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

Abstract

The present invention relates to a battery diagnostic method capable of diagnosing the state of a battery including a plurality of battery cells connected in parallel, and a battery diagnostic device and a battery system providing the method. The battery diagnostic device of the present invention includes a measurement unit that measures a battery voltage, which is a voltage across both ends of a battery including a plurality of battery cells, a battery current, which is a current flowing through the battery, and a battery temperature, which is the temperature of the battery; a memory unit that stores an internal resistance value calculated based on the battery voltage and the battery current, a state of charge (SOC) estimated by a predetermined method, and the measured battery temperature for each diagnosis time point at which a defect in the battery is diagnosed; and a control unit that extracts, for each diagnosis time point, a plurality of diagnosis time points that satisfy a first condition under which environmental data belongs to a predetermined environment section to which environmental data at the diagnosis time point belongs and a second condition corresponding to a predetermined number of samples from among the previous diagnosis time points based on the diagnosis time point, calculates a moving average that is an average of the plurality of internal resistance values ​​corresponding to each of the plurality of diagnosis time points, and diagnoses a defect in the battery by comparing the internal resistance value calculated for each diagnosis time point with an upper limit value that is higher than the moving average value by a predetermined value and a lower limit value that is lower than the moving average value by a predetermined value, wherein the environmental data includes at least one of the state of charge and the battery temperature.

Description

[Technical Field]

[0001] Cross-citation with related applications (etc.) This application claims the benefit of priority based on Korean Patent Application No. 10-2022-0061918, filed May 20, 2022, and all contents disclosed in the documents of this 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 including a plurality of battery cells connected in parallel, a battery diagnostic device that provides the method, and a battery system. [Background technology]

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

[0004] However, as the number of battery cells in a battery increases, defects can 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 disconnections and short circuits between battery cells can occur. When a battery defect occurs, 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 difficult to directly measure 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 for diagnosing battery defects by estimating the direct current internal resistance (DCIR) for each battery and comparing the estimated DCIR value with a pre-set (fixed) reference value has limitations in that it cannot detect defects when multiple battery cells are simultaneously open or short-circuited within the battery. Furthermore, there is a risk of misdiagnosing the degree of change in DC internal resistance due to aging as a defect.

[0007] Furthermore, the technology for diagnosing battery defects based on the DCIR value of the battery has the problem that the error range becomes excessively large depending on the battery's State of Charge (SOC) when the external temperature changes suddenly, resulting in inaccurate diagnosis. Summary of the Invention [Problem to be solved by the invention]

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

[0009] A battery diagnostic device according to one aspect of the present invention includes a measurement unit that measures a battery voltage, which is a voltage across both ends of a battery including a plurality of battery cells, a battery current, which is a current flowing through the battery, and a battery temperature, which is the temperature of the battery; a memory unit that stores, for each diagnostic time point at which a defect in the battery is diagnosed, an internal resistance value calculated based on the battery voltage and the battery current, and environmental data, which are factors that affect the internal resistance value; and a control unit that extracts, for each diagnostic time point, a plurality of diagnostic time points that satisfy a first condition that the plurality of diagnostic time points include environmental data corresponding to the diagnostic time point within a predetermined range and a second condition that the plurality of diagnostic time points are previous diagnostic time points corresponding to a predetermined number of samples based on the diagnostic time point, calculates a moving average that is an average of the plurality of internal resistance values ​​corresponding to each of the plurality of diagnostic time points, and diagnoses a defect in the battery by comparing the internal resistance value calculated for each diagnostic time point with an upper limit value that is higher than the moving average value by a predetermined value and a lower limit value that is lower than the moving average value by a predetermined value, wherein the environmental data includes at least one of a state of charge (SOC) of the battery estimated by a predetermined method and the battery temperature.

[0010] According to another aspect of the present invention, a battery system includes a battery including a plurality of battery cells; a measurement unit that measures a battery voltage, which is a voltage across the battery, a battery current, which is a current flowing through the battery, and a battery temperature, which is a temperature of the battery; a memory unit that stores, for each diagnostic time point at which a defect in the battery is diagnosed, an internal resistance value calculated based on the battery voltage and the battery current, and environmental data, which are factors that affect the internal resistance value; and a control unit that extracts, for each diagnostic time point, a plurality of diagnostic time points that satisfy a first condition that the plurality of diagnostic time points include environmental data corresponding to the diagnostic time point within a predetermined range and a second condition that the plurality of diagnostic time points are previous diagnostic time points corresponding to a predetermined number of samples based on the diagnostic time point, calculates a moving average that is an average of the plurality of internal resistance values ​​corresponding to each of the plurality of diagnostic time points, and diagnoses a defect in the battery by comparing the internal resistance value calculated for each diagnostic time point with an upper limit value that is higher than the moving average value by a predetermined value and a lower limit value that is lower than the moving average value by a predetermined value, wherein the environmental data includes at least one of a state of charge (SOC) estimated by a predetermined method and the battery temperature.

[0011] The control unit can calculate an error value by multiplying a standard deviation average value, which is the average of multiple standard deviations corresponding to each of the multiple diagnostic 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] According to another aspect of the present invention, a battery diagnostic method includes: a battery data collection step of collecting measured values ​​of a battery voltage, which is the voltage across a battery, a battery current, which is the current flowing through the battery, and a battery temperature, which is the temperature of the battery; a sample group determination step of extracting a plurality of diagnostic time points that satisfy a first condition that the environmental data corresponding to the diagnostic time point is included within a predetermined range and a second condition that the diagnostic time point is an earlier diagnostic time point that belongs to a predetermined number of samples based on the diagnostic time point; a reference value determination step of calculating a moving average value that is an average of a plurality of internal resistance values ​​corresponding to each of the plurality of diagnostic time points, 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; and a defect diagnosis step of diagnosing a defect in the battery by comparing the internal resistance value corresponding to the diagnostic time point with the upper limit value and the lower limit value, wherein the environmental data includes at least one of a State of Charge (SOC) estimated by a predetermined method and the battery temperature.

