CN116008817A - Lithium battery fault early warning method, device, equipment and computer readable storage medium - Google Patents
Lithium battery fault early warning method, device, equipment and computer readable storage medium Download PDFInfo
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- CN116008817A CN116008817A CN202310047121.0A CN202310047121A CN116008817A CN 116008817 A CN116008817 A CN 116008817A CN 202310047121 A CN202310047121 A CN 202310047121A CN 116008817 A CN116008817 A CN 116008817A
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- 238000000034 method Methods 0.000 title claims abstract description 35
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 18
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 18
- 238000012417 linear regression Methods 0.000 claims abstract description 23
- 238000004590 computer program Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 208000032953 Device battery issue Diseases 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
- 238000005562 fading Methods 0.000 description 2
- 206010016766 flatulence Diseases 0.000 description 2
- 229910001416 lithium ion Inorganic materials 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000005802 health problem Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
Embodiments of the present application provide a method, apparatus, device, and computer-readable storage medium for lithium battery fault early warning. The method comprises the steps of obtaining a state parameter of battery operation; based on the state parameters and the battery cycle times, establishing a linear regression model between a main peak value and cycle times for describing a safe operation stage of the battery; based on the linear regression model, obtaining X ten-digit regression of the main peak value; x is a positive integer; and determining the early warning triggering condition of the battery based on the X ten-digit regression. In this way, accurate prediction of lithium battery failure is achieved.
Description
Technical Field
Embodiments of the present disclosure relate to the field of battery fault early warning, and in particular, to a method, an apparatus, a device, and a computer readable storage medium for lithium battery fault early warning.
Background
With the increasing application range and number of lithium ion batteries, the short plates and the technical problems which are not overcome at present are exposed gradually, wherein typical problems are health problems and safety problems of the lithium ion batteries.
Therefore, how to perform accurate fault early warning of the lithium battery is a problem to be solved at present.
Disclosure of Invention
According to the embodiment of the application, a lithium battery fault early warning scheme is provided.
In a first aspect of the present application, a method for early warning of a lithium battery fault is provided. The method comprises the following steps:
acquiring a state parameter of battery operation;
based on the state parameters and the battery cycle times, establishing a linear regression model between a main peak value and cycle times for describing a safe operation stage of the battery;
based on the linear regression model, obtaining X ten-digit regression of the main peak value; x is a positive integer;
and determining the early warning triggering condition of the battery based on the X ten-digit regression.
Further, the loss function of the linear regression model is as follows:
wherein τ represents the quantile value;
y i representing actual data points;
Further, the determining the early warning trigger condition of the battery based on the X tenth digit regression includes:
obtaining X groups of residual data based on the X ten-digit regression;
carrying out normal distribution inspection on the X groups of residual data to obtain a group of quantile regression results with maximum residual normal significance;
calculating the slope and intercept of a group of quantile regression results with the largest residual normal significance;
and determining the early warning triggering condition of the battery based on the slope and the intercept.
Further, residual data is calculated by the following formula:
wherein p is i Primary peak-to-peak raw data;
Further, the determining the early warning trigger condition of the battery based on the slope and the intercept comprises:
based on the slope and the intercept, determining a central straight line and upper and lower boundaries of the banded region in a safe running state of the battery;
and determining the early warning triggering condition of the battery based on the center straight line and the upper and lower boundaries.
Further, the center straight line of the band-shaped region in the battery safe operation state is determined by the following formula:
p ′ ＝k s n+b s +μ
wherein n is the number of battery cycles;
h is the main peak-to-peak point;
k s slope of a set of quantile regression results with maximum residual normal significance;
b s intercept of a set of quantile regression results with maximum residual normal significance;
mu is the mean of quantile regression residuals.
Further, the upper and lower boundaries of the band-shaped region in the safe operation state of the battery are determined by the following formula:
p′ ± ＝k s n+b s +μ±3σ
wherein σ is the standard deviation of the residual error.
In a second aspect of the present application, a lithium battery fault pre-warning device is provided. The device comprises:
the acquisition module is used for acquiring the state parameters of battery operation;
the establishing module is used for establishing a linear regression model between a main peak value and the circulation times for describing the safe operation stage of the battery based on the state parameters and the circulation times of the battery;
the calculation module is used for obtaining X ten-digit regression of the main peak value based on the linear regression model; x is a positive integer;
and the determining module is used for determining the early warning triggering condition of the battery based on the X ten-digit regression.
