Battery diagnostic method, battery diagnostic device providing said method, and battery system

Abstract

The battery diagnostic device includes a measurement unit that measures a battery voltage, which is a voltage across a battery, and a battery current, which is a current flowing through the battery; a memory unit that stores an actual internal resistance value calculated based on at least one of the battery voltage and the battery current for each diagnostic time point at which a defect in the battery is diagnosed, and at least one test internal resistance value determined according to a predetermined criterion; and a control unit that extracts, for each diagnostic time point, a plurality of previous diagnostic time points corresponding to a predetermined number of samples based on the diagnostic time point, calculates a moving average value that is an average of the plurality of actual internal resistance values ​​corresponding to each of the plurality of diagnostic time points, and diagnoses defects in the battery by comparing the actual internal resistance value calculated for each diagnostic time point with a reference value calculated based on the moving average value, and if the number of diagnostic time points is smaller than the number of samples, calculates the moving average value based on the actual internal resistance values ​​and the test internal resistance values ​​corresponding to the number of samples.

Description

[Technical field]

[0001] [Cross-reference to related applications] This application claims the benefit of priority based on Korean Patent Application No. 10-2022-0061921 dated May 20, 2022, and all contents disclosed in the documents of that Korean patent application are incorporated herein by reference.

[0002] The present invention relates to a battery diagnostic method capable of diagnosing the state of a battery, a battery diagnostic device providing the method, and a battery system. [Background technology]

[0003] Large batteries installed in electric vehicles, energy storage batteries, robots, satellites, etc., are required to have a larger capacity than small batteries installed in portable terminals, laptops, etc. Large capacity batteries can be configured by connecting multiple batteries in series and / or parallel. In this case, the multiple batteries can include multiple battery cells connected in parallel.

[0004] Meanwhile, as the number of battery cells included in a battery increases, defects may occur in the battery due to problems with the battery cells themselves and / or problems with the connections between the battery cells. For example, defects such as disconnection and short circuit between battery cells may occur. When a defect occurs in a battery, it is necessary to quickly diagnose and correct the defect so that the system in which the battery is installed (e.g., automobile, energy storage device, etc.) can operate normally.

[0005] However, when multiple battery cells are connected in parallel, it is not easy to directly sense the cell voltage of each battery cell due to structural problems in the connection, etc. In other words, it is difficult to directly estimate defects in the battery cell itself and diagnose defects in the entire battery.

[0006] In addition, the technology of estimating direct current internal resistance (DCIR) for each battery and comparing the estimated DCIR value with a preset (fixed) reference value to diagnose battery defects has a limitation in that it cannot detect defects when multiple battery cells are simultaneously disconnected or shorted in the battery. In addition, there is a possibility that the degree of change in the DC internal resistance due to aging may be erroneously diagnosed as a defect. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Korean Patent Publication No. 10-2022-0030824 Summary of the Invention [Problem to be solved by the invention]

[0008] The present invention has been devised to solve the above problems, and provides a battery diagnostic method capable of precisely diagnosing the state of a battery including a plurality of battery cells connected in parallel, and a battery system that provides the same. [Means for solving the problem]

[0009] According to one aspect of the present invention, a battery diagnostic device includes a measurement unit that measures a battery voltage, which is a voltage across a battery, and a battery current, which is a current flowing through the battery; a memory unit that stores an actual internal resistance value calculated based on at least one of the battery voltage and the battery current for each diagnostic time point at which a defect in the battery is diagnosed, and at least one test internal resistance value determined according to a predetermined criterion; and a control unit that extracts, for each diagnostic time point, a plurality of previous diagnostic time points corresponding to a predetermined sample number based on the diagnostic time point, calculates a moving average value that is an average of the plurality of actual internal resistance values ​​corresponding to each of the plurality of diagnostic time points, and diagnoses a defect in the battery by comparing the actual internal resistance value calculated for each diagnostic time point with a reference value calculated based on the moving average value, and if the plurality of diagnostic time points are smaller than the sample number, calculates the moving average value based on the actual internal resistance values ​​and the test internal resistance values ​​corresponding to the sample number.

[0010] The reference value may include an upper limit value that is greater than the moving average value by a predetermined value and a lower limit value that is less than the moving average value by a predetermined value.

[0011] The control unit can calculate an error value by multiplying a standard deviation average value, which is the average of multiple actual standard deviations corresponding to each of the multiple diagnosis time points, by a predetermined multiple, calculate the upper limit value by adding the error value to the moving average value, and calculate the lower limit value by subtracting the error value from the moving average value.

[0012] The memory unit further stores an actual standard deviation calculated for each diagnostic time point and at least one test standard deviation determined according to a predetermined criterion, and the control unit can calculate the average standard deviation based on the actual standard deviation and the test standard deviation corresponding to the sample number if the multiple diagnostic time points are smaller than the sample number.

[0013] The control unit may diagnose that an open circuit defect has occurred in at least one of a plurality of battery cells included in the battery if the actual internal resistance calculated at each diagnosis time point exceeds the upper limit value.

[0014] The control unit may diagnose that a short circuit defect has occurred in at least one of a plurality of battery cells included in the battery if the actual measured internal resistance value calculated at each diagnosis time point is less than the lower limit value.

[0015] According to another aspect of the present invention, a battery system includes a battery including a plurality of battery cells; a measuring unit for measuring a battery voltage, which is a voltage across the battery, and a battery current, which is a current flowing through the battery; a memory unit for storing an actual internal resistance value calculated based on at least one of the battery voltage and the battery current for each diagnosis time point at which defects in the battery are diagnosed, and at least one test internal resistance value determined according to a predetermined criterion; and a control unit for extracting, for each diagnosis time point, a plurality of previous diagnosis time points corresponding to a predetermined sample number based on the diagnosis time point, calculating a moving average value that is an average of the plurality of actual internal resistance values ​​corresponding to each of the plurality of diagnosis time points, and comparing the actual internal resistance value calculated for each diagnosis time point with a reference value calculated based on the moving average value to diagnose defects in the battery, wherein if the plurality of diagnosis time points is smaller than the sample number, the control unit can calculate the moving average value based on the actual internal resistance values ​​and the test internal resistance values ​​corresponding to the sample number.

[0016] The reference value may include an upper limit value that is greater than the moving average value by a predetermined value and a lower limit value that is less than the moving average value by a predetermined value.

[0017] The control unit can calculate an error value by multiplying a standard deviation average value, which is the average of multiple actual standard deviations corresponding to each of the multiple diagnosis time points, by a predetermined multiple, calculate the upper limit value by adding the error value to the moving average value, and calculate the lower limit value by subtracting the error value from the moving average value.

[0018] The memory unit further stores an actual standard deviation calculated for each diagnostic time point and at least one test standard deviation determined according to a predetermined criterion, and the control unit can calculate the average standard deviation based on the actual standard deviation and the test standard deviation corresponding to the sample number if the multiple diagnostic time points are smaller than the sample number.

[0019] The control unit may diagnose that an open circuit defect occurs in at least one of the plurality of battery cells if an actual measured internal resistance value calculated at each diagnosis time point exceeds the upper limit value.

