Battery fault diagnosis method and server for providing same

Abstract

The present invention relates to a battery fault diagnosis method and a server for providing the same, and the server of the present invention diagnoses a battery fault by configuring a reference value reflecting a change in an internal resistance value of a battery at each diagnosis time point for battery fault diagnosis, thereby being capable of improving the accuracy of diagnosis. Furthermore, by configuring the reference value based on a plurality of internal resistance values in an environment similar to the environment at the current diagnosis time, the problem of erroneously diagnosing the external environment difference as the fault of the battery itself can be solved, thereby improving the accuracy of the diagnosis.

Description

Technical Field

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2022-0077533 filed in the Korean Intellectual Property Office on June 24, 2022, the entire contents of which are incorporated herein by reference.

[0003] The present invention provides a method of diagnosing a fault in a battery and a server for providing the method, which is capable of accurately diagnosing the state of a battery including a plurality of battery cells connected in parallel. Background Art

[0004] Large batteries installed in electric vehicles, energy storage batteries, robots, satellites, etc. need to have higher capacity than small batteries installed in portable terminals, laptops, etc. A high-capacity battery can be configured by connecting a plurality of batteries in series and / or in parallel. In this case, the plurality of batteries may include a plurality of battery cells connected in parallel.

[0005] Meanwhile, when the number of battery cells included in a battery increases, a fault may occur in the battery due to a problem with the battery cell itself and / or a problem with a connection between the battery cells. For example, a fault between the battery cells, such as disconnection and a short circuit, may occur. When a fault occurs in a battery, it is necessary to enable a system (e.g., a vehicle and an energy storage device) in which the battery is installed to operate normally by quickly diagnosing and correcting the fault.

[0006] However, when a plurality of battery cells are connected in parallel, it is not easy to directly sense the cell voltage of a single battery cell, etc., due to structural problems in the connection, etc. That is, it is difficult to diagnose a failure of the entire battery by directly estimating a failure of the battery cell itself.

[0007] In addition, the technology for diagnosing a fault in a battery by estimating a direct current internal resistance (DCIR) on a battery-by-battery basis and comparing the estimated DCIR value with a preset (fixed) reference value has the following limitations: a fault cannot be detected when a plurality of battery cells in the battery are disconnected or short-circuited at the same time. In addition, a problem may occur where a change in the DC internal resistance value with aging is mistakenly diagnosed as the occurrence of a fault.

[0008] Furthermore, the technology for diagnosing a fault in a battery based on the DCIR value of the battery has a problem in that when the external temperature changes rapidly, an error range becomes too large according to the state of charge (SOC) of the battery, etc., making the diagnosis inaccurate.

[0009] In order to solve this problem, various studies have been conducted on methods for diagnosing battery faults in the related art. However, most of the methods in the related art are simple fault diagnosis methods performed in a battery management system (BMS), but there is a problem of low accuracy of diagnosis. The high-precision fault diagnosis method that requires a large amount of accumulated data has the following limitations: it cannot be easily implemented with a small memory capacity included in the BMS. Summary of the invention

[0010] Technical issues

[0011] The present invention provides a method of diagnosing a fault in a battery, which is capable of accurately diagnosing a state of a battery including a plurality of battery cells connected in parallel, and a server for providing the method.

[0012] The present invention provides a method of diagnosing a fault in a battery and a server for providing the method, which can accurately diagnose the fault in the battery while overcoming the memory limitation in a battery management system (BMS).

[0013] Technical Solution

[0014] A server for diagnosing faults in a battery according to one feature of the present invention comprises: a server communication unit for receiving battery data from a battery management system (BMS), the battery data comprising at least one of a battery voltage, a battery current and a battery temperature, the battery voltage being the voltage across the battery, the battery current being the current flowing through the battery, and the battery temperature being the temperature of the battery; a server storage unit for storing an internal resistance value of the battery calculated based on the battery data at each diagnostic time point for diagnosing faults in the battery; and a server control unit for extracting, at each diagnostic time point, a plurality of previous diagnostic time points corresponding to a predetermined number of samples based on the diagnostic time point, calculating a moving average value which is an average value of a plurality of internal resistance values ​​respectively corresponding to the plurality of diagnostic time points, and comparing the internal resistance value with an upper limit threshold value which is a predetermined value greater than the moving average value and a lower limit threshold value which is a predetermined value less than the moving average value, so as to diagnose faults in the battery.

[0015] The server control unit can calculate the error value by multiplying the standard deviation average by a predetermined multiple, where the standard deviation average is the average of multiple standard deviations corresponding to multiple diagnostic time points respectively; calculate the upper limit threshold by adding the error value to the moving average; and calculate the lower limit threshold by subtracting the error value from the moving average.

[0016] When the internal resistance value exceeds the upper limit threshold, the server control unit may diagnose that a disconnection failure occurs in at least one of the plurality of battery cells included in the battery.

[0017] When the internal resistance value is less than the lower limit threshold, the server control unit may diagnose that a short circuit fault occurs in at least one of the plurality of battery cells included in the battery.

[0018] The server storage unit may also store environmental data, the 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 may extract multiple diagnostic time points based on a first condition and a second condition, the first condition including environmental data corresponding to the environmental data at the diagnostic time point within a predetermined range, and the second condition being a previous diagnostic time point corresponding to a sample number based on the diagnostic time point.

[0019] The first condition may be a condition in which the battery temperature at a predetermined diagnosis time point belongs to a predetermined temperature section to which the battery temperature corresponding to the diagnosis time point belongs, among a plurality of temperature sections set at predetermined temperature size intervals.

[0020] The first condition may be a condition in which the SOC at a predetermined diagnosis time point belongs to a predetermined SOC section to which the SOC corresponding to the diagnosis time point belongs, among a plurality of SOC sections set at predetermined SOC size intervals.

