CN116466237B - Charging safety monitoring and early warning method and system for lithium battery - Google Patents
Charging safety monitoring and early warning method and system for lithium battery Download PDFInfo
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- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 202
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 201
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000012544 monitoring process Methods 0.000 title claims abstract description 38
- 238000004458 analytical method Methods 0.000 claims abstract description 46
- 238000012502 risk assessment Methods 0.000 claims abstract description 33
- 238000003745 diagnosis Methods 0.000 claims abstract description 28
- 238000004140 cleaning Methods 0.000 claims abstract description 14
- 230000010354 integration Effects 0.000 claims abstract description 9
- 238000012423 maintenance Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000012549 training Methods 0.000 claims description 27
- 238000012706 support-vector machine Methods 0.000 claims description 26
- 238000012360 testing method Methods 0.000 claims description 23
- 238000005070 sampling Methods 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 4
- 238000011156 evaluation Methods 0.000 claims description 4
- 230000009469 supplementation Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 2
- 239000007789 gas Substances 0.000 description 59
- 230000008569 process Effects 0.000 description 11
- 230000006870 function Effects 0.000 description 5
- 230000005856 abnormality Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 125000004435 hydrogen atom Chemical class [H]* 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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Abstract
The invention discloses a method and a system for monitoring and early warning charging safety of a lithium battery, which are applied to the technical field of data processing, wherein the method comprises the following steps: and acquiring a multi-dimensional lithium battery charging data stream, performing data cleaning and integration to obtain a multi-dimensional standard lithium battery charging data stream, and performing fault parameter identification to obtain charging fault clue information. And constructing a charging safety risk analysis model based on the charging fault clue information. And carrying out charging safety monitoring on the target lithium battery by using the sensor group to obtain a charging operation real-time data stream. And inputting the charging operation real-time data stream into a charging safety risk analysis model for analysis, and obtaining lithium battery charging risk diagnosis information. And carrying out risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information. The technical problems of low early warning accuracy and high hysteresis in the prior art of lithium battery charging safety early warning are solved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a system for monitoring and early warning of charging safety of a lithium battery.
Background
Along with the development of new energy technology, lithium batteries have become a mainstream product for battery energy storage, and lithium metal is very active due to its chemical characteristics, so that safety accidents are very easy to occur in the charging process, and serious explosion accidents are also possible to occur. However, in the prior art, the charging safety monitoring and early warning method for the lithium battery can only monitor and early warn individual parameters in the charging process, and the actual state of the battery cannot be accurately estimated, so that the problem of low accuracy and high hysteresis of the charging safety monitoring and early warning of the lithium battery is caused.
Therefore, the technical problems of low early warning accuracy and high hysteresis of the safety early warning of the lithium battery charging in the prior art exist.
Disclosure of Invention
The method and the system for monitoring and early warning the charging safety of the lithium battery solve the technical problems of low early warning accuracy and high hysteresis in the prior art of the charging safety early warning of the lithium battery.
The application provides a charging safety monitoring and early warning method of a lithium battery, which comprises the following steps: acquiring a multi-dimensional lithium battery charging data stream through big data, wherein the multi-dimensional lithium battery charging data stream comprises a voltage charging data stream, an internal resistance charging data stream and a temperature charging data stream; data cleaning and integration are carried out on the multi-dimensional lithium battery charging data stream, and a multi-dimensional standard lithium battery charging data stream is obtained; performing fault parameter identification based on the multi-dimensional standard lithium battery charging data stream to obtain charging fault clue information; based on the charging fault clue information, a charging safety risk analysis model is constructed, wherein the charging safety risk analysis model comprises a charging risk level analysis model, a charging risk type analysis model and a charging risk incentive analysis model; the method comprises the steps of performing charging safety monitoring on a target lithium battery by using a sensor group to obtain a charging operation real-time data stream; inputting the charging operation real-time data stream into the charging safety risk analysis model for analysis to obtain lithium battery charging risk diagnosis information; and carrying out risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information.
