CN111934032A - Graphical representation of temperature characteristic of power battery and temperature abnormal single body identification method - Google Patents
Graphical representation of temperature characteristic of power battery and temperature abnormal single body identification method Download PDFInfo
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- CN111934032A CN111934032A CN202010757125.4A CN202010757125A CN111934032A CN 111934032 A CN111934032 A CN 111934032A CN 202010757125 A CN202010757125 A CN 202010757125A CN 111934032 A CN111934032 A CN 111934032A
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/4285—Testing apparatus
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K1/00—Details of thermometers not specially adapted for particular types of thermometer
- G01K1/02—Means for indicating or recording specially adapted for thermometers
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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
Abstract
The invention relates to the technical field of battery diagnosis, and particularly discloses a graphical representation of temperature characteristics of a power battery and a temperature abnormal monomer identification method, which comprises the following steps: s1, acquiring a temperature frequency distribution table comprising each battery temperature probe of the vehicle; s2, converting the temperature frequency distribution table into temperature characteristic data and storing the temperature characteristic data; and S3, judging whether the temperature probe is abnormal or not based on the temperature characteristic data, and if so, generating and outputting an abnormal record. By adopting the technical scheme of the invention, whether the battery temperature probe is abnormal or not can be identified, and whether the battery temperature is normal or not can be indirectly estimated.
Description
Technical Field
The invention relates to the technical field of battery diagnosis, in particular to a graphical representation of temperature characteristics of a power battery and a temperature abnormal single body identification method.
Background
In recent years, environmental protection is more and more high, and new energy automobiles are more and more popular. The new energy automobile is an environment-friendly travel tool adopting unconventional automobile fuel as a power source, and comprises a pure electric automobile, a range-extended electric automobile, a hybrid electric automobile, a fuel cell electric automobile, a hydrogen engine automobile and the like. Compared with the existing fuel oil automobile, the new energy automobile has the characteristics of zero emission of pollutants, high energy utilization rate, simple structure, low noise and the like, and the new energy automobile is vigorously advocated to be used in the social aspect due to the characteristics of the new energy automobile.
The power battery of the new energy automobile is used as an energy storage component, and is related to the cruising ability of the new energy automobile in the driving process. The power battery comprises a plurality of battery monomers, the power battery is used as an energy supply part for the running of the vehicle, and the battery monomers are always operated and used; when a certain battery cell of the new energy automobile fails and is not disposed in time, the battery cell at the periphery is easily affected to fail, and further, the safety accident of the whole automobile is caused.
For the improvement of the safety performance of the new energy automobile and the timely discovery of the faults of the automobile, the collection of big data in the driving process of the new energy automobile is very important, for this reason, relevant national standards are set by the country, various operation data such as batteries, engines and the like in the driving process of the new energy automobile are collected on the basis of new energy automobile enterprises, the collected operation data are sent to an enterprise platform, and the enterprise collects the data and then sends the data to the national platform for monitoring and analysis.
However, due to the huge data volume of the operation data, such as the temperature data, after collection and aggregation, cannot be well utilized. In order to better analyze the temperature of the battery cell and find a fault in time, a method capable of accurately identifying whether the temperature of the battery is abnormal is needed.
Disclosure of Invention
The invention provides a graphical representation of temperature characteristics of a power battery and a temperature abnormal single identification method, which can accurately identify whether a battery temperature probe is abnormal or not and further indirectly estimate whether the battery temperature is normal or not.
In order to solve the technical problem, the present application provides the following technical solutions:
the graphical representation of the temperature characteristic of the power battery and the temperature abnormal monomer identification method comprise the following steps:
s1, acquiring a temperature frequency distribution table comprising each battery temperature probe of the vehicle;
s2, converting the temperature frequency distribution table into temperature characteristic data and storing the temperature characteristic data;
s3, judging whether the temperature probe is abnormal or not based on the temperature characteristic data, and if so, generating an abnormal record and outputting the abnormal record; the method specifically comprises the following steps:
s301, establishing a coordinate image;
s302, generating color blocks of all frequency numbers of the temperature probes in the coordinate images;
s303, repeating the step S302 for each temperature probe, and finally generating a probe temperature characteristic diagram;
s304, identifying whether the overall graph formed by any temperature probe color block is different from the overall graph formed by other electric temperature probe color blocks on the whole in the probe temperature characteristic graph, and if so, marking the temperature probe as abnormal.
