Battery pack failure warning method, system, device and storage medium

Abstract

The present invention discloses a battery pack fault warning method, system, device and storage medium, including: obtaining voltage data of a battery pack, the voltage data including: single cell voltage and combination voltage, the combination referring to two adjacent single cells and a resistor connected between the two single cells; expanding the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix; standardizing the two-dimensional data analysis matrix to obtain a standardized matrix; and then obtaining a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five sub-matrices; calculating the expectation of the maximum eigenvalue of each sub-matrix; determining the expected value of a fault indicator of a voltage sensor fault according to the expectation of the maximum eigenvalue of each sub-matrix; and determining whether a voltage sensor fault occurs according to the expected value of the fault indicator.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology, and in particular to a battery pack fault early warning method, system, device and storage medium. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] Lithium-ion battery safety is the main factor restricting the further promotion and application of lithium-ion batteries. Due to the manufacturing problems of the battery itself and the influence of subsequent use conditions, various faults may occur in the battery system, which seriously affect the service life and safety of lithium-ion batteries.

[0004] The inventors have discovered that existing battery fault diagnosis cannot accurately and in real time diagnose the type of battery fault. Summary of the invention

[0005] In order to solve the deficiencies of the prior art, the present invention provides a battery pack fault warning method and system;

[0006] On the one hand, a battery pack failure early warning method is provided;

[0007] The battery pack failure early warning method includes:

[0008] Obtaining voltage data of the battery pack, the voltage data including: single cell voltage and combination voltage, the combination refers to two adjacent single cells and a resistor connected between the two single cells;

[0009] The voltage data of the battery pack is expanded using a sliding window to obtain a two-dimensional data analysis matrix;

[0010] The two-dimensional data analysis matrix is ​​standardized to obtain a standardized matrix; and then a covariance matrix corresponding to the standardized matrix is ​​obtained; the covariance matrix corresponding to the standardized matrix includes five sub-matrices;

[0011] Calculate the expectation of the maximum eigenvalue of each submatrix; determine the expected value of the fault indicator of the voltage sensor fault according to the expectation of the maximum eigenvalue of each submatrix; and determine whether the voltage sensor fails according to the expected value of the fault indicator.

[0012] On the other hand, a battery pack failure warning system is provided;

[0013] Battery pack failure warning system, including:

[0014] An acquisition module is configured to: acquire voltage data of a battery pack, wherein the voltage data includes a single cell voltage and a combination voltage, wherein the combination refers to two adjacent single cells and a resistor connected between the two single cells;

[0015] An expansion module is configured to: expand the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix;

[0016] A processing module is configured to: perform standardization processing on a two-dimensional data analysis matrix to obtain a standardized matrix; and then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five sub-matrices;

[0017] The determination module is configured to: calculate the expectation of the maximum eigenvalue of each submatrix; determine the fault indicator expectation value of the voltage sensor fault according to the expectation of the maximum eigenvalue of each submatrix; and determine whether the voltage sensor fails according to the fault indicator expectation value.

[0018] On the other hand, there is also provided an electronic device, comprising:

[0019] a memory for non-transitory storage of computer-readable instructions; and

[0020] a processor for executing the computer readable instructions,

[0021] When the computer-readable instructions are executed by the processor, the method described in the first aspect is executed.

[0022] On the other hand, a storage medium is provided, which non-temporarily stores computer-readable instructions, wherein when the non-temporary computer-readable instructions are executed by a computer, the instructions of the method described in the first aspect are executed.

[0023] On the other hand, a computer program product is provided, comprising a computer program, wherein the computer program is used to implement the method described in the first aspect when running on one or more processors.

[0024] The above technical solution has the following advantages or beneficial effects:

[0025] (1) The present invention is based on a non-redundant measurement topology, using N voltage sensors to measure the voltage of N battery strings and the voltage of N+1 equivalent connection resistors, with a compact structure and high detection efficiency;

[0026] (2) The present invention adopts a data-driven fault feature recognition method. Due to the inherent orthogonality, this method is sensitive to the inconsistency of battery voltage changes, but is insensitive to voltage changes caused by current mutations. It has high timeliness and low false alarm rate.