The application also provides a lithium battery's safety monitoring early warning system that charges, the system includes: the charging data stream acquisition module is used for acquiring a multi-dimensional lithium battery charging data stream through big data, wherein the multi-dimensional lithium battery charging data stream comprises a voltage charging data stream, an internal resistance charging data stream and a temperature charging data stream; the charging data stream processing module is used for carrying out data cleaning and integration on the multi-dimensional lithium battery charging data stream to obtain a multi-dimensional standard lithium battery charging data stream; the charging fault clue acquisition module is used for carrying out fault parameter identification based on the multi-dimensional standard lithium battery charging data stream to acquire charging fault clue information; the risk analysis model construction module is used for constructing a charging safety risk analysis model based on the charging fault clue information, wherein the charging safety risk analysis model comprises a charging risk level analysis model, a charging risk type analysis model and a charging risk incentive analysis model; the real-time charging data stream acquisition module is used for carrying out charging safety monitoring on the target lithium battery by utilizing the sensor group to acquire a charging operation real-time data stream; the risk diagnosis module is used for inputting the charging operation real-time data stream into the charging safety risk analysis model for analysis, and obtaining lithium battery charging risk diagnosis information; and the risk early warning module is used for carrying out risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information.
The application also provides an electronic device, comprising:
a memory for storing executable instructions;
and the processor is used for realizing the charge safety monitoring and early warning method of the lithium battery when executing the executable instructions stored in the memory.
The embodiment of the application provides a computer readable storage medium, which stores a computer program, and when the program is executed by a processor, the method for monitoring and early warning of the charging safety of the lithium battery is realized.
According to the charging safety monitoring and early warning method and system for the lithium battery, provided by the application, the charging operation real-time data flow is evaluated from multiple aspects by acquiring the charging safety risk analysis model and acquiring the charging fault clue, the accuracy of the charging safety early warning of the lithium battery is improved, and the abnormality existing in the charging process can be quickly found due to the fact that the monitoring data is the charging data flow, so that early warning hysteresis is further reduced. The technical problems of low early warning accuracy and high hysteresis in the prior art of lithium battery charging safety early warning are solved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
Fig. 1 is a schematic flow chart of a method for monitoring and early warning charging safety of a lithium battery according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a multi-dimensional standard lithium battery charging data flow obtained by the method for monitoring and early warning charging safety of a lithium battery according to an embodiment of the present application;
fig. 3 is a schematic flow chart of obtaining charging fault clue information by the charging safety monitoring and early warning method of the lithium battery according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of a system of a method for monitoring and early warning charging safety of a lithium battery according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system electronic device of a method for monitoring and early warning charging safety of a lithium battery according to an embodiment of the present invention.
Reference numerals illustrate: the system comprises a charging data stream acquisition module 11, a charging data stream processing module 12, a charging fault clue acquisition module 13, a risk analysis model construction module 14, a real-time charging data stream acquisition module 15, a risk diagnosis module 16, a risk early warning module 17, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
Example 1
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only.
While the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, the modules are merely illustrative, and different aspects of the system and method may use different modules.
A flowchart is used in this application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
As shown in fig. 1, an embodiment of the present application provides a method for monitoring and early warning charging safety of a lithium battery, where the method includes:
s10: acquiring a multi-dimensional lithium battery charging data stream through big data, wherein the multi-dimensional lithium battery charging data stream comprises a voltage charging data stream, an internal resistance charging data stream and a temperature charging data stream;
s20: data cleaning and integration are carried out on the multi-dimensional lithium battery charging data stream, and a multi-dimensional standard lithium battery charging data stream is obtained;
s30: performing fault parameter identification based on the multi-dimensional standard lithium battery charging data stream to obtain charging fault clue information;
specifically, a multi-dimensional lithium battery charging data stream is obtained through big data, namely lithium battery charging data of multiple dimensions is obtained from a lithium battery charging process, and the multi-dimensional lithium battery charging data stream comprises voltage record data of a voltage charging data stream, namely a charging process, internal resistance record data of an internal resistance charging data stream, namely a battery charging process, and temperature record data of a temperature charging data stream, namely a battery temperature record data of the charging process. And carrying out data cleaning and integration on the multi-dimensional lithium battery charging data stream to obtain a multi-dimensional standard lithium battery charging data stream. Further, fault parameter identification is carried out based on the multi-dimensional standard lithium battery charging data stream, fault parameter characteristics existing in the lithium battery charging fault data stream are identified, and charging fault clue information is obtained.