[0020] The control unit may diagnose that a short circuit defect occurs in at least one of the plurality of battery cells if the actual measured internal resistance value calculated at each diagnosis time point is less than the lower limit value.

[0021] According to another aspect of the present invention, a battery diagnosis method includes the steps of: collecting measured values ​​of a battery voltage, which is a voltage across a battery, and a battery current, which is a current flowing through the battery; determining a sample group by extracting a plurality of previous diagnosis time points corresponding to a predetermined sample number based on a predetermined diagnosis time point for diagnosing defects in the battery; calculating a moving average value, which is an average of a plurality of actual internal resistance values ​​corresponding to each of the plurality of diagnosis time points belonging to the sample group, and calculating a reference value, which is a criterion for diagnosing defects in the battery, based on the moving average value; and diagnosing defects in the battery by comparing the actual internal resistance value corresponding to the diagnosis time point with the reference value, wherein if the plurality of diagnosis time points are smaller than the sample number, the step of calculating the reference value calculates the moving average value based on the actual internal resistance values ​​and test internal resistance values ​​corresponding to the sample number, and the actual internal resistance value is calculated based on at least one of the battery voltage and the battery current for each diagnosis time point, and the test internal resistance value is determined according to a predetermined criterion.

[0022] The reference value may include an upper limit value that is greater than the moving average value by a predetermined value and a lower limit value that is less than the moving average value by a predetermined value.

[0023] The step of calculating the reference value may further include a step of calculating an error value by multiplying a standard deviation average value, which is an average of a plurality of actually measured standard deviations corresponding to each of the plurality of diagnosis time points, by a predetermined multiple, and a step of calculating the upper limit value by adding the error value to the moving average value and calculating the lower limit value by subtracting the error value from the moving average value.

[0024] In the step of calculating the reference value, if the number of diagnosis time points is smaller than the number of samples, the standard deviation average value is calculated based on an actual standard deviation and a test standard deviation corresponding to the number of samples, and the actual standard deviation is calculated based on a number of actual internal resistance values ​​corresponding to the number of samples for each diagnosis time point, and the test standard deviation can be determined according to a predetermined criterion.

[0025] If the plurality of diagnostic time points do not exist, the calculating of the reference value may calculate the moving average value based on test internal resistance values ​​corresponding to the number of samples.

[0026] The step of diagnosing a defect in the battery may include diagnosing that an open circuit defect has occurred in at least one of a plurality of battery cells included in the battery if an actual measured internal resistance value calculated at each diagnosis time point exceeds the upper limit value.

[0027] The step of diagnosing a defect in the battery may include diagnosing that a short circuit defect has occurred in at least one of a plurality of battery cells included in the battery if the actual measured internal resistance value calculated at each diagnosis time point is less than the lower limit value. Effect of the Invention

[0028] The present invention is capable of diagnosing battery defects with high accuracy even when a plurality of battery cells are connected in parallel.

[0029] Unlike the conventional technology that diagnoses battery defects using a fixed reference value, the present invention diagnoses battery defects by setting a reference value that reflects changes in the internal resistance value of the battery for each diagnosis point in time when diagnosing battery defects, thereby preventing problems of misdiagnosing an increase in the internal resistance value due to battery aging as a battery defect or misdiagnosing a temporary increase in the internal resistance value as a battery defect.

[0030] Even if there is no previous diagnosis point corresponding to the number of samples, the present invention can calculate a reference value based on a plurality of internal resistance values ​​and a plurality of standard deviations pre-stored in a memory unit, so that a battery defect can be accurately diagnosed even at the initial diagnosis point. [Brief description of the drawings]

[0031] [Figure 1] FIG. 1 is a diagram illustrating a battery diagnostic device according to an embodiment. [Diagram 2] FIG. 13 is a diagram illustrating a battery system according to another embodiment. [Diagram 3] FIG. 13 is a diagram showing an example in which moving average values, upper limit values, and lower limit values ​​calculated for each of a plurality of diagnosis time points are accumulated and displayed. [Figure 4] 4 is a flowchart illustrating a battery diagnosis method according to an embodiment. [Diagram 5] 5 is a flowchart illustrating in detail the sample population determination step (S200) of FIG. 4. [Figure 6] 5 is a flowchart illustrating in detail the reference value determination step (S300) of FIG. 4. [Figure 7] 5 is a flowchart for explaining in detail the defect diagnosis step (S400) of FIG. 4. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0032] Hereinafter, the embodiments disclosed herein will be described in detail with reference to the attached drawings, and identical or similar components will be given the same or similar drawing numbers, and redundant description thereof will be omitted. The suffixes "module" and / or "part" for components used in the following description are given or mixed for the sake of ease of specification writing only, and do not have any meaning or role that is different from each other by themselves. In addition, in describing the embodiments disclosed herein, if it is determined that a detailed description of related known technology may obscure the gist of the embodiments disclosed herein, the detailed description will be omitted. In addition, the attached drawings are merely for the purpose of making the embodiments disclosed herein easily understandable, and the technical ideas disclosed herein are not limited by the attached drawings, and it should be understood that the drawings include all modifications, equivalents, or alternatives included in the ideas and technical scope of the present invention.

[0033] Terms including ordinal numbers such as first, second, etc. may be used to describe various components, but the components are not limited to the terms. The terms are used only to distinguish one component from another.

[0034] When a component is referred to as being "coupled" or "connected" to another component, it should be understood that the component may be directly coupled or connected to the other component, but that there may be other components in between. Conversely, when a component is referred to as being "directly coupled" or "directly connected" to another component, it should be understood that there are no other components in between.

[0035] In this application, the terms "comprise" or "have" and the like are intended to specify the presence of any feature, number, step, operation, component, part, or combination thereof set forth in the specification, and should be understood as not precluding the presence or additional possibility of one or more other features, number, step, operation, component, part, or combination thereof.

[0036] FIG. 1 is a diagram illustrating a battery diagnostic device according to an embodiment.

[0037] Referring to FIG. 1, a battery diagnostic device 1 includes a measurement unit 110, a storage unit 130, and a control unit 150.

[0038] The measurement unit 110 can measure a battery voltage, which is a voltage across the battery, and a battery current, which is a current flowing through the battery. The battery voltage and the battery current can be battery data required to calculate the internal resistance of the battery. For example, the internal resistance can include a direct current internal resistance (DCIR).

[0039] The measurement unit 110 may include a voltage sensor (not shown) electrically connected to both ends of the battery to measure the battery voltage, and a current sensor (not shown) connected in series with the battery to measure the battery current. For example, the measurement unit 110 may measure the battery voltage and the battery current at each diagnosis time point for diagnosing a defect in the battery, and transmit the measurement result to the control unit 150.

[0040] The memory unit 130 can store the internal resistance value calculated by the control unit 150 based on at least one of the battery voltage and the battery current for each diagnosis time point when the battery defect is diagnosed. Also, the control unit 150 can store the battery voltage value and the battery current value received from the measurement unit 110 in the memory unit 130 for each diagnosis time point when the battery defect is diagnosed.

