Battery defect diagnosis method and server providing the method

Abstract

The present invention relates to a battery defect diagnosis method and a server that provides the method. The server of the present invention diagnoses battery defects by setting a reference value that reflects a change in the internal resistance value of a battery at each diagnosis point in time when diagnosing battery defects, thereby improving the accuracy of diagnosis.

Description

Technical Field

[0001] Mutual citation with related applications This application claims the benefit of priority based on Korean Patent Application No. 10-2022-0077533 filed on Jun. 24, 2022, and all the contents disclosed in the literature of the Korean patent application are incorporated herein by reference in their entirety.

[0002] The present invention provides a method for diagnosing a battery defect that can accurately diagnose the state of a battery including a plurality of battery cells connected in parallel, and a server providing the method.

Background Art

[0003] Large batteries mounted in electric vehicles, energy storage batteries, robots, satellites, etc. are required to have a higher capacity than small batteries mounted in portable terminals, notebook computers, etc. A high-capacity battery can be configured by connecting a plurality of batteries in series and / or in parallel. At this time, the plurality of batteries can include a plurality of battery cells connected in parallel.

[0004] On the other hand, 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 connection problems between the battery cells. For example, defects such as disconnection or 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 (e.g., an automobile, an energy storage device, etc.) in which the battery is mounted can be operated normally.

[0005] However, when a plurality of battery cells are connected in parallel, it is not easy to directly measure (sense) the cell voltage of an individual battery cell due to structural problems in the connection, etc. That is, it is difficult to directly estimate a defect in the battery cell itself and diagnose a defect in the entire battery.

[0006] Furthermore, the technique of estimating the direct current internal resistance (DCIR) on a battery-by-battery basis and comparing the estimated DCIR value to a preset (fixed) reference value to diagnose battery defects has limitations; it cannot detect defects when a large number of battery cells are simultaneously disconnected or short-circuited. Additionally, there is a risk of misdiagnosing a defect based on a change in DC internal resistance due to aging.

[0007] Furthermore, the technology that diagnoses battery defects based on the battery's DC internal resistance (DCIR) value suffers from problems where the error range becomes excessively large when the external temperature changes rapidly or when the battery's state of charge (SOC) changes, resulting in inaccurate diagnoses.

[0008] To address these issues, various studies have been conducted on conventional methods for diagnosing battery defects. However, most conventional methods were simple defect diagnostic methods performed by a Battery Management System (BMS), but they suffer from low diagnostic precision. High-precision defect diagnostic methods that require a large amount of accumulated data are difficult to implement with the limited memory capacity included in a BMS. [Overview of the project] [Problems that the invention aims to solve]

[0009] The present invention provides a method for diagnosing defects in a battery that can precisely diagnose the condition of a battery including multiple battery cells connected in parallel, and a server that provides this method.

[0010] This invention provides a method for diagnosing battery defects and a server that provides this method, which enables precise diagnosis of battery defects while overcoming the memory limitations within a Battery Management System (BMS). [Means for solving the problem]

[0011] A battery defect diagnosis server according to one feature of the present invention includes a server communication unit that receives battery data from a BMS (Battery Management System) including at least one of the following: battery voltage, which is the voltage across the battery terminals; battery current, which is the current flowing through the battery; and battery temperature, which is the temperature of the battery; a server storage unit that stores the internal resistance value of the battery calculated based on the battery data for each diagnosis point in time in which a defect in the battery is diagnosed; and a server control unit that, for each diagnosis point in time, extracts a plurality of previous diagnosis points corresponding to a predetermined number of samples based on the diagnosis point in time, calculates a moving average value which is the average of a plurality of internal resistance values ​​corresponding to each of the plurality of diagnosis points, and diagnoses a defect in the battery by comparing the internal resistance value with an upper limit value which is a predetermined value greater than the moving average value and a lower limit value which is a predetermined value less than the moving average value.

[0012] The server control unit can calculate an error value by multiplying the average standard deviation, 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 by adding the error value to the moving average; and calculate the lower limit by subtracting the error value from the moving average.

[0013] The server control unit can diagnose that a wire break defect has occurred in at least one of the multiple battery cells contained in the battery if the internal resistance value exceeds the upper limit.

[0014] The server control unit can diagnose that a short-circuit defect has occurred in at least one of the multiple battery cells contained in the battery if the internal resistance value is less than the lower limit value.

[0015] The server storage unit further stores environmental data including at least one of the charge state (SOC) estimated by a predetermined method and the measured battery temperature, and the server control unit can extract the plurality of diagnostic time points based on a first condition which includes environmental data corresponding to the environmental data at the diagnostic time point within a predetermined range and a second condition which is an earlier diagnostic time point corresponding to the number of samples relative to the diagnostic time point.

[0016] The first condition may also be that the battery temperature at a predetermined diagnostic time belongs to a predetermined temperature interval among a plurality of temperature intervals set at predetermined temperature intervals, to which the battery temperature corresponding to the diagnostic time belongs.

[0017] The first condition may also be a condition that the charge state at a predetermined diagnostic time belongs to a predetermined charge state interval, which is one of a plurality of charge state intervals set at predetermined charge state size intervals, to which the charge state corresponding to the diagnostic time belongs.

[0018] A battery diagnostic method according to other features of the present invention may include a data reception step in which a server receives battery data from a BMS (Battery Management System) including at least one of battery voltage, which is the voltage across the terminals of the battery; battery current, which is the current flowing through the battery; and battery temperature, which is the temperature of the battery; a sample group determination step in which, at a diagnostic time for diagnosing defects in the battery, a plurality of previous diagnostic time points corresponding to a predetermined number of samples based on the diagnostic time point; a reference value determination step in which a moving average value, which is the 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 in which a defect in the battery is diagnosed by comparing the internal resistance value corresponding to the diagnostic time point with the upper limit value and the lower limit value.