The basic scheme principle and the beneficial effects are as follows:
according to the physicochemical characteristics of the power battery, the more consistent the graphs of all the temperature probes are, the better the consistency of the PACK of the whole battery is, otherwise, if the graphs of a certain temperature probe are obviously different, the temperature abnormity can be visually judged. Experience repeatedly proves that the charging and discharging curve of the battery is a powerful tool for describing the essential characteristics of the battery, and experience also repeatedly proves that the temperature change curve of the battery is a powerful tool for describing the essential characteristics of the battery. Because the temperature frequency distribution table is essentially a temperature characteristic curve under the real working condition of the battery, the temperature characteristic data acquired under the support of a large amount of data becomes an effective and accurate description tool for the temperature characteristic of the battery.
The temperature frequency distribution of the battery is the reflection of the essential characteristics of the battery, and in most cases, the essential abnormality of the battery is expressed as the abnormality of the temperature frequency distribution, so the scheme becomes an effective checking means of the battery temperature identification algorithm. That is, the pattern of the fault temperature probe identified by a certain battery temperature fault identification algorithm is firstly required to satisfy the condition that the temperature frequency is remarkably abnormal (similar to the necessary condition in the mathematical sense), otherwise, the battery temperature fault identification algorithm is very likely to be defective or even incorrect.
In conclusion, the scheme can accurately identify whether the battery temperature probe is abnormal or not, and further indirectly presumes whether the battery temperature is normal or not.
Further, in S301, a coordinate image is established by taking the temperature of the temperature probe as a vertical coordinate and the serial number of the temperature probe as a horizontal coordinate;
in S302, starting from the temperature probe with the smallest number, a temperature value corresponding to the frequency maximum value is firstly found out, and a maximum color block is generated in a corresponding area of the coordinate image; and then generating color blocks with different sizes by the frequency numbers corresponding to other temperatures of the temperature probe in equal proportion until all the frequency numbers of the temperature probe generate the color blocks.
The temperature probes are arranged on the same vehicle, and the temperature probes are arranged on the same coordinate image.
Further, in S2, the battery temperature characteristic data includes a temperature value and a frequency corresponding to the voltage value.
And the distribution of the frequency corresponding to the voltage values is convenient for subsequently judging whether the single battery is abnormal or not.
Further, in the 301, each probe occupies the same width in the coordinate image.
And the variable is controlled, so that the problem that the identification standard is difficult to unify due to different occupied widths of each battery cell is solved.
Further, in S304, a convolutional neural network model is used for identification.
The convolutional neural network module is accurate in image identification, and can effectively replace manual work under the condition of large data volume to perform automatic identification.
Furthermore, in the coordinate image, the width occupied by each temperature probe is 10-20 pixels.
The temperature probes can be accommodated in the coordinate image as many as possible while ensuring easy recognition.
Drawings
FIG. 1 is a flow chart of a method for graphical representation of temperature characteristics of a power battery and identification of abnormal temperature cells according to an embodiment;
fig. 2 is a probe temperature characteristic diagram in a power battery temperature characteristic graphical representation and temperature abnormality single body identification method according to an embodiment.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
As shown in fig. 1, the graphical representation of the temperature characteristic of the power battery and the method for identifying the abnormal temperature single body of the power battery of the embodiment include the following steps:
and S1, acquiring a temperature frequency distribution table comprising each battery temperature probe of the vehicle, and acquiring a recent temperature frequency distribution table for subsequent analysis in the embodiment.
S2, converting the temperature frequency distribution table into temperature characteristic data and storing the temperature characteristic data, wherein the battery temperature characteristic data comprise temperature values and frequency numbers corresponding to the voltage values; as shown in table 1;
TABLE 1
And S3, judging whether the temperature probe is abnormal or not based on the temperature characteristic data, and if so, generating and outputting an abnormal record.
