CN117310543A - Battery abnormality diagnosis method and device - Google Patents
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 32
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- 238000010276 construction Methods 0.000 claims description 6
- 230000009286 beneficial effect Effects 0.000 abstract description 5
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- 230000006870 function Effects 0.000 description 5
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
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Classifications
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- G—PHYSICS
- 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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- G—PHYSICS
- 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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- G—PHYSICS
- 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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
Abstract
The invention provides a battery abnormality diagnosis method and device, and relates to the technical field of battery detection, wherein the method comprises the following steps: reconstructing a single voltage sequence on the t th day according to a voltage signal matrix containing continuous voltage sequence signals of all battery units in the battery to be detected; calculating multi-scale arrangement entropy values of the voltages of all battery units on the t th day, and carrying out standard deviation processing on the multi-scale arrangement entropy values through a mathematical rule to obtain a voltage abnormal threshold value of the battery on the t th day; determining the voltage variation coefficient of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix; under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormality threshold, the battery to be tested is abnormal, and an abnormal battery unit of the battery to be tested is determined through the voltage variation coefficient, so that whether the battery to be tested is abnormal or not is determined through the multi-scale arrangement entropy value, and the abnormal battery unit is determined through the voltage variation coefficient, thereby being beneficial to improving the accuracy of abnormality diagnosis under the actual working condition.
Description
Technical Field
The present invention relates to the field of battery detection technologies, and in particular, to a method and apparatus for diagnosing battery abnormalities, an electronic device, and a storage medium.
Background
The battery is a complex nonlinear time-varying system with a plurality of inconsistencies, wherein there may be different types of faults, such as battery mismatch, capacity imbalance, battery life imbalance, etc., and particularly during charge and discharge, the batteries in the battery pack affect each other, and the fault condition is more complicated. In the related art, early diagnosis of battery faults is often difficult without obvious anomalies, and is easily affected by random errors. Therefore, it is necessary to develop an abnormality diagnosis method that combines accuracy and simplicity to make a preliminary diagnosis of the battery condition.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first object of the present invention is to provide a battery abnormality diagnosis method, which determines whether a battery to be tested is abnormal by multi-scale arrangement of entropy values, and determines an abnormal battery unit by a voltage variation coefficient, so as to be beneficial to improving the accuracy of abnormality diagnosis under actual working conditions.
A second object of the present invention is to provide a battery abnormality diagnosis device.
A third object of the present invention is to propose an electronic device.
A fourth object of the present invention is to propose a non-transitory computer readable storage medium storing computer instructions.
To achieve the above object, an embodiment of a first aspect of the present invention provides a battery abnormality diagnosis method, including:
constructing a voltage signal matrix of the battery to be tested according to continuous voltage sequence signals of each battery unit in the battery to be tested, and reconstructing a single voltage sequence of each battery unit on the t th day based on the voltage signal matrix;
calculating the multiscale arrangement entropy of each battery unit voltage on the t-th day based on the single voltage sequence, and carrying out standard deviation processing on the multiscale arrangement entropy through a mathematical rule to obtain a voltage abnormality threshold of the battery on the t-th day;
based on Gaussian distribution of each column vector in the voltage signal matrix, determining a voltage average value and a voltage standard deviation of each battery unit, and calculating a voltage variation coefficient of each battery unit according to the voltage average value and the voltage standard deviation of each battery unit;
and under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormality threshold, the battery to be tested is abnormal, and an abnormal battery unit of the battery to be tested is determined through a voltage variation coefficient.
In order to achieve the above objective, a second aspect of the present invention provides a battery abnormality diagnosis device, a construction module, configured to construct a voltage signal matrix of a battery to be tested according to continuous voltage sequence signals of each battery unit in the battery to be tested, and reconstruct a single voltage sequence of each battery unit on a t-th day based on the voltage signal matrix;
the first calculation module is used for calculating multiscale arrangement entropy values of the voltages of all battery units on the t th day based on the single voltage sequence, and carrying out standard deviation processing on the multiscale arrangement entropy values through a mathematical rule so as to obtain a voltage abnormal threshold value of the battery on the t th day;
the second calculation module is used for determining the voltage average value and the voltage standard deviation of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix, and calculating the voltage variation coefficient of each battery unit according to the voltage average value and the voltage standard deviation of each battery unit;
and the determining module is used for determining the abnormal battery unit of the battery to be tested according to the voltage variation coefficient under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormal threshold value.