[0041] According to an exemplary embodiment, the memory unit 130 may store the internal resistance value and the standard deviation corresponding to each of a plurality of test diagnosis time points determined according to a predetermined criterion. At this time, the test diagnosis time point may not be an actual diagnosis time point for diagnosing defects in the battery 10. The test diagnosis time point may be a theoretical diagnosis time point including an experimental value, etc. In particular, the test diagnosis time point may be a supplementary diagnosis time point when the number of actual measurement diagnosis time points is smaller than the number of samples constituting a sample population described below. Therefore, the test diagnosis time point may be a mapping of the internal resistance value and the standard deviation provided to calculate a moving average (MA), an upper band threshold (UB_Th), and a lower band threshold (LB_Th) described below and stored in the memory unit 130.

[0042] When a diagnosis time point (N) based on preset conditions arrives, the control unit 150 calculates a moving average value (MA), an upper limit value (UB_Th) that is a predetermined value greater than the moving average value, a lower limit value (LB_Th) that is a predetermined value less than the moving average value, and an internal resistance value corresponding to the diagnosis time point (N).

[0043] According to an embodiment, the time when the charging of the battery starts or the time when the discharging of the battery ends may be the diagnosis time (N) for diagnosing the battery defect. When the diagnosis time (N) arrives, the measurement unit 110 may measure the battery voltage and the battery current at a predetermined period and at a predetermined cycle, and transmit the measurement results to the control unit 150.

[0044] First, the control unit 150 may determine a sample group by extracting a plurality of diagnosis time points included in a preset number of samples (SN) when counting diagnosis time points in the direction of a previous diagnosis time point based on a current diagnosis time point (N). At this time, the sample number (SN) is the number of diagnosis time points included in the sample group, and may be determined as an optimal number based on an experiment, etc. The sample group is a subgroup of a plurality of past diagnosis time points, which is a population, and may be a group for calculating a moving average (MA) and a standard deviation average (σ_ave), which will be described below.

[0045] Table 1 below shows an example of the internal resistance (DCIR), moving average (MA), upper limit (UB_Th), and lower limit (LB_Th) calculated at each of a plurality of diagnostic time points. It is assumed that the sample size (SN) is 5.

[0046] [Table 1]

[0047] For reference, in Table 1, the moving average value (MA), standard deviation (σ), standard deviation average value (σ_ave), upper limit value (UB_Th), and lower limit value (LB_Th) at the initial diagnosis time point (1) may be difficult to calculate directly (thus, the corresponding values ​​are displayed as blanks in Table 1). In addition, the moving average value (MA), standard deviation (σ), standard deviation average value (σ_ave), upper limit value (UB_Th), and lower limit value (LB_Th) at the diagnosis time points (2, 3, ...) adjacent to the initial diagnosis time point (1) may also be difficult to calculate directly due to a lack of past diagnosis values ​​to calculate them. In this case, values ​​calculated on average by experiments can be substituted for the moving average value (MA), standard deviation (σ), standard deviation average value (σ_ave), upper limit value (UB_Th), and lower limit value (LB_Th) at the initial diagnosis time points (1, 2, 3, ...). The following Table 2 is an example of this.

[0048] [Table 2]

[0049] In Table 2, the -5th to -1st diagnostic time points (-5, -4, -3, -2, -1) may be the test diagnostic time points described above. The internal resistance value and standard deviation corresponding to each of the -5th to -1st diagnostic time points may be calculated experimentally or may be calculated as an average value of the internal resistance values ​​and standard deviations of multiple batteries with the same performance.

[0050] The diagnostic time points disclosed in Table 1 are defined as actual measurement diagnostic time points, and the internal resistance value and standard deviation corresponding to the actual measurement diagnostic time points can be explained as the actual measurement internal resistance value and the actual measurement standard deviation. Also, the internal resistance value and standard deviation corresponding to the test diagnostic time points disclosed in Table 2 can be explained as the test internal resistance value and the test standard deviation.

[0051] For example, referring to Tables 1 and 2, at the first diagnosis time point (1), there is no previous actual measurement diagnosis time point corresponding to the sample number (SN). The control unit 150 can calculate a moving average value (MA1, 23.8) based on a plurality of test internal resistance values ​​(25, 27, 23, 24, 20) corresponding to the -5th to -1st diagnosis time points (-5, -4, -3, -2, -1), which are test diagnosis time points. The control unit 150 can also calculate a standard deviation average value (σ1_ave, 1.656) based on a plurality of test standard deviations (1.56, 1.60, 1.70, 1.60, 1.82) corresponding to the -5th to -1st diagnosis time points (-5, -4, -3, -2, -1), which are test diagnosis time points, respectively.

[0052] As another example, referring to Tables 1 and 2, at the third diagnosis time point (3), there is a shortage of previous actual measurement diagnosis time points corresponding to the number of samples (SN). Although Table 1 does not disclose the internal resistance values ​​and standard deviation values ​​corresponding to the first diagnosis time point and the second diagnosis time point, let us assume that they are disclosed. In a similar manner to that described above, the control unit 150 can calculate the moving average value (MA3) and the average standard deviation value (σ3_ave) based on the internal resistance values ​​and standard deviation values ​​corresponding to the -3rd to -1st diagnosis time points (-3, -2, -1) which are test diagnosis time points and the 1st to 2nd diagnosis time points (1, 2) which are actual measurement diagnosis time points.

[0053] For the sake of convenience, the actual measurement diagnosis time, the actual measurement internal resistance, the actual measurement standard deviation, etc. disclosed in Table 1 may be described as diagnosis time, internal resistance, standard deviation, etc. However, in order to distinguish between Tables 1 and 2, in the case of the test diagnosis time disclosed in Table 2, the internal resistance, standard deviation, etc. will be described as test internal resistance, test standard deviation, etc.

[0054] Referring to Table 1, the control unit 150 can determine a sample group by counting the diagnosis time points from the current diagnosis time point (N) toward the previous diagnosis time point and extracting the N-1th diagnosis time point, the N-2th diagnosis time point, the N-3th diagnosis time point, the N-4th diagnosis time point, and the N-5th diagnosis time point, which correspond to 5 sample numbers (SN).

[0055] The control unit 150 may extract a plurality of diagnosis time points (N-5, N-4, N-3, N-2, N-1) to determine a sample group, and may determine reference values ​​(upper limit and lower limit values ​​described below) to be used for defect diagnosis based on the internal resistance values ​​calculated at each of the plurality of diagnosis time points in the sample group. This may solve the problem of misdiagnosing the degree of aging and / or temporary fluctuations in the internal resistance value due to long-term use of the battery as a battery defect.

[0056] Next, the control unit 150 determines reference values ​​(upper limit and lower limit) for diagnosing a battery defect at the Nth diagnosis point based on the internal resistance values ​​calculated at each of the multiple diagnosis points (N-5, N-4, N-3, N-2, N-1) belonging to the sample population.

