Battery fault diagnosis method, server providing the same

Abstract

The present invention relates to a battery diagnostic method capable of diagnosing the condition of a battery comprising a plurality of parallel-connected battery cells, and a battery defect diagnostic server providing the method. The battery defect diagnostic server of the present invention comprises: a server communication unit that receives battery data from a Battery Management System (BMS), the battery data including at least one of a battery voltage, which is the voltage across the battery terminals; a battery current, which is the current flowing through the battery; and a battery temperature, which is the temperature of the battery; a server storage unit that stores an internal resistance value of the battery calculated based on the battery data at each diagnostic point in time for diagnosing a defect in the battery; and a server control unit that, at each diagnostic point in time, extracts a plurality of previous diagnostic points corresponding to a predetermined number of samples based on the diagnostic 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 diagnostic points in time, 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 smaller than the moving average value.

Description

Technology Field

[0001] The present invention provides a method for diagnosing defects in a battery capable of precisely diagnosing the condition of a battery comprising a plurality of parallel-connected battery cells, and a server providing the method. Background Technology

[0002] Large batteries installed in electric vehicles, energy storage batteries, robots, satellites, etc., are required to have a higher capacity than small batteries installed 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.

[0003] Meanwhile, as the number of battery cells increases, defects may occur due to issues with the cells themselves and / or connection problems between them. For example, defects such as disconnections or short circuits between battery cells may occur. In the event of a battery defect, it is necessary to ensure that the system in which the battery is installed (e.g., automobiles, energy storage devices, etc.) can operate normally through rapid diagnosis and correction.

[0004] However, when multiple battery cells are connected in parallel, it is not easy to directly sense the cell voltage of individual cells due to structural issues in the connection. In other words, it is difficult to diagnose defects in the entire battery by directly estimating defects in the battery cells themselves.

[0005] Furthermore, the technology that diagnoses battery defects by estimating Direct Current Internal Resistance (DCIR) at the battery level and comparing the estimated DCIR value with a preset (fixed) reference value has a limitation in that it fails to detect defects when multiple battery cells within the battery are simultaneously disconnected or short-circuited. Additionally, there is a risk of misdiagnosing changes in DCIR values ​​due to aging as the occurrence of a defect.

[0006] Furthermore, the technology for diagnosing battery defects based on the DC Internal Resistance (DCIR) value has a problem in that the error range becomes too large when the external temperature changes rapidly or depending on the battery's State of Charge (SOC), resulting in inaccurate diagnosis.

[0007] To address these issues, various studies have been conducted on methods for diagnosing defects in conventional batteries. However, most conventional methods involved simple defect diagnosis performed within a Battery Management System (BMS), which suffers from low diagnostic precision. Furthermore, high-precision defect diagnosis methods requiring a large amount of accumulated data face limitations as they cannot be easily implemented within the limited memory capacity of a BMS. The problem to be solved

[0008] The present invention provides a method for diagnosing defects in a battery capable of precisely diagnosing the condition of a battery comprising a plurality of parallel-connected battery cells, and a server providing the method.

[0009] The present invention provides a method for diagnosing battery defects capable of precise diagnosis of battery defects while overcoming memory limitations within a Battery Management System (BMS), and a server providing the method. means of solving the problem

[0010] A battery defect diagnosis server according to one feature of the present invention comprises: a server communication unit that receives battery data from a Battery Management System (BMS), the data including at least one of a battery voltage, which is the voltage across the battery terminals; a battery current, which is the current flowing through the battery; and a battery temperature, which is the temperature of the battery; a server storage unit that stores an internal resistance value of the battery calculated based on the battery data at each diagnosis point in time for diagnosing a defect in the battery; and a server control unit that, at 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 in time, 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 smaller than the moving average value.

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

[0012] The server control unit above can diagnose that a disconnection defect has occurred in at least one of the plurality of battery cells included in the battery if the internal resistance value exceeds the upper limit value.

[0013] The server control unit can diagnose that a short-circuit fault has occurred in at least one of the plurality of battery cells included in the battery if the internal resistance value is less than the lower limit value.

[0014] The server storage unit further stores environmental data including at least one of a State of Charge (SOC) estimated by a predetermined method and a measured battery temperature, and the server control unit can extract the plurality of diagnostic times based on a first condition in which the environmental data at the diagnostic time belongs to a predetermined environmental time among a plurality of environmental time sections set according to a predetermined standard, and a second condition which is a previous diagnostic time corresponding to the number of samples based on the diagnostic time.

[0015] The first condition above may be a condition in which the battery temperature belongs to a predetermined temperature range among a plurality of temperature ranges set at predetermined temperature intervals, to which the battery temperature corresponding to the diagnosis time belongs.

[0016] The first condition above may be a condition in which the charging state belongs to a predetermined charging state interval to which the charging state corresponding to the diagnosis time belongs among a plurality of charging state intervals set at a predetermined charging state size interval.