To achieve the above object, an embodiment of a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
To achieve the above object, an embodiment of a fourth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method according to the first aspect.
According to the battery abnormality diagnosis method, the battery abnormality diagnosis device, the electronic equipment and the storage medium, a single voltage sequence on the t th day is reconstructed according to a voltage signal matrix containing continuous voltage sequence signals of all battery units in a battery to be detected; calculating multi-scale arrangement entropy values of the voltages of all battery units on the t th day, and carrying out standard deviation processing on the multi-scale arrangement entropy values through a mathematical rule to obtain a voltage abnormal threshold value of the battery on the t th day; determining the voltage variation coefficient of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix; under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormality threshold, the battery to be tested is abnormal, and an abnormal battery unit of the battery to be tested is determined through the voltage variation coefficient, so that whether the battery to be tested is abnormal or not is determined through the multi-scale arrangement entropy value, and the abnormal battery unit is determined through the voltage variation coefficient, thereby being beneficial to improving the accuracy of abnormality diagnosis under the actual working condition.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a battery abnormality diagnosis method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another battery abnormality diagnosis method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a battery abnormality diagnosis device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The technical scheme of the invention is to acquire, store, use, process and the like data, which all meet the relevant regulations of national laws and regulations.
The battery abnormality diagnosis method, apparatus, electronic device, and storage medium of the embodiments of the present invention are described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a battery abnormality diagnosis method according to an embodiment of the present invention.
As shown in fig. 1, the method comprises the steps of:
step 101, constructing a voltage signal matrix of the battery to be tested according to continuous voltage sequence signals of each battery unit in the battery to be tested, and reconstructing a single voltage sequence of each battery unit on the t th day based on the voltage signal matrix.
Optionally, an implementation manner of constructing a voltage signal matrix of the battery to be measured according to the continuous voltage sequence signals of each battery unit in the battery to be measured and reconstructing a single voltage sequence of each battery unit on the t-th day based on the voltage signal matrix may be that continuous voltage sequence signals of each battery unit in the battery to be measured are obtained to construct the voltage signal matrix of the battery to be measured; and reconstructing a single voltage sequence of the t th day of each battery unit by a reconstruction phase space method.
In particular, a specific time can be assumedAnd j battery units, then constructing a voltage signal matrix X of the battery to be tested:
(1)
(2)
then, the lithium battery state is filtered and found. Next, the sequence X is updated and a new sequence is obtained
(3)
To reconstruct the phase spaceSetting m as the embedded dimension, λ is the time delay factor, r is a positive integer, and，/>equal to->，/>Then, the coefficients areλAndradded to create a new sequence space (4), where m is the embedding dimension.
(4)
The new sequence isGenerating
And ordered in ascending order, defined as +.>。
Alternatively, the continuous voltage sequence signal of each battery cell in the battery to be tested may be obtained by performing data preprocessing such as NaNs data deletion, time sequencing, and the like on the original voltage signal of each battery cell, but is not limited thereto.
The battery to be measured may be a lithium battery, but is not limited thereto, and the embodiment is not particularly limited thereto.
Step 102, calculating multiscale arrangement entropy values of the voltages of all battery units on the t th day based on the single voltage sequence, and performing standard deviation processing on the multiscale arrangement entropy values through a mathematical rule to obtain a voltage abnormality threshold value of the battery on the t th day.
Optionally, in one embodiment, calculating the multiscale permutation entropy of each battery unit voltage on the t-th day according to the single voltage sequence, and performing standard deviation processing on the multiscale permutation entropy by using a mathematical rule to obtain the voltage abnormality threshold of the battery on the t-th day, the multiscale permutation entropy of each battery unit voltage on the t-th day may be calculated based on the single voltage sequence, and performing standard deviation processing on the multiscale permutation entropy by using the mathematical rule to obtain the minimum multiscale permutation entropy of each battery unit voltage; and taking the minimum multi-scale permutation entropy value as a voltage abnormality threshold value of the t th day of the battery.
Specifically, the single voltage sequence at the t-th day of each battery cell isIn the case of (a) the number of the cells,
(5)
wherein,
p isIs a minimum multiscale permutation entropy value (voltage anomaly threshold value)/(frequency of the system)>Is calculated as follows:
(6)
(7)
step 103, determining the average voltage value and the standard voltage deviation of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix, and calculating the voltage variation coefficient of each battery unit according to the average voltage value and the standard voltage deviation of each battery unit.