[0057] According to an exemplary embodiment, the control unit 150 diagnoses a defect in the battery by comparing the internal resistance (DCIR) value corresponding to the Nth diagnosis time point with the upper limit (UB_Th) and the lower limit (LB_Th). For example, referring to Table 1, at the Nth diagnosis time point, the control unit 150 diagnoses a defect in the battery by comparing the internal resistance (

[0058]

number

[0059] ), upper limit (

[0060]

number

[0061] ) and lower limit (

[0062]

number

[0063] ) and calculate the calculated internal resistance value (

[0064]

number

[0065] ) to the upper limit (

[0066]

number

[0067] ) and lower limit (

[0068]

number

[0069] ) to diagnose battery defects.

[0070]

number

[0071] ) and lower limit (

[0072]

number

[0073] ), the moving average (

[0074]

number

[0075] ) and standard deviation mean (

[0076]

number

[0077] ) is required. However, the standard deviation (

[0078]

number

[0079] ) is not a value required for defect diagnosis at the Nth diagnosis point, but is required for defect diagnosis at the subsequent diagnosis points (N+1, N+2, ...), so it can be calculated at the Nth diagnosis point and stored in the memory unit 130. The internal resistance value (

[0080]

number

[0081] ), moving average (

[0082]

number

[0083] ), standard deviation (

[0084]

number

[0085] ), standard deviation mean (

[0086]

number

[0087] ), upper limit (

[0088]

number

[0089] ) and lower limit (

[0090]

number

[0091] ) will be explained.

[0092] The control unit 150 calculates an internal resistance (DCIR N For example, the internal resistance (DCIR N ,

[0093]

number

[0094] ) values ​​can be calculated.

[0095]

number

[0096] For example, the control unit 150 can calculate the voltage difference (ΔV=|V1-V2|) between the battery voltage (V1) corresponding to a first point in time when charging begins and the battery voltage (V2) corresponding to a second point in time a predetermined time has elapsed since the first point in time. The control unit 150 can calculate the internal resistance (DCIR N For example, the internal resistance (DCIR N ) value is assumed to be calculated as 30 Ω.

[0097] Referring to Table 1, the control unit 150 calculates the moving average value (MA) corresponding to the diagnosis time point (N) by averaging (23Ω+24Ω+20Ω+21Ω+23Ω / 5=22.2Ω) the internal resistance values ​​(23Ω, 24Ω, 20Ω, 21Ω, 23Ω) corresponding to the diagnosis time points (N-5, N-4, N-3, N-2, N-1) belonging to the sample group. N ,

[0098]

number

[0099] That is, the internal resistance (DCIR N ) value may be 22.2 Ω.

[0100]

number

[0101] The control unit 150 calculates the standard deviation (

[0102]

number

[0103] ) can be calculated.

[0104] [Table 3]

[0105] As explained above, the standard deviation (

[0106]

number

[0107] ) is not a value required for defect diagnosis at the Nth diagnosis time point, but is required for defect diagnosis at the subsequent diagnosis times (N+1, N+2, ...). Therefore, the standard deviation (

[0108]

number

[0109] ) can be calculated at the Nth diagnosis point in time and stored in the storage unit 130.

[0110] The control unit 150 may refer to Table 4 below to obtain a plurality of standard deviations (σ N-5 , σ N-4 , σ N-3 , σ N-2 , σ N-1 ) based on the standard deviation mean value (

[0111]

number

[0112] ) can be calculated.

[0113] [Table 4]

[0114] The control unit 150 calculates the moving average value (MA N ) is greater than the upper limit (UB N _Th) and the lower limit (LB) which is a specified value smaller than the moving average value (MA). N According to an exemplary embodiment, the control unit 150 calculates the standard deviation average value (

[0115]

number

[0116] ) is multiplied by a first predetermined multiple to calculate the first error value, and the moving average (MA N ) plus the first error value to obtain the upper limit (UB N _Th). In addition, the control unit 150 can calculate the standard deviation average value (

[0117]

number

[0118] ) is multiplied by a second predetermined multiple to calculate the second error value, and the moving average (MA N ) to obtain the lower limit (LB N In this case, the first and second multiples may be the same, but are not limited thereto and may be calculated using various multiples.

[0119] According to an exemplary embodiment, the control unit 150 calculates the standard deviation mean (

[0120]

number

[0121] ) multiplied by a given multiple (Q) to get the error value (

[0122]

number

[0123] ) can be calculated. In this case, the multiple (Q) is a value for reflecting a predetermined error, and can be determined to various values ​​through experiments. For example, the multiple (Q) is assumed to be a natural number 3.

[0124] The control unit 150 calculates the moving average value (MA N =22.2) with error value ((

[0125]

number

[0126] ) and add the upper limit (UB NThe control unit 150 can calculate the moving average (MA N =22.2) to the error value (

[0127]

number

[0128] ) to get the lower limit (LB N _Th)17.1 can be calculated.

[0129]

number

[0130] Next, the control unit 150 detects the internal resistance (DCIR N ) value corresponding to the Nth diagnosis time point N _Th) and lower limit (LB N _Th) to diagnose battery defects.

[0131] According to an exemplary embodiment, the internal resistance (DCIR N ) value is the upper limit (UB N If the internal resistance (DCIR _Th) is exceeded, the control unit 150 may diagnose that a disconnection defect (DD) has occurred in at least one of the battery cells included in the battery. N ) value is the lower limit (LB N If the internal resistance (DCIR_Th) is less than the threshold voltage, the control unit 150 may diagnose that a short defect (SD) has occurred in at least one of the battery cells included in the battery. N ) value is the lower limit (LB N _Th) or higher upper limit (UB NIf the internal resistance (DCIR N If the value is within the normal range, the control unit 150 can diagnose the battery state as normal.

[0132] For example, as described above through Tables 1 and 4 and equations (1) to (4), the internal resistance value (DCIR N ), upper limit (UB N _Th), and lower limit (LB N _Th) can be calculated as 30 (Ω), 27.3, and 17.1, respectively. In this case, the control unit 150 calculates the internal resistance value (DCIR N =30) is the upper limit (UB N _Th=27.3), a battery defect (disconnection defect) can be diagnosed.

[0133] FIG. 2 is a diagram illustrating a battery system according to another embodiment.

[0134] Referring to FIG. 2, the battery system 2 includes a battery 10, a relay 20, a current sensor 30, and a battery management system (BMS) 40.

[0135] The battery 10 may include a plurality of battery cells connected in series and / or parallel. Although three battery cells connected in parallel are shown in FIG. 2, the battery 10 may include various numbers of battery cells connected in series and / or parallel. In some embodiments, the battery cells may be rechargeable secondary batteries. Also, For example, the battery 10 may supply a desired power to an external device by forming a battery bank with a predetermined number of battery cells connected in parallel and forming a battery pack with a predetermined number of battery banks connected in series. As another example, the battery 10 may supply a desired power to an external device by forming a battery bank with a predetermined number of battery cells connected in parallel and forming a battery pack with a predetermined number of battery banks connected in parallel. However, the battery 10 is not limited to such a connection, and may include a plurality of battery banks including a plurality of battery cells connected in series and / or parallel, and the plurality of battery banks may also be connected in series and / or parallel.