[0021] According to another feature of the present invention, a method for diagnosing a battery includes: a data receiving operation for receiving battery data from a battery management system (BMS) by a server, the battery data including at least one of a battery voltage, a battery current and a battery temperature, the battery voltage being the voltage across the battery, the battery current being the current flowing through the battery, and the battery temperature being the temperature of the battery; a sample group determination operation for extracting, at a diagnosis time point for diagnosing a battery fault, a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on the diagnosis time point; a reference value determination operation for calculating a moving average, an upper threshold value greater than the moving average by a predetermined value, and a lower threshold value less than the moving average by a predetermined value, the moving average being an average value of a plurality of internal resistance values ​​corresponding to the plurality of diagnosis time points respectively; and a fault diagnosis operation for diagnosing a fault in the battery by comparing the internal resistance value corresponding to the diagnosis time point with the upper threshold value and the lower threshold value.

[0022] The reference value determination operation may include calculating an error value by multiplying a standard deviation average by a predetermined multiple, the standard deviation average being an average of multiple standard deviations corresponding to multiple diagnostic time points, calculating an upper threshold by adding the error value to a moving average, and calculating a lower threshold by subtracting the error value from the moving average.

[0023] In the fault diagnosis operation, when the internal resistance value corresponding to the diagnosis time point exceeds the upper limit threshold, it may be diagnosed that a disconnection fault occurs in at least one of the plurality of battery cells included in the battery.

[0024] In the fault diagnosis operation, when the internal resistance value corresponding to the diagnosis time point is less than the lower limit threshold, it may be diagnosed that a short circuit fault occurs in at least one of the plurality of battery cells included in the battery.

[0025] The sample group determination operation may include extracting multiple diagnostic time points based on a first condition and a second condition, the first condition including environmental data corresponding to the environmental data at the diagnostic time point within a predetermined range, the second condition being 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 a measured battery temperature.

[0026] The first condition may be a condition in which the battery temperature at a predetermined diagnosis time point belongs to a predetermined temperature section to which the battery temperature corresponding to the diagnosis time point belongs, among a plurality of temperature sections set at predetermined temperature size intervals.

[0027] The first condition may be a condition in which the SOC at a predetermined diagnosis time point belongs to a predetermined SOC section to which the SOC corresponding to the diagnosis time point belongs, among a plurality of SOC sections set at predetermined SOC size intervals.

[0028] Beneficial Effects

[0029] In the present invention, the server plays the role of storing a large amount of data and complex algorithms necessary for fault diagnosis, so that the battery system has the effect of reducing the storage space burden and receiving high-precision fault diagnosis at the same time.

[0030] Unlike the prior art that diagnoses faults in a battery by using a fixed reference value, the present invention diagnoses battery faults by setting a reference value that reflects the change in the internal resistance value of the battery at each diagnostic time point used to diagnose the battery fault, thereby improving the accuracy of the diagnosis by solving the problem of misdiagnosing the battery as deterioration over the period of use of the battery is a fault in the battery itself.

[0031] The present invention sets reference values ​​based on multiple internal resistance values ​​in an environment similar to the environment at the current diagnosis time point for diagnosing faults in the battery (e.g., external temperature and SOC), thereby improving the accuracy of diagnosis by solving the problem of misdiagnosing differences in the external environment as faults in the battery itself. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a diagram illustrating a battery failure diagnosis system according to an exemplary embodiment.

[0033] Figure 2 It is shown Figure 1 A block diagram of a battery system 100 is shown.

[0034] Figure 3 It is shown Figure 1 A block diagram of the configuration of the server 200 is shown.

[0035] Figure 4 is a flowchart illustrating a battery failure diagnosis method according to an exemplary embodiment.

[0036] Figure 5 It is shown in detail Figure 4 Flowchart of the reference value determining operation S300. DETAILED DESCRIPTION

[0037] Hereinafter, the exemplary embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, and regardless of the figure numerals, the same or similar constituent elements are represented by the same figure numerals, and repeated descriptions thereof will be omitted. The suffixes "module" and / or "unit" used for the constituent elements described below are given or mixed only in consideration of the ease of writing the specification, and the suffixes themselves have no different meanings or functions. In addition, when describing the exemplary embodiments disclosed in the present disclosure, when it is determined that the detailed description related to the known functions or configurations may make the subject matter of the exemplary embodiments disclosed in the present disclosure unnecessarily obscure, the detailed description will be omitted. In addition, the drawings are provided to help easily understand the exemplary embodiments disclosed in the present specification, and the technical spirit disclosed in the present specification is not limited by the drawings, and it will be understood that the present invention includes all modifications, equivalents and substitutes included in the spirit and technical scope of the present invention.

[0038] Terms including ordinal numbers such as first and second are used to describe various constituent elements, but the constituent elements are not limited by the terms. The terms are only used to distinguish one constituent element from another constituent element.

[0039] It should be understood that when a component is referred to as being "coupled to" or "connected to" another component, the component may be directly coupled to or connected to the other component, but other intermediate elements may also exist. In contrast, when a component is referred to as being "directly coupled to" or "directly connected to" another component, it should be understood that there are no other intermediate elements.

[0040] In the present application, it should be understood that the terms "including" and "having" are intended to specify the existence of the features, numbers, operations, actions, constituent elements and parts or a combination thereof described in the specification, and do not exclude the possibility of pre-existing or adding one or more other features, numbers, operations, actions, constituent elements and parts or a combination thereof.

[0041] Figure 1is a diagram illustrating a battery failure diagnosis system according to an exemplary embodiment.

[0042] Reference Figure 1 The battery fault diagnosis system 1 includes a battery system 100 and a server 200 .

