document can store instructions that are executed to perform the method of claim 21.

[0059] According to the embodiments disclosed in this document, data used for battery abnormality diagnosis can be simplified.

[0060] According to the embodiments disclosed in this document, the accuracy of battery abnormality diagnosis can be improved by diagnosing battery cell abnormalities based on an OCV moving average obtained by applying a weighted moving average to the OCV deviation.

[0061] According to embodiments disclosed in this document, a battery fire can be prevented by diagnosing long-term decreasing voltage behavior.

[0062] In addition, various effects may be provided, either directly or indirectly, through this document.

[0063] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0064] FIG. 2 is a drawing for explaining an embodiment in which a battery diagnostic device diagnoses an abnormality in a battery cell.

[0065] FIGS. 3A to 3C are drawings for explaining an embodiment in which a battery diagnostic device diagnoses an abnormality in a battery cell.

[0066] Figure 4 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment.

[0067] FIG. 5 is a block diagram of a battery system including a battery diagnostic device according to one embodiment.

[0068] FIG. 6 is a diagram illustrating measured OCV at multiple points in time for a predetermined period of time for each battery cell of a battery module according to one embodiment.

[0069] FIG. 7 is a diagram showing measured OCV at multiple points in time for multiple battery cells including the first to fifth battery cells and an average OCV over a predetermined period of time.

[0070] FIG. 8A is a diagram illustrating the measured OCV of a first battery cell along with an average OCV according to one embodiment.

[0071] Figure 8B is a diagram showing the OCV values ​​of the first battery cell and the deviation from the average OCV.

[0072] Figure 8C is a diagram showing the slope of the OCV deviation value for a short period of time of the first battery cell compared to the short-term threshold S.

[0073] Figure 8D is a diagram showing the number of occurrences when the slope shown in Figure 8C falls below the short-term threshold S.

[0074] Figure 8E is a diagram showing the moving average of the first battery cell over a short period of time.

[0075] Figure 8F is a diagram showing the number of occurrences based on a moving average operation.

[0076] FIG. 8G is a diagram showing a weighted moving average for a short period of time of the first battery cell.

[0077] Figure 8H is a diagram showing the number of occurrences based on a weighted moving average operation.

[0078] FIG. 8I is a diagram showing the average slope of multiple battery cells and the slope shown in FIG. 8C together.

[0079] FIG. 8J is a diagram showing the slope of the OCV deviation value for a long-term period of the first battery cell compared to a long-term threshold L.

[0080] FIG. 8K is a diagram showing the number of occurrences when the slope shown in FIG. 8F falls below the long-term threshold L.

[0081] Figure 8L is a diagram showing the moving average for a long period of time of the first battery cell.

[0082] Figure 8M is a diagram showing the number of occurrences based on a moving average operation.

[0083] FIG. 8N is a diagram showing a weighted moving average over a long period of time of the first battery cell.

[0084] Figure 8O is a diagram showing the number of occurrences based on a weighted moving average operation.

[0085] Figure 8P is a diagram showing the average slope of multiple battery cells and the slope shown in Figure 8J together.

[0086] Figure 8Q is a diagram showing the slopes shown in Figures 8C and 8F, along with the corresponding short-term threshold S and long-term threshold L, respectively.

[0087] FIG. 9A is a diagram illustrating the measured OCV of a second battery cell along with an average OCV according to one embodiment.

[0088] Figure 9B is a diagram showing the OCV values ​​of the second battery cell and the deviation from the average OCV.

[0089] Figure 9C is a diagram showing the slope of the OCV deviation value for a short period of time of the second battery cell compared to the short-term threshold S.

[0090] Figure 9D is a diagram showing the number of occurrences when the slope shown in Figure 9C falls below the short-term threshold S.

[0091] Figure 9E is a diagram showing the moving average for a short period of time of the second battery cell.

[0092] Figure 9F is a diagram showing the number of occurrences based on a moving average operation.

[0093] Figure 9G is a diagram showing a weighted moving average for a short period of time of the second battery cell.

[0094] Figure 9H is a diagram showing the number of occurrences based on a weighted moving average operation.

[0095] FIG. 9I is a diagram showing the average slope of multiple battery cells and the slope shown in FIG. 9C together.

[0096] FIG. 9J is a diagram showing the slope of the OCV deviation value for a long-term period of the second battery cell compared to a long-term threshold L.

[0097] Figure 9K is a diagram showing the number of occurrences when the slope shown in Figure 9F falls below the long-term threshold L.

[0098] Figure 9L is a diagram showing the moving average for a long period of time of the second battery cell.

[0099] Figure 9M is a diagram showing the number of occurrences based on a moving average operation.

[0100] FIG. 9N is a diagram showing a weighted moving average over a long period of time of the second battery cell.

[0101] Figure 9O is a diagram showing the number of occurrences based on a weighted moving average operation.

[0102] Figure 9P is a diagram showing the average slope of multiple battery cells and the slope shown in Figure 9J together.

[0103] Figure 9Q is a diagram showing the slopes shown in Figures 9C and 9F, along with the corresponding short-term threshold S and long-term threshold L, respectively.

[0104] FIG. 10A is a diagram illustrating the measured OCV of a fourth battery cell along with an average OCV according to one embodiment.

[0105] Figure 10B is a diagram showing the OCV values ​​of the fourth battery cell and the deviation from the average OCV.

[0106] Figure 10C is a diagram showing the slope of the OCV deviation value for a short period of time of the fourth battery cell compared to the short-term threshold S.

[0107] Figure 10D is a diagram showing the number of occurrences when the slope shown in Figure 10C falls below the short-term threshold S.

[0108] Figure 10E is a diagram showing the moving average for a short period of time of the fourth battery cell.

[0109] Figure 10F is a diagram showing the number of occurrences based on a moving average operation.

[0110] FIG. 10G is a diagram showing the weighted moving average for a short period of time of the fourth battery cell.

[0111] Figure 10H is a diagram showing the number of occurrences based on a weighted moving average operation.

[0112] FIG. 10I is a diagram showing the average slope of multiple battery cells and the slope shown in FIG. 10C together.

[0113] FIG. 10J is a diagram showing the slope of the OCV deviation value for a long-term period of the fourth battery cell compared to the long-term threshold L.

[0114] Figure 10K is a diagram showing the number of occurrences when the slope shown in Figure 10F falls below the long-term threshold L.

[0115] Figure 10L is a diagram showing the moving average for a long period of time of the fourth battery cell.

[0116] Figure 10M is a diagram showing the number of occurrences based on a moving average operation.

[0117] Figure 10N is a diagram showing the weighted moving average for a long period of time of the fourth battery cell.

[0118] Figure 10O is a diagram showing the number of occurrences based on a weighted moving average operation.

[0119] Figure 10P is a diagram showing the average slope of multiple battery cells and the slope shown in Figure 10J together.

[0120] Figure 10Q is a diagram showing the slopes shown in Figures 10C and 10F, along with the corresponding short-term threshold S and long-term threshold L, respectively.

[0121] FIG. 11A is a drawing showing an enlarged view of the measured OCV of a plurality of battery cells illustrated in FIG. 3 for a given period of time.

[0122] Figure 11B is a drawing showing an enlarged view of Figure 4 for a given period of time.

[0123] Figure 11C is a drawing showing an enlarged view of Figure 8A for a given period of time.

[0124] Figure 11D is a drawing showing an enlarged view of Figure 8B over a given period of time.

[0125] Figure 11E is a drawing showing an enlarged view of Figure 8C for a given period of time.

[0126] Figure 11F is a drawing showing an enlarged view of Figure 8E for a given period of time.

[0127] Figure 11G is a drawing showing an enlarged view of Figure 8G for a given period of time.

[0128] Figure 11H is a drawing showing an enlarged view of Figure 8J for a given period of time.

[0129] Figure 11I is a drawing showing an enlarged view of Figure 8L for a given period of time.

[0130] Figure 11J is a drawing showing an enlarged view of Figure 8N for a given period of time.

[0131] FIGS. 12A to 14 are flowcharts illustrating a method for diagnosing a battery abnormality according to one embodiment.

[0132] Figure 15 illustrates one or more computing systems.

[0133] Hereinafter, various embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0134] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0135] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0136] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0137] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0138] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0139] Referring to FIG. 1, a battery pack (102) includes a plurality of battery modules (110, 120, 130), and each of the plurality of battery modules (110, 120, 130) may include a plurality of battery cells (112, 114, 116, 122, 124, 126, 132, 134, 136). According to one embodiment, the battery pack (102) may be a battery mounted inside an electric vehicle to provide power to the electric vehicle.

[0140] According to one embodiment, the battery diagnostic device (100) can diagnose an abnormality of a battery unit based on OCV data obtained from the battery unit. In the present disclosure, the battery unit may mean a battery pack (102), a battery module (110, 120, or 130), or a battery cell (112, 114, 116, 122, 124, 126, 132, 134, 136).

[0141] According to one embodiment, the battery diagnostic device (100) may be formed integrally with the battery unit. In this case, the battery diagnostic device (100) may be included in the BMS (Battery Management System) of the battery unit.

[0142] According to one embodiment, the battery diagnostic device (100) may be formed separately from the battery unit. In this case, the battery diagnostic device (100) may be implemented as an external server connected to the battery unit via a wireless network.

[0143] In addition, the operation of the battery diagnostic device (100) below can be performed by a BMS (Battery management system) in a vehicle, and can also be performed in various devices such as a server, cloud, charger, or charger / discharger.

[0144] According to one embodiment, the battery diagnostic device (100) may include an interface (106) and one or more processors (108).

[0145] According to one embodiment, the interface (106) can obtain OCV data of battery cells (112, 114, 116, 122, 124, 126, 132, 134, and / or 136). For example, the interface (106) can obtain information about voltage, current, and / or temperature of the battery cells (112, 114, 116, 122, 124, 126, 132, 134, and / or 136) and construct OCV data based on the obtained information. In this case, the interface (106) can include a sensor for obtaining information about voltage, current, and / or temperature and a processor for constructing OCV data based on the obtained information. As another example, the interface (106) may receive OCV data of battery cells (112, 114, 116, 122, 124, 126, 132, 134, and / or 136) acquired by the battery unit. In this case, the interface (106) may include communication circuitry capable of wired and / or wireless network communication.

[0146] For reference, internal and external factors during the manufacturing process and / or use may cause characteristic deviations between multiple battery cells, and characteristic deviations between multiple battery cells may cause voltage unevenness. A balancer may be utilized to resolve voltage unevenness between multiple battery cells by performing balancing processing (e.g., discharging) on ​​at least some of the multiple battery cells. However, when balancing processing is performed, the voltage unevenness is completely or partially resolved even for battery cells with abnormalities, so balancing processes performed in the past may act as an obstacle in detecting battery cells with abnormalities.