[0136] 2, a battery 10 is connected between two output terminals OUT1 and OUT2 of a battery system 2. In addition, a relay 20 is connected between a positive terminal of the battery system 2 and the first output terminal OUT1, and a current sensor 30 is connected between a negative terminal of the battery system 2 and the second output terminal OUT2. The configurations and the connections between the configurations shown in FIG. 2 are merely examples, and the invention is not limited thereto.

[0137] The relay 20 controls an electrical connection between the battery system 2 and an external device. When the relay 20 is turned on, the battery system 2 and the external device are electrically connected to perform charging or discharging, and when the relay 20 is turned off, the battery system 2 and the external device are electrically separated. At this time, the external device may be a charger in a charging cycle in which the battery 10 is charged by supplying power to the battery 10, and may be a load in a discharging cycle in which the battery 10 discharges power to the external device.

[0138] The current sensor 30 is connected in series to a current path between the battery 10 and an external device. The current sensor 30 measures a battery current, i.e., a charging current and a discharging current, flowing through the battery 10, and transmits the measurement result to the BMS 40.

[0139] The BMS 40 includes a measurement unit 41, a storage unit 43, and a control unit 45. The battery diagnostic device 1 shown in FIG. 1 may correspond to the BMS 40 shown in FIG. 2. More specifically, the functions performed by the measurement unit 110, the storage unit 130, and the control unit 150 of the battery diagnostic device 1 may correspond to the functions performed by the measurement unit 41, the storage unit 43, and the control unit 45 of the BMS 40, respectively. For example, the battery diagnostic device 1 may be configured separately from the battery system 1. As another example, as shown in FIG. 2, the BMS 40 may perform the function of the battery diagnostic device 1 in the battery system 1.

[0140] The measurement unit 41 is electrically connected to both ends of the battery 10 and can measure the battery current and the battery voltage. For example, the measurement unit 41 can be realized by an ASIC (Application Specific Integrated Circuit) that monitors the battery 10 and measures battery data (voltage, current, etc.) corresponding to the state of the battery 10.

[0141] For example, the measurement unit 41 may collect the battery voltage by sensing a voltage value across the battery 10. The measurement unit 41 may receive a battery current value from the current sensor 30. The measurement unit 41 may transmit the battery voltage value and the battery current value to the control unit 150.

[0142] The memory unit 43 can store the internal resistance value calculated by the control unit 45 based on at least one of the battery voltage and the battery current for each diagnosis time point when the battery 10 is diagnosed for a defect. The control unit 45 can also store in the memory unit 43 the battery voltage value and the battery current value received from the measurement unit 41 for each diagnosis time point when the battery is diagnosed for a defect.

[0143] According to an exemplary embodiment, the memory unit 43 may store the internal resistance value and the standard deviation corresponding to each of a plurality of test diagnosis time points determined according to a predetermined criterion. At this time, the test diagnosis time point may not be an actual diagnosis time point for diagnosing defects in the battery 10. The test diagnosis time point may be a theoretical diagnosis time point including an experimental value, etc. In particular, the test diagnosis time point may be a supplementary diagnosis time point when the number of actual measurement diagnosis time points is smaller than the number of samples constituting a sample group described below. Therefore, the test diagnosis time point may be a mapping of the internal resistance value and the standard deviation provided to calculate a moving average (MA), an upper band threshold (UB_Th), and a lower band threshold (LB_Th) described below and stored in the memory unit 130.

[0144] When a diagnosis time point (N) based on a preset condition arrives, the control unit 45 calculates the moving average value (MA N ; Moving Average), Upper Limit (UB N _Th;Upper Band Threshold, and Lower Limit (LB N _Th; Lower Band Threshold), and the internal resistance (DCIR N Then, the control unit 45 calculates the internal resistance (DCIR N ) value to the upper limit (UB N _Th) and lower limit (LB N _Th), the condition of the battery 10 can be diagnosed.

[0145] First, the control unit 45 may determine a sample group by extracting a plurality of diagnosis time points included in a preset number of samples (SN) when counting diagnosis time points in the direction of a previous diagnosis time point based on a diagnosis time point (N). At this time, the sample number (SN) is the number of diagnosis time points included in the sample group, and may be determined to be an optimal number based on an experiment, etc. The sample group is a subgroup of a plurality of past diagnosis time points, which is a population, and may be a group for calculating a moving average (MA) and a standard deviation average (σ_ave), which will be described below.

[0146] For example, assume that the sample number (SN) is 5. In Table 1, when counting diagnostic time points from the current diagnostic time point (N) toward the previous diagnostic time point, the control unit 45 can extract the N-1th diagnostic time point, the N-2th diagnostic time point, the N-3th diagnostic time point, the N-4th diagnostic time point, and the N-5th diagnostic time point, which correspond to the sample number (SN) of 5, to determine a sample group.

[0147] According to an exemplary embodiment, if the number of diagnostic time points before the current diagnostic time point (N) is smaller than the sample number (SN), the control unit 45 can add a test diagnostic time point to the sample group. For example, if the number of actual diagnostic time points before the current diagnostic time point (N) is three, the control unit 45 can add two predetermined test diagnostic time points to the sample group. As another example, if the number of actual diagnostic time points before the current diagnostic time point (N) is three, the control unit 45 can form a sample group only from the predetermined test diagnostic time points. In other words, if the actual diagnostic time points do not correspond to the sample number, the control unit 45 can form a sample group from the actual diagnostic time points and the test diagnostic time points, or form a sample group only from the test diagnostic time points.

[0148] The control unit 45 calculates a moving average (MA), which is an average of a plurality of internal resistance values ​​corresponding to a plurality of diagnostic time points included in the sample population. NFor example, referring to Table 1 and formula (2), a moving average value (MA) corresponding to the diagnosis time point (N) can be calculated by averaging a plurality of internal resistance values ​​(23Ω, 24Ω, 20Ω, 21Ω, 23Ω) corresponding to each of a plurality of diagnosis time points (N-5, N-4, N-3, N-2, N-1) belonging to the sample population. N )22.2 can be calculated.

[0149] For example, by referring to Tables 1 and 4, the control unit 45 calculates standard deviations (σ N-5 , σ N-4 , σ N-3 , σ N-2 , σ N-1 ) based on the standard deviation mean value (

[0150]

number

[0151] ) can be calculated.

[0152] The control unit 45 calculates the internal resistance (DCIR N ), the upper limit (UB), which is the reference value for diagnosing a battery defect at the current diagnosis point (N), i.e., the Nth diagnosis point, is set. N _Th) and lower limit (LB N _Th) is determined.

[0153] The control unit 45 calculates the moving average value (MA N ) is greater than the upper limit (UB N _Th) and the lower limit (LB) which is a specified value smaller than the moving average value (MA). N _Th). In some embodiments, the control unit 45 may calculate the standard deviation average value (

[0154]

number

[0155] ) is multiplied by a first predetermined multiple to calculate the first error value, and the moving average (MA N ) plus the first error value to obtain the upper limit (UB N _Th). The control unit 45 can also calculate the standard deviation average value (

[0156]

number

[0157] ) is multiplied by a second multiple to calculate the second error value, and the moving average (MA N ) to obtain the lower limit (LB N _Th) can be calculated. In this case, the first and second multiples may be the same, but are not limited thereto and can be calculated using various multiples.