[0017] A battery diagnosis method according to another feature of the present invention may include: a data receiving step in which a server receives battery data from a Battery Management System (BMS), the data including at least one of a battery voltage, which is the voltage across the 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 in which, at a diagnosis point in which a defect in the battery is diagnosed, a plurality of previous diagnosis points corresponding to a predetermined number of samples are extracted based on the diagnosis 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 diagnosis points, 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 smaller than the moving average value, are calculated; and a defect diagnosis step in which the internal resistance value corresponding to the diagnosis point is compared with the upper limit value and the lower limit value to diagnose the defect in the battery.

[0018] The above reference value determination step may calculate an error value by multiplying the average standard deviation value, which is the average of a plurality of standard deviations corresponding to each of the plurality of diagnostic points, by a predetermined multiple, calculate an upper limit value by adding the error value to the moving average value, and calculate a lower limit value by subtracting the error value from the moving average value.

[0019] The above defect diagnosis step can diagnose that a disconnection defect has occurred in at least one of the plurality of battery cells included in the battery if the internal resistance value corresponding to the diagnosis time exceeds the upper limit value.

[0020] The above defect diagnosis step can diagnose that a short-circuit defect has occurred in at least one of the plurality of battery cells included in the battery if the internal resistance value corresponding to the diagnosis time is less than the lower limit value.

[0021] The above sample group determination step extracts the plurality of diagnostic time points based on a first condition in which the environmental data at the diagnostic time point belongs to a predetermined environmental section among the plurality of environmental sections set according to a predetermined standard, and a second condition which is a previous diagnostic time point corresponding to the number of samples based on the diagnostic time point, and the environmental data may include at least one of a state of charge (SOC) estimated by a predetermined method and the measured battery temperature.

[0022] The first condition above may be a condition in which the battery temperature belongs to a predetermined temperature range among a plurality of temperature ranges set at predetermined temperature intervals, to which the battery temperature corresponding to the diagnosis time belongs.

[0023] The first condition above may be a condition in which the charging state belongs to a predetermined charging state interval to which the charging state corresponding to the diagnosis time belongs among a plurality of charging state intervals set at a predetermined charging state size interval. Effects of the invention

[0024] The present invention has the effect of enabling the battery system to receive highly accurate defect diagnosis while reducing the burden on storage space, as the server assumes the role of storing large amounts of data and complex algorithms required for defect diagnosis.

[0025] 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 the change in the battery's internal resistance value at each diagnostic point, thereby solving the problem of misdiagnosing battery aging due to usage time as a defect in the battery itself and improving the precision of the diagnosis.

[0026] The present invention can improve the precision of diagnosis by solving the problem of misdiagnosing external environmental differences as defects in the battery itself, by setting a reference value based on a plurality of internal resistance values ​​in an environment similar to the current diagnosis time (e.g., external temperature, SOC, etc.) at the time of diagnosing battery defects. Brief explanation of the drawing

[0027] FIG. 1 is a drawing illustrating a battery defect diagnosis system according to one embodiment. FIG. 2 is a block diagram illustrating the battery system (100) shown in FIG. 1. FIG. 3 is a block diagram illustrating the configuration of the server (200) shown in FIG. 1. FIG. 4 is a flowchart illustrating a battery defect diagnosis method according to an embodiment. FIG. 5 is a flowchart that explains in detail the reference value determination step (S300) of FIG. 4. Specific details for implementing the invention

[0028] Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Identical or similar components are assigned identical or similar reference numerals, and redundant descriptions thereof will be omitted. The suffixes "module" and / or "part" for components used in the following description are assigned or used interchangeably solely for the ease of drafting the specification and do not have distinct meanings or roles in themselves. Furthermore, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of related prior art could obscure the essence of the embodiments disclosed in this specification, such detailed description will be omitted. Additionally, the attached drawings are intended only to facilitate understanding of the embodiments disclosed in this specification; the technical concept disclosed in this specification is not limited by the attached drawings, and it should be understood that they include all modifications, equivalents, and substitutions that fall within the spirit and technical scope of the invention.

[0029] Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but said components are not limited by said terms. These terms are used solely for the purpose of distinguishing one component from another.

[0030] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0031] In this application, terms such as “comprising” or “having” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0033] FIG. 1 is a drawing illustrating a battery defect diagnosis system according to one embodiment.

[0034] Referring to FIG. 1, the battery defect diagnosis system (1) includes a battery system (100) and a server (200).

[0035] The battery system (100) may be a system for supplying power stored in a battery to an external device. In FIG. 1, the battery system (100) is shown mounted on a vehicle system (A) to supply power to the vehicle, but it is not limited thereto. The battery system (100) can be mounted on any system that requires power from the battery. For example, the battery system (100) can be mounted on various systems such as an energy storage system (ESS), a robot, a rocket, an airplane, etc., to supply power to the mounted upper system.

[0036] The server (200) may receive battery data from the battery system (100) at a predetermined period or in real time. At this time, the battery data may include at least one of the data, 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.

[0037] According to one embodiment, the server (200) may set a reference value based on battery data received from the battery system (100) at each diagnosis time for diagnosing a defect in the battery, and may diagnose a defect in the battery based on the set reference value. When it is diagnosed that a defect has occurred, the server (200) may send a warning message corresponding to the defect diagnosis to the battery system (100).

