Battery diagnosis device, battery system, and battery diagnosis method

Abstract

The battery diagnostic device according to the present application includes a voltage measurement circuit for generating voltage signals indicative of respective battery voltages of two terminals of each of a plurality of batteries, a database for recording a plurality of abnormality types, a plurality of reference time series, and a predetermined correspondence between the plurality of abnormality types and the plurality of reference time series, and a control unit for generating a plurality of input time series representing a history of changes in the respective battery voltages of the plurality of batteries based on the voltage signals. The control unit extracts an abnormal input time series representing an abnormal voltage behavior by comparing the plurality of input time series with each other. The control unit identifies an abnormality type of the abnormal input time series among the plurality of abnormality types by comparing the abnormal input time series with each of the plurality of reference time series one by one.

Description

TECHNICAL FIELD

[0001] This application claims priority to Korean Patent Application No. 10-2021-0002849, filed on January 8, 2021 in Korea, the disclosure of which is incorporated herein by reference.

[0002] The present disclosure relates to a technology for diagnosing an abnormality in a battery, and more particularly to a battery diagnosis device capable of identifying an abnormal pattern causing abnormal voltage behavior of a battery, a battery system, and a battery diagnosis method. BACKGROUND

[0003] Recently, there has been a sharp increase in demand for portable electronic products such as notebook computers, camcorders, and portable phones, and electric vehicles, energy storage batteries, robots, satellites, etc. have been developed vigorously. Accordingly, high-performance batteries that allow repeated charging and discharging are being actively researched.

[0004] Currently marketed batteries include nickel-cadmium batteries, nickel-hydrogen batteries, nickel-zinc batteries, lithium batteries, etc. Among them, lithium batteries are attracting attention because they have almost no memory effect compared to nickel-based batteries and also have a very low self-charging rate and high energy density.

[0005] As applications requiring high voltage become common, battery systems employing a structure in which a plurality of batteries are connected in series are widely used. As the number of batteries included in the battery system increases, the frequency of battery abnormalities inevitably increases. Therefore, the demand for diagnosis technology that accurately detects battery abnormalities is increasing.

[0006] Recently, a method of detecting a battery abnormality based on battery information including a plurality of parameters related to a battery (e.g., voltage, current, temperature, etc. of the battery) and a usage state (e.g., charging, discharging, hibernation) of the battery is widely used.

[0007] However, the above-described detection method has a disadvantage in that a large amount of calculation and a long time are required to detect an abnormality of each battery because a process in which a battery diagnosis device monitors battery information using various sensors is necessary.

[0008] Meanwhile, an attempt is made to detect an abnormal voltage behavior of each battery by comparing a plurality of battery voltages obtained from a plurality of batteries at a certain point in time or a predetermined time unit with each other while excluding parameters other than the voltage. However, the voltage-based abnormality detection can only detect whether each battery exhibits an abnormal voltage behavior, and does not provide information about why the abnormal voltage behavior occurs. SUMMARY

[0009] TECHNICAL PROBLEM

[0010] The present disclosure is designed to solve problems of the related art, and thus the present disclosure aims to provide a battery diagnostic device, a battery system, and a battery diagnostic method which can identify an abnormal pattern causing abnormal voltage behavior of each battery by comparing each battery input time series extracted as an abnormal input time series indicating abnormal voltage behavior through voltage-based abnormality detection with a plurality of reference time series one-to-one associated with a plurality of different abnormal patterns without monitoring parameters other than a battery voltage.

[0011] These and other objects and advantages of the present disclosure can be understood from the following detailed description, and will become more fully apparent from the exemplary embodiments of the present disclosure. Furthermore, it will be readily understood that the objects and advantages of the present disclosure can be achieved by the structures shown in the claims and combinations thereof.

[0012] Technical Solution

[0013] A battery diagnostic device according to one aspect of the present disclosure includes a voltage measurement circuit configured to generate a voltage signal representing a battery voltage that is a voltage across each of a plurality of batteries, a database in which a plurality of abnormal patterns, a plurality of reference time series, and a predetermined correspondence between the plurality of abnormal patterns and the plurality of reference time series are recorded, and a control unit configured to generate a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries based on the voltage signal. The control unit is configured to extract an abnormal input time series representing abnormal voltage behavior from among the plurality of input time series by comparing the plurality of input time series with each other. The control unit is configured to identify an abnormal pattern of the abnormal input time series among the plurality of abnormal patterns by comparing the abnormal input time series with the plurality of reference time series one by one.