The step of judging whether the temperature probe has abnormity or not based on the temperature characteristic data specifically comprises the following steps:
s301, establishing a coordinate image by taking the temperature of the temperature probes as a vertical coordinate and the serial number of the temperature probes as a horizontal coordinate, wherein each probe occupies the same width, and the occupied width is 10-20 pixel widths, namely 10 pixel widths in the embodiment.
S302, starting from the temperature probe with the minimum number, firstly, the temperature value corresponding to the maximum frequency value is found out, and the maximum color block is generated in the corresponding area of the rectangular coordinate image. The corresponding area refers to the area defined by the abscissa and the ordinate of the temperature probe. In the embodiment, a color block with a width of 10 pixels and a certain height is generated; the pixel value of the height can be set according to actual conditions. And then generating color blocks with different sizes by the frequency numbers corresponding to other temperatures of the temperature probe in equal proportion until all the frequency numbers of the temperature probe generate the color blocks.
And S303, repeating the step S302 for each temperature probe, and finally generating a probe temperature characteristic diagram shown in FIG. 2. In the figure, the abscissa is the number of temperature probes, and the ordinate represents the frequency of the statistical temperature of all temperature probes at a certain temperature during the observation period, for example, the frequency of the occurrence of a certain probe at 25 ℃.
S304, identifying whether the overall graph formed by any temperature probe color block is different from the overall graph formed by other electric temperature probe color blocks in the probe temperature characteristic graph, and if so, marking the temperature probe as abnormal. The difference of the overall graph can be identified by adopting a manual identification mode and a machine identification mode. In this embodiment, a machine recognition mode is adopted, and specifically, a trained convolutional neural network model is adopted for recognition.
According to the physicochemical characteristics of the power battery, the more consistent the graphs of all the temperature probes are, the better the consistency of the PACK of the whole battery is, otherwise, if the graphs of a certain temperature probe are obviously different, the temperature abnormity can be visually judged.
Experience repeatedly proves that the charging and discharging curve of the battery is a powerful tool for describing the essential characteristics of the battery, and experience also repeatedly proves that the temperature change curve of the battery is a powerful tool for describing the essential characteristics of the battery. Because the temperature frequency distribution table is essentially a temperature characteristic curve under the real working condition of the battery, the temperature characteristic data acquired under the support of a large amount of data becomes an effective and accurate description tool for the temperature characteristic of the battery.
The temperature probes are arranged on the same vehicle, and the temperature probes are arranged on the same coordinate image. For the battery monomer with abnormal performance, under the cooperation of human experience, the fault types of the battery can be directly judged under partial conditions, such as damage of a temperature probe, damage of a collecting line, damage of a battery body and the like.
The temperature frequency distribution of the battery is the reflection of the essential characteristics of the battery, and in most cases, the essential abnormality of the battery is represented as the abnormality of the temperature frequency distribution, so that the scheme also becomes an effective checking means of the battery temperature identification algorithm. That is, the pattern of the fault temperature probe identified by a certain battery temperature fault identification algorithm is firstly required to satisfy the condition that the temperature frequency is remarkably abnormal (similar to the necessary condition in the mathematical sense), otherwise, the battery temperature fault identification algorithm is very likely to be defective or even incorrect.
Example two
The present embodiment is different from the first embodiment in that the present embodiment further includes step S4, mounting a temperature sensor on the bottom of the vehicle;
s5, judging whether the position of the abnormal temperature probe is adjacent to the bottom of the vehicle; in this embodiment, the distance between the adjacent finger and the bottom of the vehicle is less than the preset distance.
S6, if adjacent, acquiring bottom temperature from the temperature sensor; judging whether the bottom temperature exceeds a first temperature threshold value, if so, jumping to S7, and if so, generating alarm information, wherein the second temperature threshold value is larger than the first temperature threshold value;
s7, acquiring the speed of the automobile from the automobile ECU; and judging whether the speed of the automobile is smaller than a preset speed interval or larger than the preset speed interval, and if so, generating alarm information.