[0039] FIG. 2 is a block diagram illustrating the battery system (100) shown in FIG. 1.

[0040] Referring to FIG. 2, the battery system (100) includes a battery (10), a relay (20), a current sensor (30), and a battery management system (hereinafter BMS) (40).

[0041] The battery (10) may include a plurality of battery cells connected in series and / or parallel. In FIG. 2, three battery cells connected in parallel are shown, but are not limited thereto, and the battery (10) may include various numbers of battery cells connected in series and / or parallel. In some embodiments, the battery cells may be rechargeable secondary batteries. Also,

[0042] For example, the battery (10) may form a battery bank by connecting a predetermined number of battery cells in parallel, and form a battery pack by connecting a predetermined number of battery banks in series to supply desired power to an external device. As another example, the battery (10) may form a battery bank by connecting a predetermined number of battery cells in parallel, and form a battery pack by connecting a predetermined number of battery banks in parallel to supply desired power to an external device. However, it is not limited to such connections, and the battery (10) may include a plurality of battery banks comprising 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.

[0043] In FIG. 2, the battery (10) is connected between two output terminals (OUT1, OUT2) of the battery system (2). Additionally, 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 configurations and the connection relationships between the configurations shown in FIG. 2 are merely examples, and the invention is not limited thereto.

[0044] 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 disconnected. At this time, the external device may be a charger in the charging cycle, which supplies power to the battery (10) to charge it, and may be a load in the discharging cycle, which discharges power from the battery (10) to the external device.

[0045] The current sensor (30) is connected in series to the current path between the battery (10) and an 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).

[0046] The BMS (40) includes a measurement unit (41), a storage unit (43), a communication unit (45), and a control unit (47).

[0047] The measuring unit (41) can measure the battery voltage, which is the voltage across the battery terminals, the battery temperature, and the battery current, which is the current flowing through the battery. The battery voltage and battery current may be battery data necessary for calculating the battery's internal resistance or state of charge (SOC). The battery temperature may be battery data necessary for determining the environment range to be described below. For example, the internal resistance may include direct current internal resistance (DCIR).

[0048] The measuring 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) at a location adjacent to the battery to measure the battery temperature. For example, the measuring unit (41) may measure the battery voltage, battery current, and battery temperature at each diagnostic time for diagnosing a defect in the battery, and transmit the measurement results to the control unit (47).

[0049] The storage unit (43) can store the battery voltage value, battery current value, and battery temperature value measured by the measurement unit (41).

[0050] The communication unit (45) may include a wireless communication module for communicating with the server (200) via a network. Additionally, the communication unit (45) may include a CAN communication module or a daisy communication module for communicating with the vehicle system (A).

[0051] The communication unit (45) can transmit battery data to the server (200) under the control of the control unit (47) at each diagnostic time when diagnosing a defect in the battery (10). At this time, the battery data may include at least one of battery voltage, battery current, and battery temperature.

[0052] The control unit (47) controls the BMS (40) overall. According to one embodiment, the control unit (47) can transmit necessary battery data to the server (200) in accordance with the command of the server (200). For example, when the communication unit (45) receives a warning message corresponding to a defect diagnosis of the battery (10) from the server (200), the control unit (47) can transmit an alarm message, etc. corresponding to the warning message, to the vehicle system (A) through the communication unit (45).

[0054] FIG. 3 is a block diagram illustrating the configuration of the server (200) shown in FIG. 1.

[0055] Referring to FIG. 3, the server (200) includes a server communication unit (210), a server storage unit (230), and a server control unit (250).

[0056] The server communication unit (210) may include a wireless communication module for communicating with the BMS (40) via a network. For example, at each diagnostic time when diagnosing a defect in the battery (10), the server communication unit (210) may receive battery data from the BMS (40). The battery data may include at least one of a battery voltage value, a battery current value, and a battery temperature value.

[0057] The server storage unit (230) can store the internal resistance value calculated by the server control unit (250) based on battery data at each diagnosis time when diagnosing a defect in the battery. The server storage unit (230) can store the State of Charge (SOC) estimated by the server control unit (250) based on the battery data. Additionally, the server storage unit (230) can store battery data received from the BMS (40).

[0058] According to one embodiment, for the battery defect diagnosis described below, the server storage unit (230) must accumulate and store the internal resistance value calculated at each diagnosis cycle, the estimated SOC, and the measured battery data. For example, the server storage unit (230) may be installed inside the server (200), but is not limited thereto and may be configured as a database (DB) installed in a separate external space.

[0059] When a diagnosis time (N) according to a preset condition arrives, the server control unit (250) calculates a moving average value (MA; Moving Average), an upper band threshold (UB_Th; Upper Band Threshold) which is greater than the moving average value, a lower band threshold (LB_Th; Lower Band Threshold) which is smaller than the moving average value, and an internal resistance value corresponding to the diagnosis time (N).

[0060] According to the embodiment, the time when charging of the battery begins or the time when discharging of the battery ends may be the diagnosis time (N) for diagnosing defects in the battery. For example, when the diagnosis time (N) arrives, the BMS (40) may transmit battery data to the server (200) along with a message indicating the arrival of the diagnosis time (N). However, it is not limited thereto, and the server control unit (250) may determine whether the diagnosis time (N) has arrived in various ways.