[0013] The reference value determination step can calculate an error value by multiplying a standard deviation average value, which is the average of multiple standard deviations corresponding to each of the multiple diagnostic 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.

[0014] The sample group determination step can determine that the first condition is satisfied when the battery temperature at a predetermined diagnosis time point belongs to a predetermined temperature interval to which the battery temperature at the diagnosis time point belongs, among a plurality of temperature intervals set at predetermined temperature intervals.

[0015] The sample group determination step can determine that the first condition is satisfied when the charge state at a predetermined diagnosis time point belongs to a predetermined charge state section to which the charge state at the diagnosis time point belongs, among a plurality of charge state sections set at predetermined charge state intervals.

[0016] The defect diagnosis step may diagnose that an open defect has occurred in at least one of the plurality of battery cells when the internal resistance value corresponding to the diagnosis time point exceeds the upper limit value.

[0017] The defect diagnosis step may diagnose that a short circuit defect has occurred in at least one of the plurality of battery cells if the internal resistance value corresponding to the diagnosis time point is less than the lower limit value. [Effects of the Invention]

[0018] The present invention can diagnose battery defects with high accuracy even when multiple battery cells are connected in parallel.

[0019] Unlike conventional methods that diagnose battery defects using a fixed reference value, the present invention diagnoses battery defects by setting a reference value that reflects changes in the battery's internal resistance value at each diagnosis point in time, thereby taking into account the degree of deterioration according to the battery's usage period and enabling highly accurate diagnosis of battery defects.

[0020] The present invention sets a reference value that reflects the change in the internal resistance value of the battery calculated under an environment (e.g., temperature, SOC, etc.) similar to the time when the current defect is diagnosed among multiple defect diagnosis points, and diagnoses the battery defect based on the calculated reference value, thereby reducing the problem of misdiagnosing differences due to the external environment as a defect in the battery itself. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a diagram illustrating a battery diagnostic device according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating a battery system according to another embodiment. [Figure 3] FIG. 3 is an exemplary diagram showing cumulative moving average values, upper limit values, and lower limit values ​​calculated at multiple diagnostic time points. [Figure 4]FIG. 4 is a flowchart illustrating a battery diagnostic method according to an embodiment. [Figure 5] FIG. 5 is a flowchart illustrating the reference value determination step (S300) of FIG. 4 in detail. DETAILED DESCRIPTION OF THE INVENTION

[0022] Hereinafter, the embodiments disclosed herein will be described in detail with reference to the accompanying drawings. Identical or similar elements will be designated by identical or similar reference numerals, and redundant descriptions thereof will be omitted. The suffixes "module" and / or "section" for elements used in the following description are given or used interchangeably solely for the convenience of writing the specification, and do not have any distinguishing meanings or functions. Furthermore, when describing the embodiments disclosed herein, if it is determined that a detailed description of related publicly known technology may obscure the gist of the embodiments disclosed herein, such a detailed description will be omitted. Furthermore, the accompanying drawings are intended to facilitate understanding of the embodiments disclosed herein, and should not be construed as limiting the technical concepts disclosed herein, and should be understood to include all modifications, equivalents, and alternatives within the concept and technical scope of the present invention.

[0023] Terms including ordinal numbers such as "first," "second," etc. may be used to describe various elements, but the elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0024] When a component is referred to as being "connected" or "coupled" to another component, it should be understood that the component may be directly connected or coupled to the other component, but that there may be other components in between. Conversely, when a component is referred to as being "directly connected" or "directly coupled" to another component, it should be understood that there are no other components in between.

[0025] 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 stated in the specification, but should be understood as not precluding the possible presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0026] FIG. 1 is a diagram illustrating a battery diagnostic device according to an embodiment.

[0027] Referring to FIG. 1, the battery diagnostic device 1 includes a measurement unit 110, a storage unit 130, and a control unit 150.

[0028] The measurement unit 110 can measure the battery voltage, which is the voltage across the battery, the battery temperature, the battery current, which is the current flowing through the battery, etc. The battery voltage and the battery current may be battery data required to calculate the internal resistance or the state of charge (SOC) of the battery. The battery temperature may be battery data required to determine the environmental zone described below. For example, the internal resistance may include direct current internal resistance (DCIR).

[0029] The measurement unit 110 may include a voltage sensor (not shown) electrically connected to both ends of the battery to measure the battery voltage, a current sensor (not shown) connected in series with the battery to measure the battery current, and a temperature sensor (not shown) located adjacent to the battery to measure the battery temperature. For example, the measurement unit 110 may measure the battery voltage, battery current, and battery temperature at each diagnosis time point for diagnosing a battery defect and transmit the measurement results to the control unit 150.

[0030] The internal resistance value and environmental data are stored in the memory unit 130. The environmental data may include information on environmental factors that affect the internal resistance value, such as battery temperature and state of charge (SOC).

[0031] The memory unit 130 may store an internal resistance value calculated by the control unit 150 based on at least one of the battery voltage, the battery current, and the battery temperature for each diagnostic time point when the battery is diagnosed for a defect. The memory unit 130 may also store a state of charge (SOC) estimated by the control unit 150 based on at least one of the battery voltage and the battery current. Furthermore, the control unit 150 may store in the memory unit 130 the battery voltage value, the battery current value, and the battery temperature value received from the measurement unit 110 for each diagnostic time point when the battery is diagnosed for a defect.

[0032] When the diagnostic time point N according to the previously set conditions arrives, the control unit 150 calculates a moving average value (MA), an upper band threshold (UB_Th) that is a predetermined value higher than the moving average value, a lower band threshold (LB_Th) that is a predetermined value lower than the moving average value, and an internal resistance value corresponding to the diagnostic time point N.

[0033] According to an embodiment, the time when the battery starts charging or the time when the battery finishes discharging may be the diagnosis time N for diagnosing a battery defect. When the diagnosis time N arrives, the measurement unit 110 may measure the battery voltage, battery current, and battery temperature at predetermined intervals for a predetermined period of time and transmit the measurement results to the control unit 150.