[0027] (3) The present invention uses a time window of limited length to limit the effective time of the fault indicator. After a fault occurs, the fault indicator responds and recovers after a fixed time, avoiding being blocked by the fault and affecting the timely diagnosis of new faults, thereby avoiding missed fault reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0029] Figure 1 is a flow chart of the method of embodiment 1;

[0030] Figure 2 This is a schematic diagram of redundant cross-measurement topology of Embodiment 1;

[0031] FIG3( a ) is a short circuit fault diagnosis result-fault assessment value curve of Example 1;

[0032] FIG3( b ) is a short circuit fault diagnosis result of Example 1 - curves of each element of the characteristic vector;

[0033] FIG4( a ) is a diagram showing an electrical connection fault diagnosis result-fault assessment value curve of Example 1;

[0034] FIG4( b ) is a curve of each element of the characteristic vector of the electrical connection fault diagnosis result of the first embodiment;

[0035] FIG5( a ) is a voltage sensor fault diagnosis result-fault assessment value curve of Example 1;

[0036] FIG. 5( b ) is a curve of each element of the voltage sensor fault diagnosis result-characteristic vector of the first embodiment. DETAILED DESCRIPTION

[0037] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0038] The present invention belongs to the field of battery management technology and discloses a battery system early fault diagnosis method, which can detect, distinguish and locate whether the battery system has a battery short circuit fault, an electrical connection fault and a voltage sensor fault. The method mainly includes the following steps: using a non-redundant cross measurement topology to monitor the battery cell voltage and the voltage of the connection resistor in real time; setting up a time window and a "first in first out" data cache, and arranging the detection data within a fixed time into M k Matrix; According to the statistical method, M kPerform feature matching to obtain the fault type and fault location.

[0039] Embodiment 1

[0040] This embodiment provides a battery pack failure early warning method;

[0041] like Figure 1 As shown, the battery pack failure warning method includes:

[0042] S101: Acquire voltage data of a battery pack, the voltage data including: a single cell voltage and a combination voltage, the combination refers to two adjacent single cells and a resistor connected between the two single cells;

[0043] S102: Expanding the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix;

[0044] S103: performing standardization processing on the two-dimensional data analysis matrix to obtain a standardized matrix; and then obtaining a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five sub-matrices;

[0045] S104: Calculate the expectation of the maximum eigenvalue of each submatrix; determine the expected value of the fault indicator of the voltage sensor fault according to the expectation of the maximum eigenvalue of each submatrix; determine whether the voltage sensor fails according to the expected value of the fault indicator.

[0046] Furthermore, the method further comprises:

[0047] S105: normalizing the two-dimensional data analysis matrix to obtain a normalized matrix; and then obtaining a covariance matrix corresponding to the normalized matrix; the covariance matrix corresponding to the normalized matrix includes three new sub-matrices;

[0048] S106: Calculate the expectation of the maximum eigenvalue of each new submatrix; determine the early battery short circuit fault threshold and the early electrical connection fault threshold based on the expectation of the maximum eigenvalue of each new submatrix; determine whether the battery pack has a battery short circuit fault based on the early battery short circuit fault threshold; determine whether the battery pack has an electrical connection fault based on the early electrical connection fault threshold.

[0049] Furthermore, the method further comprises:

[0050] S107: Determine the location where the fault occurs by using the minimum value of the eigenvector element corresponding to the maximum eigenvalue.

[0051] Furthermore, if Figure 2As shown, the S101: obtaining voltage data of the battery pack, the voltage data including: single cell voltage and combination voltage, the combination refers to two adjacent single cells and the resistor connected between the two single cells, specifically including:

[0052] The battery pack includes: a negative electrode of the battery pack, a first connection point, a resistor R 0,1 , the second connection point, the first battery cell, the third connection point, the resistor R 1,2 , 4th connection point, 2nd battery cell, 5th connection point, resistor R 2,3 , 6th connection point, 3rd battery cell, 7th connection point, resistor R 3,4 , the 8th connection point, the 4th battery cell...the 2n-1th connection point, the resistor R n-1,n , 2nth connection point, nth battery cell, 2n+1th connection point, resistor Rn,b, 2n+2th connection point, positive electrode of battery pack; n is greater than or equal to 1;