[0043] The battery system 100 may be a system for supplying power stored in a battery to an external device. Figure 1 , the battery system 100 is shown to be installed on the vehicle system A and supply power to the vehicle, but is not limited thereto. The battery system 100 can be installed in any system as long as it is a system that requires battery power. For example, the battery system 100 can be installed in various systems such as an energy storage system (ESS), a robot, a rocket, and an aircraft, and supply power to the installed upper system.

[0044] The server 200 may receive the battery data from the battery system 100 in a regular or real-time manner. In this case, the battery data may include at least one of a battery voltage, a battery current, and a battery temperature, wherein the battery voltage is the voltage across the battery, the battery current is the current flowing through the battery, and the battery temperature is the temperature of the battery.

[0045] According to an exemplary embodiment, the server 200 may set a reference value based on the battery data received from the battery system 100 at each diagnosis time point for diagnosing a fault in the battery, and diagnose the fault in the battery based on the set reference value. When a fault is diagnosed to have occurred in the battery, the server 200 may send a warning message corresponding to the fault diagnosis to the battery system 100.

[0046] Figure 2 It is shown Figure 1 A block diagram of a battery system 100 is shown.

[0047] Reference Figure 2 , the battery system 100 includes a battery 10 , a relay 20 , a current sensor 30 , and a battery management system (BMS) 40 .

[0048] The battery 10 may include a plurality of battery cells connected in series and / or in parallel. Figure 2 , three battery cells connected in parallel are shown, but the present invention is not limited thereto, and the battery 10 may include various numbers of battery cells connected in series and / or in parallel. In some exemplary embodiments, the battery cells may be rechargeable secondary batteries.

[0049] For example, in the battery 10, a predetermined number of battery cells are connected in parallel to form a battery pack, and a predetermined number of battery packs are connected in series to form a battery pack to supply desired power to an external device. For another example, in the battery 10, a predetermined number of battery cells are connected in parallel to form a battery pack, and a predetermined number of battery packs are connected in parallel to form a battery pack to supply desired power to an external device. However, the present invention is not limited to such a connection, and the battery 10 may include a plurality of battery packs including a plurality of battery cells connected in series and / or in parallel, and a plurality of battery packs may also be connected in series and / or in parallel.

[0050] exist Figure 2 In the embodiment, the battery 10 is connected between two output terminals OUT1 and OUT2 of the battery system 2. Furthermore, the relay 20 is connected between the positive electrode of the battery system 2 and the first output terminal OUT1, and the current sensor 30 is connected between the negative electrode of the battery system 2 and the second output terminal OUT2. Figure 2 The illustrated configurations and connection relationships between the configurations are examples, but the present invention is not limited thereto.

[0051] The relay 20 controls the electrical connection between the battery system 2 and the external device. When the relay 20 is turned on, the battery system 2 and the external device are electrically connected to perform charging or discharging, and when the relay 20 is turned off, the battery system 2 and the external device are electrically separated. In this case, the external device may be a charger in a charging cycle in which power is supplied to the battery 10 for charging, and may be a load in a discharging cycle in which the battery 10 discharges power to the external device.

[0052] The current sensor 30 is connected in series to a current path between the battery 10 and an external device. The current sensor 30 may measure a battery current, ie, a charging current and a discharging current, flowing through the battery 10 and transmit the measurement result to the BMS 40.

[0053] The BMS 40 includes a measuring unit 41 , a storage unit 43 , a communication unit 45 , and a control unit 47 .

[0054] The measurement unit 41 may measure a battery voltage, a battery temperature, a battery current, etc. The battery voltage is the voltage across the battery, and the battery current is the current flowing through the battery. The battery voltage and the battery current may be battery data required to calculate the internal resistance or the state of charge (SOC) of the battery. The battery temperature may be battery data required to determine the environmental segment to be described below. For example, the internal resistance may include a direct current internal resistance (DCIR).

[0055] 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 to the battery to measure the battery current, and a temperature sensor (not shown) for measuring the battery temperature at a position adjacent to the battery. For example, the measuring unit 41 may measure the battery voltage, the battery current, and the battery temperature at each diagnosis time point for diagnosing a fault in the battery, and send the measurement results to the control unit 47.

[0056] The storage unit 43 may store the battery voltage value, the battery current value, and the battery temperature value measured by the measurement unit 41 .

[0057] The communication unit 45 may include a wireless communication module for communicating with the server 200 through a network. In addition, the communication unit 45 may include a CAN communication module or a daisy communication module, etc., for communicating with the vehicle system A.

[0058] The communication unit 45 may transmit the battery data to the server 200 at each diagnosis time point for diagnosing a fault in the battery 10 under the control of the control unit 47. In this case, the battery data may include at least one of battery voltage, battery current, and battery temperature.

[0059] The control unit 47 generally controls the BMS 40. According to the exemplary embodiment, the control unit 47 may transmit necessary battery data to the server 200 according to a command of the server 200. For example, when the communication unit 45 receives a warning message corresponding to a fault diagnosis of the battery 10 from the server 200, the control unit 47 transmits a prompt message corresponding to the warning message, etc. to the vehicle system A through the communication unit 45.

[0060] Figure 3 It is shown Figure 1 A block diagram of the configuration of the server 200 is shown.

[0061] Reference Figure 3 The server 200 includes a server communication unit 210 , a server storage unit 230 , and a server control unit 250 .

[0062] The server communication unit 210 may include a wireless communication module for communicating with the BMS 40 through a network. For example, the server communication unit 210 may receive battery data from the BMS 40 at each diagnosis time point for diagnosing a fault in the battery 10. The battery data may include at least one of a battery voltage value, a battery current value, and a battery temperature value.