[0034] First, the control unit 150 determines a sample group by extracting a plurality of diagnostic time points included in a pre-set number of samples (SN) when the environmental data belongs to an environment section to which the environmental data of the current diagnostic time point N belongs and counting diagnostic time points toward the previous diagnostic time point from the current diagnostic time point N. Here, the sample number SN is the number of diagnostic time points included in the sample group, and can be determined as an optimal number based on experiments, etc. The sample group is a subset of a plurality of past diagnostic time points of the population, and may be a group for calculating a moving average value MA, a standard deviation average value σ_ave, etc., which will be described below.

[0035] Table 1 below shows an example of the battery temperature, state of charge SOC, internal resistance DCIR value, moving average MA, upper limit UB_Th, and lower limit LB_Th measured, estimated, and calculated at each of multiple diagnostic time points. It is assumed that the number of samples SN is 5, multiple temperature ranges (e.g., 0°C to 20°C, 21°C to 40°C, 41°C to 60°C, ...) distinguished by predetermined temperature intervals (e.g., 20°C intervals) are set, and multiple charging ranges (e.g., 11% to 30%, 31% to 50%, 51% to 70%, 71% to 90%) distinguished by predetermined state of charge SOC intervals (e.g., 20% intervals) are set.

[0036] According to one embodiment, in Table 1 below, it is assumed that the environmental data includes battery temperature and state of charge SOC.

[0037] For reference, in Table 1, the moving average value MA, standard deviation σ, average standard deviation σ_ave, upper limit value UB_Th, and lower limit value LB_Th at initial diagnosis time point 1 may be difficult to calculate directly (therefore, the corresponding values ​​are left blank in Table 1). Also, the moving average value MA, standard deviation σ, average standard deviation σ_ave, upper limit value UB_Th, and lower limit value LB_Th at diagnosis times 2, 3, ... adjacent to initial diagnosis time point 1 may also be difficult to calculate directly due to lack of or insufficient past diagnosis values ​​for calculation. In this case, values ​​calculated on average through experiments can be substituted for the moving average value MA, standard deviation σ, average standard deviation σ_ave, upper limit value UB_Th, and lower limit value LB_Th at initial diagnosis times 1, 2, 3, .... Also, example values ​​for the internal resistance value, moving average value, etc., at the N-8th diagnosis cycle, which are not used in the following explanation, are omitted.

[0038] [Table 1]

[0039] In Table 1, the battery temperature (25° C.) at the current diagnosis point N belongs to a temperature range of 21° C. to 40° C. Also, the state of charge (60%) at the current diagnosis point N belongs to a state of charge range of 51% to 70%.

[0040] The controller 150 can determine the sample group by extracting the N-1st, N-2nd, N-3rd, N-5th, and N-7th diagnostic time points, which belong to an environment similar to that of the current diagnostic time point N, i.e., the Nth diagnostic time point (21°C to 40°C and 51% to 70%), and which correspond to five sample numbers SN when counting diagnostic time points from diagnostic time point N toward previous diagnostic time points. In this case, the N-4th diagnostic time point has a corresponding battery temperature (8°C) in a temperature range (0°C to 20°C) different from the temperature range (21°C to 40°C) to which the battery temperature (25°C) at the Nth diagnostic time point belongs. The N-6th diagnostic time point has a corresponding state of charge (11%) in a state of charge range (11% to 30%) different from the state of charge range (51% to 70%) to which the state of charge (60%) at the Nth diagnostic time point belongs. The N-8 diagnosis time point is in a similar environment to the N diagnosis time point (21°C to 40°C and 51% to 70%), but is not included in the sample size SN because of the large time interval between the N diagnosis time point and the N diagnosis time point. In other words, the N-4 diagnosis time point, the N-6 diagnosis time point, and the N-8 diagnosis time point are not included in the sample population.

[0041] In other embodiments, the environmental data may include battery temperature or state of charge (SOC). For example, assuming that the environmental data in Table 1 includes only battery temperature, the control unit 150 may include in the sample group the N-1 diagnostic time point (23° C.), the N-2 diagnostic time point (23° C.), the N-3 diagnostic time point (20° C.), the N-5 diagnostic time point (22° C.), and the N-6 diagnostic time point (23° C.), which belong to an environment similar to the N-th diagnostic time point (21° C. to 40° C.). As another example, assuming that the environmental data in Table 1 includes only the SOC, the control unit 150 may include in the sample group the N-1 diagnostic time point (55%), the N-2 diagnostic time point (55%), the N-3 diagnostic time point (50%), the N-4 diagnostic time point (60%), and the N-5 diagnostic time point (55%), which belong to an environment similar to the N-th diagnostic time point (51% to 70%).

[0042] In summary, the control unit 150 can determine a sample group by extracting multiple diagnostic time points that are in an environment similar to a predetermined diagnostic time point N and are adjacent to the diagnostic time point N. For example, the control unit 150 can determine a sample group by extracting multiple diagnostic time points N-7, N-5, N-3, N-2, and N-1 according to the criteria described above, and determine reference values ​​(upper and lower limit values ​​described below) used for defect diagnosis based on internal resistance values ​​calculated at multiple diagnostic time points within the sample group. This can solve the problem of misdiagnosing the degree of deterioration and / or temporary fluctuations in internal resistance value due to long-term use of a battery as a battery defect.

[0043] Next, the control unit 150 determines the reference values ​​(upper and lower limits) for diagnosing battery defects at the Nth diagnosis point based on the internal resistance values ​​calculated at each of the multiple diagnosis points N-7, N-5, N-3, N-2, and N-1 belonging to the sample population.

[0044] According to one embodiment, the control unit 150 compares the internal resistance DCIR value corresponding to the Nth diagnostic time point with the upper limit UB_Th and the lower limit LB_Th to diagnose a battery defect. For example, referring to Table 1, at the Nth diagnostic time point, the control unit 150 calculates the internal resistance value [1], the upper limit [5], and the lower limit [6], and then compares the calculated internal resistance value [1] with the upper limit [5] and the lower limit [6] to diagnose a battery defect. To calculate the upper limit [5] and the lower limit [6], the moving average [2] and the standard deviation average [4] are required. However, although the standard deviation [3] is not a required value for the defect diagnosis at the Nth diagnostic time point, it is required for the defect diagnosis at the subsequent diagnostic time points N+1, N+2, etc., and therefore may be calculated at the Nth diagnostic time point and stored in the storage unit 130. Below, we will explain the internal resistance value [1], moving average value [2], standard deviation [3], standard deviation average value [4], upper limit value [5] and lower limit value [6] calculated by the control unit 150 at the Nth diagnosis point in Table 1.

