Battery diagnosis device, battery pack, battery system, and battery diagnosis method

Abstract

A battery diagnostic device according to the present disclosure includes: a memory configured to store an observation matrix including a plurality of observed voltage vectors indicating a time series of cell voltages of each of a plurality of battery cells; and a control unit configured to determine a plurality of principal component vectors, a plurality of singular values, and a plurality of coefficient vectors based on the observation matrix. Each coefficient vector includes a plurality of coefficients corresponding to the plurality of observed voltage vectors in a one-to-one relationship. The control unit is configured to, for each coefficient vector, determine an invalid coefficient among the plurality of coefficients by comparing the plurality of coefficients included in the corresponding coefficient vector, and detect an abnormality of the battery cell corresponding to the invalid coefficient based on the principal component vector corresponding to the corresponding coefficient vector, the singular values corresponding to the corresponding coefficient vector, and the invalid coefficient.

Description

Technical Field

[0001] The present disclosure relates to technology for battery cell abnormality detection.

[0002] This application claims the benefit of Korean Patent Application No. 10-2020-0096786 filed on August 3, 2020, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. Background Art

[0003] Recently, the demand for portable electronic products such as laptop computers, camcorders, and mobile phones has increased rapidly, and with the widespread development of electric vehicles, energy storage devices, robots, and satellites, much research is being conducted on rechargeable high-performance batteries.

[0004] Currently, commercial batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, lithium batteries, etc. Among them, lithium batteries have almost no or no memory effect, so they are more popular than nickel-based batteries because they have the advantages of being able to be charged at any time when convenient, having a very low self-discharge rate, and having a high energy density.

[0005] With the recent rise in applications requiring high voltage, battery packs, comprising multiple battery cells connected in series, have become widely used. As the number of battery cells in a battery pack increases, the likelihood of a battery cell anomaly increases. Consequently, the need for diagnostic technologies that can accurately detect battery cell anomalies is growing.

[0006] The prior art monitors cell information including multiple parameters associated with the status of the battery cells (eg, voltage, current, temperature), and detects abnormalities of the battery cells based on the operating status of the battery cells (eg, charging, discharging, resting) and the monitored cell information.

[0007] However, the above-mentioned abnormality detection method requires the battery management system (BMS) to use multiple sensors to monitor the cell information of the battery cells, so abnormality detection requires a lot of calculations and a long time. Specifically, in a structure where the power of the BMS is supplied by the battery cells, the power of the battery cells may be continuously consumed during the BMS operation for abnormality detection.

[0008] Furthermore, existing technologies detect battery cell anomalies based on rapid changes in battery cell information within a short period of time. However, in some cases, the cell information of a faulty battery cell does not always change rapidly within a short period of time, but may tend to change slowly over a long period of time, making it impossible to detect the battery cell anomaly at the appropriate time. Summary of the Invention

[0009] Technical issues

[0010] The present disclosure is designed to solve the above-mentioned problems, and thus the present disclosure aims to provide a battery diagnostic device, a battery pack, a battery system, and a battery diagnostic method, which use the cell voltage of each battery cell of a plurality of battery cells connected in series as a single parameter for abnormality detection.

[0011] The present disclosure further aims to provide a battery diagnostic device, a battery pack, a battery system, and a battery diagnostic method for battery cell abnormality detection, wherein an observation matrix is generated, and abnormal behavior of the cell voltage of each battery cell is identified based on the results of analyzing the observation matrix, the observation matrix being a data set including a plurality of observation voltage vectors, the plurality of observation voltage vectors indicating a voltage history (time series) of the cell voltage of each battery cell among a plurality of battery cells observed during the same period.

[0012] These and other purposes and advantages of the present disclosure can be understood through the following description and become apparent according to the embodiments of the present disclosure.In addition, it is easy to understand that the purposes and advantages of the present disclosure can be achieved by the means given in the accompanying claims and their combinations.

[0013] Technical Solution

[0014] A battery diagnostic device according to the disclosed solution includes: a memory configured to store an observation matrix including a plurality of observation voltage vectors indicating a time series of cell voltages of each of a plurality of battery cells; and a control unit configured to determine a plurality of principal component vectors, a plurality of singular values, and a plurality of coefficient vectors based on the observation matrix. Each coefficient vector includes a plurality of coefficients corresponding to the plurality of observation voltage vectors in a one-to-one relationship. The control unit is configured to, for each coefficient vector, determine an invalid coefficient among the plurality of coefficients by comparing the plurality of coefficients included in the corresponding coefficient vector, and detect an abnormality of a battery cell corresponding to the invalid coefficient among the plurality of battery cells based on the principal component vector corresponding to the corresponding coefficient vector among the plurality of principal component vectors, the singular value corresponding to the corresponding coefficient vector among the plurality of singular values, and the invalid coefficient.

[0015] The control unit may be configured to determine a first submatrix, a second submatrix, and a third submatrix by applying a matrix decomposition algorithm to the observation matrix. The first submatrix includes a plurality of principal component vectors as column vectors. The second submatrix includes a plurality of singular values as elements of the main diagonal. The third submatrix includes a plurality of coefficient vectors as row vectors.

[0016] The control unit may be configured to determine, as an invalid coefficient, a coefficient among the plurality of coefficients, the coefficient having an absolute value of a difference from an average value of the plurality of coefficients being greater than a first reference value.

