CN114970734A - Abnormal battery determination method, abnormal battery determination device, computer equipment and storage medium - Google Patents
Abnormal battery determination method, abnormal battery determination device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to an abnormal battery determination method, an abnormal battery determination device, a computer device and a storage medium. The method comprises the following steps: acquiring battery test data of a battery to be detected aiming at any battery to be detected in a group to be detected, wherein the battery test data is data generated in the pre-charging process and/or the constant volume process of the battery; respectively constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected; and determining abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected. By adopting the method, the screening precision of the abnormal battery can be improved.
Description
Technical Field
The present application relates to the field of battery technologies, and in particular, to a method and an apparatus for determining an abnormal battery, a computer device, and a storage medium.
Background
In order to alleviate the problems of energy shortage and environmental pollution, new energy automobiles are listed in strategic emerging technology industries in China, so that the driving of automobiles by using new energy such as electric energy instead of chemical energy gradually becomes one of the main trends of automobile technology development. The lithium ion power battery has the characteristics of high specific energy, low self-discharge rate and long cycle life, and is the most practical pure electric vehicle energy source at present.
With the large-scale application of lithium ion batteries, safety accidents of lithium ion batteries characterized by thermal runaway sometimes occur. The battery defect introduced in the battery production and manufacturing process is one of potential causes of thermal runaway of the battery, and if abnormal batteries can be screened out in the battery production and manufacturing process to avoid flowing into a using link, the possibility of thermal runaway can be reduced.
The conventional abnormal battery screening method is usually used for screening abnormal batteries only according to a few indexes, and the indexes are usually selected only according to the experience of technicians, so that the problem that the screening standard is rigid and the actual situation of all production cannot be covered exists, and the screening precision of the abnormal batteries is low.
Disclosure of Invention
In view of the above, it is necessary to provide an abnormal battery determination method, apparatus, computer device and storage medium for solving the above technical problems.
In a first aspect, the present application provides a method of determining an abnormal battery. The method comprises the following steps:
the method comprises the steps that battery test data of a battery to be detected are obtained for any battery to be detected in a group to be detected, wherein the battery test data are data generated in the pre-charging process and/or the constant volume process of the battery;
respectively constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected;
and determining abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected.
In one embodiment, the battery test data includes target characteristic data, the target characteristic data includes data corresponding to a target characteristic at each time in the pre-charging process and/or the constant volume process, and the constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected includes:
for any battery to be detected, extracting detection data corresponding to the target characteristic data through the target characteristic data of the battery to be detected;
and constructing a battery quality data set of the battery to be detected according to the detection data corresponding to each target characteristic data.
In one embodiment, the extracting, by using the target feature data of the battery to be detected, detection data corresponding to the target feature data includes:
dividing time periods of the battery production process according to the production strategy in the battery production process corresponding to the battery test data to obtain a plurality of time periods;
determining a target time period corresponding to the target characteristic data from the plurality of time periods;
acquiring target data from the target characteristic data according to a target time period corresponding to the target characteristic data;
and obtaining detection data corresponding to the target characteristic data according to the target data corresponding to the target characteristic data.
In one embodiment, the battery test data includes index data, and the constructing a battery quality data set of each battery to be tested according to the battery test data of each battery to be tested includes:
and aiming at any battery to be detected, constructing a battery quality data set of the battery to be detected according to detection data corresponding to each target characteristic data of the battery to be detected and/or each index data of the battery to be detected.
In one embodiment, the determining abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected includes:
for any battery to be detected, determining an abnormal value corresponding to the battery to be detected by adopting an outlier detection algorithm according to the battery quality data set of the battery to be detected;
and taking the battery to be detected with the abnormal value larger than a threshold value in the group to be detected as an abnormal battery.
In a second aspect, the present application also provides an abnormal battery determination apparatus. The device comprises:
the battery testing device comprises an acquisition module, a pre-charging module and a volume fixing module, wherein the acquisition module is used for acquiring battery testing data of a battery to be detected aiming at any battery to be detected in a group to be detected, and the battery testing data is data generated in the pre-charging process and/or the volume fixing process of the battery;
the construction module is used for constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected;
and the determining module is used for determining the abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected.
In one embodiment, the battery test data includes target feature data, the target feature data includes data corresponding to a target feature at each time in the pre-charging process and/or the constant volume process, and the building module is further configured to:
for any battery to be detected, extracting detection data corresponding to the target characteristic data through the target characteristic data of the battery to be detected;
and constructing a battery quality data set of the battery to be detected according to the detection data corresponding to each target characteristic data.
In one embodiment, the building module is further configured to:
dividing time periods of the battery production process according to the production strategy in the battery production process corresponding to the battery test data to obtain a plurality of time periods;
determining a target time period corresponding to the target characteristic data from the plurality of time periods;
acquiring target data from the target characteristic data according to a target time period corresponding to the target characteristic data;
and obtaining detection data corresponding to the target characteristic data according to the target data corresponding to the target characteristic data.
In one embodiment, the battery test data includes index data, and the building module is further configured to:
and aiming at any battery to be detected, constructing a battery quality data set of the battery to be detected according to detection data corresponding to each target characteristic data of the battery to be detected and/or each index data of the battery to be detected.