[0147] When charging a faulty battery cell, some of the charging power may be consumed as leakage current without being stored in the faulty cell. Furthermore, when discharging a faulty battery cell, some of the discharged power may be consumed as leakage current without being supplied to the electrical load. Consequently, during charging, the voltage change (i.e., the increase in SOC) of a faulty battery cell is smaller than that of a normal battery cell. Conversely, during discharging, the voltage change (i.e., the decrease in SOC) of a faulty battery cell is larger than that of a normal battery cell. Furthermore, even under no-load conditions, the energy stored in a faulty battery cell may be consumed as leakage current.

[0148] A balancer (not shown) may be configured to perform balancing processing on at least some battery cells among a plurality of battery cells that require balancing.

[0149] The present invention can compensate for the pre-compensation OCV data of each of a plurality of battery cells by utilizing the balancing capacity of each of the plurality of battery cells. The compensated OCV data of the battery cell may be an estimate of the OCV data of the battery cell in the case where balancing processing for the battery cell has not been performed.

[0150] According to one embodiment, the OCV data may be a result of compensating the pre-compensation OCV data of each battery cell using the balancing capacity resulting from the balancing process performed by the balancer on each battery cell.

[0151] In one embodiment, the balancing capacity may be the accumulated discharge capacity of a battery cell resulting from a balancing process. For example, the balancing capacity may be the accumulated discharge capacity of a battery cell resulting from a balancing process performed over a specific period, i.e., the total discharge capacity over a specific period. For example, the balancing capacity of a battery cell that has not been balanced even once over a specific period may be 0.

[0152] According to one embodiment, (i) the SOC estimate of each battery cell may be determined by applying a SOC-OCV map to the pre-compensation OCV data of each battery cell, (ii) the SOC estimate of each battery cell may be compensated by adding a SOC change corresponding to a balancing capacity to the SOC estimate of each battery cell, and (iii) the OCV data (i.e., compensated OCV data) may be determined by applying the SOC-OCV map to the compensated SOC estimate of each battery cell.

[0153] In this way, by compensating the OCV data before compensation of the battery cells on which the balancing process has been performed, it is possible to accurately detect a battery cell with an abnormality among the multiple battery cells even when the voltage unevenness between the multiple battery cells has been resolved by the balancing process.

[0154] According to one embodiment, the processor (108) may calculate a judgment value (e.g., OCV deviation, OCV deviation change, OCV moving average, and / or diagnostic count) based on the OCV data acquired by the interface (106). According to one embodiment, the processor (108) may extract OCV data of a specified voltage range from among the OCV data. The processor (108) may calculate the judgment value based on the extracted OCV data of the specified voltage range. Various embodiments in which the processor (108) calculates the judgment value may be specifically described in FIGS. 2 to 3C, which will be described later.

[0155] According to one embodiment, the processor (108) can diagnose an abnormality in the battery unit based on the calculated judgment value.

[0156] According to one embodiment, the processor (108) can diagnose an abnormality of the battery unit by comparing the judgment value with a corresponding threshold value. For example, if the judgment value (e.g., diagnosis count) is greater than or equal to the threshold value (e.g., 40), the processor (108) can diagnose the battery unit (e.g., battery cells (112, 114, 116, 122, 124, 126, 132, 134, and / or 136)) as an abnormal battery unit.

[0157] The processor (108) described above may be implemented as a single processor or as separate processors. Here, the processor may execute software to control at least one other component (e.g., hardware or software component) of the battery diagnostic device (100) and perform various data processing or calculations.

[0158] According to an embodiment, the battery diagnosis device (100) may transmit the battery diagnosis results to an external device (e.g., a cloud server or a user terminal). Here, the cloud server may provide a service for providing the battery diagnosis results to each of a plurality of users. In addition, the user terminal may include a terminal such as a personal computer (PC) or a smartphone. For example, the battery diagnosis device (100) may provide information on abnormal battery cells to the user terminal through a communication unit (not shown), and may also provide information on abnormal battery cells through a display equipped in a vehicle or a charger.

[0159] Hereinafter, various embodiments in which the processor (108) diagnoses an abnormality of a battery unit will be described with reference to FIGS. 2 to 3C. FIGS. 2 to 3C can be described using the configuration of FIG. 1 (e.g., interface (106), processor (108)).

[0160] Fig. 2 is a diagram illustrating an embodiment in which a battery diagnostic device calculates a judgment value. Figs. 3a to 3c are diagrams illustrating an embodiment in which a battery diagnostic device diagnoses an abnormality in a battery cell.

[0161] Referring to FIG. 2, the processor (108) may include a first processor (108_1), a second processor (108_2), a third processor (108_3), and a fourth processor (108_4).

[0162] According to one embodiment, the interface (106) may transmit OCV data of a plurality of battery cells included in a plurality of battery units (e.g., a plurality of battery modules) to the first processor (108_1). For example, the interface (106) may transmit OCV data (OCV1, OCV2, OCV3) of a plurality of battery cells (121, 122, 123) of a specific battery module (120) to the first processor (108_1). For convenience of explanation, one battery module (120) will be described as an example below, but the number of battery modules is not limited thereto.

[0163] According to one embodiment, the first processor (108_1) generates a plurality of OCV deviations (OCVs) representing the difference between the average OCV corresponding to a plurality of points in time and the OCVs (OCV1, OCV2, OCV3) of the battery cells (121, 122, 123) for each of a plurality of battery cells (121, 122, 123) included in a specific battery unit (e.g., a specific battery module (120)) based on the OCV data (OCV1, OCV2, OCV3). D1 , OCV D2 , OCV D3 ) can be produced. For example, the first processor (108_1) may calculate an average OCV (e.g., (OCV)) corresponding to a first specific point in time of a plurality of battery cells (121, 122, 123) included in a specific battery module (120). 1_1 +OCV 2_1 +OCV 3_1) / 3) and OCV (OCV) corresponding to the first specific point in time of the battery cell (121) 1_1 ) is the OCV deviation (OCV D1_1 ) and calculates the average OCV (e.g., (OCV) corresponding to a second specific point in time of a plurality of battery cells (121, 122, 123) included in a specific battery module (120). 1_2 +OCV 2_2 +OCV 3_2 ) / 3) and OCV (OCV) corresponding to the second specific point in time of the battery cell (121) 1_2 ) is the OCV deviation (OCV D1_2 ) can be produced.

[0164] According to one embodiment, the first processor (108_1) generates a plurality of OCV deviations (OCV) corresponding to the plurality of points in time. D1 , OCV D2 , OCV D3 ) can be transmitted to the second processor (108_2).

[0165] According to one embodiment, the second processor (108_2) outputs a plurality of OCV deviation change amounts (OCV) representing the degree of change in a plurality of OCV deviations at a plurality of time points for each of a plurality of battery cells (121, 122, 123). slope1 , OCV slope2 , OCV slope3 ) can be obtained. Here, multiple OCV deviation changes (OCV slope1 , OCV slope2 , OCV slope3 ) may each correspond to a plurality of battery cells (121, 122, 123). For example, the second processor (108_2) may correspond to a plurality of OCV deviations (OCV) corresponding to each of a plurality of time points of the battery cell (121). D1 ) based on a plurality of OCV deviation changes corresponding to each of a plurality of time points of the battery cell (121) slope1 ) can be obtained.

[0166] According to one embodiment, the plurality of OCV deviation changes may be slopes of the plurality of OCV deviations at a plurality of time points. For example, the k-th OCV deviation change corresponding to the k-th time point of the battery cell (121) may be a value obtained by subtracting the (k-1)-th OCV deviation corresponding to the (k-1)-th time point from the k-th OCV deviation corresponding to the k-th time point, and dividing the value by the interval between the k-th time point and the (k-1)-th time point.

[0167] However, the slopes of the plurality of OCV deviations are merely examples for explaining the degree of change in the plurality of OCV deviations, and the plurality of OCV deviation changes disclosed in this document are not limited to the slopes of the plurality of OCV deviations. For example, the plurality of OCV deviation changes may be difference values ​​between the plurality of OCV deviations at multiple points in time. For example, the k-th OCV deviation change corresponding to the k-th point in time of the battery cell (121) may be a value obtained by subtracting the (k-1)-th OCV deviation corresponding to the (k-1)-th point in time from the k-th OCV deviation corresponding to the k-th point in time.

[0168] Referring to Fig. 3a, for a specific battery cell, OCV deviations at multiple points in time are illustrated, and it can be confirmed that multiple OCV deviation changes corresponding to each of multiple points in time of a specific battery cell can be obtained through this. For reference, Fig. 3a and Figs. 3b to 3c described below calculate OCV deviations at regular periodic intervals based on OCV data acquired at regular intervals (e.g., 10 days), and illustrate an OCV moving average, an OCV moving average deviation, and an OCV evaluation value acquired based on the OCV deviations. The horizontal axis represents time (days), one notch on the horizontal axis corresponds to three months, and the vertical axis represents voltage (mV). However, for convenience of explanation, it is described in Figs. 3a to 3c that OCV data is acquired at regular intervals, but a case in which OCV data is acquired non-periodically is not excluded.

[0169] According to one embodiment, the second processor (108_2) calculates a plurality of OCV deviation variations (OCV slope1 , OCV slope2 , OCV slope3 ) can be transmitted to the third processor (108_3).

[0170] According to one embodiment, the third processor (108_3) may be configured to calculate a plurality of OCV deviation variations (OCV slope1 , OCV slope2 , OCV slope3 ) by applying a weighted moving average to the OCV moving average (OCV mv1 , OCV mv2 , OCV mv3 ) can be obtained. For example, the third processor (108_3) can obtain a plurality of OCV deviation changes (OCV) corresponding to a plurality of time points of the battery cell (121). slope1 ) by applying a weighted moving average to the OCV moving average (OCV mv1 ) can be obtained.

[0171] For reference, the OCV moving average (OCV mv1 , OCV mv2 , OCV mv3 ) each has a value corresponding to each of multiple points in time, and the OCV moving averages corresponding to two different points in time may be different from each other, but are not limited thereto. For example, the OCV moving averages corresponding to two different points in time may have the same value. For example, the OCV moving average (OCV) of the battery cell (121) mv1 ) The values ​​of the OCV moving average corresponding to the first point in time and the OCV moving average corresponding to the second point in time may be the same or different from each other.

[0172] According to one embodiment, the third processor (108_3) may be configured to calculate a plurality of OCV deviation variations (OCV slope1 , OCV slope2 , OCV slope3) by applying the Exponentially Weighted Moving Average to the OCV Moving Average (OCV mv1 , OCV mv2 , OCV mv3 ) can be obtained. For example, the third processor (108_3) can obtain a plurality of OCV deviation changes (OCV) corresponding to a plurality of time points of the battery cell (121). slope1 ) by applying exponential weighted moving average to OCV moving average (OCV mv1 ) can be obtained.