[0017] The control unit may be configured to determine the first reference value to be equal to a value obtained by multiplying a standard deviation of the plurality of coefficients by a first scale factor.

[0018] The control unit can be configured to, for each coefficient vector, extract a partial voltage vector of an observation voltage vector corresponding to the invalid coefficient from among multiple observation voltage vectors by multiplying a principal component vector corresponding to the corresponding coefficient vector from among multiple principal component vectors, a singular value corresponding to the corresponding coefficient vector from among multiple singular values, and an invalid coefficient, and detect a battery cell corresponding to the invalid coefficient from among multiple battery cells as a fault when a voltage characteristic value of the partial voltage vector is greater than a second reference value.

[0019] The control unit may be configured to determine the voltage characteristic value to be equal to a difference between a maximum partial voltage and a minimum partial voltage among a plurality of partial voltages included in the partial voltage vector.

[0020] The control unit may be configured to determine the second reference value to be equal to a value obtained by multiplying a voltage resolution of the voltage measurement circuit by a second scale factor.

[0021] The control unit may be configured to output a fault message when a ratio of a maximum singular value to a minimum singular value among the plurality of singular values is less than a preset value.

[0022] A battery pack according to another aspect of the present disclosure includes a battery diagnostic device.

[0023] A battery system according to another aspect of the present disclosure includes a battery pack.

[0024] A battery diagnostic method according to another embodiment of the present disclosure includes determining a plurality of principal component vectors, a plurality of singular values, and a plurality of coefficient vectors based on an observation matrix, wherein the observation matrix includes a plurality of observation voltage vectors indicating a time series of cell voltages for each of a plurality of battery cells. Each coefficient vector includes a plurality of coefficients corresponding to the plurality of observation voltage vectors in a one-to-one relationship. The battery diagnostic method further includes, for each coefficient vector, determining an invalid coefficient among the plurality of coefficients by comparing the plurality of coefficients included in the corresponding coefficient vector; and detecting an abnormality in a battery cell corresponding to the invalid coefficient among the plurality of battery cells based on the principal component vector corresponding to the corresponding coefficient vector among the plurality of principal component vectors, the singular values corresponding to the corresponding coefficient vector among the plurality of singular values, and the invalid coefficient.

[0025] Determining the invalid coefficient among the plurality of coefficients may include determining a coefficient among the plurality of coefficients, a coefficient having an absolute value of a difference between the coefficient and an average value of the plurality of coefficients being greater than a first reference value, as the invalid coefficient.

[0026] Detecting an abnormality of a battery cell corresponding to an invalid coefficient among multiple battery cells may include: extracting a partial voltage vector of an observation voltage vector corresponding to the invalid coefficient among multiple observation voltage vectors by multiplying a principal component vector corresponding to the corresponding coefficient vector among multiple principal component vectors, a singular value corresponding to the corresponding coefficient vector among multiple singular values, and the invalid coefficient, and detecting the battery cell corresponding to the invalid coefficient among the multiple battery cells as a fault when a voltage characteristic value of the partial voltage vector is greater than a second reference value.

[0027] Beneficial effects

[0028] According to at least one embodiment of the present disclosure, by detecting abnormality of each of a plurality of battery cells connected in series using only cell voltage in addition to current or temperature, the amount of calculation, time, and power required for abnormality detection can be reduced.

[0029] In addition, according to at least one embodiment of the present disclosure, the accuracy of battery cell abnormality detection can be improved by generating an observation matrix, and the abnormal behavior of the cell voltage of each battery cell can be identified based on the results of analyzing the observation matrix, where the observation matrix is a data set including multiple observation voltage vectors, and the observation voltage vector indicates the voltage history (time series) of the cell voltage of each battery cell among multiple battery cells observed within the same time period.

[0030] The effects of the present disclosure are not limited to the above-mentioned effects, and those skilled in the art will clearly understand these and other effects from the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings illustrate preferred embodiments of the present disclosure, and together with the detailed description of the present disclosure described below, are used to provide a further understanding of the technical solutions of the present disclosure. Therefore, the present disclosure should not be construed as being limited to the accompanying drawings.

[0032] Figure 1 is a schematic diagram exemplarily showing the configuration of a battery system according to the present disclosure.

[0033] Figure 2 is a graph exemplarily showing changes in cell voltage of a battery cell over time.

[0034] Figure 3 is described as an indication Figure 2 An exemplary observation matrix of a data set of voltage histories of battery cells is shown as a schematic diagram for reference.

[0035] Figure 4 is a graph exemplarily showing a coefficient vector.

[0036] Figure 5This is a schematic diagram for reference when describing the relationship between the ineffective coefficients and the partial voltage vectors.

[0037] Figure 6 FIG. 1 is a flowchart exemplarily illustrating a battery diagnosis method according to the first embodiment of the present disclosure.

[0038] Figure 7 FIG. 1 is a flowchart exemplarily illustrating a battery diagnosis method according to a second embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] The preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Before the description, it should be understood that the terms or words used in the specification and the appended claims should not be interpreted as limited to the general meaning and dictionary meaning, but should be interpreted based on the meaning and concept corresponding to the technical solution of the present disclosure on the basis of the principle that the inventor is allowed to appropriately define the terms to obtain the best interpretation.