[0014] For each of the plurality of reference time series, the control unit can be configured to calculate a first similarity value representing a signal distance between the abnormal input time series and each reference time series by using dynamic time warping, to convert the abnormal input time series and each reference time series into first and second permutation time series having the same time length, respectively, by using dynamic time warping, to calculate a second similarity value representing a Pearson correlation coefficient between the first and second permutation time series, and to determine a matching index between the abnormal input time series and each reference time series to be identical to a value obtained by dividing the first similarity value by the second similarity value. The control unit can be configured to recognize an abnormal pattern of the abnormal input time series to be identical to an abnormal pattern of a reference time series corresponding to one of the matching indexes determined for the plurality of reference time series.

[0015] The control unit can be configured to recognize an abnormal pattern of the abnormal input time series to be identical to an abnormal pattern of a reference time series corresponding to a minimum matching index among the plurality of matching indexes.

[0016] For each of the plurality of reference time series, the control unit can be configured to convert the abnormal input time series and each reference time series into first and second normalized time series, respectively, by using max-min normalization, to calculate a first similarity value representing a signal distance between the first and second normalized time series by using dynamic time warping, to convert the first and second normalized time series into first and second permutation time series having the same time length, respectively, by using dynamic time warping, to calculate a second similarity value representing a Pearson correlation coefficient between the first and second permutation time series, and to determine a matching index between the abnormal input time series and each reference time series to be identical to a value obtained by dividing the first similarity value by the second similarity value. The control unit can be configured to recognize an abnormal pattern of the abnormal input time series to be identical to an abnormal pattern of a reference time series corresponding to one of the matching indexes determined for the plurality of reference time series.

[0017] The control unit can be configured to recognize an abnormal pattern of the abnormal input time series to be identical to an abnormal pattern of a reference time series corresponding to a minimum matching index among the plurality of matching indexes.

[0018] For each of the plurality of reference time series, the control unit can be configured to calculate a first similarity value representing a signal distance between the abnormal input time series and each reference time series by using dynamic time warping, calculate a second similarity value representing a Pearson correlation coefficient between the first and second permutation time series by converting the abnormal input time series and each reference time series into first and second permutation time series, respectively, having the same time length by using dynamic time warping, calculate a third similarity value representing a signal distance between the first and second normalized time series by using dynamic time warping, calculate a fourth similarity value representing a Pearson correlation coefficient between the first and second normalized time series, and determine a matching index between the abnormal input time series and each reference time series by dividing any one of or a product of the first and third similarity values by any one of or a product of the second and fourth similarity values. The control unit can be configured to identify an abnormal pattern of the abnormal input time series as being the same as an abnormal pattern of a reference time series corresponding to one of a plurality of matching indexes determined for the plurality of reference time series.

[0019] The control unit can be configured to identify an abnormal pattern of the abnormal input time series as being the same as an abnormal pattern of a reference time series corresponding to a minimum matching index among the plurality of matching indexes.

[0020] The control unit can be configured to add the abnormal input time series to the database as a new reference time series corresponding to the identified abnormal pattern.

[0021] A battery system according to another aspect of the disclosure includes a battery diagnostic device.

[0022] A battery diagnostic method according to another aspect of the disclosure includes collecting a voltage signal representing a battery voltage that is a voltage across each of a plurality of batteries, generating a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries based on the voltage signal, extracting an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other, and identifying an abnormal pattern of the abnormal input time series among a plurality of abnormal patterns having a predetermined correspondence relationship with a plurality of reference time series by comparing the abnormal input time series with the plurality of reference time series one by one.

[0023] Advantageous Effects

[0024] According to at least one embodiment of the present disclosure, an abnormal pattern causing abnormal voltage behavior of each battery can be identified by comparing an input time series of each battery extracted through voltage-based abnormality detection as indicating abnormal voltage behavior with a plurality of reference time series one-to-one associated with a plurality of different abnormal patterns, without monitoring parameters other than battery voltage.

[0025] Further, according to at least one embodiment of the present disclosure, for a pair of an input time series and each reference time series, a matching index between the input time series and each reference time series can be determined by combining two similarity values determined through dynamic time warping and a Pearson correlation coefficient. Then, an abnormal pattern of a battery exhibiting abnormal voltage behavior can be identified according to a one-to-one correspondence relationship between the plurality of reference time series, the plurality of matching indexes, and the plurality of abnormal patterns.

[0026] Effects of the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, provide further understanding of the technical features of the present disclosure, and therefore the present disclosure is not to be construed as being limited to the drawings.

[0028] Figure 1 FIG. 1 is an exemplary view illustrating a configuration of a battery system according to the present disclosure.

[0029] Figure 2 FIG. 2 is an exemplary view illustrating a result of battery voltage of a normal battery and battery voltage of an abnormal battery obtained on a time region having the same use condition.