[0173] At this time, when applying the exponential weighted moving average, the size of the window (e.g., the number of data to which the exponential weighted moving average is applied) may be 19, the weight applied to the current OCV deviation change may be 0.1, and the weight applied to the past exponential weighted moving average may be 0.9.

[0174] According to one embodiment, the third processor (108_3) may change the k-th OCV deviation change amount corresponding to the k-th time point among the plurality of time points to a first threshold OCV deviation change amount, and the (k-1)-th OCV deviation corresponding to the (k-1)-th time point before the k-th time point to a specific OCV deviation change amount, and may obtain an OCV moving average by applying a weighted moving average to the plurality of OCV deviation changes including the changed OCV deviation change amount (e.g., the specific OCV deviation change amount). For example, the third processor (108_3) may obtain an OCV moving average by changing the k-th OCV deviation change amount (OCV) corresponding to the k-th time point among the plurality of time points of the battery cell (121). slope1_k ) is less than the first critical OCV deviation change amount, and the (k-1) OCV deviation (OCV) corresponding to the (k-1) time point before the k-th time point of the battery cell (121) D1_(k-1) ) is greater than the critical OCV deviation, the kth OCV deviation change (OCV slope1_k) can be changed (or updated) to a specific OCV deviation change amount, and a weighted moving average can be applied to multiple OCV deviation changes including the changed OCV deviation change amount (e.g., the specific OCV deviation change amount) to obtain an OCV moving average.

[0175] According to one embodiment, the first threshold OCV deviation change amount may be set to -0.2, the threshold OCV deviation may be set to 0, and the specific OCV deviation change amount may be set to 0. For reference, values ​​such as -0.2 and 0 are merely examples to help understanding, and the values ​​of the first threshold OCV deviation change amount, the threshold OCV deviation, and the specific OCV deviation change amount disclosed in this document are not limited to the above values. In addition, at least some of the first threshold OCV deviation changes corresponding to each cell may be the same or different. In addition, at least some of the threshold OCV deviations corresponding to each cell may be the same or different. In addition, at least some of the specific OCV deviation changes corresponding to each cell may be the same or different. Through this, the over-detection rate that may occur due to the OCV deviation changes having excessively large absolute values ​​can be reduced.

[0176] According to one embodiment, the third processor (108_3) may be configured to calculate a plurality of OCV deviation variations (OCV slope1 , OCV slope2 , OCV slope3 ) among which, some OCV deviation changes that are less than the second threshold OCV deviation changes are changed to the second threshold OCV deviation changes, and a weighted moving average is applied to a plurality of OCV deviation changes including some OCV deviation changes to obtain an OCV moving average. For example, the third processor (108_3) may obtain a plurality of OCV deviation changes (OCV) of the battery cell (121) by changing the weighted moving average to a plurality of OCV deviation changes (OCV) of the battery cell (121). slope1), some OCV deviation changes that are less than the second threshold OCV deviation change amount are changed to the second threshold OCV deviation change amount, and a weighted moving average is applied to a plurality of OCV deviation changes including some OCV deviation changes to obtain an OCV moving average corresponding to the battery cell (121).

[0177] In one embodiment, the second threshold OCV deviation change amount may be set to -0.7. This may reduce the over-inspection rate that may occur due to an OCV deviation change amount having an excessively large absolute value.

[0178] According to one embodiment, the third processor (108_3) calculates the OCV moving average (OCV mv1 , OCV mv2 , OCV mv3 ) can be transmitted to the fourth processor (108_4).

[0179] According to one embodiment, the fourth processor (108_4) calculates an OCV moving average (OCV mv1 , OCV mv2 , OCV mv3 ), it is possible to diagnose an abnormality of the battery cells (121, 122, 123). For example, the fourth processor (108_4) may diagnose the OCV moving average (OCV) of the battery cell (121). mv1 ), the abnormality of the battery cell (121) can be diagnosed.

[0180] According to one embodiment, the fourth processor (108_4) calculates an OCV moving average (OCV mv1 , OCV mv2 , OCV mv3 ) and the critical moving average can be used to diagnose an abnormality of the battery cell. For example, the fourth processor (108_4) can diagnose an abnormality of the battery cell based on the result of comparing the OCV moving average (OCV) of the battery cell (121). mv1 ) and the critical moving average, an abnormality of the battery cell (121) can be diagnosed.

[0181] According to one embodiment, the critical moving average may be determined by multiplying a standard deviation of the OCV moving average corresponding to multiple points in time of a specific battery module by a first preset value and adding the average value of the OCV moving average of the specific battery module, comparing the value with a second preset value to select a smaller value, and comparing the selected value with a third preset value to determine a larger value as the critical moving average.

[0182] For example, the first preset value may be -6, the second preset value may be -0.02, and the third preset value may be -0.05, but these are only examples to help understanding, and the present invention is not limited to the above examples.

[0183] At this time, the standard deviation and average value can be calculated based on the values ​​excluding the maximum and minimum values ​​among the OCV moving averages corresponding to multiple points in time of a specific battery module.

[0184] Referring to FIG. 3b, an OCV moving average (310) can be confirmed, which is a result of applying an exponentially weighted moving average to the OCV deviation change amount at multiple points in time obtained based on the OCV deviation at multiple points in time for a specific battery cell of FIG. 3a. In addition, a critical moving average (320) for comparison with the OCV moving average can also be confirmed.

[0185] According to one embodiment, the fourth processor (108_4) calculates an OCV moving average (OCV mv1 , OCV mv2 , and / or OCV mv3 ) is less than the critical moving average, the value of the diagnostic count is increased by the first increment, and an abnormality of the battery cell (121, 122, and / or 123) can be diagnosed based on the result of comparing the diagnostic count and the critical count. For example, the fourth processor (108_4) may diagnose the OCV moving average (OCV) of the battery cell (121) mv1) is less than the critical moving average, the value of the diagnostic count corresponding to the battery cell (121) is increased by a first increment (e.g., 10), and an abnormality of the battery cell (121, 122, and / or 123) can be diagnosed based on the result of comparing the increased diagnostic count and the critical count.

[0186] According to one embodiment, the fourth processor (108_4) calculates an OCV moving average (OCV mv1 , OCV mv2 , and / or OCV mv3 ) is less than the critical moving average, and if the diagnostic count increase condition is additionally satisfied, the value of the diagnostic count can be increased by the first increment. At this time, the diagnostic count increase condition for increasing the diagnostic count based on the OCV data at a specific point in time may be a condition in which (i) the OCV deviation of a specific battery cell at a specific point in time is less than the OCV deviation of the previous point in time, and the OCV deviation of the previous point in time is less than a preset value (e.g., 0), and (ii) the number of OCV deviations of a specific battery cell is a predetermined number or more, and the data accumulation collection period is a predetermined period or more.

[0187] At this time, the fourth processor (230) is the OCV moving average (OCV mv1 , OCV mv2 , and / or OCV mv3 ) is smaller than the critical moving average, the more the value of the diagnostic count can be increased.

[0188] According to one embodiment, the fourth processor (108_4) calculates an OCV moving average (OCV mv1 , OCV mv2 , and / or OCV mv3) is less than the critical moving average, and additionally calculates a second increment, increases the value of the diagnostic count by the second increment (e.g., 1), and diagnoses an abnormality of the battery cell (121, 122, and / or 123) based on the result of comparing the increased diagnostic count and the critical count. For example, the fourth processor (108_4) may diagnose an abnormality of the battery cell (121) by calculating the OCV moving average (OCV) of the battery cell (121). mv1 ) is less than the critical moving average, a second increment is additionally calculated, the value of the diagnostic count corresponding to the battery cell (121) is additionally increased by the second increment, and an abnormality of the battery cell (121) can be diagnosed based on the result of comparing the increased diagnostic count and the critical count.

[0189] According to one embodiment, the fourth processor (108_4) calculates an OCV moving average (OCV mv1 , OCV mv2 , and / or OCV mv3 ) is greater than or equal to the critical moving average, the value of the diagnostic count may be reduced. For example, the fourth processor (108_4) may decrease the OCV moving average (OCV) corresponding to the battery cell (121). mv1 ) is greater than or equal to the critical moving average, the value of the diagnostic count corresponding to the battery cell (121) can be decreased by a first decrease (e.g., 1). At this time, the lower limit of the diagnostic count can be set to a specific value (e.g., 0).

[0190] Referring to FIG. 3c, the result of increasing or decreasing the value of the diagnostic count can be confirmed based on the result of comparing the OCV moving average (310) and the critical moving average (320) obtained by applying a weighted moving average to the OCV deviation change amount for multiple time points for a specific battery cell of FIG. 3b. Referring again to FIG. 3b, it can be confirmed that the OCV moving average (310) for a specific battery cell is less than the critical moving average (320) from around October 2022, and accordingly, as shown in FIG. 3c, it can be confirmed that the value of the diagnostic count for a specific battery cell increases from around October 2022.

[0191] If the critical count is 100, as shown in FIG. 3c, the value of the diagnostic count corresponding to a specific battery cell has a value of 100 around April 2023, and since the diagnostic count corresponding to a specific battery cell is greater than or equal to the critical count, the fourth processor (108_4) can diagnose that an abnormality has occurred in the specific battery cell.

[0192] The first processor (108_1) to the third processor described above may be implemented as a single processor or as separate processors. Here, the processor may execute software to control at least one other component (e.g., hardware or software component) of the battery diagnostic device (100) and perform various data processing or calculations.

[0193] According to an embodiment, the battery diagnostic device (100) may transmit battery diagnostic results to an external device (e.g., a cloud server or a user terminal). Here, the cloud server may provide a service for providing battery diagnostic results to each of multiple users. Furthermore, the user terminal may include a terminal such as a personal computer (PC) or a smartphone.

[0194] Fig. 4 is a flowchart illustrating the operation of a battery diagnostic device according to an embodiment. Fig. 4 may be an explanation of the operation of the battery diagnostic device (100) of Fig. 1, and may be explained using the configuration of Fig. 1.

[0195] The embodiment illustrated in FIG. 4 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 4, and some of the steps illustrated in FIG. 4 may be omitted, the order between steps may be changed, or steps may be merged.

[0196] Referring to FIG. 4, in operation 405, the battery diagnostic device (100) can obtain OCV data of battery cells (121, 122, 123, 131, 132, 133, 141, 142 and / or 143). For example, the battery diagnostic device (100) can configure the OCV data based on voltage, current, and / or temperature measurement information of the battery cells (121, 122, 123, 131, 132, 133, 141, 142 and / or 143). As another example, the battery diagnostic device (100) can receive OCV data of a battery cell (121, 122, 123, 131, 132, 133, 141, 142 and / or 143) acquired by a battery module (120, 130, 140) or a battery cell (121, 122, 123, 131, 132, 133, 141, 142 and / or 143).