[0063] The server storage unit 230 may store the internal resistance value calculated by the server control unit 250 based on the battery data at each diagnosis time point for diagnosing a fault in the battery. The server storage unit 230 may store the state of charge (SOC) estimated by the server control unit 250 based on the battery data. In addition, the server storage unit 230 may store the battery data received from the BMS 40.

[0064] According to an exemplary embodiment, for battery fault diagnosis to be described below, the server storage unit 230 may accumulate and store the internal resistance value calculated for each diagnosis period, 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.

[0065] When the diagnosis time point N according to the preset conditions arrives, the server control unit 250 calculates the moving average MA, the upper threshold UB_Th which is a predetermined value greater than the moving average, the lower threshold LB_Th which is a predetermined value less than the moving average, and the internal resistance value corresponding to the diagnosis time point N.

[0066] According to an exemplary embodiment, a time point at which charging of the battery starts or a time point at which discharging of the battery ends may be a diagnostic time point N for diagnosing a fault in the battery. For example, when the diagnostic time point N arrives, the BMS 40 may transmit the battery data to the server 200 together with a message notifying the arrival of the diagnostic time point N. However, the present invention is not limited thereto, and in various ways, the server control unit 250 may determine whether the diagnostic time point N has arrived.

[0067] According to an exemplary embodiment, the server control unit 250 may extract a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on the current diagnosis time point N, and calculate a moving average value, which is an average value of a plurality of internal resistance values ​​corresponding to the extracted plurality of diagnosis time points, respectively. In addition, the server control unit 250 may determine an upper threshold value greater than the moving average value by a predetermined value and a lower threshold value less than the moving average value by a predetermined value. The server control unit 250 may diagnose a fault in the battery 10 by comparing the internal resistance value corresponding to the current diagnosis time point N with the upper threshold value and the lower threshold value.

[0068] According to another exemplary embodiment, the server control unit 250 can determine a sample group by extracting multiple diagnostic time points that satisfy a first condition and a second condition, in which the environmental data belongs to the environmental segment to which the environmental data of the current diagnostic time point N among multiple environmental segments belongs, and in which the number of diagnostic time points corresponds to a predetermined sample number SN when counting diagnostic time points based on the current diagnostic time point N in the direction toward the previous diagnostic time point. In this case, the sample number SN is the number of multiple diagnostic time points included in the sample group, and can be determined as an optimal number based on experiments, etc. The sample group is a subgroup of multiple past diagnostic time points as a whole, and can be a group used to calculate the moving average MA and standard deviation average σ_ave, etc., which will be described below.

[0069] Hereinafter, Table 1 is an example of a lookup table for storing battery temperature, SOC, internal resistance (DCIR) value, moving average MA, upper threshold UB_Th, and lower threshold LB_Th measured, estimated, and calculated at each of a plurality of diagnosis time points. The lookup table may be stored in the server storage unit 230.

[0070] In the following, it is assumed that the sample number SN is 5, a plurality of temperature sections (e.g., 0°C to 20°C, 21°C to 40°C, 41°C to 60°C, ...) distinguished by a predetermined temperature size interval (e.g., 20°C interval) are set, and a plurality of charging sections (e.g., 11% to 30%, 31% to 50%, 51% to 70%, 71% to 90%) distinguished by a predetermined SOC size interval (e.g., 20% interval) are set.

[0071] According to an exemplary embodiment, in the following Table 1, it is assumed that the environmental data includes at least one of the battery temperature and the SOC. In this case, the environmental data may be a factor that affects the internal resistance value of the battery 10. For example, when all other conditions are assumed to be the same but the battery temperature is different, the internal resistance value of the battery 10 may be different. For another example, when all other conditions are assumed to be the same but the SOC is different, the internal resistance value of the battery 10 may be different.

[0072] For reference, in Table 1 below, it may be difficult to directly calculate the moving average MA, standard deviation σ, standard deviation average σ_ave, upper threshold UB_Th and lower threshold LB_Th at the initial diagnostic time point 1. (Therefore, the corresponding values ​​in Table 1 are marked with spaces). In addition, it may also be difficult to directly calculate the moving average MA, standard deviation σ, standard deviation average σ_ave, upper threshold UB_Th and lower threshold LB_Th at the diagnostic time points 2, 3, ... adjacent to the initial diagnostic time point 1, because there are no past diagnostic values ​​or not enough past diagnostic values ​​for calculation. In this case, the value calculated by averaging the experiment can replace the moving average MA, standard deviation σ, standard deviation average σ_ave, upper threshold UB_Th and lower threshold LB_Th at the initial diagnostic time points 1, 2, 3, ... In addition, the example values ​​of the internal resistance value, moving average value, etc. in the N-8th diagnostic period that are not used in the following description are omitted.

[0073] (Table 1)

[0074]

[0075] In Table 1, the battery temperature (25° C.) at the current diagnosis time point N belongs to a temperature range of 21° C. to 40° C. In addition, the state of charge (60%) at the current diagnosis time point N belongs to a state of charge range of 51% to 70%.

[0076] The server control unit 250 may extract the N-1th diagnostic time point, the N-2nd diagnostic time point, the N-3rd diagnostic time point, the N-5th diagnostic time point, and the N-7th diagnostic time point and determine a sample group, which belong to an environment similar to that of the current diagnostic time point N (i.e., the Nth diagnostic time point) (21°C to 40°C and 51% to 70%), and at the same time correspond to the sample number SN of 5 when the diagnostic time point is counted in the direction toward the previous diagnostic time point based on the current diagnostic time point N. In this case, at the N-4th diagnostic time point, the corresponding battery temperature (8°C) belongs to a temperature range (0°C to 20°C) different from the temperature range (21°C to 40°C) to which the battery temperature (25°C) at the Nth diagnostic time point belongs. At the N-6th diagnostic time point, the corresponding state of charge (11%) belongs to a state of charge range (11% to 30%) different from the state of charge range (51% to 70%) to which the state of charge (60%) at the Nth diagnostic time point belongs. The N-8th diagnosis time point is in a similar environment (21°C to 40°C and 51% to 70%) to the Nth diagnosis time point, but the time interval with the Nth diagnosis time point is large, so the N-8th diagnosis time point is not included in the sample number SN. That is, the N-4th diagnosis time point, the N-6th diagnosis time point, and the N-8th diagnosis time point are not included in the sample group.