[0040] Therefore, the embodiments described herein and the descriptions shown in the accompanying drawings are only the most preferred embodiments of the present disclosure, but are not intended to fully describe the technical solutions of the present disclosure. Therefore, it should be understood that various other equivalents and modifications may be given thereto when submitting this application.

[0041] Terms including ordinal numbers such as “first,” “second,” etc. are used to distinguish one element from another among various elements, but are not intended to limit the elements by the terms.

[0042] Unless the context clearly indicates otherwise, it should be understood that the term "comprising" used in this specification specifies the presence of the elements described, but does not exclude the presence or addition of one or more other elements. In addition, the term "control unit" used herein refers to a processing unit having at least one function or operation, and this can be implemented by hardware and software alone or in combination.

[0043] Furthermore, throughout this specification, it will be further understood that when an element is referred to as being “connected to” another element, it can be directly connected to the other element or intervening elements may be present.

[0044] Figure 1 is a schematic diagram exemplarily showing the configuration of a battery system according to the present disclosure.

[0045] Figure 1 An energy storage system is shown as an example of a battery system 1. Figure 1, the battery system 1 includes a battery pack 10 and a switch 20. The battery system 1 may further include at least one of a remote controller 240 or a power conversion system 30. The battery system 1 is not limited to an energy storage system and may include any battery system having a charging function and / or a discharging function of the battery pack 10 provided therein, such as an electric vehicle or a battery tester.

[0046] The battery pack 10 includes a positive terminal P+, a negative terminal P−, a cell group 11, and a battery management system 100. The cell group 11 includes a plurality of battery cells BC1 to BC2 electrically connected between the positive terminal P+ and the negative terminal P−. n (n is a natural number greater than or equal to 2). Figure 1 The plurality of battery cells BC1 to BC2 connected in series in the cell group 11 are shown. n Next, a plurality of battery cells BC1 to BC n When describing together, the reference sign “BC” is used to refer to a battery cell.

[0047] The positive and negative terminals of the battery cells BC are electrically coupled to other battery cells BC via conductors such as bus bars. The battery cells BC may be lithium-ion battery cells. The battery cells BC are not limited to a specific type and may include any type of battery cells that can be repeatedly recharged.

[0048] The switch 20 is mounted on the power line PL of the battery pack 10. When the switch 20 is turned on, power can be transmitted from either the battery pack 10 or the power conversion system 30 to the other. The switch 20 can be implemented as at least one of well-known switching devices such as a relay and a field effect transistor (FET).

[0049] The power conversion system 30 is operably coupled to at least one of the battery management system 100 and the remote controller 240. Operable coupling refers to a direct or indirect connection for unidirectional or bidirectional signal transmission and reception. The power conversion system 30 can generate DC power for charging the cell stack 11 from AC power supplied by the grid 40. The power conversion system 30 can generate AC power from DC power supplied by the battery stack 10.

[0050] The battery management system 100 may include a voltage measurement circuit 110 and a battery controller 140. The battery management system 100 may further include at least one of a current sensor 120, a temperature sensor 130, or an interface unit 150. The interface unit 150 may be included in the battery controller 140.

[0051] The voltage measurement circuit 110 is configured to be electrically connected to the positive and negative terminals of the battery cells BC. The voltage measurement circuit 110 can measure a cell voltage or a voltage across the battery cells BC and output a signal indicating the measured cell voltage to the battery controller 140.

[0052] The current sensor 120 is electrically connected in series with the cell stack 11 via the power line PL. For example, a shunt resistor or a Hall effect device can be used as the current sensor 120. The current sensor 120 can measure the current flowing through the cell stack 11 and output a signal indicating the measured current to the battery controller 140.

[0053] The temperature sensor 130 is arranged within a predetermined distance range from the cell stack 11. For example, a thermocouple may be used as the temperature sensor 130. The temperature sensor 130 may measure the temperature of the cell stack 11 and output a signal indicating the measured temperature to the battery controller 140.

[0054] The battery controller 140 is operatively coupled to the voltage measurement circuit 110, the current sensor 120, the temperature sensor 130, and / or the interface unit 150. At least one of the battery controller 140 or the remote controller 240 may control the switch 20 to be turned on / off according to the diagnosis result of the cell pack 11.

[0055] The interface unit 150 can be coupled to the remote controller 240 of the battery system 1 to enable communication. The interface unit 150 can transmit signals from the remote controller 240 to the battery controller 140, and vice versa. Signals from the battery controller 140 may include information for notifying the user of battery cell abnormalities. Communication between the interface unit 150 and the remote controller 240 can utilize, for example, wired networks such as a local area network (LAN), a controller area network (CAN), and a daisy chain, and / or wireless networks such as Bluetooth, Zigbee, and Wi-Fi. The interface unit 150 may include an output device (e.g., a display, a speaker) to provide information received from the battery controller 140 and / or the remote controller 240 in a recognizable format. The remote controller 240 can control at least one of the battery pack 10, the switch 20, or the power conversion system 30 based on cell information collected through communication with the battery management system 100 (e.g., cell voltage, current, temperature, SOC, battery cell abnormalities).

[0056] The battery controller 140 includes a memory 141 and a control unit 142. The remote controller 240 may also include the memory 241 and the control unit 242. The remote controller 240 may also include a communication circuit 243. The remote controller 240 may be implemented as a cloud server or a mobile diagnostic device. The communication circuit 243 is used for wired / wireless communication with the battery management system 100.