The reasons for the abnormality of the temperature probe may be damage of the temperature probe, damage of the battery body, etc., or the temperature of the battery near the bottom of the vehicle may be too high due to too high temperature of the road surface; in the implementation, when the position of the abnormal temperature probe is adjacent to the bottom of the vehicle, whether the temperature of the bottom of the vehicle exceeds a first temperature threshold value is also judged, and if the temperature of the bottom of the vehicle exceeds the first temperature threshold value, the temperature of a battery close to the bottom of the vehicle possibly caused by overhigh road surface temperature is overhigh; if the temperature exceeds the second temperature threshold, the battery is possibly damaged to cause abnormal heating, so alarm information needs to be generated, and the driver can be conveniently informed at the first time.
When the speed of the automobile is lower than a preset speed interval, the speed of the automobile is slow or the automobile is static, the air circulation is slow, the heat dissipation effect of the bottom of the automobile is poor, and the problem that the temperature of a battery close to the bottom of the automobile is too high due to too high temperature of a road surface cannot be reduced by means of air flow, so that alarm information needs to be generated, and the driver can be informed at the first time conveniently; when the speed of the automobile is higher than a preset speed interval, the speed of the automobile is high, the air circulation is high, the heat dissipation effect of the bottom of the automobile is good, the temperature of the bottom of the automobile still exceeds a first temperature threshold, the heat of a battery accounts for a main factor, alarm information needs to be generated, and the automobile can be conveniently informed to a driver at the first time; when the automobile speed equals to the preset speed interval, the automobile speed is proper, the air circulation is fast, the automobile can be cooled to the bottom of the automobile by the aid of the air flow for a period of time, whether the temperature of the bottom of the automobile can be reduced or not is judged, the alarm is given out at the first time, and the false alarm probability is reduced.
The above are merely examples of the present invention, and the present invention is not limited to the field related to this embodiment, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein too much, and those skilled in the art can know all the common technical knowledge in the technical field before the application date or the priority date, can know all the prior art in this field, and have the ability to apply the conventional experimental means before this date, and those skilled in the art can combine their own ability to perfect and implement the scheme, and some typical known structures or known methods should not become barriers to the implementation of the present invention by those skilled in the art in light of the teaching provided in the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (6)
1. The graphical representation of the temperature characteristic of the power battery and the identification method of the temperature abnormal single body are characterized by comprising the following steps:
s1, acquiring a temperature frequency distribution table comprising each battery temperature probe of the vehicle;
s2, converting the temperature frequency distribution table into temperature characteristic data and storing the temperature characteristic data;
s3, judging whether the temperature probe is abnormal or not based on the temperature characteristic data, and if so, generating an abnormal record and outputting the abnormal record; the method specifically comprises the following steps:
s301, establishing a coordinate image;
s302, generating color blocks of all frequency numbers of the temperature probes in the coordinate images;
s303, repeating the step S302 for each temperature probe, and finally generating a probe temperature characteristic diagram;
s304, identifying whether the overall graph formed by any temperature probe color block is different from the overall graph formed by other electric temperature probe color blocks on the whole in the probe temperature characteristic graph, and if so, marking the temperature probe as abnormal.
2. The graphical representation of power battery temperature characteristics and temperature anomaly cell identification method according to claim 1, characterized in that: in S301, a coordinate image is established by taking the temperature of the temperature probe as a vertical coordinate and the serial number of the temperature probe as a horizontal coordinate;
in S302, starting from the temperature probe with the smallest number, a temperature value corresponding to the frequency maximum value is firstly found out, and a maximum color block is generated in a corresponding area of the coordinate image; and then generating color blocks with different sizes by the frequency numbers corresponding to other temperatures of the temperature probe in equal proportion until all the frequency numbers of the temperature probe generate the color blocks.
3. The graphical representation of the temperature characteristic of the power battery and the temperature abnormality single body identification method according to claim 2, characterized in that: in S2, the battery temperature characteristic data includes a temperature value and a frequency corresponding to the voltage value.
4. The graphical representation of power battery temperature characteristics and temperature anomaly cell identification method according to claim 3, characterized in that: in the 301, each probe occupies the same width in the coordinate image.
5. The graphical representation of power battery temperature characteristics and temperature anomaly cell identification method according to claim 4, characterized in that: in S304, a convolutional neural network model is used for identification.
6. The graphical representation of power battery temperature characteristics and temperature anomaly cell identification method according to claim 5, characterized in that: in the coordinate image, the width occupied by each temperature probe is 10-20 pixels.
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