[0061] According to one embodiment, the server control unit (250) can extract a plurality of previous diagnostic points corresponding to a predetermined number of samples based on the current diagnostic 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 plurality of diagnostic points. Additionally, the server control unit (250) can determine an upper limit value which is greater than a predetermined value than the moving average value and a lower limit value which is smaller than the moving average value. The server control unit (250) can diagnose a defect in the battery (10) by comparing the internal resistance value corresponding to the current diagnostic point (N) with the upper limit value and the lower limit value.

[0062] According to another embodiment, the server control unit (250) can determine a sample group by extracting a plurality of diagnostic points that correspond to a preset number of samples (the number of samples, SN) when counting diagnostic points in the direction of previous diagnostic points based on the current diagnostic point (N) (first condition), wherein the environmental data belongs to the environmental section to which the environmental data of the current diagnostic point (N) belongs (second condition). At this time, the number of samples (SN) is the number of diagnostic points included in the sample group, and can be determined as an optimal number based on experiments, etc. The sample group is a subgroup of past diagnostic points that is a population, and may be a group for calculating the moving average value (MA) and the mean value of the standard deviation (σ_ave), etc., which will be described below.

[0063] Table 1 below is an example of a look-up table that stores battery temperature, state of charge (SOC), internal resistance (DCIR) values, moving average (MA), upper limit (UB_Th), and lower limit (LB_Th) measured, estimated, and calculated at each of multiple diagnostic points. The look-up table may be stored in a server storage unit (230).

[0064] Hereinafter, it is assumed that the sample size (SN) is 5, and multiple temperature ranges (e.g., 0°C to 20°C, 21°C to 40°C, 41°C to 60°C, …) are set by a predetermined temperature size interval (e.g., 20°C interval), and multiple charge ranges (e.g., 11% to 30%, 31% to 50%, 51% to 70%, 71% to 90%) are set by a predetermined state of charge (SOC) size interval (e.g., 20% interval).

[0065] According to one embodiment, in Table 1 below, the environmental data is assumed to include at least one of battery temperature and state of charge (SOC).

[0066] For reference, in Table 1 below, the moving average (MA), standard deviation (σ), average standard deviation (σ_ave), upper limit (UB_Th), and lower limit (LB_Th) of the initial diagnosis time point (1) may be difficult to calculate directly (thus, the corresponding values ​​in Table 1 are marked as blank). In addition, the moving average (MA), standard deviation (σ), average standard deviation (σ_ave), upper limit (UB_Th), and lower limit (LB_Th) of diagnosis time points (2, 3, …) adjacent to the initial diagnosis time point (1) may also be difficult to calculate directly because there are no past diagnosis values ​​to calculate them or there is insufficient data. In this case, values ​​calculated on average according to the experiment can be substituted for the moving average (MA), standard deviation (σ), average standard deviation (σ_ave), upper limit (UB_Th), and lower limit (LB_Th) of the initial diagnosis time point (1, 2, 3, …). Additionally, example values ​​for internal resistance values, moving average values, etc., in the N-8 diagnostic cycle, which are not used in the following description, have been omitted.

[0067]

[0068] In Table 1 above, the temperature range to which the battery temperature (25℃) at the current diagnosis time (N) belongs is 21℃ to 40℃. Also, the charge state range to which the charge state (60%) at the current diagnosis time (N) belongs is 51% to 70%.

[0069] The server control unit (250) can determine a sample group by extracting the N-1, N-2, N-3, N-5, and N-7 diagnostic times corresponding to the sample count (SN) of 5 when counting diagnostic times in the direction of previous diagnostic times relative to the current diagnostic time (N), that is, the Nth diagnostic time, which belongs to an environment similar to the current diagnostic time (N), i.e., the Nth diagnostic time. At this time, the N-4 diagnostic time has a corresponding battery temperature (8℃) that belongs to a temperature range (0℃~20℃) different from the temperature range (21℃~40℃) to which the battery temperature (25℃) of the Nth diagnostic time belongs. At the N-6th diagnostic time, the corresponding charge state (11%) falls within a charge state range (11%–30%) different from the charge state range (51%–70%) to which the charge state (60%) of the Nth diagnostic time belongs. At the N-8th diagnostic time, the environment is similar to that of the Nth diagnostic time (21°C–40°C and 51%–70%), but the time interval from the Nth diagnostic time is large, so it is not included in the sample count (SN). That is, the N-4th diagnostic time, the N-6th diagnostic time, and the N-8th diagnostic time are not included in the sample group.

[0070] According to another embodiment, the environment data may include battery temperature or state of charge (SOC). For example, assuming that the environment data in Table 1 includes only battery temperature, the server control unit (250) may include the N-1 diagnosis time (23°C), N-2 diagnosis time (23°C), N-3 diagnosis time (20°C), N-5 diagnosis time (22°C), and N-6 diagnosis time (23°C) in the sample group, which belong to an environment (21°C to 40°C) similar to the N diagnosis time. As another example, assuming that the environment data in Table 1 above includes only the state of charge (SOC), the server control unit (250) may include the N-1 diagnosis time (55%), N-2 diagnosis time (55%), N-3 diagnosis time (50%), N-4 diagnosis time (60%), and N-5 diagnosis time (55%) in the sample group, which belong to an environment (51% to 70%) similar to the N diagnosis time.