[0045] The control unit 150 calculates an internal resistance DCIR corresponding to the Nth diagnosis time point based on the battery voltage, which is the voltage across the battery, and the battery current, which is the current flowing through the battery. N For example, the internal resistance (DCIR N , [1]) values ​​can be calculated.

[0046]

number

[0047] For example, the control unit 150 can calculate the voltage difference (ΔV=|V1-V2|) between the battery voltage V1 corresponding to the first time point when charging starts and the battery voltage V2 corresponding to the second time point a predetermined time has elapsed since the first time point. The control unit 150 can calculate the internal resistance DCIR based on the charging current I flowing through the battery and the voltage difference ΔV. N For example, the value of the internal resistance DCIR corresponding to the Nth diagnostic point in time can be calculated. N Assume the value is calculated to be 30 Ω.

[0048] Referring to Table 1, the control unit 150 averages the internal resistance values ​​25Ω, 23Ω, 20Ω, 21Ω, and 23Ω corresponding to the diagnostic time points N-7, N-5, N-3, N-2, and N-1 of the sample population (25Ω+23Ω+20Ω+21Ω+23Ω / 5=22.4) to calculate a moving average value (MA) corresponding to the diagnostic time point N. N , [2]) can be calculated. That is, the internal resistance DCIR corresponding to the Nth diagnostic point in time can be calculated. N The value may be 22.4 Ω.

[0049]

number

[0050] Referring to Table 2 below, the control unit 150 calculates the standard deviation (σ) corresponding to the diagnostic time point N based on the internal resistance values ​​DCIR and the moving average value MA corresponding to each of the multiple diagnostic time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample population. N , [3]) can be calculated.

[0051] [Table 2]

[0052] As explained above, the standard deviation (σ N , [3]) is not a value required for defect diagnosis at the Nth diagnosis time point, but is required for defect diagnosis at the subsequent diagnosis time points N+1, N+2, .... Therefore, the standard deviation (σ N , [3]) may be calculated at the Nth diagnosis time point and stored in the storage unit 130.

[0053] Referring to Table 3 below, the control unit 150 calculates a plurality of standard deviations σ corresponding to the plurality of diagnostic time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample population. N-7 , σ N-5 , σ N-3 , σ N-2 , σ N-1 Based on this, the standard deviation mean value (σ N , [4]) can be calculated.

[0054] [Table 3]

[0055] The control unit 150 calculates the moving average value MA N The upper limit value UB is a predetermined value greater than N _Th and the lower limit LB, which is a predetermined value smaller than the moving average value MA N According to the embodiment, the control unit 150 calculates the standard deviation average value σ N_ ave is multiplied by the first predetermined multiple to calculate the first error value, and the moving average value MAN Add the first error value to obtain the upper limit value UB N The control unit 150 can calculate the standard deviation average value σ N_ ave is multiplied by a predetermined second multiple to calculate the second error value, and the moving average value MA N Subtract the second error value from 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.

[0056] According to one embodiment, the control unit 150 calculates the standard deviation mean value σ, which is the average of the standard deviations of the sample population. N_ The error value (E=σ N_ ave × Q) can be calculated. In this case, the multiple Q is a value that reflects a predetermined error and may be determined as various values ​​through experiments. For example, the multiple Q is assumed to be the natural number 3.

[0057] The control unit 150 calculates the moving average value (MA) of the sample population as shown in the following formula 3. N =22.4) to the error value (E=σ N_ ave×Q=1.69×3) to obtain the upper limit UB N The control unit 150 can calculate the moving average value (MA N =22.4) to the error value (E=σ N_ ave×Q=1.69×3) to obtain the lower limit LB N _Th can be calculated as 17.33.

[0058]

number

[0059] Next, the control unit 150 calculates the internal resistance DCIR corresponding to the Nth diagnostic time point. N The value is the upper limit value UB corresponding to the Nth diagnosis point. N _Th and lower limit LB N _Th to diagnose battery defects.

[0060] According to one embodiment, the internal resistance DCIR N The value is the upper limit UB N If the internal resistance DCIR exceeds the threshold value _Th, 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 The value is the lower limit LB N If the internal resistance DCIR is less than _Th, the control unit 150 may diagnose that a short circuit (SD) has occurred in at least one of the battery cells included in the battery. N The value is the lower limit LB N _Th or greater and upper limit UB N If the internal resistance DCIR is outside the normal range, the control unit 150 may diagnose that a defect (open circuit defect or short circuit defect) has occurred in the battery. N If the value falls within the normal range, the control unit 150 can diagnose the battery state as normal.

[0061] For example, as explained above with reference to Tables 1 and 3 and Equations 1 to 4, the internal resistance value DCIR corresponding to the Nth diagnosis point in time is N , upper limit UB N _Th and lower limit LB N In this case, the control unit 150 calculates the internal resistance value (DCIR N =30) is the upper limit (UB N _Th=27.47), a battery defect (disconnection defect) can be diagnosed.

[0062] FIG. 2 is a diagram illustrating a battery system according to another embodiment.

[0063] Referring to FIG. 2, the battery system 2 includes a battery 10, a relay 20, a current sensor 30, and a battery management system (hereinafter referred to as BMS) 40.

[0064] Battery 10 may include multiple battery cells connected in series and / or parallel. While Figure 2 shows three battery cells connected in parallel, this is not limiting and 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.

[0065] For example, the battery 10 may have a predetermined number of battery cells connected in parallel to form a battery bank, and a predetermined number of battery banks connected in series to form a battery pack, thereby supplying a desired power to an external device. As another example, the battery 10 may have a predetermined number of battery cells connected in parallel to form a battery bank, and a predetermined number of battery banks connected in parallel to form a battery pack, thereby supplying a desired power to an external device. However, the battery 10 is not limited to such a connection, and may include a plurality of battery banks, each 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.

