CN113311348A - Battery fault identification method based on wavelet decomposition and envelope spectrum analysis - Google Patents
Battery fault identification method based on wavelet decomposition and envelope spectrum analysis Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 31
- 238000010183 spectrum analysis Methods 0.000 title claims abstract description 20
- 230000002159 abnormal effect Effects 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims description 14
- 238000001514 detection method Methods 0.000 claims description 13
- 238000012935 Averaging Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
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- 238000003745 diagnosis Methods 0.000 abstract description 9
- 239000000284 extract Substances 0.000 abstract description 3
- 238000013450 outlier detection Methods 0.000 abstract 1
- 238000001228 spectrum Methods 0.000 abstract 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 208000032953 Device battery issue Diseases 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
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Abstract
The invention provides a battery fault identification method based on wavelet decomposition and envelope spectrum analysis. The method comprises the following steps: the method comprises the steps of firstly obtaining a proper decomposition signal by utilizing wavelet decomposition, selecting a proper detail decomposition signal to carry out Hilbert transform to obtain an envelope spectrum, extracting effective fault characteristics, and detecting a fault battery by utilizing an outlier detection algorithm. The method can effectively extract fault characteristics and realize battery fault diagnosis in advance, and can accurately find abnormal batteries in the battery pack.
Description
Technical Field
The invention belongs to the technical field of battery management, and particularly relates to a battery fault identification method based on wavelet decomposition and envelope spectrum analysis.
Background
With the increase of market share of new energy automobiles, the number of electric automobiles is rapidly increased, and meanwhile, safety accidents of power lithium batteries also frequently occur, so that the life and property safety of people is seriously threatened, and the problem of battery safety becomes a key bottleneck restricting the large-scale rapid development of the electric automobiles. As an intervention means for avoiding serious consequences caused by battery faults, early warning of the battery faults plays an important role in reducing the occurrence rate of the battery safety accidents, and is a research hotspot in recent years.
In the actual operation process of the vehicle-mounted lithium ion battery system, a single battery fault, a battery pack fault, a battery management system hardware fault and the like may exist, and the faults not only can cause the rapid degradation of the battery performance, but also can cause the battery to be on fire to cause serious safety accidents to a certain extent. In order to avoid such situations, it is necessary to rapidly and accurately diagnose a fault occurring in the battery and perform a safety precaution, thereby improving the safety of the use of the battery.
At present, the lithium ion battery fault diagnosis technology is still a hotspot and difficulty in the research of battery safety problems, and the data which can be detected and is easily obtained for a battery system at present are generally voltage, temperature and current. In practice, the battery voltage is usually analyzed because the current changes in a complicated manner and the temperature measurement is influenced by a temperature sensor. The current common method is to extract relevant features by performing time domain analysis on the voltage. The method is used for analyzing from the direction of a frequency domain and effectively realizing fault diagnosis by combining an unsupervised algorithm.
Disclosure of Invention
In view of the above, it is desirable to provide a more accurate method for identifying a faulty cell.
The invention provides a battery fault identification method based on wavelet decomposition and envelope spectrum analysis, which extracts a plurality of characteristic parameters capable of reflecting voltage signal change characteristics and fault frequency from voltage data of each single battery innovatively by using a signal analysis method through acquiring the voltage data of each single battery in the using process of a lithium ion battery system in real time, and realizes fault diagnosis and early warning by combining abnormal value detection of unsupervised learning. Therefore, the battery fault diagnosis algorithm based on wavelet decomposition and envelope spectrum analysis is specifically described as follows:
a battery fault identification method based on wavelet decomposition and envelope spectrum analysis comprises the following steps:
s1, collecting cell voltage data in the use process of the battery pack in a set time window as an original data set, wherein the original data set comprises voltage data of normal single batteries and voltage data of fault single batteries;
s2, performing wavelet decomposition on all voltage data of the acquired original data set to obtain detail components, and performing Hilbert transform envelope spectrum analysis on the detail components to obtain a mean value amplitude value corresponding to each cell voltage;
and S3, taking the mean amplitude value as a fault characteristic value and taking the fault characteristic value as a basic sample point, and carrying out abnormal value detection based on distance, wherein the abnormal value detection is to calculate the distance from each sample point to the center of the sample, and if the distance deviation of one or more single sample points exceeds a set threshold value, the sample point can be judged to be an abnormal value.