[0019] The aforementioned reference value determination step can be performed by calculating an error value by multiplying the mean standard deviation, which is the average of multiple standard deviations corresponding to each of the multiple diagnostic time points, by a predetermined multiple; calculating the upper limit by adding the error value to the moving average value; and calculating the lower limit by subtracting the error value from the moving average value.

[0020] The defect diagnosis step can diagnose that a wire break defect has occurred in at least one of the multiple battery cells contained in the battery if the internal resistance value corresponding to the diagnosis time exceeds the upper limit.

[0021] The defect diagnosis step can diagnose that a short-circuit defect has occurred in at least one of the multiple battery cells contained in the battery, if the internal resistance value corresponding to the diagnosis time is less than the lower limit value.

[0022] The sample group determination step extracts a plurality of diagnostic time points based on a first condition which includes environmental data corresponding to the environmental data at the diagnostic time point within a predetermined range, and a second condition which is an earlier diagnostic time point corresponding to the number of samples relative to the diagnostic time point. The environmental data may include at least one of the charge state (SOC) estimated by a predetermined method and the measured battery temperature.

[0023] The first condition may also be that the battery temperature at a predetermined diagnostic time belongs to a predetermined temperature interval among a plurality of temperature intervals set at predetermined temperature intervals, to which the battery temperature corresponding to the diagnostic time belongs.

[0024] The first condition may also be a condition that the charge state at a predetermined diagnostic time belongs to a predetermined charge state interval, which is one of a plurality of charge state intervals set at predetermined charge state size intervals, to which the charge state corresponding to the diagnostic time belongs. [Effects of the Invention]

[0025] This invention has the effect of reducing the burden on the battery system's memory space while simultaneously enabling highly accurate defect diagnosis, by having the server handle roles such as storing large amounts of data and complex algorithms necessary for defect diagnosis.

[0026] Unlike conventional methods that diagnose battery defects using fixed reference values, this invention diagnoses battery defects by setting reference values ​​that reflect changes in the battery's internal resistance at each diagnostic point. This solves the problem of misdiagnosing battery aging due to usage time as a defect in the battery itself, thereby improving the precision of the diagnosis.

[0027] This invention solves the problem of misdiagnosing external environmental differences as defects in the battery itself and improves the accuracy of the diagnosis by setting a reference value based on multiple internal resistance values ​​in an environment similar to the current diagnostic point (e.g., external temperature, SOC, etc.) and the current diagnostic point. [Brief explanation of the drawing]

[0028] [Figure 1] This figure illustrates a battery defect diagnosis system according to one embodiment. [Figure 2] This is a block diagram illustrating the battery system 100 shown in Figure 1. [Figure 3] Figure 1 is a block diagram illustrating the configuration of server 200. [Figure 4] This is a flowchart illustrating a battery defect diagnosis method according to an embodiment. [Figure 5] This is a flowchart that explains in detail the reference value determination stage (S300) in Figure 4. [Modes for carrying out the invention]

[0029] The embodiments disclosed herein will be described in detail below with reference to the attached drawings, but identical or similar components will be given the same or similar reference numerals, and redundant descriptions thereof will be omitted. The suffixes “module” and / or “part” used for components in the following description are added or mixed for the sole purpose of ease of specification preparation and do not have any distinguishing meaning or role in themselves. Furthermore, in describing the embodiments disclosed herein, if it is determined that a specific description of the relevant prior art may obscure the gist of the embodiments disclosed herein, such detailed description will be omitted. In addition, the attached drawings are merely for the purpose of making the embodiments disclosed herein easy to understand, and it should be understood that the technical ideas disclosed herein are not limited by the attached drawings and include all modifications, equivalents or substitutes that fall within the idea and technical scope of the present invention.

[0030] Terms including ordinal numbers, such as "first," "second," etc., can be used to describe a variety of components, but the components are not limited by such terms. These terms are used solely for the purpose of distinguishing one component from another.

[0031] When it is mentioned that one component is “combined” or “connected” to another component, it should be understood that it may be directly combined or connected to the other component, but there may also be other components in between. Conversely, when it is mentioned that one component is “directly combined” or “directly connected” to another component, it should be understood that there are no other components in between.

[0032] In this application, terms such as “includes” or “having” are intended to specify the presence of features, figures, stages, operations, components, parts, or combinations thereof as described in the specification, and should be understood not to pre-exist to exclude the presence or possibility of adding one or more other features, figures, stages, operations, components, parts, or combinations thereof.

[0033] Figure 1 illustrates a battery defect diagnosis system according to one embodiment.

[0034] Referring to Figure 1, the battery defect diagnostic system 1 includes a battery system 100 and a server 200.

[0035] The battery system 100 may also be a system for supplying power stored in a battery to an external device. Figure 1 shows the battery system 100 mounted on automobile system A to supply power to the automobile, but it is not limited to this. The battery system 100 can be mounted anywhere a system requires battery power. For example, the battery system 100 can be mounted on a variety of systems such as energy storage systems (ESS), robots, rockets, and airplanes to supply power to the higher-level systems on which it is mounted.

[0036] The server 200 can receive battery data from the battery system 100 at predetermined intervals or in real time. At this time, the battery data may include at least one of the following: battery voltage (voltage across the battery terminals), battery current (current flowing through the battery), and battery temperature (battery temperature).