[0030] Figure 3 FIG. 3 is an exemplary view illustrating a result of battery voltage of a normal battery and battery voltage of an abnormal battery obtained on a time region having the same use condition. Figure 4 FIG. 4 is an exemplary graph for explaining a comparison process between an abnormal input time series and a plurality of reference time series.

[0031] Figure 5 FIG. 5 is an exemplary flowchart illustrating a battery diagnosis method according to the present disclosure. DETAILED DESCRIPTION

[0032] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before the description, it is to be understood that the terms used in the specification and the appended claims should not be construed as limited to general and dictionary meanings but interpreted based on the meanings and concepts under the principles of the inventor's application considering the technical scope of the present disclosure.

[0033] Therefore, the description proposed herein merely illustrates preferred examples and is not intended to limit the scope of the present disclosure, and it should be understood that other equivalents and modifications can be made thereto without departing from the scope of the present disclosure.

[0034] The terms including ordinal numbers such as "first," "second," or the like can be used to distinguish one element from another element in various elements, but are not intended to limit the elements by the terms.

[0035] Throughout the specification, when a part is referred to as "including" or "comprising" any element, it means that the part can further include other elements, not excluding the other elements, unless otherwise specified. Also, the terms used in the specification, such as "control unit," refer to a unit processing at least one function or operation, and can be implemented as hardware, software, or a combination of hardware and software.

[0036] Also, throughout the specification, when a part is referred to as "connected" to another part, it is not limited to the case where they are "directly connected," but also includes the case where they are "indirectly connected" with another element interposed therebetween.

[0037] Figure 1 FIG. 1 is a diagram exemplarily illustrating a configuration of a battery system according to the present disclosure.

[0038] Referring to Figure 1 , the battery system 1 can be a power device including an electrical load driven using discharge power of a battery, such as an electric vehicle. Alternatively, the battery system 1 can be a charge-discharge test device for manufacturing a battery and provided for testing battery performance in a manufacturing process.

[0039] The battery system 1 includes a battery assembly 10, a switch 20, a charge-discharge circuit 30, an upper controller 2, and a battery diagnosis device 100. The upper controller 2 is responsible for the entire charging and discharging process of the battery assembly 10. That is, the upper controller 2 directly or indirectly controls the charge-discharge circuit 30 through the battery diagnosis device 100 according to a predetermined charging and discharging schedule.

[0040] The battery assembly 10 includes a plurality of batteries B1 to B n (n is a natural number equal to or greater than 2).

[0041] The plurality of batteries B1 to Bn may be connected in series with each other as a single group. Alternatively, the plurality of batteries B1 to B n may be divided into two or more groups to enable charging and discharging independently of each other. Hereinafter, in describing common contents of the plurality of batteries B1 to B n The type of the battery B is not particularly limited as long as it can be repeatedly charged and discharged, such as a lithium-ion battery.

[0042] The switch 20 is installed on a power line PL connecting the power input / output terminals of the battery assembly 10 and the power input / output terminals of the charge-discharge circuit 30. When the switch 20 is on, power can be transferred from one of the battery assembly 10 and the charge-discharge circuit 30 to the other. The switch 20 can be implemented by using any one of known switching devices such as a relay, an FET (Field Effect Transistor), etc., or by combining two or more thereof. The control unit 130 can turn on / off the switch 20 according to the state of the battery assembly 10.

[0043] The charge-discharge circuit 30 is operatively coupled to the battery diagnosis device 100 via the upper controller 2. When two components are operatively coupled, it means that the two components are connected to transmit and receive signals in one direction or two directions. The charge-discharge circuit 30 can generate DC power for charging each group of the battery assembly 10 from AC power supplied by an external power source. The charge-discharge circuit 30 can convert DC power from each group of the battery assembly 10 into AC power and / or DC power having different voltage levels, and transmit the converted power to an electrical load (not shown).

[0044] The battery diagnosis device 100 includes a voltage measurement circuit 110, a database 120, and a control unit 130, which are operatively coupled to each other. The battery diagnosis device 100 can further include an interface unit 140.

[0045] The voltage measurement circuit 110 is provided to be electrically connectable to the positive and negative terminals of each battery B. The voltage measurement circuit 110 is configured to measure a battery voltage as a voltage across each battery B, and to generate a voltage signal representing the measured battery voltage.

[0046] The database 120 records a plurality of abnormal patterns, a plurality of reference time series, and a predetermined correspondence between the plurality of abnormal patterns and the plurality of reference time series. The plurality of reference time series is associated one-to-one or many-to-one with a plurality of different abnormal patterns.