[0053] A first voltage sensor is connected between the second connection point and the fifth connection point. The voltage collected by the first voltage sensor is A second voltage sensor is connected between the 4th connection point and the 7th connection point. The voltage collected by the second voltage sensor is A third voltage sensor is connected between the 6th connection point and the 9th connection point. The voltage collected by the third voltage sensor is And so on;

[0054] The nth voltage sensor is connected between the 2nth connection point and the 2n+3th connection point. The voltage collected by the nth voltage sensor is

[0055] Get the measurement data vector Where n is the number of batteries connected in series;

[0056] According to the measurement topology and Kirchhoff's voltage law, the decomposition of the measurement data vector is:

[0057]

[0058] in, is the voltage value vector measured by the voltage sensor, is the battery terminal voltage vector, R n,n+1 is the equivalent connection resistance between the nth single cell and the n+1th single cell, R b,1 is the resistance connected to the negative terminal of the battery pack, R n,b It is the resistance connected to the positive terminal of the battery pack; each voltage sensor is used to measure the voltage of two adjacent single cells and the equivalent connection resistance between them.

[0059] It should be understood that the change in voltage detection caused by current change is highly consistent with the current, while the change in voltage detection caused by a fault is poorly correlated with the current. This feature can be used to detect and distinguish battery system faults. Each single battery includes one cell or multiple parallel cells.

[0060] Furthermore, the S102: expanding the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix specifically includes:

[0061] Set the size of the sliding window and the sliding step size w, and move the sliding window over the measured data vector V s Slide up, and each slide step will get a two-dimensional data analysis sub-matrix, and finally get a two-dimensional data analysis matrix M k :

[0062] M k =[V s|k - w+1 ,V s|k - w+2 ,...,V s|k ] T (2)

[0063] in,

[0064]

[0065] V s|k represents e at the kth moment in the time window s state.

[0066] Abstract the influence of the error as vector e and simplify formula (1) to m = Ax1 + Bx2 + e = m * +e, where m * represents the fault-free part of vector m, e is the measurement error, which can be considered to satisfy of white noise, and

[0067]

[0068]

[0069] Further, the S103: standardizes the two-dimensional data analysis matrix to obtain a standardized matrix; and then obtains a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five sub-matrices, specifically including:

[0070] The two-dimensional data analysis matrix M k Standardization, order represents a fault-free, error-free component, where

[0071]

[0072] in, Yes k The standard deviation diagonal matrix of and are the mean vectors of x1 and x2 respectively.

[0073] Adding the failure volume, we get:

[0074]

[0075] in, represents the fault-free component; f k represents the fault component in the time window, which contains the characteristics of the fault duration; ξ represents the fault weight, only the fault channel is non-zero and ||ξ||2=1; is the error component, representing the normal measurement error of the voltage sensor.

[0076] The covariance matrix of For the convenience of analysis, it is divided into 5 parts.

[0077] in

[0078]

[0079] It should be understood that the voltage sensor fault mode matches. When a voltage sensor fails, it can be considered that its detection value is superimposed with the fault component f, so m=m * +ξf. m is the actual detection value, m * is the fault-free component, ξf is the fault component, ξ is the fault weight, only the fault channel is non-zero, and ||ξ||2=.

[0080] It should be understood that the fault detection index and the covariance matrix C k The eigenvalue distribution of is directly related to the eigenvalue distribution of . Therefore, the expectation of the eigenvalue is studied to gain a deeper understanding of the correlation between the fault amplitude and the detection threshold. Each submatrix {C i} is the lower bound of .

[0081] Furthermore, the step S104: calculating the expectation of the maximum eigenvalue of each submatrix specifically includes:

[0082] (1)C 1,k

[0083] According to the commutative law of matrix multiplication, C 1,kIt is expressed as:

[0084]

[0085] in,

[0086] After eigenvalue decomposition, we can get:

[0087]

[0088] in, Sort in descending order The eigenvalue diagonal matrix of is the corresponding feature vector.