Optionally by calculating each column vector in the voltage signal matrixTo build a diagnostic model to identify potential faults of the battery:
(10)
further, a coefficient of variation (Coefficient of Variation, CV) rule for each battery cell voltage is calculated using the voltage average and the voltage standard deviation, and CV is defined as follows:
(11)
then calculate the sequenceAnd the voltage mean and the voltage standard deviation of (2) and to obtain the Gaussian distribution thereof +.>。
(12)
Is to find the threshold value (voltage variation coefficient) of the abnormal cell position. If the CV value exceeds the threshold, the position of the battery cell is deemed to be an abnormal position.
Step 104, when the real-time target voltage of the battery to be tested is greater than the voltage abnormality threshold, the battery to be tested is abnormal, and the abnormal battery unit of the battery to be tested is determined through the voltage variation coefficient.
Optionally, under the condition that the real-time target voltage of the battery to be tested is smaller than or equal to the voltage abnormality threshold, determining that the battery to be tested is in a normal running state, and not carrying out abnormality diagnosis of each battery unit.
According to the battery abnormality diagnosis method, a single voltage sequence on the t th day is reconstructed according to a voltage signal matrix containing continuous voltage sequence signals of all battery units in the battery to be detected; calculating multi-scale arrangement entropy values of the voltages of all battery units on the t th day, and carrying out standard deviation processing on the multi-scale arrangement entropy values through a mathematical rule to obtain a voltage abnormal threshold value of the battery on the t th day; determining the voltage variation coefficient of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix; under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormality threshold, the battery to be tested is abnormal, and an abnormal battery unit of the battery to be tested is determined through the voltage variation coefficient, so that whether the battery to be tested is abnormal or not is determined through the multi-scale arrangement entropy value, and the abnormal battery unit is determined through the voltage variation coefficient, thereby being beneficial to improving the accuracy of abnormality diagnosis under the actual working condition.
In order to clearly illustrate the above embodiment, fig. 2 is a schematic flow chart of another battery abnormality diagnosis method according to an embodiment of the present invention.
Step 201, a voltage signal matrix of the battery to be measured is constructed according to the continuous voltage sequence signals of each battery unit in the battery to be measured, and a single voltage sequence of each battery unit on the t-th day is reconstructed based on the voltage signal matrix.
Step 202, calculating multiscale arrangement entropy values of voltages of all battery units on the t-th day based on a single voltage sequence, and performing standard deviation processing on the multiscale arrangement entropy values through a mathematical rule to obtain a voltage abnormality threshold value of the battery on the t-th day.
Step 203, determining the average voltage value and the standard voltage deviation of each battery unit based on the gaussian distribution of each column vector in the voltage signal matrix, and calculating the voltage variation coefficient of each battery unit according to the average voltage value and the standard voltage deviation of each battery unit.
It should be noted that, regarding the specific implementation of steps 201 to 204, reference may be made to the related description in the above embodiments.
Step 204, when the real-time target voltage of the battery to be tested is greater than the voltage abnormality threshold, the battery to be tested is abnormal, and the target battery unit corresponding to the target voltage is determined through the voltage variation coefficient.
Optionally, under the condition that the real-time target voltage of the battery to be detected is larger than the voltage abnormality threshold, the battery to be detected is abnormal, and then the target voltage is positioned through the voltage variation coefficient, so that a target battery unit corresponding to the target voltage is accurately determined, accurate positioning of the battery unit is realized, and random errors are reduced.
In step 205, the target battery cell is used as an abnormal battery cell of the battery to be tested.
Optionally, after determining the abnormal battery unit, an artificial check can be performed to determine whether the abnormal battery unit is abnormal, and under the condition of determining the abnormality, the operation of the battery to be tested is stopped, and the abnormal single-cell battery is operated and maintained, so that early diagnosis of the battery abnormality is realized under the condition of no obvious abnormality, and the safety of the battery to be tested is ensured.