[0057] Each of the control units 142 and 242 may be implemented in hardware using at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a microprocessor, or an electrical unit for performing other functions.

[0058] At least one of the memory 141 or the memory 142 may pre-store programs and data required to execute the battery diagnosis method (diagnostic process) according to the embodiment described below. Each of the memory 141 and the memory 142 may include, for example, at least one type of storage medium of a flash memory type, a hard disk type, a solid state disk (SSD) type, a silicon disk drive (SDD) type, a multimedia card micro, a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), or a programmable read-only memory (PROM). At least one of the memory 141 or the memory 142 may be pre-stored by executing the following diagnostic process ( Figures 2 to 6 ) to record the data and algorithm required for detecting abnormalities of the battery BC. The memory 141 and the control unit 142 may be integrated into a single chip. The memory 241 and the control unit 242 may be integrated into a single chip.

[0059] The battery controller 140 is an example of a battery diagnostic device according to the present disclosure, and the remote controller 240 is another example of a battery diagnostic device according to the present disclosure. That is, the following reference is performed by at least one of the battery controller 140 or the remote controller 240 provided as a battery diagnostic device. Figures 2 to 7 The diagnostic process described.

[0060] The battery diagnostic device according to the present disclosure can perform a diagnostic process (see Figure 6 and Figure 7 ) to detect multiple battery cells BC1~BC n The diagnostic procedure can be based on voltage data acquired within a specified period of time (e.g., a predetermined time in the past) (see Figure 2 X1~X n ), during which specified period, the cell group 11 is maintained in a predetermined diagnosis-possible state (eg, rest, constant current charging, constant voltage charging, constant current discharging). The following description will be made under the assumption that the battery controller 140 is provided as a battery diagnostic device.

[0061] Figure 2 is a graph exemplarily showing changes in cell voltage of a battery cell over time, Figure 3 is described as an indication Figure 2An exemplary observation matrix of a data set of voltage histories of battery cells is shown as a schematic diagram for reference.

[0062] The control unit 142 may determine the voltage of the plurality of battery cells BC1 to BC2 at predetermined time intervals based on the voltage signal from the voltage measurement circuit 110. n The voltage value of the cell voltage of each battery cell in the battery is determined, and the determined voltage value is recorded in the memory 141. The preset time may be equal to the time length of the abnormality detection period, as described below.

[0063] The control unit 142 determines a plurality of observed voltage vectors X1 to X2 over a specified period Δt in the past predetermined time. n The observation matrix X can be determined using a moving window 200. For example, multiple observation voltage vectors X1 to X n Indicates a plurality of battery cells BC1 to BC2 measured at preset time intervals within the moving window 200. n The cell voltage of each battery cell in the battery depends on the change of time. The moving window 200 is used to set the time period Δt, during which the moving window 200 is moved at a preset time interval to obtain multiple observation voltage vectors X1 to X2 at a preset time interval. n The size Δt of the moving window 200 can be preset or adjusted by the control unit 142 .

[0064] The cell voltage of the battery cell BC may be measured multiple times in a time series (e.g., a total of m, where m is a natural number greater than or equal to 2) by the voltage measurement circuit 110, and the measured cell voltages may be recorded in the memory 141 by the control unit 142. For example, where the size of the moving window 200 is 200 seconds, the preset time is 1 second, and m is 200, the cell voltage of the battery cell BC is measured 200 times within the moving window 200.

[0065] Reference Figure 2 , curve 210 exemplarily indicates a plurality of battery cells BC1 to BC n The kth battery cell BC k The cell voltage varies with time. The abnormal state may be a state that triggers abnormal behavior of the cell voltage, such as an internal short circuit. Figure 2 In the middle, t1 and t m are the start time and end time of the specified period Δt, t i is the time point corresponding to the time index i within the specified period Δt. k In the example, k is a natural number n or smaller, which can be used to distinguish multiple battery cells BC1 to BC n The single index of .

[0066] The following will be based on the kth battery cell BC k The abnormality detection operation according to the present disclosure is described. k The description can be applied to multiple battery cells BC1~BC n The remaining battery cells BC.

[0067] Reference Figure 3 , the observation matrix X is an m×n matrix including m rows and n columns. For the convenience of description below, it is assumed that m is a natural number greater than n, i is a natural number greater than 1 and less than m, j is a natural number greater than 1 and less than n, and k is a natural number less than n.

[0068] The n column vectors of the observation matrix X can be associated with multiple observation voltage vectors X1 to X2 in a one-to-one relationship. n That is to say, multiple observation voltage vectors X1~X n Each observation voltage vector in is a column vector of the observation matrix X and includes m elements (measured cell voltages). k It is the kth battery cell BC k The cell voltage is measured m times in a time series array, that is, the kth battery cell BC k The measured cell voltage x 1k ~x mk The k-th observation voltage vector X k It can be the k-th column vector of the observation matrix X. Figure 2 , in the observation matrix X, "x ik " indicates that the kth battery cell BC is measured m times in total within the specified period Δt j The element (called data or component) of the i-th measured cell voltage among the cell voltages.

[0069] The control unit 142 can extract the first sub-matrix A, the second sub-matrix B and the third sub-matrix C from the observation matrix X by performing matrix decomposition on the observation matrix X. T That is, the observation matrix X can be decomposed into the first sub-matrix A, the second sub-matrix B and the third sub-matrix C T . The algorithms used in matrix decomposition may include, for example, singular value decomposition (SVD) and principal component analysis (PCA). In the specification, the superscript "T" on the right side of the matrix indicates the transposed matrix. As shown in the figure, the first sub-matrix A, the second sub-matrix B and the third sub-matrix C T The product of the multiplication is equal to the observation matrix X.