[0071] In summary, the server control unit (250) can determine a sample group by extracting multiple diagnostic points that are in an environment similar to a predetermined diagnostic point (N) and are simultaneously adjacent to the diagnostic point (N). For example, the server control unit (250) can determine a sample group by extracting multiple diagnostic points (N-7, N-5, N-3, N-2, N-1) according to the criteria described above, and determine a reference value (upper limit and lower limit, to be described below) used for defect diagnosis based on the internal resistance values ​​calculated at multiple diagnostic points belonging to the sample group. Then, the problem of misdiagnosing the degree of aging and / or temporary fluctuations in internal resistance values ​​due to long-term use of the battery as a defect in the battery can be resolved.

[0072] Next, the server control unit (250) determines a reference value (upper limit and lower limit) for diagnosing a defect in the battery at the Nth diagnosis time point based on the internal resistance value calculated at each of the multiple diagnosis times points (N-7, N-5, N-3, N-2, N-1) belonging to the sample group.

[0073] According to one embodiment, the server control unit (250) diagnoses a defect in the battery by comparing the internal resistance (DCIR) value corresponding to the Nth diagnosis time point with the upper limit value (UB_Th) and the lower limit value (LB_Th). For example, referring to Table 1, at the Nth diagnosis time point, the server control unit (250) calculates the internal resistance value (①), the upper limit value (⑤), and the lower limit value (⑥), and diagnoses a defect in the battery by comparing the calculated internal resistance value (①) with the upper limit value (⑤) and the lower limit value (⑥). At this time, in order to calculate the upper limit value (⑤) and the lower limit value (⑥), a moving average value (②) and a standard deviation average value (④) are required. However, the standard deviation (③) is not a value required for the defect diagnosis at the Nth diagnosis time point, but is required for the defect diagnosis at subsequent diagnosis times (N+1, N+2, …), so it can be calculated at the Nth diagnosis time point and stored in the storage unit (43).

[0074] Below, 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 (47) at the Nth diagnosis point of Table 1 are explained.

[0075] The server control unit (250) has an internal resistance (DCIR) corresponding to the Nth diagnostic 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 of ) can be calculated. For example, by the following equation (1), the internal resistance (DCIR) N , ①) The value can be calculated.

[0076] [Mathematical Formula 1]

[0077]

[0078] For example, the control unit (47) can calculate the voltage difference (ΔV = |V1 - V2|) between the battery voltage (V1) corresponding to a first time point when charging begins and the battery voltage (V2) corresponding to a 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 of ) can be calculated. For example, the internal resistance (DCIR) corresponding to the Nth diagnosis time point (N). N It is assumed that the value is calculated as 30Ω.

[0079] Referring to Table 1 above, the server control unit (250) averages (25Ω + 23Ω + 20Ω + 21Ω + 23Ω / 5 = 22.4) the multiple internal resistance values ​​(25Ω + 23Ω + 20Ω + 21Ω + 23Ω / 5 = 22.4) corresponding to each of the multiple diagnostic time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample group, and obtains 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 diagnosis point (N) N The value of ) can be 22.4Ω.

[0080] [Mathematical Formula 2]

[0081]

[0082] Referring to Table 2 below, the server control unit (250) [calculates] the standard deviation ([calculated]) corresponding to the Nth diagnosis time point (N) based on the internal resistance value (DCIR) and moving average value (MA) corresponding to each of the multiple diagnosis time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample group. , ③) can be produced.

[0083]

[0084] As explained above, the standard deviation corresponding to the Nth diagnosis time point (N) , ③) is not a value required for defect diagnosis at the Nth diagnosis point, but is required for defect diagnosis at subsequent diagnosis points (N+1, N+2, …). Therefore, the standard deviation corresponding to the Nth diagnosis point (N) , ③) can be calculated at the Nth diagnosis time (N) and stored in the server storage unit (230).

[0085] Referring to Table 3 below, the server control unit (250) has a plurality of standard deviations (σ) corresponding to each of the plurality of diagnosis time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample group. N-7 , σ N-5 , σ N-3 , σ N-2 , σ N-1 Based on ) the mean value of the standard deviation corresponding to the Nth diagnosis time point (N) ,④) can be produced.

[0086]

[0087] The server control unit (250) is a moving average value (MA N An upper limit value (UB) greater than a predetermined value N _Th) and a lower limit value (LB) smaller than a predetermined value of the moving average (MA). N _Th) can be calculated. According to an embodiment, the server control unit (250) can calculate the standard deviation average value ( A first error value is calculated by multiplying ) by a predetermined first multiple, and the moving average value (MA N The upper limit value (UB) is calculated by adding the first error value to ) N _Th) can be calculated. In addition, the server control unit (250) can calculate the standard deviation average value ( Calculate a second error value by multiplying ) by a predetermined second multiple, and the moving average value (MA N Subtract the second error value from ) to obtain the lower limit value (LB N _Th) can be calculated. At this time, the first and second multiples may be identical, but are not limited thereto, and may be calculated as various multiples.