As shown in fig. 2, the method S20 provided in the embodiment of the present application further includes:
s21: carrying out missing numerical identification based on the multi-dimensional lithium battery charging data stream to obtain a vacant charging data stream;
s22: performing numerical value limitation analysis on the multi-dimensional lithium battery charging data stream to obtain a negative threshold charging data stream;
s23: cleaning the vacant charging data stream and the negative threshold charging data stream to obtain a reference lithium battery charging data stream;
s24: performing attribute marking based on the reference lithium battery charging data stream to obtain charging data stream attribute information;
s25: and integrating the reference lithium battery charging data stream according to the charging data stream attribute information to obtain the multi-dimensional standard lithium battery charging data stream.
Specifically, missing numerical value recognition is performed based on a multi-dimensional lithium battery charging data stream, and a charging data stream with data missing at the recording moment in the charging data stream is recognized to obtain a vacant charging data stream. And then, carrying out numerical limitation analysis on the multi-dimensional lithium battery charging data flow, wherein the numerical limitation is the data threshold limit which can appear for each charging data, and if the numerical limitation exceeds the data threshold limit, the corresponding data is abnormal, for example, the voltage range cannot have negative values, the internal resistance cannot have negative values and the like. A negative threshold charge data stream is obtained, i.e., data that exceeds the threshold limit at which data can occur is obtained. And further, cleaning the acquired multidimensional lithium battery charging data stream with a gap charging data stream and the negative threshold charging data stream, and cleaning and correcting the problematic data to obtain a reference lithium battery charging data stream. And finally, marking the attribute based on the reference lithium battery charging data stream, and marking the data types of voltage data, internal resistance data, temperature data and the like in the reference lithium battery charging data stream to obtain charging data stream attribute information. And finally, integrating the reference lithium battery charging data stream according to the charging data stream attribute information, namely grouping the attribute information in the reference lithium battery charging data stream, classifying and grouping the data stream according to the attribute information, and obtaining the multi-dimensional standard lithium battery charging data stream.
The method S20 provided in the embodiment of the present application further includes:
s26: carrying out distribution relevance evaluation on the vacant charging data stream to obtain a data stream distribution relevance coefficient;
s27: performing data supplementation on the vacant charging data stream based on the data stream distribution association coefficient to obtain a complete lithium battery charging data stream;
s28: normalizing the negative threshold charging data stream to obtain a normalized lithium battery charging data stream;
s29: and obtaining the reference lithium battery charging data stream based on the complete lithium battery charging data stream and the standard lithium battery charging data stream.
Specifically, the distribution relevance evaluation is performed on the vacant charging data stream, the relevance function of a plurality of data distributed before and after the vacant charging data is evaluated, namely, the relevance function fitting, such as the proportional or additive function, is performed on the plurality of data distributed before and after the vacant charging data, and then a specific function fitting relation is obtained, so that the distribution relevance coefficient is obtained. And further, calculating the vacant charging data based on the data flow distribution relevance coefficient and a plurality of data distributed in front and back, and carrying out data supplementation on the vacant charging data flow according to the vacant charging data obtained by calculation to obtain a complete lithium battery charging data flow. And normalizing the negative threshold charging data stream, and normalizing the data with abnormality in the negative threshold charging data stream according to the filling mode of the vacant charging data to obtain the normalized lithium battery charging data stream. And obtaining the reference lithium battery charging data stream based on the complete lithium battery charging data stream and the standard lithium battery charging data stream.
As shown in fig. 3, the method S30 provided in the embodiment of the present application further includes:
s31: constructing a charging fault identification support vector machine;
s32: inputting the multi-dimensional standard lithium battery charging data stream into the charging fault identification support vector machine for identification, and obtaining a charging fault identification data stream;
s33: extracting and classifying the charging fault identification data stream to obtain charging fault classification element information;
s34: and carrying out characteristic combing on the charging fault identification data stream based on the charging fault classification element information to obtain the charging fault clue information.