[0070] The first sub-matrix A is an m×m matrix. The second sub-matrix B is an m×n matrix. The third sub-matrix C T is an n×n matrix.

[0071] The first sub-matrix A is an orthogonal matrix and includes a plurality of principal component vectors A1 to A m . Multiple principal component vectors A1~A m Each principal component vector in may be referred to as a “left singular vector”. Each principal component vector includes m elements and may be a column vector of the first submatrix A. That is, the first submatrix A may be represented as follows.

[0072] A=[A1 A2...A m ]

[0073] A i =[a 1i a 2i ...a mi ] T

[0074] In multiple principal component vectors A1~A m Among them, the principal component vectors A1~A n Indicates the variance information of the observation matrix X. The jth principal component vector Aj corresponds to the axis where the variance of the elements of the observation matrix X is the jth maximum. That is, when the elements of the observation matrix X are mapped to multiple principal component vectors A1~A m When the axis of each principal component vector in is once, the elements of the observation matrix X are along the jth principal component vector A j The variance of the axis of can be the jth maximum value.

[0075] With the jth principal component vector A j The variance of is larger, which indicates that the j-th principal component vector A j The element distribution of the observation matrix X has a larger description factor. j The description factor increases, the j-th principal component vector A j The plurality of battery cells BC1 to BC2 in the movable window 200 are included. n In contrast, with the jth principal component vector A j The variance of the j-th principal component vector A is smaller. j The description factor is lower, that is, the j-th principal component vector A j Contains a greater amount of information associated with noise characteristics (e.g., abnormal conditions).

[0076] The second submatrix B is a diagonal matrix and includes a plurality of singular values b 11 ~b nn As the elements of the main diagonal. That is, the second sub-matrix B can be expressed as follows.

[0077] B=[B1 B2...Bn ]

[0078] B j =[b 1j b 2j ...b mj ] T

[0079] Where i≠j, b ij is 0, b jj is the jth singular value.

[0080] That is to say, among all the m×n elements of the second submatrix B, except for the n elements b on the main diagonal 11 ~b nn Except for , the rest of the elements are 0. Therefore, in the multiple principal component vectors A1~A m Among them, the principal component vector A n+1 ~A m The description of the variance information of the observation vector X may be redundant.

[0081] Multiple singular values b 11 ~b nn The following relationship can be satisfied: b 11 ≥b 22 ≥...≥b nn ≥ 0. Multiple singular values b can be sorted in descending order of size. 11 ~b nn are called the first to nth singular values, b jj Can be multiple singular values b 11 ~b nn The j-th largest singular value among them.

[0082] Multiple singular values b 11 ~b nn Indicates multiple principal component vectors A1~A n The descriptive factor information of the second submatrix B is b. jj Indicates the jth principal component vector A j The descriptive factor.

[0083] The third sub-matrix C T is an orthogonal matrix and includes multiple coefficient vectors C1 T ~C n T . Multiple coefficient vectors C1 can be T ~C n T Each coefficient vector of is called a "right singular vector". Each coefficient vector includes n components and can be the third submatrix C T The row vector of the third submatrix C T It is expressed as follows.

[0084] C T =[C1C2...C n ] T =[C1 T ; C2 T ;...;C n T ]

[0085] C j T =[c j1 c j2 ...c jn ]

[0086] Multiple coefficient vectors C1 T ~C n T Indicates multiple observation voltage vectors X1~X n For multiple principal component vectors A1~A n Specifically, through the j-th coefficient vector C j T Set multiple observation voltage vectors X1~X n Each of the j principal component vectors A j The degree of influence. The j-th coefficient vector C j T Including multiple coefficients c j1 ~c jn , which are in a one-to-one relationship with the first to nth observation voltage vectors X1~X n For example, the j-th coefficient vector C j T c jk Indicates the jth principal component vector A j For the k-th observation voltage vector X k impact.

[0087] The first to nth principal component vectors A1~A n , the first to nth singular values b 11 ~b nn and the first to nth coefficient vectors C1 T ~C n T They can correspond to each other in a one-to-one relationship.

[0088] The observation matrix X is equal to the first submatrix A, the second submatrix B and the third submatrix C T The product of , and the relationship of the following equation 1 can be satisfied.

[0089] <Equation 1>

[0090]

[0091] In Equation 1, A j is considered as an (m×1) matrix, C j T is considered as a (1×n) matrix.

[0092] Referring to Equation 1, the kth observed voltage vector X k Equivalent to depending on the first to nth principal component vectors A1~A in a one-to-one relationship n The sum of the first to n-th part voltage vectors, and the relationship of the following equation 2 can be satisfied.

[0093] <Equation 2>

[0094]

[0095] In Equation 2, Y kj =(b jj ×A j ×c jk ) is the kth observation voltage vector X k The jth part voltage vector. The kth observation voltage vector X k The voltage vector Y of the jth part kj is the kth observation voltage vector X k , depends on the jth principal component vector A j The voltage component of the j-th principal component vector A j , j-th singular value b jj and coefficient c jk That is, the voltage vector Y of the jth part kj It is possible to use only the first to nth principal component vectors A1~A n The j-th principal component vector A j To recover (approximately) the k-th observed voltage vector X k Therefore, the voltage vector Y of the jth part is kj has a one-to-one relationship with the kth observation voltage vector X k The elements of each partial voltage vector may be referred to as a “partial voltage (or approximate voltage)”, and the partial voltage vector may be referred to as a “restored voltage vector”.