[0037] In one embodiment, the server 200 can set a reference value based on battery data received from the battery system 100 at each diagnostic point in time for diagnosing battery defects, and diagnose battery defects based on the set reference value. If a defect is diagnosed, the server 200 can transmit a warning message corresponding to the defect diagnosis to the battery system 100.

[0038] Figure 2 is a block diagram illustrating the battery system 100 shown in Figure 1.

[0039] Referring to Figure 2, the battery system 100 includes a battery 10, a relay 20, a current sensor 30, and a battery management system (BMS) 40.

[0040] Battery 10 may include multiple battery cells connected in series and / or parallel. While Figure 2 shows three battery cells connected in parallel, battery 10 may include any number of battery cells connected in series and / or parallel, and is not limited to this arrangement. In one embodiment, the battery cells may be rechargeable secondary batteries.

[0041] For example, battery 10 can supply desired power to an external device by connecting a predetermined number of battery cells in parallel to form a battery bank, and by connecting a predetermined number of battery banks in series to form a battery pack. As another example, battery 10 can supply desired power to an external device by connecting a predetermined number of battery cells in parallel to form a battery bank, and by connecting a predetermined number of battery banks in parallel to form a battery pack. However, it is not limited to such connections, and battery 10 can include multiple battery banks, each containing multiple battery cells connected in series and / or in parallel, and multiple battery banks can also be connected in series and / or in parallel.

[0042] In Figure 2, the battery 10 is connected between the two output terminals OUT1 and OUT2 of the 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 configuration and connections shown in Figure 2 are examples only, and the invention is not limited thereto.

[0043] Relay 20 controls the electrical connection between the battery system 2 and the external device. When relay 20 is turned on, the battery system 2 and the external device are electrically connected for charging or discharging, and when relay 20 is turned off, the battery system 2 and the external device are electrically isolated. At this time, the external device may be a charger in a charging cycle where power is supplied to charge the battery 10, or a load in a discharging cycle where the battery 10 discharges power to the external device.

[0044] 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 the discharging current, and transmit the measurement results to the BMS 40.

[0045] The BMS40 includes a measurement unit 41, a storage unit 43, a communication unit 45, and a control unit 47.

[0046] The measurement unit 41 can measure battery voltage (voltage across the battery), battery temperature, and battery current (current flowing through the battery). Battery voltage and battery current may be battery data necessary for calculating the battery's internal resistance or state of charge (SOC). Battery temperature may be battery data necessary for determining the environmental intervals described below. For example, internal resistance may include direct current internal resistance (DCIR).

[0047] The measurement unit 41 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 41 can measure the battery voltage, battery current, and battery temperature at each diagnostic point in time for diagnosing battery defects and transmit the measurement results to the control unit 47.

[0048] The memory unit 43 can store the battery voltage value, battery current value, and battery temperature value measured by the measurement unit 41.

[0049] The communication unit 45 may include a wireless communication module for communicating with the server 200 via a network. The communication unit 45 may also include a CAN communication module or a daisy communication module for communicating with the vehicle system A.

[0050] The communication unit 45 can transmit battery data to the server 200 under the control of the control unit 47 at each diagnostic point in time for diagnosing defects in the battery 10. At this time, the battery data may include at least one of the following: battery voltage, battery current, and battery temperature.

[0051] The control unit 47 controls the BMS 40 overall. In one embodiment, the control unit 47 can transmit necessary battery data to the server 200 in response to commands from the server 200. For example, if the communication unit 45 receives a warning message from the server 200 corresponding to a defect diagnosis of the battery 10, the control unit 47 can transmit an alarm message or the like corresponding to the warning message to the vehicle system A via the communication unit 45.

[0052] Figure 3 is a block diagram illustrating the configuration of server 200 shown in Figure 1.

[0053] Referring to Figure 3, the server 200 includes a server communication unit 210, a server storage unit 230, and a server control unit 250.

[0054] The server communication unit 210 may include a wireless communication module for communicating with the BMS 40 over a network. For example, at each diagnostic point in time for diagnosing a defect in the battery 10, the server communication unit 210 can receive battery data from the BMS 40. The battery data may include at least one of the following: battery voltage value, battery current value, and battery temperature value.

[0055] The server storage unit 230 can store the internal resistance value calculated by the server control unit 250 based on battery data at each diagnostic point in time for diagnosing battery defects. The server storage unit 230 can store the State of Charge (SOC) estimated by the server control unit 250 based on battery data. In addition, the server storage unit 230 can store battery data received from the BMS 40.

[0056] In one embodiment, for the battery defect diagnosis described below, the server storage unit 230 can accumulate and store the internal resistance value calculated for each diagnostic cycle, the estimated SOC, and the measured battery data. For example, the server storage unit 230 can be installed inside the server 200, but is not limited to this, and can be composed of a database (DB) installed in a separate external space.

[0057] When a diagnostic time (N) according to pre-set conditions arrives, the server control unit 250 calculates the moving average (MA), the upper band threshold (UB_Th) which is greater than the moving average by a predetermined value, the lower band threshold (LB_Th) which is less than the moving average by a predetermined value, and the internal resistance value corresponding to the diagnostic time (N).

[0058] Depending on the embodiment, the diagnostic time (N) for diagnosing a battery defect may be the time when battery charging begins or when battery discharge ends. For example, when diagnostic time (N) arrives, the BMS 40 can transmit battery data to the server 200 along with a message indicating that diagnostic time (N) has arrived. However, the server control unit 250 can determine whether or not diagnostic time (N) has arrived in a variety of ways, without being limited to this.