Specifically, a charging fault identification support vector machine is built based on the obtained multidimensional lithium battery charging data stream. And inputting the multi-dimensional standard lithium battery charging data stream into the charging fault identification support vector machine for identification, and obtaining a charging fault identification data stream. And then, carrying out automatic extraction and classification on the acquired charging fault identification data stream according to the distribution condition of the data in the data stream, and extracting classification elements in the charging fault identification data stream, wherein the classification elements are clustering features for carrying out automatic extraction and classification, such as contained specific data, fixed values and the like, so as to obtain the charging fault classification element information. And finally, feature combing is carried out on the charging fault identification data stream based on the charging fault classification element information, elements in the normal data stream are screened, the obtained charging fault clue information reflects the fault features of the charging fault identification data stream to a certain extent, and the charging fault clue information is obtained.
The method S31 provided in the embodiment of the present application further includes:
s312: carrying out data division on the multi-dimensional lithium battery charging data stream according to a preset proportion to obtain a charging data training sample and a charging data testing sample;
s313: obtaining a training sample characteristic label and a test sample characteristic label according to the charging data training sample and the charging data test sample;
s314: taking the charging data training samples and the training sample feature labels as training data, and constructing a basic charging fault identification support vector machine;
s315: and verifying the basic charging fault identification support vector machine based on the charging data test sample and the test sample feature tag, and determining the charging fault identification support vector machine.
Specifically, when the charge fault identification support vector machine is constructed, the multi-dimensional lithium battery charge data stream is divided into a charge data training sample and a charge data test sample according to a preset proportion, and the charge data training sample and the charge data test sample are obtained. And carrying out artificial charging data flow fault identification according to the obtained charging data training sample and the charging data testing sample to obtain a training sample characteristic label and a testing sample characteristic label. And taking the charging data training samples and the training sample feature labels as training data to construct a basic charging fault identification support vector machine. And when the support vector machine is constructed, performing supervision training on the untrained support vector machine, verifying the basic charge fault identification support vector machine based on the charge data test sample and the test sample feature label until the training on the support vector machine is completed when the result which can be output by the support vector machine after the charge data test sample is input can meet the preset accuracy, and further obtaining the charge fault identification support vector machine.
S40: based on the charging fault clue information, a charging safety risk analysis model is constructed, wherein the charging safety risk analysis model comprises a charging risk level analysis model, a charging risk type analysis model and a charging risk incentive analysis model;
s50: the method comprises the steps of performing charging safety monitoring on a target lithium battery by using a sensor group to obtain a charging operation real-time data stream;
s60: inputting the charging operation real-time data stream into the charging safety risk analysis model for analysis to obtain lithium battery charging risk diagnosis information;
s70: and carrying out risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information.
Specifically, based on the charging fault clue information, a charging safety risk analysis model is built, wherein the charging safety risk analysis model comprises a charging risk level analysis model, a charging risk type analysis model and a charging risk incentive analysis model. The charging risk level analysis model obtains the number of charging fault clues of the charging data, determines the corresponding charging risk level according to the number of the charging fault clues, and the higher the number of the charging fault clues is, the higher the corresponding charging risk level is, and otherwise, the lower the charging risk level is. The charging risk type analysis model is used for obtaining a charging fault clue type of charging data, and specific risk types are matched according to the charging fault clue type to obtain the charging risk type. Before the charging risk type is obtained, the risk type corresponding to the charging fault clue is obtained through big data, a charging fault clue-risk type database is constructed, and the risk type corresponding to the charging fault clue is stored in the database. The charging risk incentive analysis model is used for carrying out cause matching on the charging fault clues, and the charging risk incentive is obtained by matching corresponding risk incentives, namely, the causes of the charging fault clues according to the types of the charging fault clues. And acquiring the risk inducement corresponding to the charging fault clue through big data before acquiring the charging risk inducement, constructing a charging fault clue-risk inducement database, and storing the risk inducement corresponding to the charging fault clue in the database. And matching the specific risk type and risk incentive of the corresponding charging data through the charging risk type analysis model and the charging risk incentive analysis model.