[0047] The plurality of abnormal patterns cause abnormal voltage behaviors of the battery B, and can depend on the internal structure of the battery B, the manufacturing method, the electrode material, etc. For example, metal lithium deposition on the negative electrode surface, partial tearing of the positive electrode tab, disconnection of the positive electrode tab and / or the negative electrode tab, bending of the negative electrode tab, short circuit between the positive electrode plate and the negative electrode plate through the separator, etc. can be respectively set as abnormal patterns.

[0048] Each reference time series is a dataset of time variation of battery voltage pre-obtained from a battery having a corresponding abnormal pattern.

[0049] In the database 120, programs and various data required to perform the battery diagnosis method according to the embodiments described later can be pre-stored. The database 120 can include at least one type of storage medium such as a flash type, a hard disk type, an SSD (Solid State Disk) type, an SDD (Silicon Disk Drive) type, a multimedia card micro type, a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), and a PROM (Programmable Read Only Memory).

[0050] The control unit 130 in the form of hardware can be implemented using at least one of an ASIC (Application-Specific Integrated Circuit), a DSP (Digital Signal Processor), a DSPD (Digital Signal Processing Device), a PLD (Programmable Logic Device), a FPGA (Field-Programmable Gate Array), a microprocessor, and an electric unit for performing other functions.

[0051] The control unit 130 can be additionally operatively coupled to the switch 20 and / or the interface unit 140.

[0052] The interface unit 140 can be communicatively coupled to the upper controller 2 of the battery system 1. The interface unit 140 can transmit a message from the upper controller 2 to the control unit 130, and can transmit a message from the control unit 130 to the upper controller 2. The message from the control unit 130 can include information for notifying of an abnormality of each battery B. For communication between the interface unit 140 and the upper controller 2, for example, a local area network such as LAN (Local Area Network), CAN (Controller Area Network), a daisy chain, and / or a short-range wireless network such as Bluetooth, Zigbee, Wi-Fi, etc. can be used. The interface unit 140 can include an output device (e.g., a display, a speaker) that provides information received from the control unit 130 and / or the upper controller 2 in a form recognizable by a user. The upper controller 2 can control the charge-discharge circuit 30 based on battery information (e.g., abnormal voltage behavior) collected by communicating with the battery diagnosis device 100.

[0053] The control unit 130 can execute the diagnosis mode to detect abnormalities of the plurality of batteries B1 to B n at all times during operation or according to a request from the upper controller 2.

[0054] Now, the operation of the control unit 130 during execution of the diagnosis mode will be described with reference to Figures 2 to 5

[0055] Figure 2 is a graph that exemplarily shows results of the battery voltage of a normal battery and the battery voltage of an abnormal battery obtained over a time region with the same use condition.

[0056] The control unit 130 collects the voltage signals from the voltage measurement circuit 110 every unit time. The unit time is predetermined, and can be, for example, an integer multiple of a time interval at which the voltage measurement circuit 110 generates the voltage signals.

[0057] The control unit 130 generates a plurality of input time series associated with the plurality of batteries B1 to B n based on the voltage signals collected from the voltage measurement circuit 110. The generation of the plurality of input time series can be repeated every predetermined unit time.

[0058] The control unit 130 can generate the plurality of input time series based on the voltage signals collected over time regions in which the plurality of batteries B1 to B n are charged, discharged, or left idle under the same use condition (e.g., charging current, charging voltage, discharging current, discharging voltage, temperature, etc.).

[0059] In a case where the plurality of batteries B1 to B n are connected in series to form a single group, the plurality of batteries B1 to B n may all be regarded as having the same use condition.

[0060] When the plurality of batteries B1 to B n ​When divided into a plurality of groups, a plurality of time regions in which the plurality of groups have the same usage condition can be identified based on a history of usage conditions of each of the plurality of groups, and a plurality of input time series can be generated based on voltage signals collected over each identified time region. As an example, when the charge and discharge circuit 30 has performed charge and discharge events for a first group having a certain usage condition in a first time region of 10:00 to 10:03 on a certain day, and the charge and discharge circuit 30 has performed charge and discharge events for a second group having the same usage condition in a second time region of 10:00 to 10:03 on the same certain day, the control unit 130 can generate an input time series associated with the first time region for each battery B of the first group, and generate an input time series associated with the second time region for each battery B of the second group. The control unit 130 can collect the input time series obtained from each battery B of the first group over the first time region and the input time series obtained from each battery B of the second group over the second time region as the same control group.