[0059] In one embodiment, the server control unit 250 can extract a predetermined number of previous diagnostic time points corresponding to the current diagnostic time point (N) and calculate a moving average value, which is the average of a plurality of internal resistance values ​​corresponding to each of the extracted diagnostic time points. The server control unit 250 can also determine an upper limit value that is greater than the moving average value and a lower limit value that is less than the moving average value. The server control unit 250 can diagnose defects in the battery 10 by comparing the internal resistance value corresponding to the current diagnostic time point (N) with the upper and lower limit values.

[0060] In another embodiment, the server control unit 250 can determine a sample population by extracting multiple diagnostic time points corresponding to a preset number of samples (SN) (second condition) when counting diagnostic time points in the direction of previous diagnostic time points with respect to the current diagnostic time point (N), provided that the environmental data belongs to the environmental interval to which the environmental data of the current diagnostic time point (N) belongs among multiple environmental intervals (first condition). In this case, the number of samples (SN) is the number of multiple diagnostic time points included in the sample population, and can be determined to an optimal number based on experiments or the like. The sample population may be a subgroup of multiple past diagnostic time points that constitute the population, and may be a population for calculating the moving mean (MA) and the average standard deviation (σ_ave) described below.

[0061] Table 1 below is an example of a lookup table that stores 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 multiple diagnostic time points. The lookup table can be stored in the server storage unit 230.

[0062] Hereinafter, we assume that the sample size (SN) is 5, that multiple temperature intervals are defined that are distinguished by predetermined temperature magnitude intervals (e.g., 20°C intervals) (e.g., 0°C to 20°C, 21°C to 40°C, 41°C to 60°C, ...), and that multiple charge intervals are defined that are distinguished by predetermined charge state (SOC) magnitude intervals (e.g., 20% intervals) (e.g., 11% to 30%, 31% to 50%, 51% to 70%, 71% to 90%).

[0063] In one embodiment, it is assumed that the environmental data, as shown in Table 1 below, includes at least one of battery temperature and state of charge (SOC). In this case, the environmental data may be factors that affect the internal resistance of battery 10. For example, assuming that all other conditions are the same but the battery temperature is different, the internal resistance of battery 10 may be different. As another example, assuming that all other conditions are the same but the state of charge (SOC) is different, the internal resistance of battery 10 may be different.

[0064] For reference, in Table 1 below, the moving mean (MA), standard deviation (σ), average standard deviation (σ_ave), upper limit (UB_Th), and lower limit (LB_Th) for the initial diagnostic time (1) may be difficult to calculate directly (therefore, the corresponding values ​​in Table 1 are shown as blank). In addition, the moving mean (MA), standard deviation (σ), average standard deviation (σ_ave), upper limit (UB_Th), and lower limit (LB_Th) for diagnostic time points adjacent to the initial diagnostic time point (1) (2, 3, ...) may also be difficult to calculate directly due to the lack of or insufficient past diagnostic values. In this case, values ​​that are calculated on average through experimentation can be substituted for the moving mean (MA), standard deviation (σ), average standard deviation (σ_ave), upper limit (UB_Th), and lower limit (LB_Th) at the initial diagnostic time points (1, 2, 3, ...). Furthermore, example values ​​for internal resistance, moving mean, etc., for the N-8 diagnostic cycles that are not used in the following explanation have been omitted.

[0065] [Table 1] In Table 1 above, the current battery temperature (25°C) at the time of diagnosis (N) falls within the temperature range of 21°C to 40°C. Furthermore, the current charge level (60%) at the time of diagnosis (N) falls within the charge level range of 51% to 70%.

[0066] The server control unit 250 can determine a sample population by extracting the N-1st, N-2nd, N-3rd, N-5th, and N-7th diagnostic times, which correspond to the sample size (SN) of 5, when counting diagnostic times in the direction of previous diagnostic times relative to the current diagnostic time (N), i.e., the Nth diagnostic time, while belonging to an environment similar to that of the Nth diagnostic time (21°C to 40°C and 51% to 70%). At this time, the N-4th diagnostic time has a corresponding battery temperature (8°C) that belongs to a different temperature range (0°C to 20°C) than the temperature range (21°C to 40°C) to which the battery temperature (25°C) at the Nth diagnostic time belongs. The N-6th diagnostic time has a corresponding charge state (11%) that belongs to a different charge state range (11% to 30%) than the charge state range (51% to 70%) to which the charge state (60%) at the Nth diagnostic time belongs. The N-8th diagnostic time point is under similar environmental conditions (21°C to 40°C and 51% to 70%) as the Nth diagnostic time point, but the time interval between them is large and therefore it is not included in the sample size (SN). In other words, the N-4th, N-6th, and N-8th diagnostic time points are not included in the sample population.

[0067] In other embodiments, 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 server control unit 250 can include 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) in the sample population, which belong to an environment similar to the (N) diagnostic time point (21°C to 40°C). As another example, assuming that the environmental data in Table 1 includes only state of charge (SOC), the server control unit 250 can include 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%) in the sample population, which belong to an environment similar to the (N) diagnostic time point (51% to 70%).

[0068] In summary, the server control unit 250 can determine a sample population by extracting multiple diagnostic time points that are in a similar environment to a predetermined diagnostic time point (N) but are simultaneously adjacent to diagnostic time point (N). For example, the server control unit 250 can determine a sample population by extracting multiple diagnostic time points (N-7, N-5, N-3, N-2, N-1) according to the criteria described above, and then determine the reference values ​​(upper and lower limits described below) used for defect diagnosis based on the internal resistance values ​​calculated at multiple diagnostic time points belonging to the sample population. This solves the problem of misdiagnosing the degree of aging due to long-term battery use and / or temporary fluctuations in internal resistance values ​​as battery defects.