And further, the sensor group is used for carrying out charging safety monitoring on the target lithium battery, so as to obtain a charging operation real-time data stream. And inputting the charging operation real-time data stream into the charging safety risk analysis model for analysis, and obtaining lithium battery charging risk diagnosis information. The lithium battery charging risk diagnosis information comprises specific risk levels, risk causes and risk types. And finally, carrying out risk early warning operation and maintenance on the target lithium battery based on specific information contained in the lithium battery charging risk diagnosis information, so that a user can acquire data such as the existing risk level and the risk category of the target lithium battery in time. By acquiring the charging safety risk analysis model and the charging fault clue, the charging operation real-time data stream is evaluated from multiple aspects, the accuracy of the lithium battery charging safety early warning is improved, the abnormality existing in the charging process can be quickly found by acquiring the charging data stream, and the early warning hysteresis is further reduced.
The method S70 provided in the embodiment of the present application further includes:
s71: the gas sampling device is used for sampling the gas of the target lithium battery to obtain a battery charging gas sample;
s72: performing composition analysis on the battery charging gas sample to obtain a charging characteristic gas component;
s73: acquiring battery pressure difference information through a pressure sensor, and determining characteristic gas concentration information according to the battery pressure difference information;
s74: performing risk prediction based on the charging characteristic gas components and the characteristic gas concentration information to obtain a charging runaway risk coefficient;
s75: and carrying out supplementary correction on the lithium battery charging risk diagnosis information based on the charging runaway risk coefficient.
Specifically, the gas sampling device is used for sampling the gas of the target lithium battery to obtain a battery charging gas sample. And carrying out composition analysis on the battery charging gas sample to obtain a charging characteristic gas component. The pressure difference information of the battery is obtained through the pressure sensor, namely, the pressure difference generated by each part inside the battery is obtained, and as the battery is mostly sealed, certain pressure is generated at the corresponding part when gas escapes, and the characteristic gas concentration information is determined according to the pressure difference information of the battery. The gas concentration information is in direct proportion to the battery pressure difference information, the higher the battery pressure difference is, the higher the corresponding gas concentration is, and specific proportion data can be obtained according to actual test results. And carrying out risk prediction based on the charging characteristic gas components and the characteristic gas concentration information to obtain a charging runaway risk coefficient. And carrying out supplementary correction on the lithium battery charging risk diagnosis information based on the charging runaway risk coefficient, namely correcting the risk level in the charging risk diagnosis information according to the acquired charging runaway risk coefficient. Setting a charging runaway risk coefficient level, and increasing the corresponding risk level by one level when the charging runaway risk coefficient is greater than a threshold value of a certain level.
The method S70 provided in the embodiment of the present application further includes:
s76: constructing a characteristic gas multi-level risk early warning strategy;
s77: dividing the charging characteristic gas components based on the characteristic gas multi-level risk early warning strategy to obtain a lithium battery gas risk level;
s78: performing curve fitting on the charging characteristic gas component and the characteristic gas concentration information to generate a lithium battery characteristic gas overcharge curve,
s79: and performing risk gain on the slope of the characteristic gas overcharge curve of the lithium battery based on the lithium battery gas risk level to obtain the risk coefficient of the charge runaway.
Specifically, a characteristic gas multi-level risk early warning strategy is constructed, and as the reaction phenomenon of each stage of thermal runaway of the lithium battery is associated with the change of the mass concentration of the gas, the charging characteristic gas components are divided based on the characteristic gas multi-level risk early warning strategy, so that a lithium battery gas risk level is obtained. According to the charging gas process, hydrogen, CO and carbon dioxide gas can be used as primary early warning, and HCl and HF are used as secondary early warning. And then, performing curve fitting on the charging characteristic gas components and the characteristic gas concentration information to generate a lithium battery characteristic gas overcharge curve, and performing curve fitting by monitoring the gas mass concentration change. And finally, performing risk gain on the slope of the characteristic gas overcharge curve of the lithium battery based on the gas risk level of the lithium battery to obtain the risk coefficient of the charge runaway, namely, taking the product of the gas early warning level and the slope as the risk coefficient of the charge runaway.