[0096] Based on the first to nth principal component vectors A1~A n , the first to nth singular values b 11 ~b nn and the first to nth coefficient vectors C1 T ~C n T Detect the first to nth battery cells BC1~BC nBefore the abnormality, the control unit 142 can calculate the first to nth singular values b 11 ~b nn The largest singular value b 11 and the minimum singular value b nn When the maximum singular value b 11 and the minimum singular value b nn When the ratio of is less than a preset ratio (eg, 200%), the control unit 142 may output a fault message indicating failure abnormality detection of the battery cell BC. Failure abnormality detection is a plurality of principal component vectors A1 to A2. n That is, in the case of failure anomaly detection, multiple principal component vectors A1~A n None of them fully includes multiple battery cells BC1~BC n The cause of the failure abnormality detection may be, for example, a fault in the voltage measurement circuit 110 or a fault in the first to nth battery cells BC1 to BC n Among them, the number of abnormal battery cells BC exceeds a predetermined ratio (for example, 50%).

[0097] When the maximum value b 11 With the minimum value b nn When the ratio of is less than the preset ratio, the control unit 142 may increase the size of the moving window 200 by a predetermined time in the next cycle. The reason for increasing the size of the moving window 200 is to fully reflect the multiple battery cells BC1 to BC n Common voltage behavior characteristics.

[0098] Figure 4 is a graph exemplarily showing the coefficient vector. Figure 4 In the figure, the horizontal axis indicates the j-th coefficient vector C j T Each coefficient in the ordinate indicates the cell index corresponding to each coefficient. For example, cell index = 1 corresponds to the first battery cell BC1.

[0099] The control unit 142 compares the first to nth coefficients c j1 ~c jn To determine the coefficient vector C included in the jth j T The first to nth coefficients c in j1 ~c jn Is there an invalid coefficient in the j-th coefficient vector C? j T The invalid coefficient indicates that the j-th principal component vector A jThe degree of abnormal voltage behavior in the voltage history of a specific battery cell corresponding to the corresponding invalid coefficient is reflected. The remaining coefficients except the invalid coefficient may be valid coefficients.

[0100] Reference Figure 4 , the control unit 142 may determine the coefficient vector C included in the jth j T The first to nth coefficients c in j1 ~c jn The average value c j_av and standard deviation. The control unit 142 may be based on the first to nth coefficients c j1 ~c jn The standard deviation is used to determine the first reference value R j1 For example, the control unit 142 may set the first reference value R j1 The first scale factor may be pre-recorded in the memory 141 .

[0101] The control unit 142 can convert the first to nth coefficients c j1 ~c jn The coefficient and average value c j_av The absolute value of the difference between the two is greater than the first reference value R j1 Each coefficient of is determined as the j-th coefficient vector C j T The invalid coefficient. Figure 4 Show coefficient c jk than the average value c j_av Small first reference value R j1 Therefore, the control unit 142 can set the coefficient c jk Determined as the j-th coefficient vector C j T The invalid coefficient.

[0102] Figure 5 This is a schematic diagram for reference when describing the relationship between the ineffective coefficients and the partial voltage vectors. Figure 5 is exemplarily shown with Figure 4 The invalid coefficient c jk The corresponding k-th observation voltage vector X k The voltage vector Y of the jth part kj The graph of the k-th observed voltage vector X k Same, the voltage vector Y of the jth part kj is an (m×1) matrix. Figure 5 , the horizontal axis indicates the time index within the moving window 200, and the vertical axis indicates the partial voltage.

[0103] Reference Figure 5, the control unit 142 may include a voltage vector Y kj The m partial voltages in the y-axis determine the partial voltage vector Y kj The voltage characteristic value can be the voltage vector indicating part Y kj For the kth observation voltage vector X k In the moving window 200, the partial voltage vector Y kj Exhibiting a large rate of change and / or slope of voltage may indicate an ineffective coefficient c jk The abnormal voltage behavior associated with the kth observation voltage vector X k For example, the control unit 142 may determine that the voltage characteristic value is equal to the partial voltage vector Y kj The maximum partial voltage y among the m partial voltages kj_max With the minimum partial voltage y kj_min The difference Δy kj Alternatively, the control unit 142 may determine the voltage characteristic value to be equal to the maximum portion voltage y kj_max With the minimum partial voltage y kj_min The slope between .

[0104] When the partial voltage vector Y kj When the voltage characteristic value is greater than the second reference value, the control unit 142 can jk The corresponding kth battery cell BC k Detected as a fault. The control unit 142 may determine a second reference value based on the voltage resolution of the voltage measurement circuit 110. For example, the control unit 142 may determine the second reference value to be equal to the product of the voltage resolution and the second proportional factor. The second proportional factor may be pre-recorded in the memory. Alternatively, the second reference value may be preset to, for example, 10.0 mV in consideration of the voltage resolution. The second reference value is used to prevent the possibility of a normal battery cell being mistakenly detected as a faulty battery cell due to a measurement error in the cell voltage measured by the voltage measurement circuit 110. When the voltage characteristic value Δy kj When the kth battery cell BC is greater than the second reference value, k Determined to be a fault.