[0088] According to one embodiment, the server control unit (250) has a standard deviation average value, which is the average of the standard deviations of the sample group ( The error value (E = by multiplying ) and a predetermined multiple (Q) ) can be calculated. In this case, the multiple (Q) is a value to reflect a predetermined error and can be determined to various values ​​through experiment. For example, the multiple (Q) is assumed to be the natural number 3.

[0089] The server control unit (250) has a moving average value (MA) of a sample group as shown in the following equation (3). N = 22.4) with error value((E = = 1.69 3) Calculate plus the upper limit value (UB N _Th) 27.47 can be calculated. In addition, the control unit (47) can calculate the moving average value (MA) of the sample group as shown in the following equation (4). N = 22.4) with error value (E= = 1.69 Subtract the lower limit value (LB) by subtracting 3)). N _Th) 17.33 can be calculated.

[0090] [Mathematical Formula 3]

[0091]

[0092] [Mathematical Formula 4]

[0093]

[0094] Next, the server control unit (250) has an internal resistance (DCIR) corresponding to the Nth diagnostic time point. N The value of ) is the upper limit value (UB) corresponding to the Nth diagnosis time point (N). N _Th) and lower limit (LB N Battery defects can be diagnosed by comparing with _Th).

[0095] According to one embodiment, internal resistance (DCIR N ) value is the upper limit (UB NIf _Th) is exceeded, the server control unit (250) can diagnose that a disconnection defect (DD) has occurred in at least one of the plurality of battery cells included in the battery (10). Internal resistance (DCIR N ) value is the lower limit (LB N If it is less than _Th), the server control unit (250) can diagnose that a short defeat (SD) has occurred in at least one of the plurality of battery cells included in the battery (10). That is, internal resistance (DCIR N ) value is the lower limit (LB N _Th) or higher upper limit (UB N If it deviates from the normal range corresponding to _Th) below, the server control unit (250) can diagnose that a defect (open circuit defect or short circuit defect) has occurred in the battery (10). In addition, internal resistance (DCIR N If the value falls within the normal range, the server control unit (250) can diagnose the state of the battery (10) as normal.

[0096] For example, as previously explained through Tables 1 and 3 and Equations (1) to (4), the internal resistance value (DCIR) corresponding to the Nth diagnosis time point N ), upper limit (UB N _Th), and lower limit (LB N Each of _Th) can be calculated as 30(Ω), 27.47, and 17.33. In this case, the server control unit (250) has an internal resistance value (DCIR N = 30) is the upper limit (UB N Based on the fact that _Th=27.47) is exceeded, a battery defect (disconnection defect) can be diagnosed.

[0098] FIG. 4 is a flowchart illustrating a battery defect diagnosis method according to an embodiment.

[0099] Hereinafter, with reference to FIGS. 1 to 4, a battery defect diagnosis method and a server providing the method will be described.

[0100] 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 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).

[0101] For example, battery voltage and battery current may be battery data required to calculate the battery's Direct Current Internal Resistance (DCIR) or State of Charge (SOC). Battery temperature may be battery data required to determine the environmental range described below.

[0102] Next, the server control unit (250) determines a sample group by extracting a plurality of diagnostic times that are in an environment similar to a predetermined diagnostic time (N) and are simultaneously adjacent to the diagnostic time (N) (S200).

[0103] The server control unit (250) determines a sample group by extracting a plurality of diagnostic points that satisfy a predetermined diagnostic point (N), i.e., 1) a first condition in which the environment data of the Nth diagnostic point belongs to a predetermined environment section to which the environment data belongs, and 2) a second condition corresponding to a preset number of samples (SN) when counting diagnostic points in the direction of the previous diagnostic point based on the Nth diagnostic point. According to one embodiment, the server control unit (250) may determine a sample group by extracting a plurality of diagnostic points that satisfy both the first condition and the second condition. According to another embodiment, the server control unit (250) may determine a sample group by extracting a plurality of diagnostic points that satisfy the second condition.

[0104] For example, let us assume that the environment data includes both battery temperature and state of charge (SOC), and the sample count (SN) is 5. In Table 1 above, the server control unit (250) can determine a sample group by extracting the N-1st diagnosis time, N-2nd diagnosis time, N-3rd diagnosis time, N-5th diagnosis time, and N-7th diagnosis time corresponding to 5 samples (SN) when counting diagnosis times in the direction of the previous diagnosis time based on the Nth diagnosis time (N), while belonging to an environment (21°C to 40°C and 51% to 70%) similar to the Nth diagnosis time (N).

[0105] Next, the server control unit (250) determines a reference value for diagnosing defects in the battery (10) (S300). According to one embodiment, the reference value is an upper limit value (UB N _Th) and lower limit (LB N It may include _Th).

[0106] In step S300, with reference to FIG. 5, the server control unit (250) averages a plurality of internal resistance values ​​corresponding to each of a plurality of diagnostic time points belonging to the sample group to obtain the moving average value (MA) of the sample group. N Calculates ) (S310).