[0061] The input time series can be generated using a moving window. The moving window has a predetermined time size, the starting point can be a time point that is the predetermined time size before a certain time point, and the ending point can be the certain time point. The signal length of the input time series corresponds to the time size of the moving window. For example, when the unit time is 0.1 seconds and the moving window is 10 seconds, the signal length of the input time series can be 10 seconds / 0.1 seconds = 100. That is, the input time series can be a vector of 100 voltage values in which the change in the battery voltage measured in order is arranged on the time axis. At each unit time, each starting point and ending point of the moving window can be increased by the unit time. Each input time series is a data set representing the temporal change in the battery voltage of the battery B associated therewith.

[0062] In Figure 2 , the horizontal axis is time and the vertical axis is battery voltage, t A are the starting point and the ending point of the time region extracted as having the same usage condition, respectively. The curve 220 corresponds to the input time series of the normal battery, and the curve 210 corresponds to the input time series of the abnormal battery. In general, in the plurality of batteries B1 to B nIn this regard, the number of normal battery B will be much greater than the number of abnormal battery B. In consideration of this, the control unit 130 extracts an abnormal input time series 210 from the plurality of input time series by comparing the plurality of input time series with each other. The abnormal input time series 210 refers to each input time series that exhibits an abnormal voltage behavior that exceeds a certain level of the overall voltage behavior (e.g., average value) of the plurality of input time series. It can be determined whether each battery's input time series has a voltage drop (or voltage rise) that exceeds a predetermined value at a certain point in time or a predetermined time, whether each battery's input time series has a difference from the average (or median) of the plurality of input time series that exceeds a predetermined value, etc., using at least one of various methods, and a detailed description thereof will be omitted.

[0063] Figure 3 and Figure 4 is an exemplary graph for explaining a comparison process between the abnormal input time series and the plurality of reference time series. In Figure 3 , the horizontal axis represents time, and the vertical axis represents the battery voltage.

[0064] The control unit 130 can identify an abnormal pattern of the abnormal input time series in the plurality of abnormal patterns given in advance by comparing the abnormal input time series 210 with the plurality of reference time series one by one. For example, the control unit 130 can compare the plurality of reference time series with the abnormal input time series 210 one by one according to an order given in advance for the plurality of reference time series. As another example, the control unit 130 can compare two or more of the plurality of reference time series with the abnormal input time series 210 at the same time and compare the remaining reference time series with the abnormal input time series 210 in the same manner. The identification of the abnormal pattern refers to determining which failure state the battery B associated with the abnormal input time series has.

[0065] In Figure 3 , the curve 210 is the same as the curve 210 of Figure 2 , and the curve 320 corresponds to any one of the plurality of reference time series. In Figure 3 , t R is the end point of the reference time series 320 when the start points of the abnormal input time series 210 and the reference time series 320 coincide with each other. That is, Figure 3 shows that the abnormal input time series 210 is shorter than the reference time series 320.

[0066] The control unit 130 can calculate a similarity value D A1When the anomalous input time series 210 is similar to the reference time series 320, the similarity value D is... A1 The similarity value D is close to 1, and if the anomalous input time series 210 is exactly the same as the reference time series 320, then the similarity value D is close to 1. A1 The control unit 130 can convert the abnormal input time series 210 and the reference time series 320 into arranged time series 310 and arranged time series 321 with the same signal length, respectively, by using dynamic time warping. That is, by dynamic time warping, the time axis of the shorter one (210) of the abnormal input time series 210 and the reference time series 320 can be extended so that the signal length of the shorter one matches the signal length of the longer one (320). As described above, Figure 3 The reference time series 320 is shown to be longer than the anomalous input time series 210, and the arranged time series 321 is shown to be the same as the reference time series 320. Since the signal distance between the two signals via dynamic time warping and the alignment of the time axes of the two signals are well-known, their detailed description will be omitted.

[0067] Control unit 130 can calculate the similarity value D, representing the Pearson correlation coefficient between time series 310 and time series 321. B1 Since time series 310 and time series 321 are similar, the similarity value D is... B1 The similarity value D is close to 1, and if the time series 310 is exactly the same as the time series 321, then the similarity value D is... B1 The value is 1.

[0068] Reference Figure 4 Curve 410 illustrates this as Figure 3 The curve 210 represents the normalized time series of the maximum-minimum normalized result, while curve 420 illustrates the normalized time series as... Figure 3 The normalized time series of the maximum-minimum normalized results for curves 320 and 321. Figure 4 In the diagram, the horizontal axis represents time, and the vertical axis represents the normalized battery voltage from 0 to 1. The control unit 130 can convert the abnormal input time series 210 and the reference time series 320 into normalized time series 410 and 420 respectively by using maximum-minimum normalization.