[0105] Figure 6 is a flowchart exemplarily illustrating a battery diagnosis method according to the first embodiment of the present disclosure. Figure 6 method.

[0106] Reference Figures 1 to 6 In step S610, the control unit 142 determines an observation matrix X, which includes a plurality of battery cells BC1 to BC nThe corresponding multiple observation voltage vectors X1~X n . Multiple observation voltage vectors X1~X n represents a plurality of battery cells BC1 to BC2 measured m times in time series within the moving window 200. n The time series of the cell voltage of each battery cell in .

[0107] In step S620, the control unit 142 determines a plurality of principal component vectors A1 to A2 according to the observation matrix X. n , multiple singular values b 11 ~b nn and multiple coefficient vectors C1 T ~C n T (See Equation 1).

[0108] Multiple coefficient vectors C1 can be T ~C n T At least one of the steps S630 to S670 is performed once. For example, the multiple singular values b can be sorted in ascending order. 11 ~b nn Steps S630 to S670 are performed on a predetermined number of coefficient vectors corresponding to a predetermined number of singular values. In another example, steps S630 to S670 may be performed on coefficient vectors corresponding to each of the following singular values: 11 ~b nn The ratio of the sum is equal to or smaller than a predetermined value.

[0109] In step S630, the control unit 142 compares the coefficient vector C j T Multiple coefficients c j1 ~c jn To determine the first reference value R j1 Alternatively, the first reference value R j1 It can be a preset constant, in which case step S630 can be omitted.

[0110] In step S640, the control unit 142 determines a plurality of coefficients c j1 ~c jn Is at least one greater than a first reference value R j1 When the value of step S640 is "No", the method may end. When the value of step S640 is "Yes", the method proceeds to step S650.

[0111] In step S650, the control unit 142 converts the plurality of coefficients c j1 ~c jn Which is greater than the first reference value Rj1 The coefficient c jk Determined as coefficient vector C j T The invalid coefficient.

[0112] In step S660, the control unit 142 calculates the value of the principal component vector A based on the principal component vector A. j , singular value b jj and the invalid coefficient c jk Extraction and invalid coefficient c jk The corresponding observation voltage vector X k Partial voltage vector Y kj (See Equation 2).

[0113] In step S670, the control unit 142 determines the partial voltage vector Y kj Voltage characteristic value Δy kj .

[0114] In step S680, the control unit 142 determines the voltage characteristic value Δy kj Is it greater than the second reference value. When the value of step S680 is "No", the method can end. The value of step S680 is "Yes" indicating that the coefficient c is invalid. jk Corresponding battery cell BC k When the value of step S680 is “yes”, the method proceeds to step S690 .

[0115] In step S690, the control unit 142 activates a predetermined protection operation. For example, the control unit 142 turns off the switch 20. In another example, the control unit 142 outputs an indication of the battery cell BC detected as a fault. k The diagnostic message may be transmitted and received between the battery controller 140 and the remote controller 240 via the interface unit 150. The interface unit 150 may output visual and / or auditory information corresponding to the diagnostic message.

[0116] Figure 7 is a flowchart exemplarily illustrating a battery diagnosis method according to a second embodiment of the present disclosure. Figure 7 method.

[0117] exist Figure 7 In the method, steps S710 to S790 are Figure 6 Steps S610 to S690 are the same, so repeated description is omitted.

[0118] and Figure 6 The methods are different. Figure 7 The method further includes steps S722 and S724.

[0119] In step S722, the control unit 142 determines a plurality of singular values b 11 ~b nn Is the maximum ratio of equal to or greater than the preset ratio. The maximum ratio is a number of singular values b 11 ~b nn The maximum value b 11 With the minimum value b nn The value of step S722 is “No”, which indicates that the ratio of the principal component vectors A1 to A n There is no principal component vector having a sufficiently large descriptive factor compared to the other principal component vectors. When the value of step S722 is "No", the method executes step S724. When the value of step S722 is "Yes", the method executes step S730.

[0120] In step S724, the control unit 142 outputs a fault message. The fault message indicates failure abnormality detection. The fault message can be transmitted and received between the battery controller 140 and the remote controller 240 via the interface unit 150. The interface unit 150 can output visual and / or auditory information corresponding to the fault message.

[0121] Although the above reference Figures 2 to 7 The description is given under the assumption that the battery controller 140 is provided as the battery diagnostic device, but as an alternative to the battery controller 140, the remote controller 240 can function as the battery diagnostic device. That is, the description of each of the control unit 142 and the memory 141 can be the same as that of the control unit 242 and the memory 241. When the remote controller 240 is provided as the battery diagnostic device, the battery management system 100 can indicate a plurality of observation voltage vectors X1 to X2 through the interface unit 150. n The data is transmitted to the communication circuit 243 of the remote controller 240.

[0122] The above-mentioned embodiments of the present disclosure are not only implemented by devices and methods, but also by programs that execute functions corresponding to the configurations of the embodiments of the present disclosure or recording media on which the programs are recorded, and those skilled in the art can easily implement these implementation methods through the disclosure of the above-mentioned embodiments.