[0158] According to an exemplary embodiment, the control unit 45 calculates the standard deviation mean (

[0159]

number

[0160] ) multiplied by a given multiple (Q) to get the error value (

[0161]

number

[0162] For example, the multiple (Q) is assumed to be a natural number 3. The control unit 45 calculates the moving average (MA N =22.2) with error value ((

[0163]

number

[0164] ) is added to the upper limit (UB N The control unit 45 can calculate the moving average (MA N =22.2) to the error value (

[0165]

number

[0166] ) is subtracted to get the lower limit (LB N _Th) 17.1 can be calculated. In this case, the multiple (Q) is a value for reflecting a predetermined error, and can be determined to various values ​​by experiment.

[0167] Next, the control unit 45 calculates the internal resistance (DCIR N ) value corresponding to the Nth diagnosis time point N _Th) and lower limit (LB N _Th) to diagnose defects in the battery 10.

[0168] According to an exemplary embodiment, the internal resistance (DCIR N ) value is the upper limit (UB N If the internal resistance (DCIR _Th) is exceeded, the control unit 45 may diagnose that a disconnection defect (DD) has occurred in at least one of the battery cells included in the battery 10. N ) value is the lower limit (LB N If the internal resistance (DCIR_Th) is less than the internal resistance (DCIR_Th), the control unit 45 may diagnose that a short defect (SD) has occurred in at least one of the battery cells included in the battery 10. N ) value is the lower limit (LB N _Th) or higher upper limit (UB NIf the internal resistance (DCIR N If the value is within the normal range, the control unit 45 can diagnose the state of the battery 10 as normal.

[0169] For example, as described above through Tables 1 and 4 and equations (1) to (4), the internal resistance value (DCIR N ), upper limit (UB N _Th), and lower limit (LB N _Th) can be calculated as 30 (Ω), 27.3, and 17.1, respectively. In this case, the control unit 45 calculates the internal resistance value (DCIR N =30) is the upper limit (UB N _Th=27.3), a defect (disconnection defect) in the battery 10 can be diagnosed.

[0170] FIG. 3 is an example diagram showing the cumulative moving average values, upper limit values, and lower limit values ​​calculated for each of a plurality of diagnosis time points.

[0171] Based on Figures 1 to 3, Tables 1 and 4, the moving average (MA N ), upper limit (UB N _Th), and lower limit (LB N An example of calculating ._Th will be described.

[0172] Referring to FIG. 3, the BMS 40 can determine a sample population by extracting a plurality of diagnostic time points adjacent to a given diagnostic time point (N) while being in a similar environment to the diagnostic time point (N). The BMS 40 can determine a moving average (MA), which is an average of a plurality of internal resistance values ​​belonging to the sample population. N ) and the standard deviation mean, which is the average of multiple standard deviations (

[0173]

number

[0174] ) based on the upper limit (UB) corresponding to the diagnosis time (N). N _Th) and lower limit (LB N _Th) can be calculated.

[0175] According to an exemplary embodiment, first, when the BMS 40 counts diagnosis times in the direction of the previous diagnosis time based on a predetermined diagnosis time (N), it can extract the N-1th diagnosis time, the N-2th diagnosis time, the N-3th diagnosis time, the N-4th diagnosis time, and the N-5th diagnosis time, which correspond to 5 sample numbers (SN).

[0176] Next, the BMS40 can average the multiple internal resistance values ​​(23Ω, 24Ω, 20Ω, 21Ω, 23Ω) corresponding to each of the extracted multiple diagnosis time points (N-5, N-4, N-3, N-2, N-1) to calculate a moving average value (23Ω+24Ω+20Ω+21Ω+23Ω) / 5=22.2Ω corresponding to the diagnosis time point (N).

[0177] Using Table 4, equation (3), and equation (4) described above, the BMS 40 can calculate the upper limit (27.3) and the lower limit (17.1).

[0178] Next, the BMS40 uses the internal resistance (DCIR N ) value to the upper limit (UB N _Th) and lower limit (LB N _Th), a defect in the battery 10 can be diagnosed. N Let's assume that the internal resistance (DCIR N =30) is the upper limit (UB N _Th=27.3), a battery defect (disconnection defect) can be diagnosed.

[0179] The internal resistance band (DCIR Band) shown in Fig. 3 can be derived by connecting the moving average (MA), the upper limit (UB_Th), and the lower limit (LB_Th) calculated at multiple diagnosis points in time. The internal resistance band (DCIR Band) can indicate the tendency of the internal resistance value that changes as the battery 10 is used.

[0180] In Figure 3, at the diagnosis time point corresponding to section A, there may be no previous diagnosis time point or the number of previous diagnosis time points corresponding to the number of samples may be insufficient. As described above, in this case, the number of actual measurement diagnosis time points that is insufficient for the number of samples (SN) can be filled with the test diagnosis time point.

[0181] FIG. 4 is a flowchart illustrating a battery diagnosis method according to an embodiment, FIG. 5 is a flowchart illustrating in detail the sample group determination step (S200) of FIG. 4, FIG. 6 is a flowchart illustrating in detail the reference value determination step (S300) of FIG. 4, and FIG. 7 is a flowchart illustrating in detail the defect diagnosis step (S400) of FIG. 4.

[0182] A battery diagnostic method, a battery diagnostic device and a battery system that provide the method will be described below with reference to Figures 1 to 7. The battery diagnostic method performed in the battery system 2 described below can be equally applied to the battery diagnostic device 1.

[0183] First, the BMS 40 collects battery data (S100). At this time, the battery data may include a battery voltage, which is a voltage across the battery 10, and a battery current, which is a current flowing through the battery 10.

[0184] For example, battery voltage and battery current may be battery data required to calculate the battery's direct current internal resistance (DCIR).

[0185] Next, the BMS 40 extracts a number of diagnostic time points adjacent to a given diagnostic time point (N) to determine a sample population (S200).

[0186] The BMS 40 can determine a sample group by extracting a plurality of diagnostic time points corresponding to a preset sample number (SN) when counting diagnostic time points in the direction of the previous diagnostic time point based on a predetermined diagnostic time point (N), i.e., the Nth diagnostic time point.

[0187] Referring to FIG. 5, in step S200, the BMS 40 determines whether or not there is an actual measurement diagnosis time point corresponding to the sample number (SN) (S210).

[0188] If the determination result in S210 is yes, the BMS 40 extracts data corresponding to the actual measurement diagnosis time point corresponding to the number of samples, and determines the sample population (S220, S240).

[0189] For example, assume that the sample number (SN) is 5. In Table 1, when counting diagnostic time points from the Nth diagnostic time point toward the previous diagnostic time point, the BMS 40 can extract the N-1th diagnostic time point, the N-2th diagnostic time point, the N-3th diagnostic time point, the N-4th diagnostic time point, and the N-5th diagnostic time point, which correspond to the sample number (SN) of 5, to determine a sample group.