[0069] Control unit 130 can calculate a similarity value D representing the signal distance between normalized time series 410 and normalized time series 420 by using dynamic time warp. A2The control unit 130 can convert the normalized time series 410 and 420 into permuted time series 411 and 421 with the same time length, respectively, using dynamic time warping. That is, through dynamic time warping, the shorter time axis 410 of the normalized time series 410 and 420 can be extended, such that the shorter time axis 410 matches the signal length of the longer time axis 420. As described above, Figure 3 It was shown that the reference time series 320 was longer than the anomalous input time series 210, and... Figure 4 The same permutation time series 421 as the normalized time series 420 is shown. Alternatively, the control unit 130 can use a maximum-minimum normalization pair. Figure 3 The curve 310 is normalized to obtain the time series 411.

[0070] Control unit 130 can calculate the similarity value D, representing the Pearson correlation coefficient between time series 411 and time series 421. B2 Since time series 411 and time series 421 are similar, the similarity value D is... B2 The similarity value D is close to 1, and if the permuted time series 411 is exactly the same as the permuted time series 421, then the similarity value D is... B2 The value is 1.

[0071] Having explained the comparison results between the abnormal input time series 210 and the reference time series 220, the control unit 130 can calculate two similarity values ​​D representing the signal distance. A1 D A2 and represent the two similarity values ​​D of the Pearson correlation coefficient. B1 D B2 Of course, the control unit 130 can calculate two similarity values ​​D representing the signal distance. A1 D A2 Or simply calculate the two similarity values ​​D. A1 D A2 One of them is calculated, while the calculation of the other is omitted. Similarly, the control unit 130 can calculate two similarity values ​​D representing the Pearson correlation coefficient. B1 D B2 Or simply calculate the two similarity values ​​D. B1 D B2 One of them is used, while the calculation of the other is omitted.

[0072] Control unit 130 can be based on two similarity values ​​D A1 D A2 At least one and two similarity values ​​D B1 D B2at least one of the reference time series 320. The matching index can be obtained from a pre-given function such that its value decreases when the abnormal input time series 210 is similar to the reference time series 320. For example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A1 by the similarity value D B1 .

[0073] As another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A1 by the similarity value D B2 .

[0074] As yet another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A2 by the similarity value D B1 .

[0075] As yet another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A2 by the similarity value D B2 .

[0076] As yet another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A1 by the product of the similarity value D A2 and the similarity value D B1 .

[0077] As yet another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A1 by the product of the similarity value D A2 and the similarity value D B2 .

[0078] As yet another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A1 by the product of the similarity value D B1 and the similarity value D B2 .

[0079] As yet another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value D A2 by the product of the similarity value D B1 and the similarity value D B2 .

[0080] As yet another example, the control unit 130 can determine the matching index to be the same as a value obtained by dividing the similarity value DA1 and similarity value D A2 The product divided by the similarity value D B1 and similarity value D B2 The product of the two terms yields the same value.

[0081] In this respect, even for the same anomalous pattern, battery voltage may be affected by other parameters of battery B (e.g., current, temperature, SOC (state of charge), and SOH (state of health)). Max-min normalization can partially reduce the dissimilarity between the anomalous input time series 210 and the reference time series 320 caused by different parameters. Therefore, when determining the matching index associated with each reference time series, when the similarity value D... A1 D B1 At least one of them has a similarity value D A2 D B2 When at least one of these combinations is used, more accurate abnormal patterns can be identified.

[0082] The control unit 130 can determine multiple matching indices corresponding one-to-one with the multiple reference time series by comparing each of the multiple reference time series with the abnormal input time series 210. Then, the control unit 130 can determine the minimum matching index among the multiple matching indices, obtain the abnormal pattern corresponding to the reference time series with the minimum matching index among the multiple abnormal patterns from the database 120, and identify the abnormal pattern of the abnormal input time series 210 as the same as the obtained abnormal pattern.

[0083] Figure 5 This is an exemplary flowchart illustrating a battery diagnostic method according to the present disclosure. Figure 5 The method can be derived from Figure 1 The battery diagnostic device 100 shown is executed.

[0084] See Figures 1 to 5 In step S510, the control unit 130 collects a voltage signal representing the battery voltage from the voltage measurement circuit 110, the battery voltage being that of the plurality of batteries B1 to B2. n The voltage across each battery in the system.

[0085] In step S520, the control unit 130 generates a representation of multiple batteries B1 to B based on the voltage signal. n Each battery B1 to B n The battery voltage is in multiple cells B1 to B n Multiple input time series with the same usage conditions and a history of change within a time region.

[0086] In step S530, the control unit 130 extracts an abnormal input time series 210 that represents an abnormal voltage behavior from among the plurality of input time series by comparing the plurality of input time series with each other.