[0197] In operation 410, the battery diagnosis device (100) may calculate a plurality of OCV deviations representing the difference between the average OCV corresponding to a plurality of points in time and the OCV of the battery cells for each of a plurality of battery cells included in a specific battery unit (e.g., a specific battery module) based on the OCV data acquired in operation 405. According to one embodiment, the battery diagnosis device (100) may extract OCV data of a specified voltage range (e.g., a voltage range of 3.9 V or higher) from among the OCV data. The battery diagnosis device (100) may calculate the plurality of OCV deviations based on the extracted OCV data of the specified voltage range.

[0198] In operation 415, the battery diagnostic device (100) can obtain a plurality of OCV deviation changes based on a plurality of OCV deviations.

[0199] In operation 420, the battery diagnosis device (100) may obtain an OCV moving average by applying a weighted moving average to a plurality of OCV deviation changes. Here, the plurality of OCV moving averages may correspond to battery cells. According to one embodiment, when a k-th OCV deviation change amount corresponding to a k-th time point among the plurality of time points is less than a first threshold OCV deviation change amount, and a (k-1)-th OCV deviation corresponding to a (k-1)-th time point before the k-th time point is greater than or equal to the threshold OCV deviation, the battery diagnosis device (100) may change the k-th OCV deviation change amount to a specific OCV deviation change amount, and obtain the OCV moving average by applying the weighted moving average to the plurality of OCV deviation changes including the specific OCV deviation change amount. According to one embodiment, the battery diagnosis device (100) may change some OCV deviation change amounts that are less than a second threshold OCV deviation change amount among the plurality of OCV deviation change amounts to the second threshold OCV deviation change amount, and may obtain the OCV moving average by applying the weighted moving average to the plurality of OCV deviation change amounts including the some OCV deviation change amounts. According to one embodiment, the battery diagnosis device (100) may obtain the OCV moving average by applying an exponentially weighted moving average to the plurality of OCV deviation change amounts.

[0200] In operation 425, the battery diagnosis device (100) can diagnose an abnormality of a battery cell based on the OCV moving average. According to one embodiment, the battery diagnosis device (100) can diagnose an abnormality of the battery cell based on a result of comparing the OCV moving average and the threshold moving average. According to one embodiment, when the OCV moving average is less than the threshold moving average, the battery diagnosis device (100) can increase the value of the diagnosis count by a first increment, and diagnose an abnormality of the battery cell based on a result of comparing the diagnosis count and the threshold count. According to one embodiment, the battery diagnosis device (100) can calculate a second increment based on the degree to which the OCV moving average is less than the threshold moving average, further increase the value of the diagnosis count by the second increment, and diagnose an abnormality of the battery cell based on a result of comparing the diagnosis count and the threshold count. According to one embodiment, the battery diagnostic device (100) can decrease the value of the diagnostic count when the OCV moving average is greater than or equal to the threshold moving average.

[0201] Detecting potential battery fires is crucial for ensuring the safety and longevity of battery systems. The present disclosure provides a method for monitoring the rate of change in the OCV deviation between the average OCV values ​​of individual battery cells and groups of battery cells, including the individual battery cells themselves. By analyzing the deviations over time, abnormal behavior that may indicate early signs of internal failure, overcharge, or thermal runaway can be identified before a hazardous condition develops.

[0202] The detection method begins by calculating the OCV deviation of each cell. Deviation △V i is the OCV (V) of individual cells. i ) of the average OCV (V) of a set of battery cells avg ) is obtained by subtracting the deviation △V iTracking how OCV changes over time provides insight into the relative performance and stability of each cell within the system. Significant deviations from the expected range can signal potential issues such as imbalance, degradation, or failure. To improve detection accuracy, the rate of change of OCV deviation, d(△V i ) / dt can be monitored. If the rate of change of the OCV deviation increases rapidly or shows an irregular trend, it may mean that the cell is behaving abnormally compared to other cells. Such deviations may indicate internal short circuits, excessive self-discharge, or other fault conditions that can lead to overheating and, in extreme cases, battery fire.

[0203] For robust fault detection, analysis can be performed over at least two different timescales: long-term and short-term. Long-term trends, which can span months, can help identify gradual degradation effects such as capacity loss due to aging, lithium deposition, or persistent cell-to-cell imbalance. Conversely, short-term trends, which can be measured over days or weeks, can detect more immediate and less predictable changes that could indicate safety hazards, such as internal shorts, abnormal self-discharge, or localized thermal issues. By combining at least two timescales, the system can distinguish between normal aging and sudden, potentially hazardous failures.

[0204] A threshold-based approach can be used to trigger alerts or preventive actions. If the rate of change in OCV deviation exceeds a preset threshold within a short- or long-term monitoring period, an alert can be generated, triggering preventive measures such as additional inspection, battery cell isolation, or system shutdown. This method improves early failure detection, reducing the likelihood of catastrophic failure while maintaining the overall health and reliability of the battery system.

[0205] Batteries are a critical component in electronic devices. They provide the power needed for electronic devices to operate and perform their functions. While batteries offer numerous benefits, they also present challenges. One of the major risks associated with battery packs is the potential for fire due to chemical and electrical processes occurring within the battery cells. Various factors, such as thermal runaway, overcharging, short circuits, overheating, and battery aging, can trigger this dangerous condition. If a fire occurs, the safety of devices and systems can be seriously compromised.

[0206] Given the critical role of batteries in electronic devices, ensuring their safety is crucial. Therefore, these devices may typically incorporate battery monitoring and / or battery diagnostic devices designed to detect potential fire hazards within the battery pack.

[0207] For example, as illustrated in FIG. 1, such an electronic device may include a battery device (102) and a battery management system (BMS) (104) that communicates with the battery device (102) to ensure that the battery device functions safely.

[0208] The battery device (102) communicates with the BMS (104) and may be composed of multiple battery modules (110, 120, 130). A "battery device" may be a device associated with a battery or a portion thereof. For example, the battery device may be a device such as a battery pack, a battery unit, a battery cell, a battery system, a battery assembly, a battery module, a power pack, an energy pack, or an energy storage device. In the example of FIG. 1, the battery device (102) may be a battery pack including a plurality of battery modules, each of which may include one or more battery cells connected to each other to meet a specific power demand. For example, each battery cell may be referred to as a battery device.

[0209] Referring to FIG. 1, each battery module (e.g., 110, 120, 130) may include multiple individual battery cells (e.g., 112, 114, 116, 132, 134, 136, etc.). The number of battery modules and cells in a pack may vary depending on the needs of the device. A battery pack may include one, two, or more battery modules depending on the design of the device, and each battery module may include one, two, three, or more battery cells depending on the design of the battery device. Each individual battery cell may be composed of several important components, such as a cathode, an anode, an anode material, a separator, an electrolyte, a polymer, a case, etc.

[0210] The BMS (104) can monitor the performance of the battery device. As illustrated in FIG. 1, the BMS may include an interface (106) and a processor (108) designed to receive and process important data from the battery. The BMS can monitor several key parameters, such as the battery's state of charge (SOC), voltage, open circuit voltage (OCV), current, and temperature. This data is crucial for maintaining the health of the battery, enhancing safety, and ensuring optimal energy management. The BMS (104) can be directly integrated into the battery or managed remotely. It collects data and can make decisions based on the information regarding the performance and safety of the battery. The BMS uses one or more processors to control various components and perform calculations, and can transmit diagnostic results to an external device, such as a cloud server or a user terminal, for further analysis. The BMS (104) can directly or indirectly monitor and assess the health of the battery pack. In one embodiment, the BMS (104) can analyze OCV data obtained from the battery unit to identify abnormalities within the battery pack (102). If an irregularity is detected, the BMS (104) can trigger an alarm, including a visual, audible, or tactile notification. The term "battery unit" may refer to a component such as a battery pack, an individual battery module, or a battery cell. In some cases, the BMS (104) may be integrated into the battery unit itself as part of a larger system. Alternatively, the BMS (104) may operate separately from the battery unit and function as an external server connected via a wireless network. In this configuration, the BMS (104) may function as an external server, communicating with the battery unit via a wireless network to ensure the safe operation of the system.

[0211] FIG. 5 is a diagram illustrating an example of an electronic device (200) that includes a battery system (202) and a control unit (204) that work together to ensure efficient function and safety. The control unit, also known as an electronic control unit (ECU) or module (ECM), may function as the brain of the device. The control unit may be used to manage and coordinate all components within the electronic device. The control unit may communicate directly with the battery system (202) to control how power flows, thereby ensuring that the device obtains the appropriate amount of energy when needed. The battery system (202) may include a battery module (206), a battery management system (BMS) (208), a battery diagnostic device (210), a sensor unit (212), and a switching unit (214). Each of these components may be designed to ensure the smooth operation of the power system of the device. For example, an electronic device (200) may include a plurality of battery modules (206), a plurality of sensor units (212), a plurality of switching units (214), and a plurality of BMSs (208).

[0212] The BMS (208) illustrated in FIG. 5 may be the same as or different from the BMS (104) illustrated in FIG. 1, and the battery module (206) of FIG. 5 may be the same as or different from the battery module (110, 120 or 130) of FIG. 1.

[0213] The sensor unit (212) may include at least one of various sensors, such as a current sensor, a voltage sensor, and a temperature sensor, each of which may serve to monitor various aspects of battery performance. The current sensor may measure the flow of electricity into and out of the battery during charge and discharge cycles. The current sensor may perform measurements periodically, particularly during charging (when the battery is powered) and discharging (when the battery is powered), and may measure each data point during a single measurement period. The collected data may be transmitted to the BMS for analysis, providing valuable real-time information about the current performance of the battery. The voltage sensor is positioned in parallel with the battery and monitors the voltage at the battery terminals. The voltage sensor may generate a voltage signal indicating the voltage level of the battery. This information may be important for ensuring proper operation and safety of the battery. Significant voltage changes may indicate potential problems. Collectively, these sensors can provide comprehensive data that allows the BMS to effectively manage the condition and performance of the battery, ensuring safe and efficient operation.

[0214] The switching unit (214) of the battery system (202) can be connected to the battery module (206) at the positive (+) or negative (-) terminal. The switching unit (214) can control the flow of charge and discharge current within the battery system. The BMS (208) can manage the on / off operation of the switching unit (214), which can be implemented as a relay or a contactor. The switching unit (214) is integrated with the battery module (206), so that the BMS can monitor the performance of the battery. The BMS (208) can control the switching unit (214) to regulate the current flow during both charge and discharge cycles, thereby ensuring safe operation of the battery. This also allows the BMS to accurately measure the OCV of each individual battery cell. Accurately measuring the OCV is essential for assessing potential fire hazards and identifying abnormal conditions such as short circuits or internal failures.