[0066] 2, a battery 10 is connected between two output terminals OUT1 and OUT2 of a battery system 2. A relay 20 is connected between the positive terminal of the battery system 2 and the first output terminal OUT1, and a current sensor 30 is connected between the negative terminal of the battery system 2 and the second output terminal OUT2. The components and connections between the components shown in FIG. 2 are merely examples, and the invention is not limited thereto.

[0067] The relay 20 controls the electrical connection between the battery system 2 and the 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. In this case, the external device may be a charger in a charging cycle that supplies power to the battery 10 to charge it, or a load in a discharging cycle that the battery 10 discharges power to the external device.

[0068] The current sensor 30 is connected in series to the current path between the battery 10 and the external device. The current sensor 30 can measure the battery current flowing through the battery 10, i.e., the charging current and discharging current, and transmit the measurement result to the BMS 40.

[0069] 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. 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 is configured separately from the battery system 1. As another example, as shown in FIG. 2, the BMS 40 may perform the functions of the battery diagnostic device 1 in the battery system 1.

[0070] The measurement unit 41 is electrically connected to both ends of the battery 10 and can collect battery current, battery voltage, and battery temperature values. For example, the measurement unit 41 may be embodied as 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.

[0071] For example, the measurement unit 41 may collect the battery voltage by sensing the voltage value across the battery 10. The measurement unit 41 may receive the battery current value from the current sensor 30. The measurement unit 41 may receive the battery temperature value measured by a temperature sensor (not shown) located inside or adjacent to the outside of the battery 10. The measurement unit 41 may transmit the battery voltage value, the battery current value, and the battery temperature value to the control unit 150.

[0072] The memory unit 43 may store the internal resistance value and environmental data of the battery 10. The environmental data may include information on environmental factors that affect the internal resistance DCIR, such as the battery temperature and state of charge (SOC).

[0073] The memory unit 43 may store an internal resistance value calculated by the control unit 45 based on at least one of the battery voltage, the battery current, and the battery temperature for each diagnostic time point when a defect in the battery 10 is diagnosed. The memory unit 43 may also store a state of charge (SOC) estimated by the control unit 45 based on at least one of the battery voltage and the battery current. The control unit 45 may also store in the memory unit 43 the battery voltage value, the battery current value, and the battery temperature value received from the measurement unit 41 for each diagnostic time point when a defect in the battery is diagnosed.

[0074] When the diagnostic time point N according to the previously set conditions 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 corresponding to the diagnostic time point N N Then, the control unit 45 calculates the internal resistance DCIR N Value upper limit UB N _Th and lower limit LB NThe state of the battery 10 can be diagnosed by comparing with the battery 10.

[0075] First, the control unit 45 determines a sample population by extracting a plurality of diagnostic time points included in a pre-set number of samples (SN) when the environmental data belongs to an environment section to which the environmental data of a predetermined diagnostic time point N belongs and counting diagnostic time points toward the previous diagnostic time point from the diagnostic time point N. In this case, the sample number SN is the number of diagnostic time points included in the sample population and can be determined as an optimal number based on experiments, etc. The sample population is a subset of a plurality of past diagnostic time points of the population and may be a population for calculating a moving average value MA, a standard deviation average value σ_ave, etc., which will be described below.

[0076] For example, assume that the environmental data includes both the battery temperature and the state of charge (SOC) and the number of samples SN is 5. In Table 1, the control unit 45 can determine the sample group by extracting the N-1st diagnostic time point, the N-2nd diagnostic time point, the N-3rd diagnostic time point, the N-5th diagnostic time point, and the N-7th diagnostic time point, which belong to an environment similar to that of the current diagnostic time point N, i.e., the Nth diagnostic time point (21°C to 40°C and 51% to 70%), and which correspond to five of the number of samples SN when counting diagnostic time points from the diagnostic time point N toward the previous diagnostic time point.

[0077] The control unit 45 calculates the moving average value MA, which is the average of the internal resistance values ​​of the sample population. N For example, referring to Table 1 and Equation 2, the moving average value MA corresponding to the diagnosis time point N is calculated by averaging the internal resistance values ​​25Ω, 23Ω, 20Ω, 21Ω, and 23Ω corresponding to the diagnosis time points N-7, N-5, N-3, N-2, and N-1, respectively, belonging to the sample population. N can be calculated as 22.4.

[0078] For example, referring to Table 1 and Table 3, the control unit 45 calculates standard deviations σ corresponding to the plurality of diagnostic time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample population.N-7 , σ N-5 , σ N-3 , σ N-2 , σ N-1 Based on this, the standard deviation mean value (σ N_ ave, 1.69) can be calculated.

[0079] The control unit 45 calculates the internal resistance values ​​DCIR calculated at each of the plurality of diagnostic time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample population. N Based on the current diagnosis point N, that is, the upper limit value UB of the reference value for diagnosing battery defects at the Nth end point N _Th and lower limit LB N _Determine Th.

[0080] The control unit 45 calculates the moving average value MA N The upper limit value UB is a predetermined value greater than N _Th and the lower limit LB, which is a predetermined value smaller than the moving average value MA N According to the embodiment, the control unit 45 calculates the standard deviation average value σ N_ ave is multiplied by the first predetermined multiple to calculate the first error value, and the moving average value MA N Add the first error value to obtain the upper limit value UB N The control unit 45 can calculate the standard deviation average value σ N_ ave is multiplied by a predetermined second multiple to calculate the second error value, and the moving average value MA N Subtract the second error value from 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.

[0081] According to one embodiment, the control unit 45 calculates the standard deviation mean value σ, which is the average of the standard deviations of the sample population. N_ The error value (E=σ N_ ave × Q). For example, suppose the multiple Q is the natural number 3.