[0089] The eigenvalue of the right side of the equation satisfy:

[0090]

[0091] when hour, exist is strictly monotonically increasing, so

[0092] For the maximum eigenvalue

[0093]

[0094] Right now:

[0095]

[0096]

[0097]

[0098] So, Expectations met:

[0099]

[0100] Among them, t f is the fault duration in the sliding window.

[0101] According to Weyl inequality, C k The lower limit of the maximum eigenvalue is:

[0102]

[0103] The definition of Weyl's inequality is as follows:

[0104] Let A,B∈Mn is a Hermite matrix, let each eigenvalue λ i (A),λ i (B),λ i (A+B) are arranged in increasing order, then for each k=1,2,…,n:

[0105] λ k (A)+λ1(B)≤λ k (A+B)≤λ k (A)+λ n (B)

[0106] (2)C 2,k +C 3,k

[0107] Due to the initial failure l =1, so C 2,k +C 3,k Can be expanded to:

[0108]

[0109] in, yes The i-th column of . Let C 2,k +C 3,k The characteristic polynomial of can be expressed as

[0110]

[0111] By mathematical induction we get:

[0112]

[0113] Then, C 2,k +C 3,k The non-zero eigenvalues ​​of are:

[0114]

[0115]

[0116] in therefore, Expectations met:

[0117]

[0118] therefore,

[0119]

[0120] (3)C 4,k

[0121] C 4,k Expand to get:

[0122]

[0123] Its characteristic value satisfy

[0124]

[0125] in, and They are and The jth column of .

[0126] make and:

[0127]

[0128] So

[0129] in therefore,

[0130]

[0131] because therefore,

[0132]

[0133] Therefore, we can get:

[0134]

[0135] (4)C 5,k

[0136] Since the noise level of each voltage channel is almost the same, let It is C 5,k The smallest eigenvalue, is normalized (n-1) -1 E T The minimum eigenvalue of E can be obtained:

[0137]

[0138] Right now

[0139]

[0140] According to the Marchenko-Pastur law in large-dimensional random matrix theory, Because n,w→+∞, c=(n-1) / w∈(0,1], therefore:

[0141]

[0142] The fault indicator is designed as

[0143]

[0144] Among them, λ i,k In the time window The covariance matrix C k The characteristic value of is its average value within the time window, is its standard deviation within the time window

[0145] Further, S104: determining an expected value of a fault indicator of a voltage sensor fault according to an expected maximum eigenvalue of each submatrix, specifically including:

[0146] According to formula (5), formula (8), formula (10), formula (11), and formula (12), the expected value of the fault indicator of the voltage sensor fault is:

[0147]

[0148] in, is j,k expected value.

[0149] Further, S104: determining whether a voltage sensor fails according to the expected value of the fault indicator, specifically includes: if the value of the fault indicator is less than the expected value of the fault indicator, it indicates that a voltage sensor failure occurs in the system.

[0150] Further, S105: normalize the two-dimensional data analysis matrix to obtain a normalized matrix; and then obtain a covariance matrix corresponding to the normalized matrix; the covariance matrix corresponding to the normalized matrix includes three new sub-matrices

[0151] Assume that the initial short circuit fault f occurs in the lth channel x 1,l , the differential measurement vector is described as:

[0152] m=A(x1+ξf)+Bx2+e;

[0153] The sample matrix M in the sliding window k Normalized to:

[0154]

[0155] The covariance matrix of in

[0156]

[0157] The short circuit fault is located at the battery cell voltage vector x1, and the connection fault is located at the contact resistance voltage vector x2. The locations of these two electrical faults are similar, and their statistical analysis methods are also similar.

[0158] Combining (12) and (14), we have

[0159]

[0160] Further, S106: calculating the expectation of the maximum eigenvalue of each new submatrix, specifically including:

[0161] If you want but where i∈{1,…,n-1},δ sc is the detection threshold for a short-circuit fault.