[0123] Although the present disclosure has been described above with respect to a limited number of embodiments and drawings, the present disclosure is not limited thereto, and it is obvious to those skilled in the art that various modifications and variations may be made thereto within the scope of equivalents of the technical solutions of the present disclosure and the appended claims.

[0124] In addition, because those skilled in the art can make many substitutions, modifications and changes to the above-mentioned disclosure without departing from the technical solution of the present disclosure, the present disclosure is not limited to the above-mentioned embodiments and drawings, and some or all of the embodiments can be selectively combined to allow various modifications.

Claims

1. A battery diagnostic device comprising: a memory configured to store an observation matrix including a plurality of observed voltage vectors indicating a time series of a cell voltage of each of a plurality of battery cells; as well as a control unit configured to determine a plurality of principal component vectors, a plurality of singular values, and a plurality of coefficient vectors based on the observation matrix, wherein each coefficient vector includes a plurality of coefficients corresponding to the plurality of observed voltage vectors in a one-to-one relationship, and The control unit is configured to, for each coefficient vector, determining an invalid coefficient among a plurality of coefficients by comparing the plurality of coefficients included in the corresponding coefficient vector, and An abnormality of a battery cell corresponding to the invalid coefficient among the plurality of battery cells is detected based on the principal component vector corresponding to the corresponding coefficient vector among the plurality of principal component vectors, the singular value corresponding to the corresponding coefficient vector among the plurality of singular values, and the invalid coefficient.

2. The battery diagnostic device according to claim 1, wherein: The control unit is configured to determine a first sub-matrix, a second sub-matrix, and a third sub-matrix by applying a matrix decomposition algorithm to the observation matrix, The first submatrix includes the plurality of principal component vectors as column vectors, The second submatrix includes the plurality of singular values as elements of the main diagonal, The third sub-matrix includes the plurality of coefficient vectors as row vectors.

3. The battery diagnostic device according to claim 1, wherein: The control unit is configured to determine, as the invalid coefficient, a coefficient among the plurality of coefficients, the coefficient having an absolute value of a difference between the coefficient and an average value of the plurality of coefficients being greater than a first reference value.

4. The battery diagnostic device according to claim 3, wherein: The control unit is configured to determine the first reference value to be equal to a value obtained by multiplying a standard deviation of the plurality of coefficients by a first scale factor.

5. The battery diagnostic device according to claim 1, wherein The control unit is configured to, for each coefficient vector, extracting a partial voltage vector of an observation voltage vector corresponding to the invalid coefficient among the plurality of observation voltage vectors by multiplying a principal component vector corresponding to the corresponding coefficient vector among the plurality of principal component vectors, a singular value corresponding to the corresponding coefficient vector among the plurality of singular values, and the invalid coefficient; and When the voltage characteristic value of the partial voltage vector is greater than a second reference value, a battery cell corresponding to the invalid coefficient among the plurality of battery cells is detected as a fault.

6. The battery diagnostic device according to claim 5, wherein: The control unit is configured to determine the voltage characteristic value to be equal to a difference between a maximum partial voltage and a minimum partial voltage among a plurality of partial voltages included in the partial voltage vector.

7. The battery diagnostic device according to claim 5, wherein: The control unit is configured to determine the second reference value to be equal to a value obtained by multiplying a voltage resolution of the voltage measurement circuit by a second scale factor.

8. The battery diagnostic device according to claim 1, wherein: The control unit is configured to output a fault message when a ratio of a maximum singular value to a minimum singular value among the plurality of singular values is less than a preset value.

9. A battery pack comprising the battery diagnostic device according to any one of claims 1 to 8.

10. A battery system comprising the battery pack according to claim 9.

11. A battery diagnosis method comprising: determining a plurality of principal component vectors, a plurality of singular values, and a plurality of coefficient vectors based on an observation matrix, the observation matrix including a plurality of observation voltage vectors indicating a time series of a cell voltage of each of a plurality of battery cells, wherein each coefficient vector includes a plurality of coefficients corresponding to the plurality of observed voltage vectors in a one-to-one relationship, and The battery diagnosis method further includes, for each coefficient vector, determining an invalid coefficient among a plurality of coefficients by comparing the plurality of coefficients included in the corresponding coefficient vector, and An abnormality of a battery cell corresponding to the invalid coefficient among the plurality of battery cells is detected based on the principal component vector corresponding to the corresponding coefficient vector among the plurality of principal component vectors, the singular value corresponding to the corresponding coefficient vector among the plurality of singular values, and the invalid coefficient.

12. The battery diagnosis method according to claim 11, wherein: Determining an invalid coefficient among the plurality of coefficients includes determining, as the invalid coefficient, a coefficient among the plurality of coefficients, wherein an absolute value of a difference between the coefficient and an average value of the plurality of coefficients is greater than a first reference value.

13. The battery diagnosis method according to claim 11, wherein: Detecting an abnormality of a battery cell corresponding to the invalid coefficient among the plurality of battery cells includes: extracting a partial voltage vector of an observation voltage vector corresponding to the invalid coefficient among the plurality of observation voltage vectors by multiplying a principal component vector corresponding to the corresponding coefficient vector among the plurality of principal component vectors, a singular value corresponding to the corresponding coefficient vector among the plurality of singular values, and the invalid coefficient; and When the voltage characteristic value of the partial voltage vector is greater than a second reference value, a battery cell corresponding to the invalid coefficient among the plurality of battery cells is detected as a fault.

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