[0077] According to another exemplary embodiment, the environmental data may include a battery temperature or SOC. For example, in Table 1 above, when it is assumed that the environmental data only includes the battery temperature, the server control unit 250 may include the N-1st diagnostic time point (23°C), the N-2nd diagnostic time point (23°C), the N-3rd diagnostic time point (20°C), the N-5th diagnostic time point (22°C), and the N-6th diagnostic time point (23°C) belonging to an environment similar to the N-th diagnostic time point (21°C to 40°C) in the sample group. As another example, in Table 1, when it is assumed that the environmental data only includes the state of charge (SOC), the server control unit 250 may include the N-1st diagnostic time point (55%), the N-2nd diagnostic time point (55%), the N-3rd diagnostic time point (50%), the N-4th diagnostic time point (60%), and the N-5th diagnostic time point (55%) belonging to an environment similar to the N-th diagnostic time point (51% to 70%) in the sample group.

[0078] In summary, the server control unit 250 can determine a sample group by extracting a plurality of diagnostic time points adjacent to the diagnostic time point N in an environment similar to that of the predetermined diagnostic time point N. For example, the server control unit 250 can determine a sample group by extracting a plurality of diagnostic time points N-7, N-5, N-3, N-2, and N-1 according to the above-mentioned criteria, and determine a reference value for fault diagnosis (an upper threshold value and a lower threshold value to be described below) based on the internal resistance values ​​calculated at the plurality of diagnostic time points belonging to the sample group. Then, the problem of erroneously diagnosing a temporary change in the degree of aging and / or internal resistance value due to long-term use of the battery as a battery fault can be solved.

[0079] Next, the server control unit 250 determines a reference value (upper threshold and lower threshold) for diagnosing a battery failure at the Nth diagnostic time point based on the internal resistance value calculated at each of the multiple diagnostic time points N-7, N-5, N-3, N-2 and N-1 belonging to the sample group.

[0080] According to an exemplary embodiment, the server control unit 250 compares the value of the internal resistance DCIR corresponding to the Nth diagnostic time point with the upper threshold UB_Th and the lower threshold LB_Th to diagnose a fault in the battery. 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 threshold (⑤) and the lower threshold (⑥), and compares the calculated internal resistance value (①) with the upper threshold (⑤) and the lower threshold (⑥) to diagnose the fault of the battery. In this case, in order to calculate the upper threshold (⑤) and the lower threshold (⑥), the moving average value (②) and the standard deviation average value (④) are required. However, the standard deviation (③) is not a value required for fault diagnosis at the Nth diagnostic time point, but a value required for fault diagnosis at the next diagnostic time points N+1, N+2, ..., so that the standard deviation (③) can be calculated at the Nth diagnostic time point and stored in the storage unit 43.

[0081] In the following, the internal resistance value (①), moving average value (②), standard deviation (③), standard deviation average value (④), upper threshold value (⑤) and lower threshold value (⑥) calculated by the control unit 47 at the Nth diagnostic time point in Table 1 will be described.

[0082] The server control unit 250 may calculate the internal resistance DCIR corresponding to the Nth diagnosis time point based on the battery voltage (ie, the voltage across the battery 10) and the battery current (ie, the current flowing through the battery 10). N For example, the internal resistance DCIR N The value of (①) can be calculated by the following formula 1.

[0083]

[0084] For example, the control unit 47 may calculate the voltage difference (ΔV=|V1-V2|) between the battery voltage V1 corresponding to a first time point when charging starts and the battery voltage V2 corresponding to a second time point after a predetermined time has passed from the first time point. The server control unit 250 may calculate the internal resistance DCIR based on the charging current I flowing through the battery and the voltage difference ΔV. N For example, assuming that the internal resistance DCIR corresponding to the Nth diagnostic time point N is N The value is calculated to be 30Ω.

[0085] Referring to Table 1, the server control unit 250 can calculate the moving average MA corresponding to the Nth diagnostic time point N by averaging 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 group (25Ω+23Ω+20Ω+21Ω+23Ω / 5=22.4).N (②) That is, the internal resistance DCIR corresponding to the Nth diagnosis time point N N The value may be 22.4Ω.

[0086]

[0087] Referring to Table 2 below, the server control unit 250 may calculate the standard deviation σ corresponding to the Nth diagnostic time point N based on the internal resistance DCIR value corresponding to each of the plurality of diagnostic time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample group and the moving average MA. N (③).

[0088] (Table 2)

[0089]

[0090] As mentioned above, the standard deviation σ corresponding to the Nth diagnosis time point N N (③) is not a value necessary for fault diagnosis at the Nth diagnostic time point, but a value necessary for diagnosing faults at the following diagnostic time points N+1, N+2, and ... Therefore, the standard deviation σ corresponding to the Nth diagnostic time point N can be calculated at the Nth diagnostic time point N N (③) and store it in the server storage unit 230.

[0091] Referring to Table 3 below, the server control unit 250 may calculate the standard deviations σ corresponding to the diagnosis time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample group based on the standard deviations σ corresponding to the diagnosis time points N-7, N-5, N-3, N-2, and N-1, respectively. N-7 , σ N-5 , σ N-3 , σ N-2 and σ N-1 , to calculate the standard deviation mean σ corresponding to the Nth diagnostic time point N N_ave (④).