[0162] According to the additivity of matrix trace, we can get Since the eigenvalue expectation satisfies We can get:

[0163]

[0164] For C 2,k , According to the commutative law between matrix trace and expectation, we can get:

[0165]

[0166] For C 3,k , according to the commutative law of matrix trace, in, express The lth column of , therefore, Then we have:

[0167]

[0168] Further, S106: determining an early battery short circuit fault threshold and an early electrical connection fault threshold according to the expectation of the maximum eigenvalue of each new submatrix; determining whether a battery short circuit fault occurs in the battery pack according to the early battery short circuit fault threshold; determining whether an electrical connection fault occurs in the battery pack according to the early electrical connection fault threshold, specifically includes:

[0169] For early battery short circuit failure, combined with (15)(16)(17)(18), we have

[0170]

[0171] The mathematical expression of electrical connection failure is similar to that of short circuit failure. Therefore, for early electrical connection failure,

[0172]

[0173] If formula (19) holds true, it means that a battery short circuit fault has occurred;

[0174] If formula (20) holds true, it means that an electrical connection failure has occurred.

[0175] When the fault assessment value is lower than the corresponding detection threshold, it means that a corresponding fault has occurred in the system.

[0176] Further, S107: determining the location where the fault occurs by the minimum value of the characteristic vector element, specifically includes:

[0177] When a voltage sensor failure occurs, the eigenvector v m,k The smallest element index in is set to i, then the fault occurs in the i-th voltage sensor;

[0178] When a short circuit occurs, find the characteristic vector v m,k A pair of adjacent elements in that are smaller than the average value, whose index is set to {i-1, i}, then the fault occurs in the i-th battery cell;

[0179] When an electrical connection failure occurs, the eigenvector v m,k The smallest element index in is set to i, and the fault occurs between the i-th battery cell and the i+1-th battery cell.

[0180] It should be understood that according to the analysis results, the i-th row of the covariance matrix has the same value as the eigenvalue λ m,k The contribution of can be expressed by the corresponding eigenvector v m,k After fault determination, the outlier eigenvalue λ can be standardized 1,k contribution to determine the location of the fault.

[0181] The present invention was tested in a battery system consisting of 5 battery strings, and the fault detection and location results are shown in Figures 3(a), 3(b), 4(a), 4(b), 5(a), and 5(b).

[0182] Embodiment 2

[0183] This embodiment provides a battery pack failure warning system, including:

[0184] An acquisition module is configured to: acquire voltage data of a battery pack, wherein the voltage data includes a single cell voltage and a combination voltage, wherein the combination refers to two adjacent single cells and a resistor connected between the two single cells;

[0185] An expansion module is configured to: expand the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix;

[0186] A processing module is configured to: perform standardization processing on a two-dimensional data analysis matrix to obtain a standardized matrix; and then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five sub-matrices;

[0187] The determination module is configured to: calculate the expectation of the maximum eigenvalue of each submatrix; determine the fault indicator expectation value of the voltage sensor fault according to the expectation of the maximum eigenvalue of each submatrix; and determine whether the voltage sensor fails according to the fault indicator expectation value.

[0188] It should be noted that the acquisition module, expansion module, processing module and determination module described above correspond to steps S101 to S104 in Embodiment 1, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in Embodiment 1 described above. It should be noted that the modules described above as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0189] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0190] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0191] Embodiment 3

[0192] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory so that the electronic device executes the method described in the above embodiment one.

[0193] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0194] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type. In the implementation process, each step of the above method may be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software.

[0195] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0196] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0197] Embodiment 4 This embodiment further provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in Embodiment 1 is completed.