[0082] The control unit 45 calculates the moving average value (MA) of the sample population as shown in the above formula 3. N =22.4) to the error value (E=σ N_ ave×Q=1.69×3) to obtain the upper limit UB N The control unit 45 can calculate the moving average value (MA N =22.4) to the error value (E=σ N_ ave×Q=1.69×3) to obtain the lower limit LB N _Th can be calculated as 17.33. In this case, the multiple Q is a value that reflects a predetermined error, and may be determined as various values ​​through experiments.

[0083] Next, the control unit 45 calculates the internal resistance DCIR corresponding to the Nth diagnostic time point. N The value is the upper limit value UB corresponding to the Nth diagnosis point. N _Th and lower limit LB N By comparing with the measured voltage, defects in the battery 10 can be diagnosed.

[0084] According to one embodiment, the internal resistance DCIR N The value is the upper limit UB N If the internal resistance DCIR exceeds the threshold value _Th, 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 The value is the lower limit LB N If the internal resistance DCIR is less than _Th, the control unit 45 may diagnose that a short circuit defect (SD) has occurred in at least one of the battery cells included in the battery 10. That is, N The value is the lower limit LB N _Th or greater and upper limit UB N If the internal resistance DCIR falls outside the normal range corresponding to the internal resistance DCIR_Th or less, the control unit 45 can diagnose that a defect (open circuit defect or short circuit defect) has occurred in the battery 10. NWhen the value falls within the normal range, the control unit 45 can diagnose the state of the battery 10 as normal.

[0085] For example, as explained above with reference to Tables 1 and 3 and Equations 1 to 4, the internal resistance value DCIR corresponding to the Nth diagnosis point in time is N , upper limit UB N _Th and lower limit LB N In this case, the control unit 45 calculates the internal resistance values ​​(DCIR N =30) is the upper limit (UB N _Th=27.47), a defect (disconnection defect) in the battery 10 can be diagnosed.

[0086] FIG. 3 is an exemplary diagram showing cumulative moving average values, upper limit values, and lower limit values ​​calculated at multiple diagnostic time points.

[0087] Based on Figures 1 to 3 and Tables 1 and 3, the moving average MA N , upper limit UB N _Th and lower limit LB N An example of calculating _Th will be described.

[0088] Referring to FIG. 3, the BMS 40 can determine a sample population by extracting multiple diagnostic time points that are in an environment similar to a predetermined diagnostic time point N and are adjacent to the diagnostic time point N. The BMS 40 calculates a moving average MA of the averages of multiple internal resistance values, etc., belonging to the sample population. N and the standard deviation mean value σ, which is the average of multiple standard deviations, etc. N_ Based on the ave, the upper limit value UB corresponding to the diagnosis time point N is calculated. N _Th and lower limit LB N _Th can be calculated.

[0089] According to one embodiment, first, when counting diagnostic time points from diagnostic time point N toward the previous diagnostic time point, BMS 40 can extract the N-1st diagnostic time point, the N-2nd diagnostic time point, the N-3rd diagnostic time point, the N-5th diagnostic time point, and the N-7th diagnostic time point, which belong to an external environment (21°C to 40°C and 51% to 70%) similar to that of a predetermined diagnostic time point N and correspond to five of the sample number SN.

[0090] Next, the BMS40 averages the multiple internal resistance values ​​25Ω, 23Ω, 20Ω, 21Ω, and 23Ω corresponding to the extracted multiple diagnostic points N-7, N-5, N-3, N-2, and N-1, respectively, to calculate the moving average value corresponding to the diagnostic point N (25Ω+23Ω+20Ω+21Ω+23Ω) / 5=22.4Ω).

[0091] Using Table 3, Equation 3, and Equation 4 described above, the BMS 40 can calculate the upper limit value to be 27.47 and the lower limit value to be 17.33.

[0092] Next, the BMS40 internal resistance DCIR N Value upper limit UB N _Th and lower limit LB N _Th, it is possible to diagnose a defect in the battery 10. At this time, for example, the internal resistance DCIR N is assumed to be 30Ω. The internal resistance value (DCIR N =30) is the upper limit (UB N _Th=27.47), a battery defect (disconnection defect) can be diagnosed.

[0093] The internal resistance band (DCIR Band) shown in FIG. 3 is derived by concatenating a moving average MA, an upper limit UB_Th, and a lower limit LB_Th calculated at each of a plurality of diagnostic time points.

[0094] The internal resistance band (DCIR Band) can indicate the tendency of the internal resistance value to change as the battery 10 is used.

[0095] FIG. 4 is a flowchart illustrating a battery diagnostic method according to an embodiment.

[0096] A battery diagnostic method, a battery diagnostic device that provides the method, and a battery system will be described below with reference to Figures 1 to 4. The battery diagnostic method performed in the battery system 2 described below can also be applied to the battery diagnostic device 1.

[0097] First, the BMS 40 collects battery data (S100). At this time, the battery data may include at least one of the battery voltage, which is the voltage across the battery 10, the battery temperature, and the battery current, which is the current flowing through the battery 10.

[0098] For example, the battery voltage and battery current may be battery data required to calculate the direct current internal resistance (DCIR) or state of charge (SOC) of the battery, and the battery temperature may be battery data required to determine the environmental zone described below.

[0099] Next, the BMS 40 extracts a plurality of diagnostic time points that are in an environment similar to the predetermined diagnostic time point N and are adjacent to the diagnostic time point N, and determines a sample population (S200).

[0100] The BMS 40 determines a sample group by extracting a plurality of diagnostic time points that satisfy the first condition that the environmental data at the Nth diagnostic time point belongs to a predetermined environmental section to which the environmental data at the Nth diagnostic time point belongs, and the second condition that the number of diagnostic time points corresponds to a previously set number of samples SN when counting diagnostic time points from the Nth diagnostic time point toward the previous diagnostic time point.

[0101] For example, assume that the environmental data includes both the battery temperature and the state of charge (SOC) and the sample number SN is 5. In Table 1, the BMS 40 can determine the sample group by extracting the N-1st diagnostic time point, the N-2nd diagnostic time point, the N-3rd diagnostic time point, the N-5th diagnostic time point, and the N-7th diagnostic time point, which belong to an environment similar to that of the Nth diagnostic time point (21°C to 40°C and 51% to 70%) and correspond to the 5 sample number SN when counting diagnostic time points from diagnostic time point N toward the previous diagnostic time point.