[0087] In step S540, the control unit 130 identifies an abnormal pattern of the abnormal input time series 210 among a plurality of abnormal patterns having a predetermined correspondence relationship with the plurality of reference time series by comparing the abnormal input time series 210 with the plurality of reference time series one by one. The control unit 130 calculates at least one of two similarity values D A1 , D A2 and at least one of two similarity values D B1 , D B2 for each of the plurality of reference time series, and combines the calculated values to obtain a plurality of matching indices in one-to-one correspondence with the plurality of reference time series. Then, the control unit 130 identifies the abnormal pattern of the abnormal input time series 210 as the same as an abnormal pattern corresponding to a reference time series associated with a minimum matching index among the plurality of matching indices.

[0088] In step S550, the control unit 130 can add the abnormal input time series 210 to the database 120 as a new reference time series corresponding to the abnormal pattern identified in step S540. Therefore, it is possible to enhance the recognition performance of the abnormal pattern using the plurality of reference time series. In addition, the control unit 130 can transmit the abnormal pattern of the abnormal input time series 210 to the upper controller 2, or output visual and / or auditory feedback for notifying the user of the abnormal pattern of the abnormal input time series 210 through the interface unit 140.

[0089] The above-described embodiments of the present disclosure can be realized not only by an apparatus and a method, but also by a program that realizes functions corresponding to the configuration of the embodiments of the present disclosure or a recording medium on which the program is recorded. The program or the recording medium can be easily realized by those skilled in the art from the description of the above embodiments.

[0090] The present disclosure has been described in detail. However, it should be understood that although a preferred embodiment of the present disclosure is indicated, the detailed description and specific examples are given by way of illustration only, since various changes and modifications within the scope of the present disclosure will become apparent to those skilled in the art from the detailed description.

[0091] In addition, those skilled in the art can make many substitutions, modifications, and changes to the above-described present disclosure without departing from the technical aspects of the present disclosure, and the present disclosure is not limited to the above-described embodiments and drawings, and each embodiment can be selectively combined partially or entirely to allow various modifications.

[0092] (Reference Signs)

[0093] 1: battery system

[0094] 10: battery assembly

[0095] B: battery

[0096] 20: switch

[0097] 30: charge / discharge circuit

[0098] 100: battery diagnosis device

[0099] 110: voltage measurement circuit

[0100] 120: database

[0101] 140: control unit

Claims

1. A battery diagnostic apparatus comprising: a voltage measurement circuit configured to generate a voltage signal representing a battery voltage, the battery voltage being a voltage across each of a plurality of batteries; a database in which a plurality of abnormal patterns, a plurality of reference time series, and a predetermined correspondence between the plurality of abnormal patterns and the plurality of reference time series are recorded; and a control unit configured to generate, based on the voltage signal, a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries; wherein the control unit is configured to: extract an abnormal input time series representing an abnormal voltage behavior from among the plurality of input time series by comparing the plurality of input time series with each other, calculate a first similarity value between the abnormal input time series and each reference time series, convert the abnormal input time series and each reference time series into a first permutation time series and a second permutation time series, respectively, calculate a second similarity value between the first permutation time series and the second permutation time series, determine a matching index between the abnormal input time series and each reference time series by combining the first similarity value and the second similarity value, and identify an abnormal pattern of the abnormal input time series as being identical to an abnormal pattern of a reference time series corresponding to a matching index determined for the plurality of reference time series.

2. The battery diagnostic apparatus according to claim 1, wherein the control unit is configured to add the abnormal input time series to the database as a new reference time series corresponding to the identified abnormal pattern.

3. A battery diagnostic apparatus comprising: a voltage measurement circuit configured to generate a voltage signal representing a battery voltage, the battery voltage being a voltage across each of a plurality of batteries; a database in which a plurality of abnormal patterns, a plurality of reference time series, and a predetermined correspondence between the plurality of abnormal patterns and the plurality of reference time series are recorded; and a control unit configured to generate, based on the voltage signal, a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries; wherein the control unit is configured to extract an abnormal input time series representing an abnormal voltage behavior from among the plurality of input time series by comparing the plurality of input time series with each other, and wherein, for each of the plurality of reference time series, the control unit is configured to: calculate a first similarity value representing a signal distance between the abnormal input time series and each reference time series by using dynamic time warping, convert the abnormal input time series and each reference time series into a first permutation time series and a second permutation time series, respectively, having the same time length by using dynamic time warping, calculating a second similarity value representing a Pearson correlation coefficient between the first aligned time series and the second aligned time series, and determining a matching index between the abnormal input time series and each reference time series by combining the first similarity value and the second similarity value, wherein the control unit is configured to recognize an abnormal pattern of the abnormal input time series as being identical to an abnormal pattern of a reference time series corresponding to a matching index among the plurality of matching indices determined for the plurality of reference time series.