[0215] When the battery is disconnected, the OCV can be obtained by measuring the voltage between the two terminals of the battery (the positive and negative terminals). The OCV value can indicate the battery's charge level and whether the battery is in good condition. How the OCV changes over time can indicate battery problems, such as a short circuit or aging. For devices such as electric vehicles (EVs) or portable electronic devices, tracking the battery's condition is important because it affects how well the device operates and how safely it can be used. In the electronic device illustrated in FIG. 2, the BMS (208) can obtain OCV data directly from the battery cell (216). The sensor unit (212) can help measure important factors such as the battery's voltage, current (power consumption), and temperature. The sensor unit can use this information to generate OCV data. This allows the BMS to determine whether the battery is operating normally. The sensor unit (212) can transmit the data to the BMS (208) via a communication circuit. The data can be transferred wired or wirelessly and can contain information the BMS needs to determine the condition and performance of the battery.

[0216] The OCV decline of individual battery cells can be monitored over time. By comparing the OCV decline of a single cell to the average OCV decline of the entire battery unit, sudden decreases that indicate problems such as short circuits or battery failure can be identified. These sudden voltage drops can be key indicators of potential fire hazards. Using short-term data collection techniques, OCV declines can be closely monitored, allowing for early detection of safety-threatening issues. This approach can help prevent accidents and improve the overall reliability of battery systems.

[0217] Meanwhile, long-term monitoring can detect early warning signs of fire risk by monitoring gradual and gentle OCV declines. Even voltages that wouldn't trigger alarms with conventional technology can slowly decrease over time, potentially leading to fire.

[0218] The present disclosure can provide a method that combines two types of voltage drops. Specifically, analyzing both steep, short-term drops and gradual, long-term drops to identify fire risks can make battery systems safer and more reliable. The method according to the present disclosure relates to analyzing voltage drops that occur over short and long periods of time. By monitoring both rapid and slow voltage changes, the method according to the present disclosure can maintain the safety of the battery system and reduce the risk of fire. The method according to the present disclosure can detect both immediate and subtle hazards.

[0219] In the examples illustrated in FIGS. 6 through 11J, the OCV of five battery cells included in one battery module can be measured. As described above, the OCV tends to decrease over time. The OCV measurements can be performed repeatedly over a period of time. However, the period of time is merely an example and can be shorter or longer depending on the situation. For example, the OCV measurements can be performed once a month, on the first day, the second day, the last day, or on any other day that suits the schedule. The measurements can be performed on the same day or on different days in each month. The frequency of these measurements can vary. For example, measurements can be taken more frequently, such as hourly (or every two hours, every three hours, etc.), daily (or every other day, every three days, etc.), weekly (or every other week, every three weeks, etc.), or at any other time that best suits the system. For example, FIGS. 11A through 11J illustrate cases where measurements are taken more frequently than those illustrated in FIGS. 6 through 10P, and illustrate how taking measurements more frequently can be helpful.

[0220] Figure 6 illustrates data measured from battery cells within a battery module. OCV measurements can be performed at regular intervals, such as daily, weekly, or monthly. Each battery cell can be measured multiple times at each interval on the graph. For example, measurements are shown once a month over 48 months. These measurements can be performed on the first day of each month, the last day of the previous month, or any other selected date. The OCV value (or power value) of the battery is displayed on the vertical axis, and time can be displayed on the horizontal axis. Referring to the graph in Figure 6, it can be seen that battery cells initially exhibit similar OCV declines, but these declines begin to vary over time. For example, Cell 2 exhibits the greatest OCV decline over time after 38 months, while Cell 4 exhibits a noticeable power decline after approximately 37 months. Conversely, the other cells exhibit a slower and more stable decline over the entire 48-month period. Detailed data measured daily or more frequently can be found in Figures 11A to 11J described below.

[0221] By measuring the OCV values ​​multiple times across five cells and averaging them at each measurement point, a synthetic voltage can be generated. This average is shown in Figure 7. This average serves as a basis for comparing the power levels of each cell and can help more easily detect abnormal changes.

[0222]

[0223] Short-term voltage dip analysis (RdV)

[0224] A process for detecting rapid voltage drops may include examining how the voltage of each battery cell compares to the average voltage of all cells. Figure 8A illustrates the results of comparing the voltage drop of an individual cell (e.g., cell 1) to the average voltage drop of all cells over time. This allows for the difference between the voltage drop of an individual cell and the average of the entire group of cells to be determined. Figure 8B illustrates that, as illustrated in Figure 8A, the deviation between the OCV value of cell 1 and the average OCV of all cells at each measurement point in time (△V = OCV of each cell - average OCV of all cells) can be calculated. These deviation values ​​are plotted against the time scale of Figure 8B. For example, at time 18 of Figure 8A, the OCV of cell 1 is 4.189 V, and the average OCV of all cells is 4.1788 V. Therefore, at time point 18, the deviation is 10.2 mV, and the deviation and time units are shown in FIG. 8B. As another example, at time point 48, the OCV of cell 1 is 4.171 V, and the average OCV of all cells is 4.1536 V. Therefore, at time point 48, the deviation is 17.4 mV, and the deviation and time units are shown in FIG. 8B. Therefore, the rate of change or slope of the OCV deviation can be calculated. For example, the slope S (slope in short-term measurement) can be calculated by measuring the change in OCV deviation over one-month time intervals, such as 37 and 38 months, 38 and 39 months, etc. For example, in FIG. 8B, at 37 time intervals (e.g., 37 months), the deviation value is 25 mV, and at 38 time intervals (e.g., 38 months), the deviation value is 16.4 mV. Therefore, the slope S for time units 37 to 38 can be calculated by dividing the change in deviation (-8.6 mV) by one time unit, which represents the rate of change in deviation over the one-month time period from time units 37 to 38.The slope L (slope in long-term measurements) can be calculated by measuring the change in OCV deviation over 8 or 7-month time intervals, such as from 0 to 8 months, 2 and 9 months, and 3 and 10 months. For example, in Figure 8B, at 3 time intervals (e.g., 3 months), the deviation value is 2.4 mV, and at 11 time intervals (e.g., 11 months), the deviation value is 11.1 mV. Therefore, the slope L for time intervals 3 to 11 can be calculated by dividing the deviation by 8 time intervals, which represents the rate of change in the deviation value over the time intervals from 3 to 11 hours (i.e., 8-month time interval). If the slope of the deviation change is too steep, it can be considered an anomaly. This can help identify problems such as overheating, damage, or other issues with the battery module.

[0225] Similar to the above, Fig. 9A shows a comparison between the OCV value of cell 2 and the average OCV value of five cells, and Fig. 9B shows the deviation value (△V = OCV of each cell - average OCV of all cells) between the OCV value of cell 2 and the average OCV value of five cells at each measurement point in Fig. 9A. Since Figs. 9A and 9B are similar to Figs. 8A and 8B, a detailed description of cell 2 will be omitted. Also, similar to the slope calculation described for battery cell 1, the slope L can be determined by the change in △V for 8 months between 16 and 24 months, and the slope S can be determined by the change in △V for 1 month between 37 and 38 months.

[0226] Fig. 10A shows the comparison results between the OCV value of cell 4 and the average OCV value of five cells, and Fig. 10B shows the deviation values ​​(△V = OCV of cell 4 - average OCV) between the OCV value of cell 4 and the average OCV of five cells at each measurement time point shown in Fig. 10A. Since Figs. 10A and 10B are similar to the graphs of Figs. 8A and 8B, a detailed description of cell 4 will be omitted. Similarly, the slope L of cell 4 can be calculated by calculating the △V change rate for 8 months between 16 and 24 months, and the slope S can be determined by calculating the △V change rate for 1 month between 37 and 38 months.

[0227] Figure 8C illustrates the rate of change (e.g., slope) of the OCV deviation value of cell 1 over a period of time (e.g., one month) as illustrated in Figure 8B. The vertical axis represents the slope (i.e., rate of change) and the horizontal axis represents time. A steep slope (large rate of change) may indicate rapid degradation or abnormal behavior, such as an internal short circuit or excessive heat generation. The slope data may be compared to a threshold (e.g., a short-term threshold S), which is a predetermined value representing the maximum allowable voltage drop before a fire hazard occurs. If the slope exceeds the short-term threshold, i.e., if the rate of voltage decrease in cell 1 over a specific time period is greater than the threshold, it may indicate that cell 1 is in an abnormal condition. The graph of Figure 8C illustrates, by way of example, that no slope value (absolute value) is greater than the absolute value of the threshold S (e.g., (-)20 mV / t).

[0228] The graph in Figure 8C can be plotted differently to show how often the slope drops below a threshold (e.g., a short-term threshold S), as shown in Figure 8D. The vertical axis in Figure 8D represents the number of occurrences, and the horizontal axis represents time or other measurement intervals. Whenever the slope drops below the threshold, it may signal that the battery is behaving abnormally, particularly if the OCV is changing at a rate faster than what is considered normal or acceptable. Tracking how often the slope drops below this threshold can be important for monitoring the health of the battery. Counting these events can help predict the likelihood of problems, such as battery failure or a potential fire hazard. For example, a count of one might simply be a minor warning, while two or more counts could indicate a more serious concern and the need for additional safety inspection. In this particular example, the count remains at zero because the slope never dropped below the short-term threshold (i.e., the absolute value of the slope was less than the absolute value of the threshold), confirming that the battery was operating normally and not posing an immediate risk.

[0229] Figure 9C shows the slope (slope S) of the OCV deviation values ​​for one month in Figure 9B. Figure 9D shows the number of occurrences where the slope shown in Figure 9C falls below a short-term threshold (threshold S). Similar to Figures 8C and 8D, it can be seen that the OCV deviation value of Cell 2 does not decrease faster than the absolute threshold value. This may indicate that the battery is operating within the expected range and that no significant problems are detected.

[0230] Figure 10C illustrates the slope (slope S) of the OCV deviation value of Figure 10B, and Figure 10D illustrates the number of occurrences in which the slope of Figure 10C falls below a threshold (threshold S). Unlike cells 1 and 2, it can be confirmed that there is one case at time point 37 in which the OCV deviation value in cell 4 falls faster than the absolute threshold value. Therefore, it can be confirmed that the count value of Figure 10D is 1 at time point 37. This count can be used to assess potential fire hazards, and for example, if it occurs at least once or more than twice, it can signal a high fire hazard and prompt additional safety measures.

[0231] The slope analysis described above is an example of a method for predicting potential risks to battery cells, and the present disclosure is not limited thereto. For example, methods such as a simple moving average (SMA) or moving average (MA) can be applied to OCV data to detect abnormal battery behavior. When the MA method is applied to OCV data, abnormal changes in battery performance can be identified by calculating the average OCV over a specific period through the moving average. For example, an increase or decrease in the OCV of a battery cell beyond a certain limit can be a warning that the battery may fail and pose a fire risk. In Figure 8E, the MA can be calculated through a fixed window, such as the average of the previous 5, 10, or 15 measurements. As new OCV data is collected, the oldest value is dropped, forming a "moving" window that tracks changes over time.