According to the battery abnormality diagnosis method, a single voltage sequence on the t th day is reconstructed according to a voltage signal matrix containing continuous voltage sequence signals of all battery units in the battery to be detected; calculating multi-scale arrangement entropy values of the voltages of all battery units on the t th day, and carrying out standard deviation processing on the multi-scale arrangement entropy values through a mathematical rule to obtain a voltage abnormal threshold value of the battery on the t th day; determining the voltage variation coefficient of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix; under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormality threshold, the battery to be tested is abnormal, and an abnormal battery unit of the battery to be tested is determined through the voltage variation coefficient, so that whether the battery to be tested is abnormal or not is determined through the multi-scale arrangement entropy value, the abnormal battery unit is determined through the voltage variation coefficient, the accurate positioning and pre-diagnosis of the abnormal battery unit in the battery to be tested are realized, and the safety of the battery to be tested is ensured.
In order to achieve the above embodiments, the present invention also proposes a battery abnormality diagnosis device.
Fig. 3 is a schematic structural diagram of a battery abnormality diagnosis device according to an embodiment of the present invention.
As shown in fig. 3, the battery abnormality diagnosis device 30 includes: the construction module 31, the first calculation module 32, the second calculation module and the determination module 34.
The construction module is used for constructing a voltage signal matrix of the battery to be tested according to continuous voltage sequence signals of all battery units in the battery to be tested, and reconstructing a single voltage sequence of each battery unit on the t th day based on the voltage signal matrix;
the first calculation module is used for calculating multiscale arrangement entropy values of the voltages of all battery units on the t th day based on the single voltage sequence, and carrying out standard deviation processing on the multiscale arrangement entropy values through a mathematical rule so as to obtain a voltage abnormal threshold value of the battery on the t th day;
the second calculation module is used for determining the voltage average value and the voltage standard deviation of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix, and calculating the voltage variation coefficient of each battery unit according to the voltage average value and the voltage standard deviation of each battery unit;
and the determining module is used for determining the abnormal battery unit of the battery to be tested according to the voltage variation coefficient under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormal threshold value.
Further, in one possible implementation manner of the embodiment of the present invention, the construction module 31 is specifically configured to:
acquiring continuous voltage sequence signals of each battery unit in the battery to be tested so as to construct a voltage signal matrix of the battery to be tested;
and reconstructing a single voltage sequence of the t th day of each battery unit by a reconstruction phase space method.
Further, in one possible implementation manner of the embodiment of the present invention, the first calculating module 32 is specifically configured to:
calculating the multi-scale arrangement entropy value of each battery unit voltage on the t th day based on the single voltage sequence, and carrying out standard deviation processing on the multi-scale arrangement entropy value through a mathematical rule to obtain the minimum multi-scale arrangement entropy value of each battery unit voltage;
and taking the minimum multi-scale permutation entropy value as a voltage abnormality threshold value of the t th day of the battery.
Further, in one possible implementation manner of the embodiment of the present invention, the determining module 34 is specifically configured to:
when the real-time target voltage of the battery to be detected is larger than the voltage abnormality threshold, the battery to be detected is abnormal, and a target battery unit corresponding to the target voltage is determined through a voltage variation coefficient;
and taking the target battery unit as an abnormal battery unit of the battery to be tested.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and will not be repeated here.
According to the battery abnormality diagnosis device, a single voltage sequence on the t th day is reconstructed according to a voltage signal matrix containing continuous voltage sequence signals of all battery units in a battery to be detected; calculating multi-scale arrangement entropy values of the voltages of all battery units on the t th day, and carrying out standard deviation processing on the multi-scale arrangement entropy values through a mathematical rule to obtain a voltage abnormal threshold value of the battery on the t th day; determining the voltage variation coefficient of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix; under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormality threshold, the battery to be tested is abnormal, and an abnormal battery unit of the battery to be tested is determined through the voltage variation coefficient, so that whether the battery to be tested is abnormal or not is determined through the multi-scale arrangement entropy value, and the abnormal battery unit is determined through the voltage variation coefficient, thereby being beneficial to improving the accuracy of abnormality diagnosis under the actual working condition.
In order to achieve the above embodiment, the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aforementioned method.
To achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned method.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in a hardware manner or in a software functional module manner. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. A battery abnormality diagnosis method, characterized by comprising:
constructing a voltage signal matrix of the battery to be tested according to continuous voltage sequence signals of each battery unit in the battery to be tested, and reconstructing a single voltage sequence of each battery unit on the t th day based on the voltage signal matrix;
calculating the multiscale arrangement entropy of each battery unit voltage on the t-th day based on the single voltage sequence, and carrying out standard deviation processing on the multiscale arrangement entropy through a mathematical rule to obtain a voltage abnormality threshold of the battery on the t-th day;
based on Gaussian distribution of each column vector in the voltage signal matrix, determining a voltage average value and a voltage standard deviation of each battery unit, and calculating a voltage variation coefficient of each battery unit according to the voltage average value and the voltage standard deviation of each battery unit;
and under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormality threshold, the battery to be tested is abnormal, and an abnormal battery unit of the battery to be tested is determined through a voltage variation coefficient.