[0092] (Table 3)

[0093]

[0094] The server control unit 250 may calculate the moving average MA N The upper threshold value UB of the larger predetermined value N _Th and the lower limit threshold LB which is smaller than the moving average MA by a predetermined value N According to an exemplary embodiment, the server control unit 250 can calculate the standard deviation average value σ N_ave The first error value is calculated by multiplying the first predetermined multiple, and the first error value and the moving average MA are added together. N Add to calculate the upper threshold UBN In addition, the server control unit 250 can calculate the standard deviation average value σ N_ave The second error value is calculated by multiplying the predetermined second multiple, and the second error value is calculated by N Subtract the second error value from the lower limit threshold LB N In this case, the first multiple and the second multiple may be the same, but are not limited thereto, and the error value may be calculated as various multiples.

[0095] According to an exemplary embodiment, the server control unit 250 may calculate the standard deviation average σ as the average value of the standard deviations of the sample group. N_ave Multiply by the predetermined multiple Q to calculate the error value (E = σ N_ave ×Q). In this case, the multiple Q is a value for reflecting a predetermined error and can be determined by various values ​​through experiments. For example, it is assumed that the multiple Q is a natural number 3.

[0096] The server control unit 250 can calculate the error value (E=σ N_ave ×Q=1.69×3) and the moving average value (MA) of the sample group N =22.4) to calculate the upper threshold UB N _Th is 27.47, as shown in the following formula 3. In addition, the control unit 47 can obtain the moving average value (MA) of the sample group by N =22.4) minus the error value (E = σ N_ave ×Q=1.69×3) to calculate the lower threshold LB N _Th is 17.33, as shown in the following formula 4.

[0097] UB N _Th=MA N +(σ N_ave ×Q)…(Formula 3)

[0098] LB N _Th=MA N -(σ N_ave ×Q)…(Formula 4)

[0099] Next, the server control unit 250 can convert the internal resistance DCIR corresponding to the Nth diagnosis time point into N The value of and the upper threshold UB corresponding to the Nth diagnostic time point N N _Th and lower threshold LB N _Th is compared to diagnose faults in the battery.

[0100] According to an exemplary embodiment, when the internal resistance DCIR N The value exceeds the upper threshold UB N_Th, the server control unit 250 may diagnose that a disconnection fault (DD) has occurred in at least one of the plurality of battery cells included in the battery 10. N The value is less than the lower threshold LB N _Th, the server control unit 250 may diagnose that a short circuit fault (SD) occurs in at least one of the plurality of battery cells included in the battery 10. That is, when the internal resistance DCIR N The value exceeds the lower threshold LB N _Th or greater and upper threshold UB N _Th or less corresponds to the normal range, the server control unit 250 can diagnose that a fault (disconnection fault or short circuit fault) has occurred in the battery 10. In addition, when the internal resistance DCIR N When the value falls within the normal range, the server control unit 250 may diagnose the state of the battery 10 as normal.

[0101] For example, as described above by Table 1 and Table 3 and Formulas 1 to 4, the internal resistance DCIR corresponding to the Nth diagnosis time point is N Value, upper threshold UB N _Th and lower threshold LB N _Th can be calculated as 30Ω, 27.47 and 17.33 respectively. In this case, the server control unit 250 can calculate the internal resistance value (DCIR N =30) exceeds the upper threshold (UB N _Th=27.47) to diagnose battery failure (disconnection failure).

[0102] Figure 4 is a flowchart illustrating a battery failure diagnosis method according to an exemplary embodiment.

[0103] In the following, reference will be made to Figures 1 to 4 A method of diagnosing a fault in a battery and a server for providing the method are described.

[0104] First, the server control unit 250 receives battery data from the battery system 100 (S100). In this case, the battery data may include at least one of a battery voltage (which is a voltage across the battery 10), a battery temperature, and a battery current (which is a current flowing through the battery 10).

[0105] For example, the battery voltage and the battery current may be battery data required to calculate the internal resistance (DCIR; direct current internal resistance) or SOC of the battery. The battery temperature may be battery data required to determine an environmental segment to be described below.

[0106] Next, the server control unit 250 may determine a sample group by extracting a plurality of diagnosis time points adjacent to the diagnosis time point N in an environment similar to that of the predetermined diagnosis time point N ( S200 ).

[0107] The server control unit 250 can determine the sample group by extracting multiple diagnostic time points, and the multiple diagnostic time points satisfy 1) a first condition in which the environmental data belongs to a predetermined environmental segment to which the environmental data at a predetermined diagnostic time point N, i.e., the Nth diagnostic time point, belongs, and 2) a second condition in which when the diagnostic time points are counted in a direction toward the previous diagnostic time point based on the Nth diagnostic time point, the number of diagnostic time points corresponds to the predetermined sample number SN. According to an exemplary embodiment, the server control unit 250 can determine the sample group by extracting multiple diagnostic time points that satisfy both the first condition and the second condition. According to another exemplary embodiment, the server control unit 250 can determine the sample group by extracting multiple diagnostic time points that satisfy the second condition.

[0108] For example, it is assumed that the environmental data includes both the battery temperature and the state of charge (SOC), and the sample number SN is 5. In Table 1, the server control unit 250 may determine a sample group by extracting the N-1th diagnostic time point, the N-2th diagnostic time point, the N-3th diagnostic time point, the N-5th diagnostic time point, and the N-7th diagnostic time point, which belong to an environment similar to that of the Nth diagnostic time point N (21° C. to 40° C. and 51% to 70%), and at the same time correspond to the sample number SN of 5 when the diagnostic time points are counted in a direction toward the previous diagnostic time point based on the Nth diagnostic time point N.