[0232] For example, over a time period of 8 months (e.g., 8 months), the MV could be the average of four slopes R (e.g., over time periods of 5, 6, 7, and 8). The number of total periods can vary, such as 2, 3, 4, 5, 6, or more. If the moving average suddenly exceeds a preset threshold, it may indicate a problem with the battery, such as overheating or component failure. Similar to how the change rate was calculated, the MA data can be compared to the threshold and the number of times the threshold is exceeded can be counted (see Figure 8F).

[0233] As shown in Figures 9E and 10E, moving averages can be calculated and graphed for comparison between cells 2 and 4. It can be seen that only one of the moving averages in cell 4 falls below the threshold (i.e., its absolute value is greater than the absolute value of the threshold), which is consistent with the slope analysis results.

[0234] A weighted moving average (WMA) assigns different weights to each data point within a window. More recent data may be given higher weighting. Like an MA, a WMA calculates the average of OCV values ​​over a specified period. The WMA formula is as follows:

[0235] WMA=(w1*x1+w2*x2+...+w n *x n ) / (w1+w2+...+w n )

[0236] At this time, x1 to x n is OCV, and w1 to w n is the weight applied to each OCV.

[0237] A disproportionately high OCV value compared to previous values ​​may indicate an abnormal battery condition. By assigning more weight to recent measurements, WMA can help detect immediate threats, such as a rapid increase in OCV, which may indicate impending failure.

[0238] In Figures 8G, 8H, 9G, and 9H, the weighted average OCV data is plotted along with the slope and simple moving average tests, indicating that battery cells 1 and 2 are normal during the measurement period. However, in Figures 10G and 10H, compared to the slope and simple moving average tests, it can be seen that cell 4 is showing signs of a fire hazard.

[0239] Another way to analyze OCV data is to use an exponential moving average (EMA). An EMA assigns more weight to the most recent data points. This means that EMA is highly sensitive to recent changes, making it effective in detecting sudden changes in OCV. This characteristic allows for the rapid detection of abnormal battery behavior, which can aid in the detection of potential fire hazards.

[0240] Additionally, the cumulative sum (CUSUM) method can be used. CUSUM can track how far data deviates from a set reference point, allowing for the detection of subtle trends in the early stages. In particular, CUSUM can detect gradual changes in OCV, potentially revealing problems that are not yet serious.

[0241] Additionally, machine learning approaches can be utilized. These algorithms can learn from past OCV data to understand how batteries normally operate. This allows them to identify when new data points deviate from expected patterns. This can lead to highly advanced methods for identifying potential problems or fire hazards in battery systems.

[0242] Combining at least some of the following anomaly detection methods: moving average (MA), weighted moving average (WMA), exponential moving average (EMA), cumulative sum (CUSUM), and machine learning-based anomaly detection methods can improve fire risk detection in battery systems. By continuously monitoring OCV values ​​and applying these techniques, potential risks can be detected early.

[0243] However, the above-described method is merely an example to aid understanding. Many other methods for analyzing OCV data can be applied.

[0244] Figure 8I illustrates the results of comparing the slope of the battery cell (1) illustrated in Figure 8C with the average slope of five battery cells. This allows comparison of the behavior of the battery cell (1) with other battery cells within the same system, and allows determination of whether the observed rate of change is normal or abnormal. The vertical axis represents the slope, and the horizontal axis represents time. A deviation from the average slope may indicate that the battery cell (1) is operating abnormally, which may indicate an increased risk of fire.

[0245]

[0246] Long-term voltage drop analysis (RdV)

[0247] Figure 8J illustrates the slope (e.g., slope L) of the OCV deviation values ​​(△V) shown in Figure 8B over an eight-month period. The vertical axis represents the slope and the horizontal axis represents time. This slope is compared to a threshold (threshold L), which may be different from the short-term threshold shown in Figure 8C. For example, the short-term threshold S may be (-)20 mV / t and the long-term threshold L may be approximately (-)0.5 mV / t or (-)0.5375 mV / t. These thresholds are examples for understanding only and are not limiting. If the slope exceeds the threshold, it may indicate an abnormal condition in the battery cell that requires further inspection. For example, it may be determined that an abnormality occurred in cell 1 at time 45.

[0248] Figure 8K shows the number of occurrences where the slope shown in Figure 8J falls below a threshold (i.e., the number of occurrences where the absolute value of the slope is greater than the absolute value of the threshold). The vertical axis represents counts, and the horizontal axis represents time or various measurement points. The number of occurrences below the threshold indicates that the battery is exhibiting abnormal behavior. In this example, since there were no slopes falling below the threshold, all counts are 0.

[0249] Figure 9J shows the slope (e.g., slope L) of the OCV deviation values ​​over the 8 months shown in Figure 9B. In Figure 9J, the slope drops below a threshold (e.g., approximately 0.5 mV per month) at the 40th time point (40 months), indicating that the battery cell is at risk of fire. Figure 9K shows the number of times the slope of battery cell 2 drops below the threshold, as shown in Figure 6J. Since the slope begins to drop below the threshold at point 34, the count value begins at that point. This count can be used to assess fire risk. For example, fire risk can be considered after counting 2 or after counting 3 as soon as the slope reaches the threshold. This indicates that battery cell 1 (shown in Figure 8K) does not pose a fire risk, but battery cell 2 (shown in Figure 9K) does. Although a detailed description is omitted, as shown in FIGS. 10J and 10K, cell 4 is also a fire hazard because the slope drops below the threshold multiple times.

[0250] Figures 8L through 8P for battery cell 1 show graphs similar to those in Figures 8C through 8I, but focus on a longer period of time using various data analysis methods, such as moving averages and weighted moving averages. The same type of analysis can be performed on battery cell 2 in Figures 9L through 9P and battery cell 4 in Figures 9L through 9P. The battery cells behave similarly over both short and long periods of time, with some variation in OCV drop observed. However, as shown in Figures 9J and 9K, observing the data over a longer period of time can detect more variation with a smaller threshold. For example, comparing the results in Figure 9D (short-term analysis) and Figure 6K (long-term analysis) demonstrates that longer observation times can detect fire hazards that may have been missed in short-term analysis. This highlights the importance of utilizing both short-term (intermittent voltage drop) and long-term (potential voltage drop) analyses to properly monitor battery operation and identify potential problems.

[0251] Figures 8P, 9P, and 10P illustrate two different types of slopes: steep and gentle voltage drops, respectively. This demonstrates how the same battery data can vary depending on the analysis method. A steep voltage drop slope can detect large, sudden changes in battery OCV, while a gentle voltage drop slope can detect slow, small changes over time. Even with the same OCV data, results can vary depending on how the data is processed. For example, a sharp drop in OCV can be detected by a steep drop analysis, while a slow, small drop can be detected by a gentle drop analysis.

[0252] Figures 11A through 11J illustrate data that is measured much more frequently than the data in previous drawings, such as Figures 3 through 10P. In Figures 3 through 10P, OCV data was collected once a month for 48 hours (e.g., 48 months). In contrast, in the third voltage drop analysis illustrated in Figures 11A through 11J, measurements may be performed daily for five battery cells in one battery module. Figures 11A through 11J are enlarged versions of portions (301, 401, 501, 502, 503, 504, 505, 506, 507, 508) shown in Figures 6 through 8C, 8E, 8G, 8J, 8L, and 8N, but illustratively, daily measurements are shown instead of monthly measurements. By measuring your battery daily, you can more easily spot small changes or noise that might not be visible with monthly measurements alone.

[0253] Looking at the graphs with more frequent time measurements, there are times when measurements significantly exceed both thresholds, which may indicate a high fire risk. However, some of this may be random noise resulting from the data collection method. Therefore, combining different data analysis methods (long-term, short-term, and medium-term) can help accurately detect fire risk.

[0254] A method for monitoring the condition of a battery will now be described with reference to the flowcharts in FIGS. 12A to 15. FIG. 12 is a diagram schematically illustrating a process (900) for diagnosing the condition of a battery according to one embodiment of the present disclosure. The purpose of the present disclosure is to ensure that batteries are maintained in a safe and good condition. Batteries are used in many devices, including mobile phones, laptops, and electric vehicles, and sometimes batteries can begin to experience problems, such as losing power too quickly, which can be dangerous. According to the present disclosure, tracking the voltage of a battery over time can help monitor the condition of a battery. If a problem symptom, such as a sudden loss of power, appears in the battery, an alert can be generated to notify the user.

[0255] Process 900 may begin with measuring the voltage of each battery cell at a specific point in time in step S902. Voltages may be measured simultaneously or sequentially for multiple battery cells during a single measurement period, allowing measurements for multiple battery cells to be completed within a generally identical time period. Each battery cell may be a small battery within a larger battery. By measuring the voltage of each cell, one can determine how much power it holds. As these voltages are recorded, a list of numbers representing the power level of each individual cell can be generated. The measured voltages can then be combined to calculate an average voltage (composite voltage) in step S904. This provides a comprehensive understanding of the battery's health. To better understand the battery's performance, the voltage of each cell can be measured multiple times over several days or weeks.

[0256] After each set of voltage measurements is completed, the average (composite) voltage can be recalculated. This process can be repeated multiple times to ensure regular battery maintenance.

[0257] Step S906 also checks whether the battery cell voltage has dropped significantly. If one of the smaller batteries loses power faster than a certain threshold (e.g., a short-term threshold), this could indicate a problem. This step allows you to determine whether a battery cell is losing power too quickly.

[0258] In step S908, a larger threshold (e.g., a long-term threshold) can be used to detect a larger power loss. This second check can identify more serious problems. If a battery cell loses power too quickly, the battery may fail soon and require repair / replacement. In this case, the long-term threshold can be set lower than the short-term threshold to detect more serious problems.

[0259] If a battery cell is determined to be losing power too quickly, a notification may be triggered at step S910. This notification may serve as a warning, such as "Caution: A problem has been detected with this battery! This may pose a safety hazard!" This notification may alert the user to the need to inspect and repair the battery to prevent further problems. The notification may take various forms, including visual signals, sounds, vibrations, or other sensory cues.

[0260] Step S912 provides an additional checkpoint for smaller issues that could worsen further. This is the third checkpoint, which examines smaller power drops over short periods of time. This step ensures that small power fluctuations are not mistaken for larger issues. This step ensures that only real problems are flagged by the system.

[0261] The diagnostic device can compare individual cell measurements to the module's average OCV. If an abnormal difference (deviation) is detected, the system can compare the data to predefined thresholds to assess the severity of the anomaly. If the deviation exceeds the threshold, an alert can be generated in step S914, indicating a potential failure or fire hazard. The system also provides detailed analysis of OCV trends, identifying both short-term and long-term degradation patterns.