[0190] If the determination result in S210 is no, the BMS 40 extracts data corresponding to the test diagnosis time point and the actual diagnosis time point from the storage unit 43, and determines the sample population (S230, S240).

[0191] For example, assume that the sample number (SN) is 5. In Table 1, when the BMS 40 counts diagnostic time points from the Nth diagnostic time point toward the previous diagnostic time point, if there is no diagnostic time point corresponding to the sample number (SN) of 5, the BMS 40 can extract the test diagnostic time points in Table 2 to determine the sample group.

[0192] For example, referring to Tables 1 and 2, there is no previous actual measurement diagnosis time corresponding to the sample number (SN) at the first diagnosis time point 1. The control unit 150 can extract the -5th to -1st diagnosis time points (-5, -4, -3, -2, -1), which are test diagnosis time points, to determine the sample group.

[0193] As another example, referring to Tables 1 and 2, at the third diagnostic time point 3, there is a shortage of previous actual measurement diagnostic time points corresponding to the sample number (SN). The control unit 150 can determine the sample group by extracting the -3rd to -1st diagnostic time points (-3, -2, -1) which are test diagnostic time points and the 1st and 2nd diagnostic time points (1, 2) which are actual measurement diagnostic time points.

[0194] Next, the BMS 40 determines (S300) a reference value for defect diagnosis of the battery 10. According to an exemplary embodiment, the reference value is an upper limit value (UB N _Th) and lower limit (LB N _Th).

[0195] Referring to FIG. 6, in step S300, the BMS 40 averages the internal resistance values ​​corresponding to a plurality of diagnostic time points belonging to the sample population to obtain a moving average value (MA) of the sample population. N ) is calculated (S310).

[0196] Referring to Table 1 and Equation (2), the BMS 40 averages a plurality of internal resistance values ​​(23Ω, 24Ω, 20Ω, 21Ω, 23Ω) corresponding to a plurality of diagnosis time points (N-5, N-4, N-3, N-2, N-1) belonging to the sample population, respectively, to obtain a moving average value (MA) corresponding to the diagnosis time point (N). N )22.2 can be calculated.

[0197] At the S300 stage, BMS40 is the standard deviation mean of the sample population (

[0198]

number

[0199] ) and calculates an error value (E) based on the result (S320).

[0200] For example, the standard deviation of the sample population (

[0201]

number

[0202] ) are multiple standard deviations (σ N-5 , σ N-4 , σ N-3 , σ N-2 , σ N-1 ) can be calculated by averaging

[0203] Referring to Tables 1 and 3, BMS40 is a set of a number of standard deviations (σ) corresponding to a number of diagnosis time points (N-5, N-4, N-3, N-2, N-1) belonging to a sample population. N-5 , σ N-4 , σ N-3 , σ N-2 , σ N-1 ) based on the standard deviation mean value (

[0204]

number

[0205] ) can be calculated. In addition, the BMS40 can calculate the standard deviation average (

[0206]

number

[0207] ) multiplied by a given multiple (Q) to get the error value (

[0208]

number

[0209] ) can be calculated. In this case, the multiple (Q) is a value that reflects a predetermined error, and can be determined to various values ​​through experiments. For example, the multiple (Q) is assumed to be a natural number 3.

[0210] At the S300 stage, BMS40 is the moving average (MA N ) and the error value ((E) based on which the upper limit (UB N _Th) and lower limit (LB N _Th) is calculated (S330).

[0211] Referring to the above formula (3), BMS40 is the moving average (MA N =22.2) with the error value (

[0212]

number

[0213] ) is added to the upper limit (UB N _Th) 27.3. Also, referring to the above formula (4), the BMS 40 can calculate the moving average (MA N =22.2) to the error value (

[0214]

number

[0215] ) to get the lower limit (LB N _Th)17.1 can be calculated.

[0216] Next, the BMS 40 calculates the internal resistance (DCIR N ) value corresponding to the diagnosis time (N) and the upper limit (UB N _Th) and lower limit (LB N _Th) to diagnose defects in the battery 10 (S400).

[0217] The BMS 40 calculates an internal resistance (DCIR N ) value can be calculated. N The value can be calculated at step S200 or step S300, and there is no restriction on the time point at which it is calculated as long as it is calculated before step S400, which is the time of diagnosis.

[0218] For example, the BMS 40 can calculate the voltage difference (ΔV=|V1-V2|) between the battery voltage (V1) corresponding to a first point in time when charging begins and the battery voltage (V2) corresponding to a second point in time a predetermined time has elapsed since the first point in time. The BMS 40 can calculate the internal resistance (DCIR N For example, the internal resistance (DCIR N ) value is assumed to be calculated as 30 Ω.

[0219] In step S400, referring to FIG. 7, the BMS40 is N ) value is the upper limit (UB N It is determined whether the time exceeds the threshold value (S410).

[0220] In step S400, if the determination result in step S410 is yes, the BMS 40 diagnoses that a disconnection defect has occurred in at least one of the plurality of battery cells included in the battery 10 (S420).

[0221] For example, if the parallel connection of some of the battery cells among the plurality of battery cells connected in parallel is broken, the internal resistance value of the battery 10 increases.

[0222] If the result of the judgment in step S400 is not exceeded (S410, No), the BMS40 sets the internal resistance value (DCIR N ) is the lower limit (LB NIt is determined whether the time is less than the threshold value (S430).

[0223] In step S400, if the determination result in step S430 is yes, the BMS 40 diagnoses that a short defect has occurred in at least one of the battery cells included in the battery 10 (S440).

[0224] For example, if some of the battery cells connected in parallel come into contact with each other (short), the internal resistance value, which is the overall resistance of the battery 10, decreases.

[0225] If the determination result at step S400 is equal to or greater than the above (S430, No), the BMS 40 diagnoses the state of the battery 10 as normal (S450).

[0226] Internal resistance (DCIR N ) value is the lower limit (LB N _Th) or more, upper limit (UB N If the internal resistance (DCIR N If the value is within the normal range, the BMS 40 can diagnose the condition of the battery 10 as normal.