According to the technical scheme provided by the embodiment of the invention, the multi-dimensional lithium battery charging data stream is obtained through big data, and comprises a voltage charging data stream, an internal resistance charging data stream and a temperature charging data stream. And carrying out data cleaning and integration on the multi-dimensional lithium battery charging data stream to obtain a multi-dimensional standard lithium battery charging data stream. And carrying out fault parameter identification based on the multi-dimensional standard lithium battery charging data stream to obtain charging fault clue information. And constructing a charging safety risk analysis model based on the charging fault clue information, wherein the charging safety risk analysis model comprises a charging risk level analysis model, a charging risk type analysis model and a charging risk incentive analysis model. And carrying out charging safety monitoring on the target lithium battery by using the sensor group to obtain a charging operation real-time data stream. And inputting the charging operation real-time data stream into the charging safety risk analysis model for analysis, and obtaining lithium battery charging risk diagnosis information. And carrying out risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information. By acquiring the charging safety risk analysis model and the charging fault clue, the charging operation real-time data stream is evaluated from multiple aspects, the accuracy of the lithium battery charging safety early warning is improved, and the abnormality existing in the charging process can be quickly found due to the fact that the monitoring data are the charging data stream, so that the early warning hysteresis is further reduced. The technical problems of low early warning accuracy and high hysteresis in the prior art of lithium battery charging safety early warning are solved.
Example two
Based on the same inventive concept as the method for monitoring and early warning the charging safety of the lithium battery in the foregoing embodiment, the present invention further provides a system for monitoring and early warning the charging safety of the lithium battery, which can be implemented by hardware and/or software, and can be generally integrated in an electronic device, for executing the method provided by any embodiment of the present invention. As shown in fig. 4, the system includes:
the charging data stream obtaining module 11 is configured to obtain a multi-dimensional lithium battery charging data stream through big data, where the multi-dimensional lithium battery charging data stream includes a voltage charging data stream, an internal resistance charging data stream, and a temperature charging data stream;
the charging data stream processing module 12 is configured to perform data cleaning and integration on the multi-dimensional lithium battery charging data stream to obtain a multi-dimensional standard lithium battery charging data stream;
the charging fault clue acquisition module 13 is used for carrying out fault parameter identification based on the multi-dimensional standard lithium battery charging data stream to obtain charging fault clue information;
the risk analysis model construction module 14 is configured to construct a charging security risk analysis model based on the charging fault clue information, where the charging security risk analysis model includes a charging risk level analysis model, a charging risk type analysis model, and a charging risk cause analysis model;
the real-time charging data stream acquisition module 15 is used for carrying out charging safety monitoring on the target lithium battery by utilizing the sensor group to obtain a charging operation real-time data stream;
the risk diagnosis module 16 is configured to input the charging operation real-time data stream into the charging security risk analysis model for analysis, so as to obtain lithium battery charging risk diagnosis information;
and the risk early warning module 17 is used for carrying out risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information.
Further, the charging data stream processing module 12 is further configured to:
carrying out missing numerical identification based on the multi-dimensional lithium battery charging data stream to obtain a vacant charging data stream;
performing numerical value limitation analysis on the multi-dimensional lithium battery charging data stream to obtain a negative threshold charging data stream;
cleaning the vacant charging data stream and the negative threshold charging data stream to obtain a reference lithium battery charging data stream;
performing attribute marking based on the reference lithium battery charging data stream to obtain charging data stream attribute information;
and integrating the reference lithium battery charging data stream according to the charging data stream attribute information to obtain the multi-dimensional standard lithium battery charging data stream.
Further, the charging data stream processing module 12 is further configured to:
carrying out distribution relevance evaluation on the vacant charging data stream to obtain a data stream distribution relevance coefficient;
performing data supplementation on the vacant charging data stream based on the data stream distribution association coefficient to obtain a complete lithium battery charging data stream;
normalizing the negative threshold charging data stream to obtain a normalized lithium battery charging data stream;
and obtaining the reference lithium battery charging data stream based on the complete lithium battery charging data stream and the standard lithium battery charging data stream.
Further, the charging failure clue obtaining module 13 is further configured to:
constructing a charging fault identification support vector machine;
inputting the multi-dimensional standard lithium battery charging data stream into the charging fault identification support vector machine for identification, and obtaining a charging fault identification data stream;
extracting and classifying the charging fault identification data stream to obtain charging fault classification element information;
and carrying out characteristic combing on the charging fault identification data stream based on the charging fault classification element information to obtain the charging fault clue information.