[0102] Next, the BMS 40 determines a reference value for defect diagnosis of the battery 10 (S300). According to one embodiment, the reference value is an upper limit value UB N _Th and lower limit LB N _Th may be included.

[0103] In step S300, referring to FIG. 5, the BMS 40 averages a plurality of 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).

[0104] Referring to Table 1 and Equation 2, the BMS 40 averages the internal resistance values ​​25Ω, 23Ω, 20Ω, 21Ω, and 23Ω corresponding to the diagnostic time points N-7, N-5, N-3, N-2, and N-1 of the sample population, respectively, to obtain a moving average value MA corresponding to the diagnostic time point N. N can be calculated as 22.4.

[0105] In step S300, the BMS 40 calculates the standard deviation mean value σ of the sample population. N_ An error value (E) is calculated based on the ave (S320).

[0106] For example, the standard deviation of the sample population is σ N_ The ave can be calculated by averaging multiple standard deviations corresponding to multiple diagnosis time points belonging to the sample population.

[0107] Referring to Tables 1 and 3, BMS40 is a set of a plurality of standard deviations σ corresponding to a plurality of diagnosis time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample population. N-7 , σ N-5 , σ N-3 , σ N-2 , σ N-1 Based on this, the standard deviation mean value σ corresponding to the diagnosis time point N N_ The average standard deviation of BMS40 is σ N_ The error value (E=σ N_ ave×Q=1.69×3) can be calculated. In this case, the multiple Q is a value that reflects a predetermined error, and may be determined as various values ​​through experiments. For example, the multiple Q is assumed to be the natural number 3.

[0108] In step S300, the BMS 40 calculates the moving average value MA of the sample population. N and the upper limit UB based on the error value (E). N _Th and lower limit LB N _Th is calculated (S330).

[0109] Referring to the above formula 3, BMS40 is the moving average value (MA N =22.4) to the error value (E=σ N_ ave×Q=1.69×3) to obtain the upper limit UB N _Th can be calculated as 27.47. Also, referring to the above formula 4, BMS40 is the moving average value (MA N =22.4) to the error value (E=σ N_ ave×Q=1.69×3) to obtain the lower limit LB N _Th can be calculated as 17.33.

[0110] Next, the BMS 40 calculates the internal resistance DCIR corresponding to the diagnostic time point N. N The upper limit value UB corresponding to the diagnostic time point N N _Th and lower limit LB N The battery 10 is compared with the battery 10 to diagnose defects (S400).

[0111] The BMS 40 calculates an internal resistance DCIR corresponding to the Nth diagnosis point based on the battery voltage, which is the voltage across the battery, and the battery current, which is the current flowing through the battery. N The value of the internal resistance DCIR can also be calculated. N The value may be calculated in step S200 or step S300, but there is no restriction on the time at which it is calculated as long as it is calculated before step S400 at the time of diagnosis.

[0112] 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 calculates the internal resistance DCIR based on the charging current I flowing through the battery 10 and the voltage difference ΔV. N For example, the value of the internal resistance DCIR corresponding to the Nth diagnostic point in time can be calculated. N Assume the value is calculated to be 30 Ω.

[0113] In step S400, the BMS 40 calculates the internal resistance DCIR N The value is the upper limit UB N It is determined whether or not the time exceeds the threshold value _Th (S410).

[0114] If the determination result in step S400 is that the threshold is exceeded (S410, Yes), the BMS 40 diagnoses that a disconnection defect has occurred in at least one of the battery cells included in the battery 10 (S420).

[0115] For example, if the parallel connection of some battery cells among a plurality of battery cells connected in parallel is broken, the internal resistance value of the battery 10 may increase.

[0116] If the determination result in step S400 is that the internal resistance value DCIR is not exceeded (S410, No), the BMS 40 N is the lower limit LB NIt is determined whether it is less than _Th (S430).

[0117] If the result of the determination in step S400 is less than the predetermined value (S430, 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).

[0118] For example, if some of the parallel-connected battery cells come into contact with each other (short circuit), the internal resistance value, which is the overall resistance of the battery 10, may decrease.

[0119] If the determination result in 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).

[0120] Internal resistance DCIR N The value is the lower limit LB N _Th or greater and upper limit UB N If the internal resistance DCIR falls outside the normal range corresponding to the internal resistance DCIR_Th or less, the BMS 40 can diagnose the state of the battery 10 as having a defect (open circuit defect or short circuit defect). N When the value falls within the normal range, the BMS 40 can diagnose the condition of the battery 10 as normal.

[0121] Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited to these examples, and various modifications and improvements made by those skilled in the art to which the present invention pertains also fall within the scope of the present invention.

Claims

1. a measuring unit that measures a battery voltage, which is a voltage across a battery, a battery current, which is a current flowing through the battery, and a battery temperature, which is a temperature of the battery; a storage unit that stores an internal resistance value calculated based on the battery voltage and the battery current at each time point when a defect in the battery is diagnosed, and environmental data that is a factor that affects the internal resistance value; At diagnosis time N, a first condition that, with the diagnostic time point N as a reference, each of the diagnostic time points N-k to N-1 includes environmental data corresponding to the environmental data at the diagnostic time point N within a predetermined range; A second condition is that k is 7 or less among the diagnosis time points Nk to N-1. Extract multiple diagnostic time points that satisfy the following: calculating a moving average value which is an average of a plurality of internal resistance values ​​corresponding to each of the plurality of diagnostic time points; calculating a standard deviation at the diagnosis time point N based on a plurality of internal resistance values ​​corresponding to the plurality of diagnosis time points and the moving average value; obtaining a plurality of standard deviations of the internal resistance values ​​corresponding to the plurality of diagnostic time points, an error value is calculated by multiplying the average standard deviation, which is the average of the plurality of standard deviations, by a predetermined multiple; an upper limit value is calculated 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 compares the internal resistance value calculated at the diagnosis time point N with the upper limit value and the lower limit value to diagnose a defect in the battery, The environmental data is The battery temperature and the state of charge (SOC) of the battery are estimated by a predetermined method. If the environmental data is the battery temperature, The control unit determining that the first condition is satisfied when the battery temperature at each of the plurality of diagnostic time points falls within a predetermined temperature section to which the battery temperature at each of the plurality of diagnostic time points falls, among a plurality of temperature sections set at predetermined temperature intervals; When the environmental data is the SOC of the battery, The control unit determining that the first condition is satisfied when the SOC at each of the plurality of diagnostic time points falls within a predetermined SOC range to which the SOC at each of the plurality of diagnostic time points belongs, among a plurality of SOC ranges set at predetermined SOC intervals; N is an integer of 5 or more, and k is an integer of 4 or more and less than N. Battery diagnostic equipment.