[0069] Next, the server control unit 250 determines the reference values ​​(upper and lower limits) for diagnosing a battery defect at the Nth diagnostic time, based on the internal resistance values ​​calculated at each of the multiple diagnostic time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample population.

[0070] In one embodiment, the server control unit 250 diagnoses a battery defect by comparing the internal resistance (DCIR) value corresponding to the Nth diagnostic time point with an upper limit (UB_Th) and a lower limit (LB_Th). For example, referring to Table 1, at the Nth diagnostic time point, the server control unit 250 calculates the internal resistance value ((1)), the upper limit ((5)), and the lower limit ((6)), and diagnoses a battery defect by comparing the calculated internal resistance value ((1)) with the upper limit ((5)) and the lower limit ((6)). At this time, the moving average value ((2)) and the standard deviation average value ((4)) are necessary to calculate the upper limit value ((5)) and the lower limit value ((6)). However, the standard deviation ((3)) is not a value required at the time of defect diagnosis at the Nth diagnostic time point, but it is required at subsequent diagnostic times (N+1, N+2, ...), so it can be calculated at the time of the Nth diagnostic time point and stored in the storage unit 43.

[0071] The following describes the internal resistance value ((1)), moving average value ((2)), standard deviation ((3)), average standard deviation ((4)), upper limit ((5)), and lower limit ((6)) calculated by the control unit 47 at the Nth diagnostic stage shown in Table 1.

[0072] The server control unit 250 determines the internal resistance (DCIR) corresponding to the Nth diagnostic time point based on the battery voltage, which is the voltage across the battery 10, and the battery current, which is the current flowing through the battery 10. N The value can be calculated. For example, the internal resistance (DCIR) can be calculated using the following formula (1). N The value can be calculated.

[0073]

number

[0074] For example, the control unit 47 can calculate the voltage difference (ΔV=|V1-V2|) between the battery voltage (V1) corresponding to the first time point when charging begins and the battery voltage (V2) corresponding to the second time point after a predetermined time has elapsed from the first time point. The server control unit 250 calculates the internal resistance (DCIR) based on the charging current (I) flowing through the battery and the voltage difference (ΔV). N The value can be calculated. For example, the internal resistance (DCIR) corresponding to the Nth diagnostic time point (N) can be calculated. N Let's assume the value is calculated to be 30Ω.

[0075] Referring to Table 1, the server control unit 250 averages (25Ω + 23Ω + 20Ω + 21Ω + 23Ω / 5 = 22.4) multiple internal resistance values ​​(25Ω + 23Ω + 20Ω + 21Ω + 23Ω / 5 = 22.4) corresponding to multiple diagnostic time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample population, and calculates a moving average value (MA) corresponding to the Nth diagnostic time point (N). N )((2)) can be calculated. That is, the internal resistance (DCIR) corresponding to the Nth diagnostic time point (N) can be calculated. N The value could be 22.4Ω.

[0076]

number

[0077] Referring to Table 2 below, the server control unit 250 calculates the standard deviation σ corresponding to the Nth diagnosis time point (N) based on the internal resistance values (DCIR) and moving average values (MA) corresponding to a plurality of diagnosis time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample population. N ((3)) can be calculated.

[0078]

Table 2

[0079] Referring to Table 3 below, the server control unit 250 calculates the standard deviation average value σ corresponding to the Nth diagnosis time point (N) based on a plurality of standard deviations (σ N-7 , σ N-5 , σ N-3 , σ N-2 , σ N-1 ) corresponding to a plurality of diagnosis time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample population. N_ave ((4)) can be calculated.

[0080]

Table 3

[0081] In one embodiment, the server control unit 250 calculates the mean of the standard deviations of the sample population, which is the mean of the standard deviations σ N_ave Multiply ((4)) by a predetermined multiple (Q) to obtain the error value (E=σ N_ave It is possible to calculate ×Q. In this case, the multiple (Q) is a value that reflects a predetermined error and can be determined to a variety of values ​​through experimentation. For example, let's assume that the multiple (Q) is a natural number 3.

[0082] The server control unit 250 calculates the moving mean (MA) of the sample population as shown in equation (3) below. N Error value (E=σ = 22.4) N_ave Add (×Q=1.69×3) to get the upper limit (UB) N _Th)27.47 can be calculated. In addition, the control unit 47 calculates the moving mean (MA) of the sample population as shown in equation (4) below. N Error value (E=σ = 22.4) N_ave Subtracting (×Q=1.69×3) gives the lower limit (LB) N _Th)17.33 can be calculated.

[0083]

number

[0084] Next, the server control unit 250 checks the internal resistance (DCIR) corresponding to the Nth diagnostic time point. N ) The value corresponds to the upper limit (UB) at the Nth diagnostic time point (N). N _Th) and lower limit (LB) N Battery defects can be diagnosed by comparing them with _Th).

[0085] In one embodiment, the internal resistance (DCIR N ) The value is the upper limit (UB) N If the internal resistance (DCIR) exceeds this value, the server control unit 250 can diagnose that a disconnection defect (DD) has occurred in at least one of the multiple battery cells contained in the battery 10. N ) The value is the lower limit (LB) N If the internal resistance (DCIR) is less than _Th, the server control unit 250 can diagnose that a short failure (SD) has occurred in at least one of the multiple battery cells contained in the battery 10. N ) The value is the lower limit (LB) N _Th) Upper limit (UB) N If the value is outside the normal range corresponding to _Th) or below, the server control unit 250 can diagnose that a defect (disconnection defect or short circuit defect) has occurred in the battery 10. In addition, the internal resistance (DCIR N If the value is within the normal range, the server control unit 250 can diagnose the battery 10 as being in a normal state.