Further, the charging failure clue obtaining module 13 is further configured to:
carrying out data division on the multi-dimensional lithium battery charging data stream according to a preset proportion to obtain a charging data training sample and a charging data testing sample;
obtaining a training sample characteristic label and a test sample characteristic label according to the charging data training sample and the charging data test sample;
taking the charging data training samples and the training sample feature labels as training data, and constructing a basic charging fault identification support vector machine;
and verifying the basic charging fault identification support vector machine based on the charging data test sample and the test sample feature tag, and determining the charging fault identification support vector machine.
Further, the risk early warning module 17 is further configured to:
the gas sampling device is used for sampling the gas of the target lithium battery to obtain a battery charging gas sample;
performing composition analysis on the battery charging gas sample to obtain a charging characteristic gas component;
acquiring battery pressure difference information through a pressure sensor, and determining characteristic gas concentration information according to the battery pressure difference information;
performing risk prediction based on the charging characteristic gas components and the characteristic gas concentration information to obtain a charging runaway risk coefficient;
and carrying out supplementary correction on the lithium battery charging risk diagnosis information based on the charging runaway risk coefficient.
Further, the risk early warning module 17 is further configured to:
constructing a characteristic gas multi-level risk early warning strategy;
dividing the charging characteristic gas components based on the characteristic gas multi-level risk early warning strategy to obtain a lithium battery gas risk level;
performing curve fitting on the charging characteristic gas component and the characteristic gas concentration information to generate a lithium battery characteristic gas overcharge curve,
and performing risk gain on the slope of the characteristic gas overcharge curve of the lithium battery based on the lithium battery gas risk level to obtain the risk coefficient of the charge runaway.
The included units and modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example III
Fig. 5 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 5, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 5, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 5, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing a software program, a computer executable program and a module, such as a program instruction/module corresponding to a method for monitoring and early warning charging safety of a lithium battery in an embodiment of the present invention. The processor 31 executes various functional applications and data processing of the computer device by running software programs, instructions and modules stored in the memory 32, namely, the method for monitoring and early warning of the charging safety of the lithium battery is realized.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. The method for monitoring and early warning the charging safety of the lithium battery is characterized by comprising the following steps of:
acquiring a multi-dimensional lithium battery charging data stream through big data, wherein the multi-dimensional lithium battery charging data stream comprises a voltage charging data stream, an internal resistance charging data stream and a temperature charging data stream;
data cleaning and integration are carried out on the multi-dimensional lithium battery charging data stream, and a multi-dimensional standard lithium battery charging data stream is obtained;
performing fault parameter identification based on the multi-dimensional standard lithium battery charging data stream to obtain charging fault clue information;
based on the charging fault clue information, a charging safety risk analysis model is constructed, wherein the charging safety risk analysis model comprises a charging risk level analysis model, a charging risk type analysis model and a charging risk incentive analysis model;
the method comprises the steps of performing charging safety monitoring on a target lithium battery by using a sensor group to obtain a charging operation real-time data stream;
inputting the charging operation real-time data stream into the charging safety risk analysis model for analysis to obtain lithium battery charging risk diagnosis information;
performing risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information;
the obtaining charging fault clue information includes:
constructing a charging fault identification support vector machine;
inputting the multi-dimensional standard lithium battery charging data stream into the charging fault identification support vector machine for identification, and obtaining a charging fault identification data stream;
extracting and classifying the charging fault identification data stream to obtain charging fault classification element information;
and carrying out characteristic combing on the charging fault identification data stream based on the charging fault classification element information to obtain the charging fault clue information.
2. The method of claim 1, wherein said obtaining a multi-dimensional standard lithium battery charge data stream comprises:
carrying out missing numerical identification based on the multi-dimensional lithium battery charging data stream to obtain a vacant charging data stream;
performing numerical value limitation analysis on the multi-dimensional lithium battery charging data stream to obtain a negative threshold charging data stream;
cleaning the vacant charging data stream and the negative threshold charging data stream to obtain a reference lithium battery charging data stream;
performing attribute marking based on the reference lithium battery charging data stream to obtain charging data stream attribute information;
and integrating the reference lithium battery charging data stream according to the charging data stream attribute information to obtain the multi-dimensional standard lithium battery charging data stream.