4. The battery diagnostic device according to claim 3, wherein, the control unit is configured to recognize an abnormal pattern of the abnormal input time series as being identical to an abnormal pattern of a reference time series corresponding to a minimum matching index among the plurality of matching indices.

5. A battery diagnostic device comprising: a voltage measurement circuit configured to generate a voltage signal representing a battery voltage that is a voltage across each of a plurality of batteries; a database in which a plurality of abnormal patterns, a plurality of reference time series, and a predetermined correspondence between the plurality of abnormal patterns and the plurality of reference time series are recorded; and a control unit configured to generate, based on the voltage signal, a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries; wherein the control unit is configured to extract, by comparing the plurality of input time series with each other, an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series, and wherein, for each of the plurality of reference time series, the control unit is configured to: convert the abnormal input time series and each reference time series into a first normalized time series and a second normalized time series, respectively, by using max-min normalization, calculate a first similarity value representing a signal distance between the first normalized time series and the second normalized time series by using dynamic time warping, convert the first normalized time series and the second normalized time series into a first aligned time series and a second aligned time series, respectively, having the same time length by using dynamic time warping, calculate a second similarity value representing a Pearson correlation coefficient between the first aligned time series and the second aligned time series, and determine a matching index between the abnormal input time series and each reference time series by combining the first similarity value and the second similarity value, wherein the control unit is configured to recognize an abnormal pattern of the abnormal input time series as being identical to an abnormal pattern of a reference time series corresponding to a matching index among the plurality of matching indices determined for the plurality of reference time series.

6. The battery diagnostic device according to claim 5, wherein The control unit is configured to recognize an abnormal pattern of the abnormal input time series as being the same as an abnormal pattern of a reference time series corresponding to one of the plurality of matching indices determined for the plurality of reference time series.

7. A battery diagnostic apparatus comprising: a voltage measurement circuit configured to generate a voltage signal representing a battery voltage that is a voltage across each of a plurality of batteries; a database in which a plurality of abnormal patterns, a plurality of reference time series, and a predetermined correspondence between the plurality of abnormal patterns and the plurality of reference time series are recorded; and a control unit configured to generate, based on the voltage signal, a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries; wherein the control unit is configured to extract, by comparing the plurality of input time series with each other, an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series, and wherein, for each of the plurality of reference time series, the control unit is configured to: calculate, by using dynamic time warping, a first similarity value representing a signal distance between the abnormal input time series and each reference time series, convert, by using dynamic time warping, the abnormal input time series and each reference time series into first and second permutation time series, respectively, having the same time length, calculate a second similarity value representing a Pearson correlation coefficient between the first and second permutation time series, convert, by using max-min normalization, the first and second permutation time series into first and second normalized time series, respectively, calculate, by using dynamic time warping, a third similarity value representing a signal distance between the first and second normalized time series, calculate a fourth similarity value representing a Pearson correlation coefficient between the first and second normalized time series, and determine a matching index between the abnormal input time series and each reference time series by combining at least one of the first and third similarity values with at least one of the second and fourth similarity values, wherein the control unit is configured to recognize an abnormal pattern of the abnormal input time series as being the same as an abnormal pattern of a reference time series corresponding to one of the plurality of matching indices determined for the plurality of reference time series.

8. The battery diagnostic apparatus according to claim 7, wherein the control unit is configured to recognize an abnormal pattern of the abnormal input time series as being the same as an abnormal pattern of a reference time series corresponding to a minimum matching index among the plurality of matching indices.

9. A battery system comprising the battery diagnostic apparatus according to any one of claims 1 to 8.

10. A battery diagnostic method comprising the steps of: collecting a voltage signal representing a battery voltage that is a voltage across each of a plurality of batteries; generating a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries based on the voltage signal; extracting an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other; and identifying an abnormal pattern of the abnormal input time series among a plurality of abnormal patterns having a predetermined correspondence relationship with a plurality of reference time series by comparing the abnormal input time series with the plurality of reference time series one by one, wherein the identifying the abnormal pattern includes: calculating a first similarity value between the abnormal input time series and each reference time series, converting the abnormal input time series and each reference time series into a first permutation time series and a second permutation time series, respectively, calculating a second similarity value between the first permutation time series and the second permutation time series, determining a matching index between the abnormal input time series and each reference time series by combining the first similarity value and the second similarity value, and identifying the abnormal pattern of the abnormal input time series as the same as an abnormal pattern of a reference time series corresponding to a matching index among a plurality of matching indexes determined with respect to the plurality of reference time series. ​

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