In the above method for identifying a battery fault based on wavelet decomposition and envelope spectrum analysis, the specific implementation process of step S2 includes:
s2.1, denoising and decomposing all voltage data of the acquired original data set, and performing 4-layer discrete wavelet decomposition on each single cell voltage signal in a time window to obtain 2 detail components and 2 approximate components, wherein the specific implementation of the discrete wavelet decomposition of the signals is as follows:
wherein Wv(m, n) represents the discrete wavelet transform of the signal function v (t), and the invention will obtain 2 approximate components W by discrete transform1(m,n),W2(m, n) and 2 detail components W3(m,n),W4(m, n), v (t) are cell voltage signals,
is a wavelet function. t is the sampling time, a0Greater than 1 is a scale factor, m belongs to Z as a discrete order, b0E R is a translation factor, and n E Z is an arbitrary constant.
S3, the detail signal obtained by the method can obviously reflect the fault information, so the detail component W is subjected to4(m, n) (hereinafter abbreviated as W)4) And performing Hilbert transform envelope spectrum analysis, wherein the specific implementation process of the Hilbert transform is as follows:
in the formula, w4And (t) is a detail component signal obtained after wavelet decomposition of the cell voltage v (t). H [ w ]* 4(t)]Is w4(t) a signal obtained by Hilbert transform. The amplitude h (t), instantaneous phase phi (t) and instantaneous frequency formula w (t) obtained after transformation correspond to an amplitude h (t) for each instantaneous frequency w (t), and the corresponding formula is as follows:
in the above method for identifying battery failure based on wavelet decomposition and envelope spectrum analysis, the mean amplitude in step S2 is obtained by averaging the signal amplitudes h (t) with different frequencies to obtain a mean amplitude a (t) as a failure characteristic value, and the failure feature set x of different cells in the battery pack is [ a ═ a [ ]1(t),a2(t)...aN(t)]And N is the number of batteries.
In the above method for identifying battery faults based on wavelet decomposition and envelope spectrum analysis, the specific implementation process of step S3 is as follows: and taking the fault characteristic value as a basic sample point, and carrying out abnormal value detection based on the distance, wherein the abnormal value detection is to calculate the distance from each sample point to the center of the sample, and if the distance deviation of one or more single sample points exceeds a set threshold value, the sample point can be judged to be an abnormal value. The specific formula is as follows:
in the formula xiFor the fault characteristic value, i is 1,2,3 … N,
is an average value and is calculated by the formula
dist(xi) Is the sample distance sought.
Compared with the prior art, the method for identifying the fault single battery analyzes the voltage data acquired in the operation process of the battery pack, and extracts the characteristic parameters reflecting the voltage signal change characteristics from the voltage data of each single battery by innovatively utilizing a frequency domain analysis method. By utilizing wavelet decomposition and envelope spectrum analysis algorithms, the detection and identification of the single battery with faults can be carried out in real time in a short time, the accuracy of fault diagnosis of the single battery is improved, and the missing report rate is reduced. The single battery fault diagnosis method provided by the invention has an important effect on improving the safety of using a battery pack product.
Drawings
Fig. 1 is a diagram of a wavelet decomposition process.
Fig. 2 is a diagnostic flowchart of a battery fault diagnosis method based on wavelet decomposition and envelope spectrum analysis.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
the method for detecting a faulty cell provided by the present invention will be described in further detail below.
The battery fault identification method provided by the invention comprises the following specific steps:
s1, acquiring cell voltage data of the battery pack in the using process as an original data set, wherein the number of the battery packs is N, and the original data set comprises voltage data of normal single batteries and voltage data of fault single batteries;
s2, 2, performing denoising and decomposition processing on the acquired original voltage data (including fault data), and performing 4-layer discrete wavelet decomposition on each cell voltage signal in the time window to obtain 2 detail components and 2 approximate components, wherein the discrete wavelet decomposition of the signals is specifically realized as follows:
wherein Wv(m, n) represents the discrete wavelet transform of the signal function v (t), and the invention will obtain 2 approximate components W by discrete transform1(m,n),W2(m, n) and 2 detail components W3(m,n),W4(m, n), v (t) are cell voltage signals,
is a wavelet function. t is the sampling time, a0Greater than 1 is a scale factor, m belongs to Z as a discrete order, b0E R is a translation factor, and n E Z is an arbitrary constant.
S3, the detail signal obtained by the method can obviously reflect the fault information, so the detail component W is subjected to4(m, n) (hereinafter abbreviated as W)4) And performing Hilbert transform envelope spectrum analysis, wherein the specific implementation process of the Hilbert transform is as follows:
in the formula, w4And (t) is a detail component signal obtained after wavelet decomposition of the cell voltage v (t). H [ w ]* 4(t)]Is w4(t) obtained by Hilbert transformA signal. The amplitude h (t), instantaneous phase phi (t) and instantaneous frequency formula w (t) obtained after transformation correspond to an amplitude h (t) for each instantaneous frequency w (t), and the corresponding formula is as follows:
s4, averaging the signal amplitudes h (t) with different frequencies to obtain an average amplitude a (t) as a fault feature, so that different battery fault feature sets x in the battery pack are [ a ═ a [ ]1(t),a2(t)...aN(t)]And N is the number of batteries.