In a third aspect of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method as described above when executing the program.
In a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as according to the first aspect of the present application.
According to the lithium battery fault early warning method, the state parameters of battery operation are obtained; based on the state parameters and the battery cycle times, establishing a linear regression model between a main peak value and cycle times for describing a safe operation stage of the battery; based on the linear regression model, obtaining X ten-digit regression of the main peak value; x is a positive integer; based on the X ten-digit regression, the early warning trigger condition of the battery is determined, and accurate prediction (early warning) of the faults of the lithium battery is realized.
It should be understood that the description in this summary is not intended to limit key or critical features of embodiments of the present application, nor is it intended to be used to limit the scope of the present application. Other features of the present application will become apparent from the description that follows.
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The above and other features, advantages and aspects of embodiments of the present application will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:
fig. 1 is a flowchart of a lithium battery fault early warning method according to an embodiment of the present application;
fig. 2 is a block diagram of a lithium battery fault early warning device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a terminal device or a server suitable for implementing an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Fig. 1 shows a flowchart of a lithium battery fault early warning method according to an embodiment of the present disclosure. The method comprises the following steps:
s110, acquiring state parameters of battery operation.
There are various faults of the battery, such as tab breakage, internal short circuit, battery perforation, deformation, swelling, and the like. In practical use, the user cannot monitor the appearance of the battery in real time, so that other methods can only be sought to identify battery faults. The battery fault often accompanies abnormal reaction of battery data, taking the battery flatulence bulge as an example, after the flatulence bulge occurs in the type II battery, an obvious inflection point appears in a capacity fading curve, the capacity fading is accelerated, and an obvious capacity 'jump' phenomenon appears, so that the inflection point is identified, and an alarm is given out at the inflection point, so that the battery is an important measure for guaranteeing the safety of the battery.
Wherein the status parameter in the present disclosure includes battery capacity.
In some embodiments, the area where the battery parameter changes in the safety state is referred to as a safety area, and is used to determine the operation state of the battery, and when the parameter passes out of the safety area, it is determined that the battery fails.
Further, the safety region in the present disclosure is a band-shaped safety region, and includes all the main peak-to-peak measurement points when the battery is safely operated. When the battery fails, the main peak value and the peak value of the battery decline in an acceleration way, and the data points gradually go out of the safety area.
And S120, based on the state parameters and the battery cycle times, establishing a linear regression model between the main peak value and the cycle times for describing the safe operation stage of the battery.
In some embodiments, a linear regression model is established between the number of cycles and the main peak to peak value that describes the safe operation phase of the battery based on the state parameters and the number of battery cycles. Different from the most common least square linear regression model, the method selects a quantile linear regression method which is more robust to the outlier and can acquire a plurality of regression lines to build the model.
The loss function of the linear regression model is shown below:
wherein τ represents the quantile value;
y i representing actual data points;
S130, obtaining X ten-digit regression of a main peak value based on the linear regression model; and X is a positive integer.
In some embodiments, based on the linear regression model, X number of ten-digit regression of the main peak-to-peak value is calculated, in this embodiment, illustrated with 9 number of ten digits of 0.1-0.9, as follows:
P＝k 1 n+b 1 (τ＝0.1)
……
P＝k 9 n+b 9 (τ＝0.9)
wherein n is the number of cycles;
p is the main peak-to-peak value data;
k and b are the slope and intercept of the regression line.
And S140, determining the early warning triggering condition of the battery based on the X ten-digit regression.
In some embodiments, the residuals of the raw data for each split into several regressions are calculated by the following formula, yielding 9 sets of residual data:
wherein p is i Primary peak-to-peak raw data;
Further, normal distribution inspection is performed on the X groups of residual data to obtain a group of quantile regression results with maximum normal significance of the residual, and normal distribution obeyed by the group of residual is expressed as:
e τ ～N(μ，σ 2 )
the center line of the band-shaped region in the safe operation state of the battery is determined by the following formula:
p ′ ＝k s n+b s +μ
wherein n is the number of battery cycles;
h is the main peak-to-peak point;
k s slope of a set of quantile regression results with maximum residual normal significance;
b s intercept of a set of quantile regression results with maximum residual normal significance;
mu is the mean of quantile regression residuals.
Further, the upper and lower boundaries of the band-shaped region in the safe operation state of the battery are determined by the following formula:
p′ ± ＝k s n+b s +μ±3σ
wherein σ is the standard deviation of the residual error.