[0086] For example, as explained earlier through Tables 1 and 3, and equations (1) to (4), the internal resistance value (DCIR) corresponding to the Nth diagnostic time point is... N ), upper limit (UB) N _Th), and lower limit (LB N _Th)These can be calculated as 30 (Ω), 27.47, and 17.33 respectively. In this case, the server control unit 250 will have an internal resistance value (DCIR N =30) is the upper limit (UB N Based on a value exceeding _Th=27.47, a battery defect (disconnection defect) can be diagnosed.

[0087] Figure 4 is a flowchart illustrating the battery defect diagnosis method according to the embodiment.

[0088] The following describes a battery defect diagnosis method and a server that provides this method, with reference to Figures 1 to 4.

[0089] First, the server control unit 250 receives battery data from the battery system 100 (S100). At this time, the battery data may include at least one of the following: battery voltage, which is the voltage across the battery 10; battery temperature; and battery current, which is the current flowing through the battery 10.

[0090] For example, battery voltage and battery current may be necessary battery data for calculating the battery's internal resistance (DCIR) or state of charge (SOC). Battery temperature may be necessary battery data for determining the environmental intervals described below.

[0091] Next, the server control unit 250 extracts multiple diagnostic time points that are in an environment similar to a predetermined diagnostic time point (N) but are also adjacent to diagnostic time point (N), and determines a sample population (S200).

[0092] The server control unit 250 determines a sample set by extracting multiple diagnostic time points that satisfy a predetermined diagnostic time point (N), i.e., 1) a first condition that the environmental data at the Nth diagnostic time point belongs to a predetermined environmental interval, and 2) a second condition that corresponds to a preset number of samples (SN) when counting diagnostic time points in the direction of previous diagnostic time points based on the Nth diagnostic time point. In one embodiment, the server control unit 250 can determine a sample set by extracting multiple diagnostic time points that satisfy all of the first and second conditions. In another embodiment, the server control unit 250 can determine a sample set by extracting multiple diagnostic time points that satisfy the second condition.

[0093] For example, let's assume that the environmental data includes all battery temperature and charge state (SOC), and the sample size (SN) is 5. In Table 1, when the server control unit 250 counts diagnostic time points in the direction of previous diagnostic time points relative to the Nth diagnostic time point (N), while belonging to an environment similar to the Nth diagnostic time point (N) (21°C to 40°C and 51% to 70%), it can extract the N-1st, N-2nd, N-3rd, N-5th, and N-7th diagnostic time points corresponding to the sample size (SN) of 5, and determine the sample population.

[0094] Next, the server control unit 250 determines the reference value for defect diagnosis of the battery 10 (S300). In one embodiment, the reference value is the upper limit value (UB). N _Th) and lower limit (LB) N It can include _Th).

[0095] At stage S300, referring to Figure 5, the server control unit 250 averages multiple internal resistance values ​​corresponding to each of the multiple diagnostic time points belonging to the sample population to obtain the moving average value (MA) of the sample population. N Calculate (S310).

[0096] Referring to Table 1 and formula (2) above, the server control unit 250 averages multiple internal resistance values ​​(25Ω, 23Ω, 20Ω, 21Ω, 23Ω) corresponding to multiple diagnostic time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample population and calculates a moving average value (MA) corresponding to the Nth diagnostic time point (N). N )22.4 can be calculated.

[0097] At stage S300, the server control unit 250 calculates the mean standard deviation σ of the sample population. N_ave The error value (E) is calculated based on this (S320).

[0098] For example, the mean of the standard deviation of a sample population σ N_ave This can be calculated by averaging multiple standard deviations corresponding to each of the multiple diagnostic time points belonging to the sample population.

[0099] Referring to Tables 1 and 3, the server control unit 250 calculates multiple standard deviations (σ) corresponding to multiple diagnostic time points (N-7, N-5, N-3, N-2, 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 mean standard deviation (σ) corresponding to the time of diagnosis (N) is used. N_ave The server control unit 250 can calculate the average standard deviation σ N_ave Multiply by a predetermined multiple (Q) to obtain the error value (E = σ N_ave The multiple (Q) can be calculated as (Q = 1.69 × 3). In this case, the multiple (Q) is a value that reflects a predetermined error and can be determined to various values ​​through experimentation. For example, let's assume that the multiple (Q) is a natural number 3.

[0100] At stage S300, the server control unit 250 calculates the moving mean (MA) of the sample population. N Based on the (E) and error values, the upper limit (UB) is calculated. N _Th) and lower limit (LB) N Calculate _Th) (S330).

[0101] Referring to equation (3) above, the server control unit 250 calculates the moving mean (MA) of the sample population. N Error value (E=σ = 22.4) N_ave Add (×Q=1.69×3) to get the upper limit (UB) N _Th)27.47 can be calculated. Also, referring to formula (4) above, the server control unit 250 calculates the moving mean (MA) of the sample population. N Error value (E=σ = 22.4) N_ave Subtracting (×Q=1.69×3) gives the lower limit (LB) N _Th)17.33 can be calculated.

[0102] Next, the server control unit 250 checks the internal resistance (DCIR) corresponding to the Nth diagnostic time point (N). N ) The value corresponds to the upper limit (UB) at the Nth diagnostic time point (N). N _Th) and lower limit (LB)N Compare with _Th) to diagnose battery defects (S400).