And S5, taking the fault characteristic value as a basic sample point, and carrying out abnormal value detection based on the distance, wherein the basic idea of the abnormal value detection is to calculate the distance from each sample point to the center of the sample, and if the distance deviation of one or more single sample points exceeds a set threshold value, the sample point can be judged to be an abnormal value.
And S6, performing real-time diagnosis on the battery by using a sliding window method.
In step S1, the battery type of the battery pack is not limited to a certain type, and in this embodiment, a lithium ion battery is detected. The battery pack comprises N single batteries with the serial numbers of 1,2 and 3 … N in sequence, wherein N is an integer larger than 1, and the N single batteries can form the battery pack in any series-parallel connection mode.
In step S5, the mahalanobis distance is used in the distance-based abnormal point detection algorithm, and the specific formula is as follows:
in the formula xiFor the fault characteristic value, i is 1,2,3 … N,
is an average value and is calculated by the formula
dist (x) is the sample distance sought.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (4)
1. A battery fault identification method based on wavelet decomposition and envelope spectrum analysis comprises the following steps:
s1, collecting cell voltage data in the use process of the battery pack in a set time window as an original data set, wherein the original data set comprises voltage data of normal single batteries and voltage data of fault single batteries;
s2, performing wavelet decomposition on all voltage data of the acquired original data set to obtain detail components, and performing Hilbert transform envelope spectrum analysis on the detail components to obtain a mean value amplitude value corresponding to each cell voltage;
and S3, taking the mean amplitude value as a fault characteristic value and taking the fault characteristic value as a basic sample point, and carrying out abnormal value detection based on distance, wherein the abnormal value detection is to calculate the distance from each sample point to the center of the sample, and if the distance deviation of one or more single sample points exceeds a set threshold value, the sample point can be judged to be an abnormal value.
2. The method for battery fault identification based on wavelet decomposition and envelope spectrum analysis according to claim 1, wherein the specific implementation process of step S2 includes:
s2.1, denoising and decomposing all voltage data of the acquired original data set, and performing 4-layer discrete wavelet decomposition on each single cell voltage signal in a time window to obtain 2 detail components and 2 approximate components, wherein the specific implementation of the discrete wavelet decomposition of the signals is as follows:
wherein Wv(m, n) represents the discrete wavelet transform of the signal function v (t), and the invention will obtain 2 approximate components W by discrete transform1(m,n),W2(m, n) and 2 detail components W3(m,n),W4(m, n), v (t) are cell voltage signals,
is a wavelet function, t is the sampling time, a0Greater than 1 is a scale factor, m belongs to Z as a discrete order, b0E is R as a translation factor, n is Z as an arbitrary constant;
s3, the detail signal obtained by the method can obviously reflect the fault information, so the detail component W is subjected to4(m, n) (hereinafter abbreviated as W)4) And performing Hilbert transform envelope spectrum analysis, wherein the specific implementation process of the Hilbert transform is as follows:
in the formula, w4(t) is a detail component signal obtained after wavelet decomposition of the cell voltage v (t); h [ w ]* 4(t)]Is w4(t) a signal obtained by hilbert transform; the amplitude h (t), instantaneous phase phi (t) and instantaneous frequency formula w (t) obtained after transformation correspond to an amplitude h (t) for each instantaneous frequency w (t), and the corresponding formula is as follows:
3. the method according to claim 1, wherein the mean amplitude in step S2 is obtained by averaging signal amplitudes h (t) with different frequencies to obtain a mean amplitude a (t) as a fault characteristic value, and different cell fault characteristic sets x ═ a in the battery pack1(t),a2(t)...aN(t)]And N is the number of batteries.
4. The method for identifying battery faults based on wavelet decomposition and envelope spectrum analysis as claimed in claim 1, wherein the specific implementation process of step S3 is: taking the fault characteristic value as a basic sample point, and carrying out abnormal value detection based on distance, wherein the abnormal value detection is to calculate the distance from each sample point to the center of the sample, and if the distance deviation of one or more single sample points exceeds a set threshold value, the sample point can be judged to be an abnormal value; the specific formula is as follows:
in the formula xiFor the fault characteristic value, i is 1,2,3 … N,
is an average value and is calculated by the formula
dist(xi) Is the sample distance sought.
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