[0109] Next, the server control unit 250 determines a reference value for diagnosing a fault of the battery 10 (S300). According to an exemplary embodiment, the reference value may include an upper threshold value UB N _Th and lower threshold LB N _Th.

[0110] In operation S300, referring to Figure 5 The server control unit 250 averages the multiple internal resistance values ​​corresponding to the multiple diagnosis time points belonging to the sample group to calculate the moving average MA of the sample group. N (S310).

[0111] Referring to Table 1 and Formula 2 above, the server control unit 250 can calculate the moving average MA corresponding to the Nth diagnostic time point N by averaging the multiple internal resistance values ​​25Ω, 23Ω, 20Ω, 21Ω and 23Ω corresponding to the multiple diagnostic time points N-7, N-5, N-3, N-2 and N-1 belonging to the sample group. N It is 22.4.

[0112] In operation S300, the server control unit 250 calculates the standard deviation average σ of the sample group based on the standard deviation average σ of the sample group. N_ave The error value E is calculated (S320).

[0113] For example, the standard deviation average σ of the sample group can be calculated by averaging a plurality of standard deviations corresponding to a plurality of diagnosis time points belonging to the sample group. N_ave .

[0114] Referring to Tables 1 and 3 above, the server control unit 250 may calculate the standard deviations σ based on the multiple diagnosis time points N-7, N-5, N-3, N-2, and N-1 belonging to the sample group. N-7 , σ N-5 , σ N-3 , σ N-2 , σ N-1 , to calculate the standard deviation mean σ corresponding to the diagnosis time point N N_ave is 1.69. In addition, the server control unit 250 can calculate the standard deviation average value σ N_ave Multiply by the predetermined multiple Q to calculate the error value (E = σ N_ave ×Q=1.69×3). In this case, the multiple Q is a value for reflecting a predetermined error, and can be determined to be various values ​​through experiments. For example, it is assumed that the multiple Q is a natural number 3.

[0115] In operation S300, the server control unit 250 calculates the moving average MA of the sample group based on the moving average MA of the sample group. N and error value E to calculate the upper threshold UB N _Th and lower threshold LB N _Th(S330).

[0116] Referring to Formula 3, the server control unit 250 can calculate the error value (E = σ N_ave ×Q=1.69×3) and the moving average value (MA) of the sample group N =22.4) to calculate the upper threshold UB N _Th is 27.47. In addition, referring to Formula 4, the server control unit 250 can obtain the moving average value (MA N =22.4) minus the error value (E = σ N_ave ×Q=1.69×3) to calculate the lower threshold LB N _Th is 17.33.

[0117] Next, the server control unit 250 can convert the internal resistance DCIR corresponding to the Nth diagnosis time point N into N The value ofN _Th and lower threshold LB N _Th is compared to diagnose a fault in the battery 10 (S400).

[0118] The server control unit 250 may calculate the internal resistance DCIR corresponding to the Nth diagnosis time point N based on the battery voltage (ie, the voltage across the battery 10) and the battery current (ie, the current flowing through the battery 10). N In addition, the internal resistance DCIR may be calculated in operation S200 or operation S300. N The calculation time point is not limited as long as the internal resistance DCIR is calculated before operation S400 as the diagnosis time point. N value.

[0119] For example, the server control unit 250 may calculate a voltage difference (ΔV=|V1-V2|) between a battery voltage V1 at a first time point corresponding to when charging of the battery 10 is started and a battery voltage V2 at a second time point after a predetermined time has passed from the first time point. The server control unit 250 may calculate the internal resistance DCIR based on the charging current I flowing through the battery 10 and the voltage difference ΔV. N For example, assuming that the internal resistance DCIR corresponding to the Nth diagnosis time point is N The value is calculated to be 30Ω.

[0120] In operation S400, the server control unit 250 determines the internal resistance DCIR N Whether the value exceeds the upper threshold UB N _Th(S410).

[0121] In operation S400, when the internal resistance DCIR is determined as a result N The value exceeds the upper threshold UB N _Th (S410, YES), the server control unit 250 diagnoses that a disconnection failure has occurred in at least one of the plurality of battery cells included in the battery 10 (S420).

[0122] For example, when the parallel connection of some of the plurality of battery cells connected in parallel is disconnected, the internal resistance value of the battery 10 may increase.

[0123] In operation S400, when the internal resistance DCIR is determined as a result N The value does not exceed the upper threshold UB N _Th (S410, No), the server control unit 250 determines the internal resistance DCIR N Is the value less than the lower limit threshold LB? N _Th(S430).

[0124] In operation S400, when the internal resistance DCIR is determined as a result N The value is less than the lower threshold LB N _Th (S430, YES), the server control unit 250 diagnoses that a short circuit fault has occurred in at least one of the plurality of battery cells included in the battery 10 (S440).

[0125] For example, when some of the plurality of battery cells connected in parallel come into contact with each other, the internal resistance value of the battery 10 , that is, the entire resistance may decrease.

[0126] In operation S400, when the internal resistance DCIR is determined as a result N The value is equal to or greater than the lower threshold LB N _Th (S430, No), the server control unit 250 diagnoses the state of the battery 10 as normal (S450).

[0127] When the internal resistance DCIR N The value exceeds the lower threshold LB N _Th or greater and upper threshold UB N _Th or less corresponds to the normal range, the server control unit 250 can diagnose the state of the battery 10 as a fault (disconnection fault or short circuit fault). In addition, when the internal resistance DCIR N When the value of falls within the normal range, the server control unit 250 may diagnose the state of the battery 10 as a normal state.

[0128] Next, the server control unit 250 may transmit a prompt message corresponding to the diagnosis result of the battery 10 to the BMS 40 ( S500 ).