[0198] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A battery pack failure early warning method, characterized in that: include: Obtaining voltage data of the battery pack, the voltage data including: single cell voltage and combination voltage, the combination refers to two adjacent single cells and a resistor connected between the two single cells; The voltage data of the battery pack is expanded using a sliding window to obtain a two-dimensional data analysis matrix; The two-dimensional data analysis matrix is ​​standardized to obtain a standardized matrix; and then a covariance matrix corresponding to the standardized matrix is ​​obtained; the covariance matrix corresponding to the standardized matrix includes five sub-matrices; Calculate the expectation of the maximum eigenvalue of each submatrix; determine the expected value of the fault indicator of the voltage sensor fault according to the expectation of the maximum eigenvalue of each submatrix; determine whether the voltage sensor fails according to the expected value of the fault indicator; Normalizing the two-dimensional data analysis matrix to obtain a normalized matrix; then obtaining a covariance matrix corresponding to the normalized matrix; the covariance matrix corresponding to the normalized matrix includes three new sub-matrices; Calculate the expectation of the maximum eigenvalue of each new submatrix; determine an early battery short circuit fault threshold and an early electrical connection fault threshold according to the expectation of the maximum eigenvalue of each new submatrix; determine whether a battery short circuit fault occurs in the battery pack according to the early battery short circuit fault threshold; determine whether an electrical connection fault occurs in the battery pack according to the early electrical connection fault threshold; The location of the fault is determined by the minimum value of the eigenvector element corresponding to the maximum eigenvalue; According to the expectation of the maximum eigenvalue of each submatrix, the expected value of the fault indicator of the voltage sensor fault is determined, which specifically includes: The expected fault indicator value for a voltage sensor fault is: in, λ i,k In the time window The covariance matrix C k The characteristic value of is its average value within the time window, is its standard deviation within the time window, is j,k Expected value; If the fault indicator value is less than the expected fault indicator value, it means that a voltage sensor fault has occurred in the system; Determine an early battery short circuit fault threshold and an early electrical connection fault threshold according to the expectation of the maximum eigenvalue of each new submatrix; determine whether a battery short circuit fault occurs in the battery pack according to the early battery short circuit fault threshold; determine whether an electrical connection fault occurs in the battery pack according to the early electrical connection fault threshold, specifically including: For early battery short circuit failure, there are Among them, δ sc is the detection threshold of short-circuit fault, express The lth column, t f is the fault duration within the sliding window; For early electrical connection failures, there are If formula (13) holds true, it means that a battery short circuit fault has occurred; If formula (14) holds true, it means that an electrical connection failure has occurred; The location of the fault is determined by the minimum value of the characteristic vector element, including: When a voltage sensor failure occurs, the eigenvector v m,k The smallest element index in is set to i, then the fault occurs in the i-th voltage sensor; When a short circuit occurs, find the characteristic vector v m,k A pair of adjacent elements in that are smaller than the average value, whose index is set to {i-1, i}, then the fault occurs in the i-th battery cell; When an electrical connection failure occurs, the eigenvector v m,k The smallest element index in is set to i, and the fault occurs between the i-th battery cell and the i+1-th battery cell.

2. The battery pack failure warning method according to claim 1, wherein the battery pack comprises: The negative pole of the battery pack, the first connection point, and the resistor R b,1 , the second connection point, the first battery cell, the third connection point, the resistor R 1,2 , 4th connection point, 2nd battery cell, 5th connection point, resistor R 2,3 , 6th connection point, 3rd battery cell, 7th connection point, resistor R 3,4 , the 8th connection point, the 4th battery cell...the 2n-1th connection point, the resistor R n-1,n , 2nth connection point, nth battery cell, 2n+1th connection point, resistor R n,b , the 2n+2th connection point, the positive electrode of the battery pack; n is greater than or equal to 1; The nth voltage sensor is connected between the 2nth connection point and the 2n+3th connection point. The voltage collected by the nth voltage sensor is Get the measurement data vector Where n is the number of batteries connected in series; According to the measurement topology and Kirchhoff's voltage law, the decomposition of the measurement data vector is: in, is the voltage value vector measured by the voltage sensor, is the battery terminal voltage vector, R n,n+1 is the equivalent connection resistance between the nth single cell and the n+1th single cell, R b,1 is the resistance connected to the negative terminal of the battery pack, R n,b It is the resistance connected to the positive terminal of the battery pack; each voltage sensor is used to measure the voltage of two adjacent single cells and the equivalent connection resistance between them.