[0107] Referring to Table 1 and Equation (2) above, the server control unit (250) averages a plurality of internal resistance values ​​(25Ω, 23Ω, 20Ω, 21Ω, 23Ω) corresponding to each of a plurality of diagnostic time points (N-7, N-5, N-3, N-2, N-1) belonging to a sample group to obtain a moving average value (MA) corresponding to the Nth diagnostic time point (N). N ) 22.4 can be calculated.

[0108] In step S300, the server control unit (250) is the mean value of the standard deviation of the sample group ( The error value (E) is calculated based on ) (S320).

[0109] For example, the mean value of the standard deviation of a sample group ( ) can be calculated by averaging multiple standard deviations corresponding to each of multiple diagnostic points belonging to the sample group.

[0110] Referring to Table 1 and Table 3 above, the server control unit (250) has a plurality of standard deviations (σ) corresponding to each of the plurality of diagnosis time points (N-7, N-5, N-3, N-2, N-1) belonging to the sample group. N-7 , σ N-5 , σ N-3 , σ N-2 , σ N-1 Based on ) the mean value of the standard deviation corresponding to the diagnosis time point (N) ,1.69) can be calculated. In addition, the server control unit (250) can calculate the standard deviation average value ( The error value (E=) is obtained by multiplying ) and a predetermined multiple (Q). = 1.69 3) can be calculated. In this case, the multiple (Q) is a value to reflect a predetermined error and can be determined to various values ​​through experiment. For example, the multiple (Q) is assumed to be the natural number 3.

[0111] In step S300, the server control unit (250) is the moving average value (MA) of the sample group. N Based on ) and error value ((E), upper limit (UB N _Th) and lower limit (LB N Calculates _Th)(S330).

[0112] Referring to the above equation (3), the server control unit (250) has a moving average value (MA) of a sample group. N = 22.4) with error value((E = = 1.69 3) perform a plus operation on the upper limit value (UB N _Th) 27.47 can be calculated. Additionally, referring to the above equation (4), the server control unit (250) can calculate the moving average value (MA) of the sample group. N = 22.4) with error value (E= = 1.69 Subtract the lower limit value (LB) by performing a subtraction operation on 3).N _Th) 17.33 can be calculated.

[0113] Next, the server control unit (250) has an internal resistance (DCIR) corresponding to the Nth diagnosis time (N). N The value of ) is the upper limit value (UB) corresponding to the Nth diagnosis time point (N). N _Th) and lower limit (LB N Diagnose defects in the battery (10) by comparing with _Th) (S400).

[0114] The server control unit (250) has an 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 of ) can be calculated. In addition, the internal resistance (DCIR N The value of ) can be calculated at step S200 or step S300, and if it is calculated before step S400, which is the diagnosis point, the time of calculation is not limited.

[0115] For example, the server control unit (250) can calculate the voltage difference (ΔV = |V1 - V2|) between a battery voltage (V1) corresponding to a first time point when charging of the battery (10) begins and a battery voltage (V2) corresponding to a 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 (10) and the voltage difference (ΔV). N The value of ) can be calculated. For example, the internal resistance (DCIR) corresponding to the Nth diagnostic point. N It is assumed that the value is calculated as 30Ω.

[0116] In step S400, the server control unit (250) has an internal resistance (DCIR N ) value is the upper limit (UB N Determine whether it exceeds _Th) (S410).

[0117] In step S400, if the judgment result exceeds (S410, Yes), the server control unit (250) diagnoses that a disconnection defect has occurred in at least one of the plurality of battery cells included in the battery (10) (S420).

[0118] For example, if the parallel connection of some of the battery cells among the multiple battery cells connected in parallel is broken, the internal resistance value of the battery (10) may increase.

[0119] In step S400, if the judgment result does not exceed (S410, No), the server control unit (250) determines the internal resistance value (DCIR N ) is the lower limit (LB N Determine whether it is less than _Th (S430).

[0120] In step 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 plurality of battery cells included in the battery (10) (S440).

[0121] For example, if some of the battery cells among the multiple battery cells connected in parallel come into contact (short) with each other, the internal resistance value, which is the total resistance of the battery (10), can be reduced.

[0122] In step S400, if the judgment result is abnormal (S430, No), the server control unit (250) diagnoses the state of the battery (10) as normal (S450).

[0123] Internal Resistance (DCIR) N ) value is the lower limit (LB N _Th) or higher upper limit (UB N If it deviates from the normal range corresponding to _Th) below, the server control unit (250) can diagnose the condition of the battery (10) as a defect (open circuit defect or short circuit defect). In addition, internal resistance (DCIR NIf the value falls within the normal range, the server control unit (250) can diagnose the state of the battery (10) as normal.

[0124] Next, the server control unit (250) can send an alarm message corresponding to the diagnosis result for the battery (10) to the BMS (40) (S500).

[0125] For example, if it is determined that there is an open circuit defect (S420) and a short circuit defect (S440), the server control unit (250) can send a warning message corresponding to the defect diagnosis to the BMS (40) (S500). As another example, if it is determined that there is a normal diagnosis, the server control unit (250) can send an alarm message corresponding to the normal state to the BMS (40) (S500).