Further, the early warning trigger condition of the battery is determined by the following formula:
(h n-1 ，h，h n+1 ≤k s n+b s +μ-3σ)‖(h n-1 ，h，h n+1 ≥k s n+b s +μ+3σ)。
according to the embodiment of the disclosure, the following technical effects are achieved:
by the method, accurate prediction (early warning) of the faults of the lithium battery is achieved.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required in the present application.
The foregoing is a description of embodiments of the method, and the following further describes embodiments of the device.
Fig. 2 shows a block diagram of a lithium battery fault early warning apparatus 200 according to an embodiment of the present application as shown in fig. 2, the apparatus 200 includes:
an obtaining module 210, configured to obtain a state parameter of battery operation;
the establishing module 220 is configured to establish a linear regression model between the cycle times and the main peak value for describing the safe operation phase of the battery based on the state parameter and the cycle times of the battery;
the calculation module 230 is configured to obtain X ten-bit regression of the main peak value based on the linear regression model; x is a positive integer;
the determining module 240 is configured to determine an early warning trigger condition of the battery based on the X number of tenth bit regression.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
Fig. 3 shows a schematic diagram of a structure of a terminal device or a server suitable for implementing an embodiment of the present application.
As shown in fig. 3, the terminal device or the server includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the terminal device or the server are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.
In particular, the above method flow steps may be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units or modules may also be provided in a processor. Wherein the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present application also provides a computer-readable storage medium that may be included in the electronic device described in the above embodiments; or may be present alone without being incorporated into the electronic device. The computer-readable storage medium stores one or more programs that when executed by one or more processors perform the methods described herein.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.
Claims (10)
1. The lithium battery fault early warning method is characterized by comprising the following steps of:
acquiring a state parameter of battery operation;
based on the state parameters and the battery cycle times, establishing a linear regression model between a main peak value and cycle times for describing a safe operation stage of the battery;
based on the linear regression model, obtaining X ten-digit regression of the main peak value; x is a positive integer;
and determining the early warning triggering condition of the battery based on the X ten-digit regression.
2. The method of claim 1, wherein the loss function of the linear regression model is as follows:
wherein τ represents the quantile value;
y i representing actual data points;
3. The method of claim 2, wherein determining the battery pre-warning trigger condition based on the X number of ten bit regression comprises:
obtaining X groups of residual data based on the X ten-digit regression;
carrying out normal distribution inspection on the X groups of residual data to obtain a group of quantile regression results with maximum residual normal significance;
calculating the slope and intercept of a group of quantile regression results with the largest residual normal significance;
and determining the early warning triggering condition of the battery based on the slope and the intercept.
4. A method according to claim 3, characterized in that the residual data is calculated by the following formula:
wherein p is i Primary peak-to-peak raw data;
5. The method of claim 4, wherein determining a pre-warning trigger condition for a battery based on the slope and intercept comprises:
based on the slope and the intercept, determining a central straight line and upper and lower boundaries of the banded region in a safe running state of the battery;
and determining the early warning triggering condition of the battery based on the center straight line and the upper and lower boundaries.
6. The method of claim 5, wherein the center line of the band-shaped region in the safe operating state of the battery is determined by the following formula:
p ′ ＝k s n+b s +μ
wherein n is the number of battery cycles;
h is the main peak-to-peak point;
k s slope of a set of quantile regression results with maximum residual normal significance;
b s intercept of a set of quantile regression results with maximum residual normal significance;
mu is the mean of quantile regression residuals.
7. The method of claim 6, wherein the upper and lower boundaries of the band-shaped zone in the safe operating state of the battery are determined by the following formula:
p′ ± ＝k s n+b s +μ±3σ
wherein σ is the standard deviation of the residual error.
8. The utility model provides a lithium cell trouble early warning device which characterized in that includes:
the acquisition module is used for acquiring the state parameters of battery operation;
the establishing module is used for establishing a linear regression model between a main peak value and the circulation times for describing the safe operation stage of the battery based on the state parameters and the circulation times of the battery;
the calculation module is used for obtaining X ten-digit regression of the main peak value based on the linear regression model; x is a positive integer;
and the determining module is used for determining the early warning triggering condition of the battery based on the X ten-digit regression.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the computer program, implements the method according to any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-7.
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| CN106462667A (en) * | 2014-06-11 | 2017-02-22 | 皇家飞利浦有限公司 | Personal emergency response system with predictive emergency dispatch risk assessment |
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Application publication date: 20230425 |