3. The battery pack failure warning method according to claim 2, characterized in that: The voltage data of the battery pack is expanded using a sliding window to obtain a two-dimensional data analysis matrix, which specifically includes: Set the size of the sliding window and the sliding step w, and move the sliding window over the measured data vector V s Slide up, and each slide step will get a two-dimensional data analysis sub-matrix, and finally get a two-dimensional data analysis matrix M k : M k =[V s|k-w+1 ,V s|k-w+2 ,...,V s|k ] T (2) Abstract the influence of the error as vector e and simplify formula (1) to m = Ax1 + Bx2 + e = m * +e, where V s|k represents V at the kth moment in the time window s Status; m * represents the fault-free part of vector m, and e is the measurement error; 4. The battery pack failure warning method as claimed in claim 3, characterized in that: The two-dimensional data analysis matrix is ​​standardized to obtain a standardized matrix; and then the covariance matrix corresponding to the standardized matrix is ​​obtained; The covariance matrix corresponding to the standardized matrix includes five sub-matrices, specifically including: The two-dimensional data analysis matrix M k Standardization, order represents a fault-free, error-free component, we get: in in, Yes k The standard deviation diagonal matrix of and are the mean vectors of x1 and x2 respectively; where, represents the fault-free component; f k represents the fault component in the time window, which contains the characteristics of the fault duration; ξ represents the fault weight, only the fault channel is non-zero and ||ξ||2=1; is the error component, representing the normal measurement error of the voltage sensor; The covariance matrix of in 5. Battery pack failure warning system, characterized by: include: An acquisition module is configured to: acquire voltage data of a battery pack, wherein the voltage data includes a single cell voltage and a combination voltage, wherein the combination refers to two adjacent single cells and a resistor connected between the two single cells; An expansion module is configured to: expand the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix; A processing module is configured to: perform standardization processing on a two-dimensional data analysis matrix to obtain a standardized matrix; and then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five sub-matrices; Normalizing the two-dimensional data analysis matrix to obtain a normalized matrix; then obtaining a covariance matrix corresponding to the normalized matrix; the covariance matrix corresponding to the normalized matrix includes three new sub-matrices; A determination module is configured to: calculate the expectation of the maximum eigenvalue of each submatrix; determine the expected value of the fault indicator of the voltage sensor fault according to the expectation of the maximum eigenvalue of each submatrix; determine whether the voltage sensor fails according to the expected value of the fault indicator; Determine an early battery short circuit fault threshold and an early electrical connection fault threshold according to the expectation of the maximum eigenvalue of each new submatrix; determine whether a battery short circuit fault occurs in the battery pack according to the early battery short circuit fault threshold; determine whether an electrical connection fault occurs in the battery pack according to the early electrical connection fault threshold; The method further includes: determining the location where the fault occurs by the minimum value of the eigenvector element corresponding to the maximum eigenvalue; According to the expectation of the maximum eigenvalue of each submatrix, the expected value of the fault indicator of the voltage sensor fault is determined, which specifically includes: The expected fault indicator value for a voltage sensor fault is: If the fault indicator value is less than the expected fault indicator value, it means that a voltage sensor fault has occurred in the system; Determine an early battery short circuit fault threshold and an early electrical connection fault threshold according to the expectation of the maximum eigenvalue of each new submatrix; determine whether a battery short circuit fault occurs in the battery pack according to the early battery short circuit fault threshold; determine whether an electrical connection fault occurs in the battery pack according to the early electrical connection fault threshold, specifically including: For early battery short circuit failure, there are Among them, δ sc is the detection threshold of short-circuit fault, express The lth column, t f is the fault duration within the sliding window; For early electrical connection failures, there are If formula (13) holds true, it means that a battery short circuit fault has occurred; If formula (14) holds true, it means that an electrical connection failure has occurred; The location of the fault is determined by the minimum value of the characteristic vector element, including: When a voltage sensor failure occurs, the eigenvector v m,k The smallest element index in is set to i, then the fault occurs in the i-th voltage sensor; When a short circuit occurs, find the characteristic vector v m,k A pair of adjacent elements in that are smaller than the average value, whose index is set to {i-1, i}, then the fault occurs in the i-th battery cell; When an electrical connection failure occurs, the eigenvector v m,k The smallest element index in is set to i, and the fault occurs between the i-th battery cell and the i+1-th battery cell.

6. An electronic device, comprising: a memory for non-transitory storage of computer readable instructions; as well as a processor for executing the computer readable instructions, When the computer-readable instructions are executed by the processor, the method described in any one of claims 1 to 4 is executed.

7. A storage medium characterized by being non-temporary The computer-readable instructions are stored, wherein when the non-transitory computer-readable instructions are executed by a computer, the instructions of the method according to any one of claims 1 to 4 are executed.

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