[0227] Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited to these embodiments, and various modifications and improvements made by a person having ordinary skill in the field to which the present invention belongs also fall within the scope of the present invention. [Explanation of symbols]

[0228] 1 Battery diagnostic device 2 Battery System 10. Battery 20 Relay 30 Current Sensor 40 Battery Management System 41 Measuring part 43 Storage section 45 Control section 110 Measuring section 130 Storage section 150 Control section

Claims

1. a measurement unit for measuring a battery voltage, which is a voltage across a battery, and a battery current, which is a current flowing through the battery; a memory unit for storing an actual internal resistance value calculated based on at least one of the battery voltage and the battery current for each diagnosis time point when diagnosing a defect in the battery, and at least one test internal resistance value which is an average value determined by experimental values; For each diagnosis time point, extracting a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on the diagnosis time point; When the number of the plurality of diagnostic time points is equal to or greater than the number of samples, at the diagnostic time point N, A moving average value, which is an average of a plurality of actually measured internal resistance values ​​corresponding to each of the diagnosis time points N-S N to N-1, is calculated, where S N is the number of samples; calculating a plurality of standard deviations corresponding to each of the diagnostic time points N-S N to N-1 based on the moving average value; Calculate an error value by multiplying a standard deviation average value, which is an average of the plurality of standard deviations, by a predetermined multiple; Calculate an upper limit value by adding the error value to the moving average value; Calculating a lower limit value by subtracting the error value from the moving average value; a control unit that diagnoses a defect in the battery by comparing the actual internal resistance value calculated at the diagnosis time point N with the upper limit value that is higher than the moving average value by a predetermined value and the lower limit value that is lower than the moving average value by a predetermined value, The control unit is If the number of the plurality of diagnosis time points is smaller than the number of samples, the moving average value is calculated based on the actual measured internal resistance value and the test internal resistance value, and the actual measured internal resistance value and the test internal resistance value correspond to the number of samples; The N is a positive integer. Battery diagnostic equipment.

2. The storage unit is Further storing an actual standard deviation calculated for each diagnostic time point and at least one test standard deviation determined by an experimental value; The control unit is The battery diagnostic device of claim 1 , wherein if the number of diagnostic time points is less than the number of samples, the standard deviation average value is calculated based on the actual standard deviation and the test standard deviation corresponding to the number of samples.

3. The control unit is If the actual internal resistance calculated at each diagnosis time point exceeds the upper limit, The battery diagnostic device according to claim 1 , wherein the battery diagnostic device diagnoses that at least one of a plurality of battery cells included in the battery has an open circuit defect.

4. The control unit is If the actual internal resistance value calculated at each diagnosis time point is less than the lower limit value, The battery diagnostic device according to claim 1 , wherein the battery diagnostic device diagnoses that a short circuit defect has occurred in at least one of a plurality of battery cells included in the battery.

5. A battery including a plurality of battery cells; a measurement unit for measuring a battery voltage, which is a voltage across both ends of the battery, and a battery current, which is a current flowing through the battery; a memory unit for storing an actual internal resistance value calculated based on at least one of the battery voltage and the battery current for each diagnosis time point when diagnosing a defect in the battery, and at least one test internal resistance value which is an average value determined by experimental values; For each diagnosis time point, extracting a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on the diagnosis time point; When the number of the plurality of diagnostic time points is equal to or greater than the number of samples, at the diagnostic time point N, A moving average value, which is an average of a plurality of actually measured internal resistance values ​​corresponding to each of the diagnosis time points N-S N to N-1, is calculated, where S N is the number of samples; calculating a plurality of standard deviations corresponding to each of the diagnostic time points N-S N to N-1 based on the moving average value; Calculate an error value by multiplying a standard deviation average value, which is an average of the plurality of standard deviations, by a predetermined multiple; Calculate an upper limit value by adding the error value to the moving average value; Calculating a lower limit value by subtracting the error value from the moving average value; a control unit that diagnoses a defect in the battery by comparing the actual internal resistance value calculated in the diagnosis N with the upper limit value that is a predetermined value higher than the moving average value and the lower limit value that is a predetermined value lower than the moving average value, The control unit is If the number of the plurality of diagnosis time points is smaller than the number of samples, the moving average value is calculated based on the actual measured internal resistance value and the test internal resistance value, and the actual measured internal resistance value and the test internal resistance value correspond to the number of samples; The N is a positive integer. Battery system.

6. The storage unit is Further storing an actual standard deviation calculated for each diagnostic time point and at least one test standard deviation determined by an experimental value; The control unit is The battery system of claim 5 , wherein if N is less than the number of samples, the average standard deviation is calculated based on the measured standard deviation and the test standard deviation corresponding to the number of samples.

7. The control unit is If the actual internal resistance value calculated at each diagnosis time point exceeds the upper limit value, The battery system according to claim 5 , wherein the battery system is diagnosed as having an open circuit defect in at least one of the plurality of battery cells.

8. The control unit is If the actual internal resistance value calculated at each diagnosis time point is less than the lower limit value, The battery system according to claim 6 , wherein a short circuit defect is diagnosed to have occurred in at least one of the plurality of battery cells.

9. collecting measurements of a battery voltage, which is a voltage across a battery, and a battery current, which is a current flowing through the battery; determining a sample population by extracting a plurality of previous diagnostic time points corresponding to a predetermined number of samples based on a predetermined diagnostic time point for diagnosing a defect in the battery; When the number of the plurality of diagnostic time points is equal to or greater than the number of samples, at a diagnostic time point N, a moving average value which is an average of a plurality of actually measured internal resistance values ​​corresponding to each of diagnostic time points N-S N to N-1 belonging to the sample population is calculated; calculating a plurality of standard deviations corresponding to each of the diagnostic time points N-S N to N-1 based on the moving average value; Calculate an error value by multiplying a standard deviation average value, which is an average of the plurality of standard deviations, by a predetermined multiple; Calculate an upper limit value by adding the error value to the moving average value; Calculating a lower limit value by subtracting the error value from the moving average value; The actual internal resistance value at the diagnosis time point N is compared with the upper limit value which is a predetermined value larger than the moving average value and the lower limit value which is a predetermined value smaller than the moving average value to diagnose a defect in the battery. The steps include: If the number of the plurality of diagnosis time points is smaller than the number of samples, the moving average value is calculated based on the actual internal resistance value and the test internal resistance value, and the actual internal resistance value and the test internal resistance value correspond to the number of samples; The actual measured internal resistance value is calculated based on at least one of the battery voltage and the battery current at each diagnosis time point; The test internal resistance value is an average value determined by experiments; wherein N is a positive integer. Battery diagnostic methods.

10. The step of calculating the upper limit value and the lower limit value includes: If the number of diagnosis time points is less than the number of samples, the standard deviation average is calculated based on the actual standard deviation and the test standard deviation corresponding to the number of samples; The actual measurement standard deviation is calculated based on a plurality of actual measured internal resistance values ​​corresponding to the number of samples for each diagnosis time point, The battery diagnostic method of claim 9 , wherein the test standard deviation is determined by experimental values.

11. The step of calculating the upper limit value and the lower limit value includes: The battery diagnosis method of claim 9, wherein if the plurality of diagnosis time points do not exist, the moving average value is calculated based on a test internal resistance value that corresponds to the number of samples and is determined by an experimental value.

12. The step of diagnosing a defect in the battery further comprises:

10. The battery diagnosis method of claim 9, further comprising diagnosing that an open circuit defect occurs in at least one of a plurality of battery cells included in the battery if the actual measured internal resistance value calculated at each diagnosis time point exceeds the upper limit value.

13. The step of diagnosing a defect in the battery further comprises:

10. The battery diagnosis method of claim 9, further comprising diagnosing that a short circuit defect has occurred in at least one of a plurality of battery cells included in the battery if the actual measured internal resistance value calculated at each diagnosis time point is less than the lower limit value.

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