[0262] The first and second verification steps S906 and S908 can each include substeps (e.g., S906-2 to S906-10 and S908-2 to S908-18). These substeps can check whether all battery cells are verified simultaneously, or at the same time if possible. By measuring all cells together, the results can be verified accurately. They can also check how quickly the battery voltage changes over time. For example, the deviation between the voltage of each cell and the average voltage of all cells can be checked to determine if the changes are occurring too quickly. This can help identify battery cells that are behaving abnormally. If a battery cell continues to experience problems over time, the number of times the problem occurs can be counted. This method can track how many times and when the problem occurs. If there are too many problems with a battery cell, it can be flagged and an alert generated.

[0263] For each time interval, the deviation between the voltage of each individual cell and the composite voltage can be calculated. For example, if the voltage of a particular cell drops from 3.8 V to 3.6 V over a given period, the deviation could be 0.2 V. This calculation can be performed for each cell at each measurement interval. After collecting the deviations for all cells over multiple time intervals, the rate at which the voltage of each cell changes (decreases) over time can be calculated. This rate can be calculated for the first, second, or third predetermined time intervals. If the voltage decrease of a cell exceeds a predetermined threshold during a given time interval, the system can identify this as a potential problem and generate an alert.

[0264] If a specific cell exhibits a significant voltage drop consistently over several hours, the system can track how often this occurs. A count can be incremented each time the cell exceeds a threshold. When the count reaches a predetermined threshold (e.g., a cell voltage drop exceeds the threshold three times in a row), the system can automatically generate an alert. This alerts the user that a specific cell is experiencing a problem and may require attention.

[0265] After all steps are completed, if a battery cell is identified as having a problem, the system can send a notification. This alerts the user that the battery is experiencing a problem and may require repair or replacement. Performing these checks ensures that the battery is safe and functioning properly. Furthermore, this allows the user to easily identify when the battery needs attention. Notifications generated by the system can include various types of information, such as battery health alerts indicating that maintenance or replacement may be necessary, service suggestions recommending that the battery system be inspected for potential defects, and replacement suggestions indicating that replacement may be necessary if cell performance continues to deteriorate.

[0266] FIG. 15 illustrates an exemplary architecture of a computing system (160) that may be used to perform one or more of the techniques described herein or depicted in other drawings. The general architecture of the computing system (160) includes an arrangement of computer hardware and software modules that may be used to implement one or more aspects of the present disclosure. The computing system (160) may include significantly more (or fewer) elements than those depicted in FIG. 15 .

[0267] As illustrated, the computing system (160) includes a processor (1610), a network interface (1620), a computer-readable medium (1630), and an input / output device interface (1640), all of which may communicate with each other via a communication bus. The network interface (1620) may provide connectivity to one or more networks or computing systems. The processor (1610) may communicate with a memory (1650) and may provide output information to one or more output devices, such as a display (e.g., display 1641), a speaker, etc., via the input / output device interface (1640). The processor (1610) may be a processor that executes various programs (e.g., a battery diagnostic program) stored in the memory (1650) and performs functions of the battery diagnostic device described above. The input / output device interface (1640) may receive input from one or more input devices, such as a camera (1642) (e.g., a 3D depth camera), a keyboard, a mouse, a digital pen, a microphone, a touch screen, a gesture recognition system, a voice recognition system, an accelerometer, a gyroscope, a thermometer, an optical temperature measurement system, a sonar, a lidar device, a laser device, and the like.

[0268] Memory (1650) may store computer program instructions (grouped into modules in some implementations) that processor (1610) executes to implement one or more aspects of the present disclosure. Memory (1650) may include RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory (1650) may store an operating system (1651) that provides computer program instructions for use by processor (1610) in the general management and operation of computing system (160). Memory (1650) may further include computer program instructions and other information for implementing one or more aspects of the present disclosure. In one implementation, for example, memory (1650) may include a user interface module (1652) that generates a user interface (and / or instructions therefor) for display, for example, via a browser or application installed on computing system (160). As another example, the memory (1650) may store various programs related to calculating the SOH of a battery cell and determining whether cell balancing is to be performed, a battery connection failure judgment program, a battery data transmission program, a battery diagnosis program, etc. In addition, the memory (1650) may store various data such as the SOC, SOH data, sensing values, and temperature of each battery cell. A plurality of such memories (1650) may be provided as needed. The memory (1650) may be a volatile memory or a non-volatile memory. RAM, DRAM, SRAM, etc. may be used as the volatile memory. ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. may be used as the non-volatile memory. The examples of the memories (1650) listed above are merely examples and are not limited to these examples.The memory (1650) may include, in addition to and / or in combination with the user interface module (1652), an image processing module (1653), which is a machine learning model (1654) that may be executed by the processor (1610).

[0269] The network interface (1620) is a component capable of transmitting and receiving various data with a server, and may be any device capable of supporting wired or wireless communication. For example, programs for calculating the SOH of battery cells, determining balancing targets, various data, battery data transmission programs, battery diagnostic programs, etc. may be transmitted and received from a separately provided external server via the network interface (1620).

[0270] In this way, the operating method of the battery management device according to one embodiment disclosed in this document can be recorded in the memory (1650) and executed by the MCU (1610).

[0271] Although the example of FIG. 15 illustrates a single processor, a single network interface, a single computer-readable medium, a single input / output device interface, a single memory, a single camera, and a single display, in other implementations the computing system (160) may include one or more of these components (e.g., two or more processors and / or two or more memories).

[0272] The logical blocks, modules, or units described in connection with the implementations disclosed herein may be implemented or performed by a computing device having at least one processor, at least one memory, and at least one communication interface. Elements of the methods, processes, or algorithms described in connection with the implementations disclosed herein may be implemented directly in hardware, as software modules executed by at least one processor, or as a combination of the two. Computer-executable instructions for implementing the methods, processes, or algorithms described in connection with the implementations disclosed herein may be stored in a non-transitory computer-readable storage medium.

[0273] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in dictionaries, should be interpreted to be consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

Claims

1. An interface for obtaining OCV (Open Circuit Voltage) data of a battery cell; and A battery diagnostic device including at least one processor that calculates a plurality of OCV deviations representing differences between an average OCV corresponding to a plurality of time points and the OCV of a battery cell for each of a plurality of battery cells included in a specific battery unit based on the OCV data, obtains a plurality of OCV deviation changes representing a degree of change in the plurality of OCV deviations for each of the plurality of time points, applies a weighted moving average to the plurality of OCV deviation changes to obtain an OCV moving average, and diagnoses an abnormality of the battery cell based on the OCV moving average.

2. In claim 1, One or more of the above processors, A battery diagnostic device, wherein, when the kth OCV deviation change amount corresponding to the kth time point among the plurality of time points is less than the first threshold OCV deviation change amount, and the (k-1)th OCV deviation corresponding to the (k-1)th time point before the kth time point is greater than or equal to the threshold OCV deviation, the kth OCV deviation change amount is changed to a specific OCV deviation change amount, and the weighted moving average is applied to the plurality of OCV deviation change amounts including the specific OCV deviation change amount to obtain the OCV moving average.

3. In claim 1, One or more of the above processors, A battery diagnostic device that diagnoses an abnormality in the battery cell based on the results of comparing the OCV moving average and the critical moving average.

4. In claim 3, One or more of the above processors, A battery diagnostic device that increases the value of the diagnostic count by a first increment when the OCV moving average is less than the threshold moving average, and diagnoses an abnormality in the battery cell based on a result of comparing the diagnostic count and the threshold count.

5. In claim 4, One or more of the above processors, A battery diagnostic device that calculates a second increment based on the degree to which the OCV moving average is less than the threshold moving average, and further increases the value of the diagnostic count by the second increment.

6. In claim 4, One or more of the above processors, A battery diagnostic device that decreases the value of the diagnostic count when the above OCV moving average is greater than or equal to the threshold moving average.

7. In claim 1, One or more of the above processors, A battery diagnostic device that changes some OCV deviation changes that are less than a second threshold OCV deviation change amount among the plurality of OCV deviation changes to the second threshold OCV deviation change amount, and obtains the OCV moving average by applying the weighted moving average to the plurality of OCV deviation changes that include the some OCV deviation changes.

8. In claim 1, One or more of the above processors, A battery diagnostic device that obtains the OCV moving average by applying an exponentially weighted moving average to the above plurality of OCV deviation changes.

9. In claim 1, A battery diagnostic device, wherein the above OCV data is a result of compensating the OCV data before compensation of at least some of the batteries using the balancing capacity obtained by balancing processing performed on at least some of the battery cells.

10. In claim 9, A battery diagnostic device wherein the above balancing capacity is a result of accumulating the discharge capacity of the battery cell through the above balancing process.

11. Action to acquire OCV (Open Circuit Voltage) data of battery cells; An operation of calculating a plurality of OCV deviations representing the difference between the average OCV corresponding to a plurality of time points and the OCV of the battery cells for each of a plurality of battery cells included in a specific battery unit based on the above OCV data; An operation of obtaining a plurality of OCV deviation changes based on the plurality of OCV deviations; An operation of obtaining an OCV moving average by applying a weighted moving average to the above plurality of OCV deviation changes; and A battery diagnosis method, comprising an operation of diagnosing an abnormality of the battery cell based on the above OCV moving average.

12. In claim 11, The operation of obtaining the OCV moving average includes an operation of changing the k-th OCV deviation change amount corresponding to a k-th point in time among the plurality of points in time to a specific OCV deviation change amount, and applying the weighted moving average to the plurality of OCV deviation change amounts including the specific OCV deviation change amount to obtain the OCV moving average, when the k-th OCV deviation change amount corresponding to a k-th point in time among the plurality of points in time is less than a first threshold OCV deviation change amount, and the (k-1)-th OCV deviation corresponding to a (k-1)-th point in time before the k-th point in time is greater than or equal to the threshold OCV deviation. A battery diagnosis method.

13. In claim 11, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of diagnosing an abnormality of the battery cell based on a result of comparing the OCV moving average and the critical moving average.

14. In claim 13, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of increasing the value of the diagnostic count by a first increment when the OCV moving average is less than the threshold moving average, and diagnosing an abnormality of the battery cell based on a result of comparing the diagnostic count and the threshold count.

15. In claim 14, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of calculating a second increment based on the degree to which the OCV moving average is less than the threshold moving average, and further increasing the value of the diagnosis count by the second increment.

16. In claim 14, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of decreasing the value of the diagnosis count when the OCV moving average is greater than or equal to the threshold moving average.

17. In claim 11, A battery diagnosis method, wherein the operation of obtaining the OCV moving average includes an operation of changing some OCV deviation change amounts that are less than a second threshold OCV deviation change amount among the plurality of OCV deviation change amounts to the second threshold OCV deviation change amount, and applying the weighted moving average to the plurality of OCV deviation change amounts including the some OCV deviation change amounts to obtain the OCV moving average.

18. In claim 11, A battery diagnosis method, wherein the operation of obtaining the above OCV moving average includes an operation of obtaining the OCV moving average by applying an exponentially weighted moving average to the plurality of OCV deviation changes.