2. The control unit When the internal resistance value exceeds the upper limit, The battery diagnostic device according to claim 1 , wherein the battery is diagnosed as having an open circuit defect in at least one of a plurality of battery cells included in the battery.

3. The control unit If the internal resistance value is less than the lower limit value, The battery diagnostic device according to claim 1 or 2, wherein the battery is diagnosed as having a short circuit defect in at least one of a plurality of battery cells included in the battery.

4. The control unit When the internal resistance value is in the range of not less than the lower limit value and not more than the upper limit value, Diagnosing that a plurality of battery cells included in the battery are in a normal state; The battery diagnostic device according to claim 1 .

5. a battery including a plurality of battery cells; a measuring unit that measures a battery voltage, which is a voltage across the battery, a battery current, which is a current flowing through the battery, and a battery temperature, which is a temperature of the battery; a storage unit that stores an internal resistance value calculated based on the battery voltage and the battery current at each time point when a defect in the battery is diagnosed, and environmental data that is a factor that affects the internal resistance value; At diagnosis time N, a first condition that, with the diagnostic time point N as a reference, each of the diagnostic time points N-k to N-1 includes environmental data corresponding to the environmental data at the diagnostic time point N within a predetermined range; A second condition is that k is 7 or less among the diagnosis time points Nk to N-1. Extract multiple diagnostic time points that satisfy the following: calculating a moving average value which is an average of a plurality of internal resistance values ​​corresponding to each of the plurality of diagnostic time points; calculating a standard deviation at the diagnosis time point N based on a plurality of internal resistance values ​​corresponding to the plurality of diagnosis time points and the moving average value; obtaining a plurality of standard deviations of the internal resistance values ​​corresponding to the plurality of diagnostic time points, an error value is calculated by multiplying the average standard deviation, which is the average of the plurality of standard deviations, by a predetermined multiple; an upper limit value is calculated 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 compares the internal resistance value calculated at the diagnosis time point N with the upper limit value and the lower limit value to diagnose a defect in the battery, The environmental data is The battery temperature and the state of charge (SOC) are estimated by a predetermined method. If the environmental data is the battery temperature, The control unit determining that the first condition is satisfied when the battery temperature at each of the plurality of diagnostic time points falls within a predetermined temperature section to which the battery temperature at each of the plurality of diagnostic time points falls, among a plurality of temperature sections set at predetermined temperature intervals; When the environmental data is the SOC of the battery, The control unit determining that the first condition is satisfied when the SOC at each of the plurality of diagnostic time points falls within a predetermined SOC range to which the SOC at each of the plurality of diagnostic time points belongs, among a plurality of SOC ranges set at predetermined SOC intervals; N is an integer of 5 or more, and k is an integer of 4 or more and less than N. Battery system.

6. a battery data collection step of collecting measured values ​​of a battery voltage, which is a voltage across a battery, a battery current, which is a current flowing through the battery, and a battery temperature, which is a temperature of the battery; At diagnosis time N, a first condition that, with the diagnostic time point N as a reference, each of the diagnostic time points N-k to N-1 includes environmental data corresponding to the environmental data at the diagnostic time point N within a predetermined range; A second condition is that k is 7 or less among the diagnosis time points Nk to N-1. A sample population determination step of extracting a plurality of diagnostic time points that satisfy the following: calculating a moving average value which is an average of a plurality of internal resistance values ​​corresponding to each of the plurality of diagnostic time points; calculating a standard deviation at the diagnosis time point N based on a plurality of internal resistance values ​​corresponding to the plurality of diagnosis time points and the moving average value; obtaining a plurality of standard deviations of the internal resistance values ​​corresponding to the plurality of diagnostic time points, an error value is calculated by multiplying the average standard deviation, which is the average of the plurality of standard deviations, by a predetermined multiple; an upper limit value is calculated by adding the error value to the moving average value; a reference value determination step of calculating a lower limit value by subtracting the error value from the moving average value; a defect diagnosis step of comparing the internal resistance value calculated at the diagnosis time point N with the upper limit value and the lower limit value to diagnose a defect in the battery, The environmental data is The battery temperature and the state of charge (SOC) of the battery are estimated by a predetermined method. If the environmental data is the battery temperature, determining that the first condition is satisfied when the battery temperature at each of the plurality of diagnostic time points falls within a predetermined temperature section to which the battery temperature at each of the plurality of diagnostic time points belongs, among a plurality of temperature sections set at predetermined temperature intervals; When the environmental data is the SOC of the battery, determining that the first condition is satisfied when the SOC at each of the plurality of diagnostic time points falls within a predetermined SOC range to which the SOC at each of the plurality of diagnostic time points belongs, among a plurality of SOC ranges set at predetermined SOC intervals; N is an integer of 5 or more, and k is an integer of 4 or more and less than N. Battery diagnostic methods.

7. The defect diagnosis step includes: When the internal resistance value corresponding to the diagnosis time exceeds the upper limit value, The battery diagnostic method according to claim 6 , wherein the battery is diagnosed as having an open circuit defect in at least one of a plurality of battery cells included in the battery.

8. The defect diagnosis step includes: If the internal resistance value corresponding to the diagnosis time point is less than the lower limit value, The battery diagnostic method according to claim 6 , wherein the battery is diagnosed as having a short circuit defect in at least one of a plurality of battery cells included in the battery.

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