2. The method according to claim 1, wherein the constructing a voltage signal matrix of the battery to be tested according to the continuous voltage sequence signals of each battery cell in the battery to be tested, and reconstructing a single voltage sequence of each battery cell on the t-th day based on the voltage signal matrix comprises:
acquiring continuous voltage sequence signals of each battery unit in the battery to be tested so as to construct a voltage signal matrix of the battery to be tested;
and reconstructing a single voltage sequence of the t th day of each battery unit by a reconstruction phase space method.
3. The method according to claim 1, wherein calculating the multi-scale arrangement entropy of the voltages of each battery cell on the t-th day based on the single voltage sequence, and performing standard deviation processing on the multi-scale arrangement entropy by using a mathematical rule to obtain the voltage abnormality threshold on the t-th day of the battery, includes:
calculating the multi-scale arrangement entropy value of each battery unit voltage on the t th day based on the single voltage sequence, and carrying out standard deviation processing on the multi-scale arrangement entropy value through a mathematical rule to obtain the minimum multi-scale arrangement entropy value of each battery unit voltage;
and taking the minimum multi-scale permutation entropy value as a voltage abnormality threshold value of the t th day of the battery.
4. The method according to claim 1, wherein in the case where the target voltage of any one of the battery cells of the battery to be tested is greater than the voltage abnormality threshold, the battery to be tested is abnormal, and the abnormal battery cell of the battery to be tested is determined by the voltage variation coefficient, comprising:
when the real-time target voltage of the battery to be detected is larger than the voltage abnormality threshold, the battery to be detected is abnormal, and a target battery unit corresponding to the target voltage is determined through a voltage variation coefficient;
and taking the target battery unit as an abnormal battery unit of the battery to be tested.
5. A battery abnormality diagnosis device, characterized by comprising:
the construction module is used for constructing a voltage signal matrix of the battery to be tested according to continuous voltage sequence signals of all battery units in the battery to be tested, and reconstructing a single voltage sequence of each battery unit on the t th day based on the voltage signal matrix;
the first calculation module is used for calculating multiscale arrangement entropy values of the voltages of all battery units on the t th day based on the single voltage sequence, and carrying out standard deviation processing on the multiscale arrangement entropy values through a mathematical rule so as to obtain a voltage abnormal threshold value of the battery on the t th day;
the second calculation module is used for determining the voltage average value and the voltage standard deviation of each battery unit based on the Gaussian distribution of each column vector in the voltage signal matrix, and calculating the voltage variation coefficient of each battery unit according to the voltage average value and the voltage standard deviation of each battery unit;
and the determining module is used for determining the abnormal battery unit of the battery to be tested according to the voltage variation coefficient under the condition that the real-time target voltage of the battery to be tested is larger than the voltage abnormal threshold value.
6. The apparatus according to claim 5, wherein the construction module is specifically configured to:
acquiring continuous voltage sequence signals of each battery unit in the battery to be tested so as to construct a voltage signal matrix of the battery to be tested;
and reconstructing a single voltage sequence of the t th day of each battery unit by a reconstruction phase space method.
7. The apparatus of claim 5, wherein the first computing module is specifically configured to:
calculating the multi-scale arrangement entropy value of each battery unit voltage on the t th day based on the single voltage sequence, and carrying out standard deviation processing on the multi-scale arrangement entropy value through a mathematical rule to obtain the minimum multi-scale arrangement entropy value of each battery unit voltage;
and taking the minimum multi-scale permutation entropy value as a voltage abnormality threshold value of the t th day of the battery.
8. The apparatus of claim 5, wherein the determining module is specifically configured to:
when the real-time target voltage of the battery to be detected is larger than the voltage abnormality threshold, the battery to be detected is abnormal, and a target battery unit corresponding to the target voltage is determined through a voltage variation coefficient;
and taking the target battery unit as an abnormal battery unit of the battery to be tested.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-4.
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