3. The method of claim 2, wherein the obtaining a reference lithium battery charge data stream comprises:
carrying out distribution relevance evaluation on the vacant charging data stream to obtain a data stream distribution relevance coefficient;
performing data supplementation on the vacant charging data stream based on the data stream distribution association coefficient to obtain a complete lithium battery charging data stream;
normalizing the negative threshold charging data stream to obtain a normalized lithium battery charging data stream;
and obtaining the reference lithium battery charging data stream based on the complete lithium battery charging data stream and the standard lithium battery charging data stream.
4. The method of claim 1, wherein building a charge failure recognition support vector machine comprises:
carrying out data division on the multi-dimensional lithium battery charging data stream according to a preset proportion to obtain a charging data training sample and a charging data testing sample;
obtaining a training sample characteristic label and a test sample characteristic label according to the charging data training sample and the charging data test sample;
taking the charging data training samples and the training sample feature labels as training data, and constructing a basic charging fault identification support vector machine;
and verifying the basic charging fault identification support vector machine based on the charging data test sample and the test sample feature tag, and determining the charging fault identification support vector machine.
5. The method of claim 1, wherein the method comprises:
the gas sampling device is used for sampling the gas of the target lithium battery to obtain a battery charging gas sample;
performing composition analysis on the battery charging gas sample to obtain a charging characteristic gas component;
acquiring battery pressure difference information through a pressure sensor, and determining characteristic gas concentration information according to the battery pressure difference information;
performing risk prediction based on the charging characteristic gas components and the characteristic gas concentration information to obtain a charging runaway risk coefficient;
and carrying out supplementary correction on the lithium battery charging risk diagnosis information based on the charging runaway risk coefficient.
6. The method of claim 5, wherein the obtaining a risk factor for charge runaway comprises:
constructing a characteristic gas multi-level risk early warning strategy;
dividing the charging characteristic gas components based on the characteristic gas multi-level risk early warning strategy to obtain a lithium battery gas risk level;
performing curve fitting on the charging characteristic gas component and the characteristic gas concentration information to generate a lithium battery characteristic gas overcharge curve,
and performing risk gain on the slope of the characteristic gas overcharge curve of the lithium battery based on the lithium battery gas risk level to obtain the risk coefficient of the charge runaway.
7. A lithium battery charging safety monitoring and early warning system, characterized in that the system performs the method of any one of claims 1 to 6, the system comprising:
the charging data stream acquisition module is used for acquiring a multi-dimensional lithium battery charging data stream through big data, wherein the multi-dimensional lithium battery charging data stream comprises a voltage charging data stream, an internal resistance charging data stream and a temperature charging data stream;
the charging data stream processing module is used for carrying out data cleaning and integration on the multi-dimensional lithium battery charging data stream to obtain a multi-dimensional standard lithium battery charging data stream;
the charging fault clue acquisition module is used for carrying out fault parameter identification based on the multi-dimensional standard lithium battery charging data stream to acquire charging fault clue information;
the risk analysis model construction module is used for constructing a charging safety risk analysis model based on the charging fault clue information, wherein the charging safety risk analysis model comprises a charging risk level analysis model, a charging risk type analysis model and a charging risk incentive analysis model;
the real-time charging data stream acquisition module is used for carrying out charging safety monitoring on the target lithium battery by utilizing the sensor group to acquire a charging operation real-time data stream;
the risk diagnosis module is used for inputting the charging operation real-time data stream into the charging safety risk analysis model for analysis, and obtaining lithium battery charging risk diagnosis information;
and the risk early warning module is used for carrying out risk early warning operation and maintenance on the target lithium battery based on the lithium battery charging risk diagnosis information.
8. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
and the processor is used for realizing the charge safety monitoring and early warning method of the lithium battery according to any one of claims 1 to 6 when executing the executable instructions stored in the memory.
9. A computer readable medium having stored thereon a computer program, which when executed by a processor implements a method for monitoring and pre-warning the charge safety of a lithium battery as claimed in any one of claims 1 to 6.
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