[0129] For example, when it is determined that the state of the battery 10 has a disconnection fault (S420) and a short circuit fault (S440), the server control unit 250 may send a warning message corresponding to the fault diagnosis to the BMS 40 (S500). For another example, when it is determined that the state of the battery 10 is normal, the server control unit 250 may send a prompt message corresponding to the normal state to the BMS 40 (S500).

[0130] While the present invention has been described in conjunction with what are presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. On the contrary, it is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A server for diagnosing a fault in a battery, the server comprising: a server communication unit, configured to receive battery data from a battery management system (BMS), wherein the battery data includes at least one of a battery voltage, a battery current, and a battery temperature, wherein the battery voltage is a voltage across two ends of a battery, the battery current is a current flowing through the battery, and the battery temperature is a temperature of the battery; a server storage unit for storing an internal resistance value of the battery calculated based on the battery data at each diagnosis time point for diagnosing a fault in the battery; as well as A server control unit is used to extract, at each diagnostic time point, a plurality of previous diagnostic time points corresponding to a predetermined number of samples based on the diagnostic time point, calculate a moving average value which is an average value of a plurality of internal resistance values ​​respectively corresponding to the plurality of diagnostic time points, and compare the internal resistance value with an upper threshold value which is a predetermined value greater than the moving average value and a lower threshold value which is a predetermined value less than the moving average value, so as to diagnose a fault in the battery.

2. The server according to claim 1, wherein: The server control unit The error value is calculated by multiplying a standard deviation average by a predetermined multiple, wherein the standard deviation average is an average of a plurality of standard deviations respectively corresponding to the plurality of diagnosis time points, calculating the upper threshold by adding the error value to the moving average, and The lower threshold is calculated by subtracting the error value from the moving average.

3. The server according to claim 2, wherein: When the internal resistance value exceeds the upper limit threshold, The server control unit diagnoses that a disconnection failure has occurred in at least one of a plurality of battery cells included in the battery.

4. The server according to claim 2, wherein: When the internal resistance value is less than the lower limit threshold, The server control unit diagnoses that a short circuit failure has occurred in at least one of a plurality of battery cells included in the battery.

5. The server according to claim 2, wherein: 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 extracts the multiple diagnostic time points based on a first condition and a second condition, wherein the first condition includes environmental data corresponding to the environmental data at the diagnostic time point within a predetermined range, and the second condition is a previous diagnostic time point corresponding to the number of samples based on the diagnostic time point.

6. The server according to claim 5, wherein: The first condition is a condition in which the battery temperature at a predetermined diagnosis time point belongs to a predetermined temperature section to which the battery temperature corresponding to the diagnosis time point belongs, among a plurality of temperature sections set at predetermined temperature size intervals.

7. The server according to claim 5, wherein: The first condition is a condition in which the SOC at a predetermined diagnosis time point belongs to a predetermined SOC section to which the SOC corresponding to the diagnosis time point belongs, among a plurality of SOC sections set at predetermined SOC size intervals.

8. A method for diagnosing a battery, the method comprising: a data receiving operation, for receiving battery data from a battery management system (BMS) by a server, wherein the battery data includes at least one of a battery voltage, a battery current, and a battery temperature, wherein the battery voltage is a voltage across two ends of a battery, the battery current is a current flowing through the battery, and the battery temperature is a temperature of the battery; a sample group determination operation for extracting, at a diagnosis time point for diagnosing a fault of the battery, a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on the diagnosis time point; a reference value determination operation, calculating a moving average value, an upper threshold value greater than the moving average value by a predetermined value, and a lower threshold value less than the moving average value by a predetermined value, wherein the moving average value is an average value of a plurality of internal resistance values ​​respectively corresponding to the plurality of diagnosis time points; as well as A fault diagnosis operation is performed to diagnose a fault in the battery by comparing the internal resistance value corresponding to the diagnosis time point with the upper threshold value and the lower threshold value.

9. The method according to claim 8, wherein: The reference value determination operation includes: The error value is calculated by multiplying a standard deviation average by a predetermined multiple, wherein the standard deviation average is an average of a plurality of standard deviations respectively corresponding to the plurality of diagnosis time points, calculating the upper threshold by adding the error value to the moving average, and The lower threshold is calculated by subtracting the error value from the moving average.

10. The method according to claim 8, wherein: In the fault diagnosis operation, When the internal resistance value corresponding to the diagnosis time point exceeds the upper limit threshold, It is diagnosed that a disconnection failure has occurred in at least one of a plurality of battery cells included in the battery.

11. The method according to claim 8, wherein: In the fault diagnosis operation, When the internal resistance value corresponding to the diagnosis time point is less than the lower limit threshold, It is diagnosed that a short circuit fault occurs in at least one of a plurality of battery cells included in the battery.

12. The method according to claim 8, wherein: The sample group determination operation includes: extracting the plurality of diagnosis time points based on a first condition and a second condition, the first condition including environmental data corresponding to the environmental data at the diagnosis time point within a predetermined range, the second condition being a previous diagnosis time point corresponding to the number of samples based on the diagnosis time point, and The environmental data includes at least one of a state of charge (SOC) estimated by a predetermined method and a measured battery temperature.

13. The method according to claim 12, wherein: The first condition is a condition in which the battery temperature at a predetermined diagnosis time point belongs to a predetermined temperature section to which the battery temperature corresponding to the diagnosis time point belongs, among a plurality of temperature sections set at predetermined temperature size intervals.

14. The method of claim 12, wherein: The first condition is a condition in which the SOC at a predetermined diagnosis time point belongs to a predetermined SOC section to which the SOC corresponding to the diagnosis time point belongs, among a plurality of SOC sections set at predetermined SOC size intervals.

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