[0103] The server control unit 250 determines the internal resistance (DCIR) corresponding to the Nth diagnostic time point (N) based on the battery voltage, which is the voltage across the battery 10, and the battery current, which is the current flowing through the battery 10. N The value can be calculated. Also, the internal resistance (DCIR) can be calculated. N The value can be calculated at the S200 or S300 stage, and there is no restriction on the timing of calculation as long as it is calculated before the S400 stage, which is the time of diagnosis.

[0104] For example, the server control unit 250 can calculate the voltage difference (ΔV=|V1-V2|) between the battery voltage (V1) corresponding to the first time point when charging of the battery 10 begins and the battery voltage (V2) corresponding to the second time point after a predetermined time has elapsed from the first time point. Based on the charging current (I) flowing through the battery 10 and the voltage difference (ΔV), the server control unit 250 calculates the internal resistance (DCIR N The value can be calculated. For example, the internal resistance (DCIR) corresponding to the Nth diagnostic time point can be calculated. N Let's assume the value is calculated to be 30Ω.

[0105] At the S400 stage, the server control unit 250 has an internal resistance (DCIR N ) The value is the upper limit (UB) N Determine whether it exceeds _Th) (S410).

[0106] At stage S400, if the judgment result exceeds the limit (S410, Yes), the server control unit 250 diagnoses that a disconnection defect has occurred in at least one of the multiple battery cells contained in the battery 10 (S420).

[0107] For example, if the parallel connection of some of the battery cells among several battery cells connected in parallel is broken, the internal resistance of battery 10 can be increased.

[0108] At stage S400, if the judgment result is not exceeded (S410, No), the server control unit 250 will check the internal resistance value (DCIR N ) is the lower limit (LB N Determine whether it is less than _Th) (S430).

[0109] At stage S400, if the judgment result is less than (S430, Yes), the server control unit 250 diagnoses that a short defect has occurred in at least one of the multiple battery cells contained in the battery 10 (S440).

[0110] For example, if some of the battery cells in a series of battery cells connected in parallel come into contact (short) with each other, the internal resistance value, which is the overall resistance of the battery 10, can be reduced.

[0111] At stage S400, if the judgment result is as above (S430, No), the server control unit 250 diagnoses the state of the battery 10 as normal (S450).

[0112] Internal resistance (DCIR N ) The value is the lower limit (LB) N _Th) or above, upper limit (UB) N If the value is outside the normal range corresponding to _Th) or below, the server control unit 250 can diagnose the state of the battery 10 as defective (disconnection defect or short circuit defect). In addition, the internal resistance (DCIR) N If the value is within the normal range, the server control unit 250 can diagnose the state of the battery 10 as normal.

[0113] Next, the server control unit 250 can transmit an alarm message corresponding to the diagnostic result for the battery 10 to the BMS 40 (S500).

[0114] For example, if a wire break defect (S420) or a short circuit defect (S440) is detected, the server control unit 250 can transmit a warning message corresponding to the defect diagnosis to the BMS 40 (S500). As another example, if a normal diagnosis is detected, the server control unit 250 can transmit an alarm message corresponding to the normal state to the BMS 40 (S500).

[0115] Although embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto. Various modifications and improvements made by persons with ordinary skill in the art to which the present invention belongs also fall within the scope of the present invention.

Claims

1. A server communication unit that receives battery data from a BMS (Battery Management System), including the battery voltage, which is the voltage across the battery terminals; the battery current, which is the current flowing through the battery; and the battery temperature, which is the temperature of the battery. A server control unit calculates the internal resistance value of the battery based on the battery voltage and battery current at each diagnostic point in time for diagnosing defects in the battery, The system includes a server storage unit that stores a lookup table including the aforementioned internal resistance value, The server control unit, At time N of diagnosis, Based on the aforementioned diagnostic time N, for each of the diagnostic time points N-k to N-1, the first condition is that the environmental data for each of the diagnostic time points N-k to N-1 is included within a predetermined range that includes the environmental data for the aforementioned diagnostic time point N, The second condition is that between the time of diagnosis N-k and the time of diagnosis N-1, k is 7 or less. Extract multiple diagnostic time points that satisfy the following criteria: A moving average value is calculated, which is the average of multiple internal resistance values ​​corresponding to each of the aforementioned multiple diagnostic time points. Based on the multiple internal resistance values ​​corresponding to each of the multiple diagnostic time points and the moving average value, the standard deviation at the diagnostic time point N is calculated. Obtain multiple standard deviations of the internal resistance value corresponding to each of the aforementioned multiple diagnostic time points, The error value is calculated by multiplying the average of the aforementioned multiple standard deviations, which is the mean of the standard deviations, by a predetermined multiple. The upper limit is calculated by adding the error value to the moving average value. The lower limit is calculated by subtracting the error value from the moving average value. The internal resistance value calculated at the diagnostic time point N is compared with the upper and lower limits to diagnose a defect in the battery. In response to diagnosing a defect, a warning message is sent. The environmental data includes at least one of the following: the battery charge state (SOC, State of Charge) estimated by the server control unit based on the battery voltage and the battery current, and the battery temperature. If the environmental data is the battery temperature, the predetermined range includes a predetermined temperature interval. The server control unit, Based on the correspondence relationships included in the lookup table, if the battery temperature at each of the multiple diagnostic time points falls within one of a set of temperature intervals, and the battery temperature at each of the multiple diagnostic time points belongs to that predetermined temperature interval, then it is determined that the first condition is satisfied. If the environmental data is the state of care of the battery, the predetermined range includes a predetermined SOC interval. The server control unit, Based on the correspondence relationships included in the lookup table, if the SOC at each of the multiple diagnostic time points belongs to the predetermined SOC interval among the multiple SOC intervals set at predetermined SOC intervals, it is determined that the first condition is satisfied. N is an integer greater than or equal to 5, and k is an integer greater than or equal to 4 and less than N. The aforementioned correspondence includes the correspondence between the multiple internal resistance values, the moving average value, the multiple standard deviations, the upper limit value, and the lower limit value, and the relationship between diagnostic time point N-k and diagnostic time point N-1. Battery defect diagnostic server.