[0126] Although embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modified and improved forms by those skilled in the art to which the present invention pertains also fall within the scope of the present invention.

Claims

Claim 1 A battery defect diagnosis server comprising: a server communication unit that receives battery data from a Battery Management System (BMS), including at least one of a battery voltage, which is the voltage across the battery terminals, and a battery current, which is the current flowing through the battery; a server storage unit that stores an internal resistance value of the battery calculated based on the battery data at each diagnosis point for diagnosing a defect in the battery; and a server control unit that, at each diagnosis point, extracts a plurality of previous diagnosis points corresponding to a predetermined number of samples based on the diagnosis point, 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 smaller than the moving average value, wherein the server control unit calculates an error value by multiplying a standard deviation average value, which is the average of a plurality of standard deviations corresponding to each of the plurality of diagnosis points, by a predetermined multiple, calculates an upper limit value by adding the error value to the moving average value, and calculates a lower limit value by subtracting the error value from the moving average value. Claim 2 delete Claim 3 In claim 1, the server control unit is a battery defect diagnosis server that diagnoses that a disconnection defect has occurred in at least one of a plurality of battery cells included in the battery when the internal resistance value exceeds the upper limit value. Claim 4 In claim 1, the server control unit is a battery defect diagnosis server that diagnoses that a short-circuit defect has occurred in at least one of a plurality of battery cells included in the battery if the internal resistance value is less than the lower limit value. Claim 5 A battery defect diagnosis server according to claim 1, wherein the server storage unit further stores environmental data including at least one of a State of Charge (SOC) estimated by a predetermined method based on the battery data and a battery temperature received from the BMS through the server communication unit, and the server control unit extracts the plurality of diagnosis times corresponding to the number of samples when counting diagnosis times in the direction of the previous diagnosis time based on the diagnosis time (2nd condition), wherein the environmental data belongs to a predetermined environmental time among a plurality of environmental time sections set according to a predetermined standard (1st condition), and the diagnosis times correspond to the number of samples when counting diagnosis times in the direction of the previous diagnosis time based on the diagnosis time. Claim 6 In paragraph 5, the first condition is a battery defect diagnosis server, wherein the battery temperature belongs to a predetermined temperature range among a plurality of temperature ranges set at predetermined temperature size intervals, to which the battery temperature corresponding to the diagnosis time belongs. Claim 7 In paragraph 5, the first condition is a battery defect diagnosis server, wherein the charge state belongs to a predetermined charge state section among a plurality of charge state sections set at predetermined charge state size intervals, to which the charge state corresponding to the diagnosis time belongs. Claim 8 A battery diagnosis method comprising: a data receiving step in which a server receives battery data including at least one of a battery voltage, which is the voltage across the battery terminals, and a battery current, which is the current flowing through the battery; a sample group determination step in which, at a diagnosis point for diagnosing a defect in the battery, a plurality of previous diagnosis points corresponding to a predetermined number of samples based on the diagnosis 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 diagnosis points, 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 smaller than the moving average value; and a defect diagnosis step in which an internal resistance value calculated based on the battery data is compared with the upper limit value and the lower limit value to diagnose a defect in the battery, wherein the reference value determination step calculates an error value by multiplying a standard deviation average value, which is the average of a plurality of standard deviations corresponding to each of the plurality of diagnosis points, by a predetermined multiple, calculates an upper limit value by adding the error value to the moving average value, and calculates a lower limit value by subtracting the error value from the moving average value. Claim 9 delete Claim 10 A battery diagnosis method according to claim 8, wherein the defect diagnosis step diagnoses that a short circuit defect has occurred in at least one of the plurality of battery cells included in the battery if the internal resistance value corresponding to the diagnosis time exceeds the upper limit value. Claim 11 A battery diagnosis method according to claim 8, wherein the defect diagnosis step diagnoses that a short-circuit defect has occurred in at least one of a plurality of battery cells included in the battery if the internal resistance value corresponding to the diagnosis time is less than the lower limit value. Claim 12 In claim 8, the sample group determination step comprises extracting the plurality of diagnosis times corresponding to the number of samples when counting diagnosis times in the direction of the previous diagnosis time based on the diagnosis time (2nd condition), wherein the environment data belongs to a predetermined environment time among a plurality of environment times set according to a predetermined standard (1st condition), and the environment data includes at least one of a state of charge (SOC) estimated by a predetermined method based on the battery data and a battery temperature received by the server from the BMS. Claim 13 A battery diagnosis method according to claim 12, wherein the first condition is a condition in which the battery temperature belongs to a predetermined temperature range among a plurality of temperature ranges set at predetermined temperature intervals, wherein the battery temperature corresponding to the diagnosis time belongs to a predetermined temperature range. Claim 14 A battery diagnosis method according to claim 12, wherein the first condition is a condition in which the charge state belongs to a predetermined charge state section among a plurality of charge state sections set at predetermined charge state size intervals, to which the charge state corresponding to the diagnosis time belongs.

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