19. In claim 11, A battery diagnosis method, wherein the above OCV data is a result of compensating the OCV data before compensation of at least some of the batteries using the balancing capacity obtained by balancing processing performed on at least some of the battery cells.

20. In claim 19, A battery diagnosis method, wherein the above balancing capacity is a result of accumulating the discharge capacity of the battery cell through the above balancing process.

21. An act of providing a battery device comprising a plurality of battery cells; An operation of measuring a voltage in a single measurement time period for each of the plurality of battery cells and providing a plurality of voltages of the plurality of battery cells measured in the single measurement time period; An operation of processing the plurality of voltages of the plurality of battery cells measured in the single measurement time period to provide a composite voltage of the plurality of battery cells in the single measurement time period; An operation of providing a plurality of sets of voltages of a plurality of battery cells by repeating voltage measurements multiple times to provide a plurality of voltages of each battery cell measured in a plurality of measurement time intervals, such that each set of voltages is a voltage of the plurality of battery cells measured in one of the plurality of measurement time intervals; An operation of providing a plurality of synthetic voltages of the plurality of battery cells by repeating the processing of each voltage set so that each of the plurality of synthetic voltages is a synthetic voltage of the plurality of battery cells at one of the plurality of measurement time periods; An operation of determining a first specific battery cell among the plurality of battery cells, wherein the voltage decrease rate in the first period is greater than a first threshold; An operation of determining a second specific battery cell among the plurality of battery cells, wherein the voltage decrease rate in the second period is greater than a second threshold; and An operation for generating a notification when at least some of the first specific battery cell and the second specific battery cell are identified; including, A battery diagnosis method, wherein the first threshold is smaller than the second threshold, the first period is longer than the second period, and the operation of determining the first specific battery cell identifies a battery cell that is not identified through the operation of determining the second specific battery cell because the voltage decrease is slower than that of the second specific battery cell.

22. In claim 21, A battery diagnosis method, wherein the measured voltage is an open circuit voltage (OCV) and the composite voltage is an average voltage.

23. In claim 21, A battery diagnosis method, wherein if the third period is shorter than the second period and the voltage decrease rates of the first specific battery cell and the second specific battery cell are within the critical variation range of the voltage decrease rates of the individual battery cells in the third period, the notification is not generated even if the voltage decrease rates of the first specific battery cell and the second specific battery cell in the third period are greater than the decrease rate of the composite voltage in the third period by the first threshold.

24. In claim 23, A battery diagnostic method, wherein the third period is within a range formed by two elements selected from the group including 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 10 hours, 11 hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 8 days, 9 days, and 10 days.

25. In claim 23, One or more of the above processors, A battery diagnosis method, wherein the above critical fluctuation range is within a range formed by two elements selected from the group comprising 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 mV / t.

26. In claim 21, A battery diagnostic method, wherein the notification includes at least a portion of information suggesting consultation on the battery status, information suggesting a service related to the battery device, and information for replacing at least a portion of the battery device.

27. In claim 21, An operation of determining a third specific battery cell among the plurality of battery cells, wherein the voltage decrease rate in the third period is greater by a third threshold than the decrease rate of the synthetic voltage in the third period; and Further comprising an action of generating a notification when at least one of the first specific battery cell, the second specific battery cell and the third specific battery cell is confirmed, A battery diagnosis method, wherein the second threshold is smaller than the third threshold, and the third period is shorter than the second period.

28. In claim 27, A battery diagnosis method, wherein when the voltage decrease rates of the first specific battery cell, the second specific battery cell, and the third specific battery cell are within a critical variation range of the voltage decrease rates of the individual battery cells in the third period, the notification is not generated even if the voltage decrease rates of the first specific battery cell, the second specific battery cell, and the third specific battery cell in the third period are greater than the decrease rate of the composite voltage in the third period by a first threshold value.

29. In claim 21, Measuring the voltage of each of the plurality of battery cells in the single measurement time interval, A battery diagnostic method, wherein measurements are performed simultaneously or sequentially for the plurality of battery cells so that measurements are completed within the same measurement time period.

30. In claim 21, In the operation of determining the first specific battery cell, For each of the plurality of battery cells, a deviation between the voltage of each of the battery cells and the composite voltage in a first measurement time period among the plurality of measurement time periods is calculated, and a first value set for the deviation in the first measurement time period of the plurality of battery cells is provided such that the first value set includes a value for the deviation of each of the plurality of battery cells in the first measurement time period, By repeating the operation of calculating the deviation in an additional measurement time interval among the plurality of measurement time intervals, the additional value set is provided for the deviation in the additional measurement time intervals of the plurality of battery cells such that the additional value set includes a value for the deviation for each of the plurality of battery cells in the additional measurement time intervals, For each of the plurality of battery cells, calculating the rate of change of the deviation in the first period using at least some of the first value set and the additional value set, A battery diagnosis method, wherein the calculated change rate is used for each of the plurality of battery cells to determine whether a first specific battery cell exists in which the voltage decrease rate in the first period is greater than the first threshold.

31. In claim 30, In the operation of calculating the rate of change of the deviation in the first period, Compute the rate of change of the deviation in the first period starting from the first measurement time interval, In the operation of determining the first specific battery cell, By repeating the operation of calculating the rate of change of the deviation in the first period starting from the one or more additional measurement time intervals for each of the plurality of battery cells, so that each rate of change of the deviation of each of the plurality of battery cells becomes the rate of change of each of the battery cells in the one or more additional measurement time intervals, and providing the rate of change of the deviation for each of the plurality of battery cells, A battery diagnosis method, wherein at least a portion of the rate of change of the deviation calculated for each of the plurality of battery cells is used to determine the first specific battery cell whose voltage decrease rate in the first period is greater than the first threshold in the first period starting from the one or more additional measurement time intervals.

32. In claim 31, A battery diagnosis method, wherein if the voltage decrease rate of the first specific battery cell among the plurality of battery cells is greater than the first threshold in the first period starting from at least one of the measurement time periods, the first specific battery cell is identified as a battery cell that generates the notification.

33. In claim 31, In the operation of determining the first specific battery cell, For each of the plurality of battery cells, count the number of times the voltage decrease rate in the first period is greater than the first threshold, A battery diagnostic method, wherein the method determines whether the number of times for each of the plurality of battery cells reaches a counting threshold.

34. In claim 33, A battery diagnosis method, wherein when the number of times for the first specific battery cell among the plurality of battery cells reaches the counting threshold, the first specific battery cell is identified as a battery cell that generates the notification.

35. In claim 30, In the operation of determining the second specific battery cell, For each of the plurality of battery cells, calculating the rate of change of the deviation in the second period using at least some of the first value set and the additional value set, A battery diagnosis method, wherein the calculated change rate is used for each of the plurality of battery cells to determine whether a second specific battery cell exists in which the voltage decrease rate in the second period is greater than the second threshold.

36. In claim 35, In the operation of calculating the rate of change of the deviation in the second period, Compute the rate of change of the deviation in the second period starting from the first measurement time period or the additional measurement time period, In the operation of determining the second specific battery cell, For each of the plurality of battery cells, the operation of calculating the rate of change of the deviation in the second period starting from the one or more additional measurement time periods is repeated, so that each rate of change of the deviation of each of the plurality of battery cells is provided as the rate of change of the deviation of each of the plurality of battery cells in the one or more additional measurement time periods, A battery diagnosis method, wherein at least a portion of the rate of change of the deviation calculated for each of the plurality of battery cells is used to determine the second specific battery cell whose voltage decrease rate in the second period is greater than the second threshold in the second period starting from the one or more additional measurement time intervals.

37. In claim 36, A battery diagnosis method, wherein if the voltage decrease rate of the second specific battery cell among the plurality of battery cells is greater than the second threshold in the second period starting from at least one of the measurement time periods, the second specific battery cell is identified as a battery cell that generates the notification.

38. In claim 36, In the operation of determining the second specific battery cell, For each of the plurality of battery cells, count the number of times the voltage decrease rate in the second period is greater than the second threshold, A battery diagnostic method, wherein the method determines whether the number of times for each of the plurality of battery cells reaches a counting threshold.

39. In claim 38, A battery diagnosis method, wherein when the number of times for the second specific battery cell among the plurality of battery cells reaches the counting threshold, the second specific battery cell is identified as a battery cell that generates the notification.

40. In claim 21, In the operation of determining the second specific battery cell, For each of the plurality of battery cells, a deviation between the voltage of each battery cell and the composite voltage in a first measurement time period among the plurality of measurement time periods is calculated, and a first set of values ​​for the deviation in the first measurement time period of the plurality of battery cells is provided such that the first set of values ​​includes a value for the deviation of each of the plurality of battery cells in the first measurement time period. By repeating the operation of calculating the deviation in an additional measurement time interval among the plurality of measurement time intervals, the additional value set is provided for the deviation in the additional measurement time intervals of the plurality of battery cells such that the additional value set includes a value for the deviation for each of the plurality of battery cells in the additional measurement time intervals, For each of the plurality of battery cells, calculating the rate of change of the deviation during the second period using at least some of the first value set and the additional value set, A battery diagnosis method, wherein the calculated change rate is used for each of the plurality of battery cells to determine whether a second specific battery cell exists in which the voltage decrease rate in the second period is greater than the second threshold.

41. In claim 40, In the operation of calculating the rate of change of the deviation in the second period, Compute the rate of change of the deviation in the second period starting from the first measurement time period or another measurement time period, In the operation of determining the second specific battery cell, By repeating the operation of calculating the rate of change of the deviation in the second period starting from the one or more additional measurement time intervals for each of the plurality of battery cells, so that each rate of change of the deviation of each of the plurality of battery cells becomes the rate of change of each of the battery cells in the one or more additional measurement time intervals, and providing the rate of change of the deviation for each of the plurality of battery cells, A battery diagnosis method, wherein at least a portion of the rate of change of the deviation calculated for each of the plurality of battery cells is used to determine the second specific battery cell whose voltage decrease rate in the second period is greater than the second threshold in the second period starting from the one or more additional measurement time intervals.

42. In claim 41, A battery diagnosis method, wherein if the voltage decrease rate of the second specific battery cell among the plurality of battery cells is greater than the second threshold in the second period starting from at least one of the measurement time periods, the second specific battery cell is identified as a battery cell that generates the notification.

43. In claim 42, In the operation of determining the second specific battery cell, For each of the plurality of battery cells, count the number of times the voltage decrease rate in the second period is greater than the second threshold, A battery diagnostic method, wherein the method determines whether the number of times for each of the plurality of battery cells reaches a counting threshold.

44. In claim 43, A battery diagnosis method, wherein when the number of times for the second specific battery cell among the plurality of battery cells reaches the counting threshold, the second specific battery cell is identified as a battery cell that generates the notification.

45. A non-transitory computer-readable medium storing instructions that are executed to perform the method of claim 21.

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