2. The server control unit, If the aforementioned internal resistance value exceeds the aforementioned upper limit, The battery defect diagnosis server according to claim 1, which diagnoses that a wire break defect has occurred in at least one of the plurality of battery cells contained in the battery.

3. The server control unit, If the internal resistance value is less than the lower limit, The battery defect diagnosis server according to claim 1, which diagnoses that a short-circuit defect has occurred in at least one of the multiple battery cells contained in the battery.

4. When the environmental data is the battery temperature, the predetermined temperature interval is one of several ranges with 20°C intervals between 0°C and 60°C. The battery defect diagnostic server according to claim 1.

5. If the environmental data is the State of Control (SOC) of the battery, the predetermined SOC interval is one of several ranges, each at 20% intervals, that fall between 10% and 90%. The battery defect diagnostic server according to claim 1.

6. The server receives battery data from the BMS (Battery Management System) via the server communication unit, including battery voltage, which is the voltage across the terminals of a battery including multiple battery cells connected in parallel; battery current, which is the current flowing through the battery; and battery temperature, which is the temperature of the battery. The server control unit performs a calculation step at each diagnostic point in time for diagnosing a defect in the battery, calculating the internal resistance value of the battery based on the battery voltage and battery current and including it in a lookup table. The server control unit performs an estimation step in which it estimates the charge state (SOC, State of Charge) of the battery based on the battery voltage and the battery current, The server control unit, at diagnostic time N, Based on the aforementioned diagnostic time N, for each of the diagnostic time points N-k to N-1, the first condition is that the environmental data for each of the diagnostic time points N-k to N-1 is included within a predetermined range that includes the environmental data for the aforementioned diagnostic time point N, The second condition is that between the time of diagnosis N-k and the time of diagnosis N-1, k is 7 or less. A sample group determination stage in which multiple diagnostic time points that satisfy the criteria are extracted, A moving average value is calculated, which is the average of multiple internal resistance values ​​corresponding to each of the aforementioned multiple diagnostic time points. Based on the multiple internal resistance values ​​corresponding to each of the multiple diagnostic time points and the moving average value, the standard deviation at the diagnostic time point N is calculated. Obtain multiple standard deviations of the internal resistance value corresponding to each of the aforementioned multiple diagnostic time points, The error value is calculated by multiplying the average of the aforementioned multiple standard deviations, which is the mean of the standard deviations, by a predetermined multiple. The upper limit is calculated by adding the error value to the moving average value. A reference value determination step in which the lower limit is calculated by subtracting the error value from the moving average, A defect diagnosis step in which the internal resistance value calculated at the aforementioned diagnosis time N is compared with the aforementioned upper limit and lower limit to diagnose a defect in the battery, The server control unit includes a message sending step in which it sends a warning message in response to diagnosing that a defect has occurred. The environmental data includes at least one of the following: the battery charge state (SOC, State of Charge) and the battery temperature. If the environmental data is the battery temperature, the predetermined range includes a predetermined temperature interval. Based on the correspondence relationships included in the lookup table, the server control unit determines that the first condition is satisfied when the battery temperature at each of the multiple diagnostic time points falls within a predetermined temperature interval set at predetermined temperature intervals, and the battery temperature at each of the multiple diagnostic time points falls within that predetermined temperature interval. If the environmental data is the state of care of the battery, the predetermined range includes a predetermined SOC interval. Based on the correspondence relationships included in the lookup table, the server control unit determines that the first condition is satisfied when the SOC at each of the multiple diagnostic time points belongs to the predetermined SOC interval among the multiple SOC intervals set at predetermined SOC intervals, and the SOC at each of the multiple diagnostic time points belongs to that interval. N is an integer greater than or equal to 5, and k is an integer greater than or equal to 4 and less than N. The aforementioned correspondence includes the correspondence between the multiple internal resistance values, the moving average value, the multiple standard deviations, the upper limit value, and the lower limit value, and the relationship between diagnostic time point N-k and diagnostic time point N-1. Battery diagnostic methods.

7. The aforementioned defect diagnosis stage is If the internal resistance value corresponding to the aforementioned diagnostic time exceeds the aforementioned upper limit, The battery diagnostic method according to claim 6, which diagnoses that a wire break defect has occurred in at least one of the multiple battery cells contained in the battery.

8. The aforementioned defect diagnosis stage is If the internal resistance value corresponding to the diagnostic time is less than the lower limit, The battery diagnostic method according to claim 6, which diagnoses that a short-circuit defect has occurred in at least one of the multiple battery cells contained in the battery.

9. When the environmental data is the battery temperature, the predetermined temperature interval is one of several ranges with 20°C intervals between 0°C and 60°C. The battery diagnostic method according to claim 6.

10. If the environmental data is the State of Control (SOC) of the battery, the predetermined SOC interval is one of several ranges, each at 20% intervals, that fall between 10% and 90%. The battery diagnostic method according to claim 6.

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