In one embodiment, the determining module is further configured to:
for any battery to be detected, determining an abnormal value corresponding to the battery to be detected by adopting an outlier detection algorithm according to the battery quality data set of the battery to be detected;
and taking the battery to be detected with the abnormal value larger than a threshold value in the group to be detected as an abnormal battery.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing any of the above methods when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, implements any of the above methods.
In a fifth aspect, the present application further provides a computer program product. The computer program product, including a computer program, the computer program product, including a computer program that, when executed by a processor, implements any of the above methods.
According to the method, the device, the computer equipment and the storage medium for determining the abnormal batteries, the battery quality data sets of the batteries to be detected can be respectively constructed by acquiring the test data generated in the pre-charging process and/or the constant volume process of the batteries to be detected in the group to be detected, and then the abnormal batteries can be screened out from the group to be detected according to the battery quality data sets. The method and the device integrate the relevant data generated in the pre-charging formation process and/or the constant volume process of the battery to be detected in the group to be detected as the battery test data to judge the abnormal battery in the group to be detected, and the adopted battery test data is the data in the battery production process, so the method and the device can adapt to the screening of the abnormal battery under various practical production conditions, the coverage of an abnormal battery screening scene is high, and the data for judging the abnormal battery is enriched due to the adoption of a large amount of battery test data in the production process, and therefore the screening precision of the abnormal battery can be improved.
Drawings
FIG. 1 is a schematic flow chart diagram of a method for abnormal battery determination in one embodiment;
FIG. 2 is a schematic flow chart of step 104 in one embodiment;
FIG. 3 is a flow chart illustrating step 202 in one embodiment;
FIG. 4 is a schematic flow chart of step 106 in one embodiment;
FIG. 5 is a diagram showing an abnormal cell determination method according to an embodiment;
FIG. 6 is a diagram illustrating an abnormal battery determination method according to an embodiment;
FIG. 7 is a diagram showing an abnormal cell determination method according to an embodiment;
FIG. 8 is a diagram showing an abnormal cell determination method according to an embodiment;
fig. 9 is a block diagram showing the structure of an abnormal battery determining apparatus in one embodiment;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In an embodiment, as shown in fig. 1, a method for determining an abnormal battery is provided, and this embodiment is illustrated by applying the method to a terminal, it is to be understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. In this embodiment, the method includes the steps of:
In the embodiment of the application, the group to be detected is composed of a plurality of batteries to be detected. For example, a group to be tested may include a plurality of batteries to be tested of a production lot. The number of the batteries to be detected included in the group to be detected is not particularly limited in the embodiment of the present application. The battery test data is data generated in the pre-charging process and/or the constant volume process of the battery to be detected, for example, voltage data and current data generated in the pre-charging process, open-circuit voltage data after the battery is aged at normal temperature, and the like, and voltage data and current data generated in the constant volume process. The pre-charging formation process comprises a pre-charging process of the battery to be detected and a formation process of the battery to be detected.
For example, the battery test data of the battery to be detected can be recorded in real time in the production process of the battery to be detected, and further, the battery test data can be obtained from the record in the screening process of the abnormal battery. Taking the battery test data as the voltage data generated in the constant volume process as an example, the voltage record can be obtained by recording the voltage value of the battery to be detected in real time in the constant volume process of the battery to be detected, and the voltage data generated in the constant volume process of the battery to be detected can be further obtained from the voltage record.
And 104, respectively constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected.
In the embodiment of the application, the battery quality data set of each battery to be detected can be constructed according to the battery test data generated in the pre-charging process and/or the constant volume process of each battery to be detected. Taking the example that the battery test data includes data generated in the pre-charging formation process as an example, the embodiment of the application can extract target data from the data generated in the pre-charging formation process, and further construct a battery quality data set of each battery to be detected according to each target data. The battery quality data set may include part of the battery test data, or may include all of the battery test data, which is not specifically limited in this embodiment of the present application.
And step 106, determining abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected.
In the embodiment of the application, according to the battery test data in the battery quality data set of each battery to be detected, abnormal batteries can be screened from the groups to be detected. Because most batteries in the group to be detected have no battery defects and meet the battery production standard, when the battery test data of a certain battery to be detected is obviously different from the battery test data of other batteries to be detected in the group to be detected, the battery to be detected is likely to be an abnormal battery.
For example, the battery to be detected, of which the battery test data is obviously different from the battery test data of other batteries to be detected, can be screened out as the abnormal battery by comparing the battery quality data sets of the batteries to be detected in the group to be detected. The method for screening abnormal batteries in the embodiment of the present application is not particularly limited, and the method for screening abnormal batteries is applicable to the embodiment of the present application according to the battery quality data set of each battery to be detected in the group to be detected.
According to the abnormal battery determining method provided by the embodiment of the application, the battery quality data sets of the batteries to be detected can be respectively constructed by acquiring the test data generated in the pre-charging process and/or the constant volume process of the batteries to be detected in the group to be detected, and then the abnormal batteries can be screened out from the group to be detected according to the battery quality data sets. The embodiment of the application integrates the relevant data generated in the pre-charging process and/or the constant volume process of the battery to be detected in the group to be detected as the battery test data to judge the abnormal battery in the group to be detected, and the adopted battery test data is the data in the battery production process, so the embodiment of the application can adapt to the screening of the abnormal battery under various production practical conditions, the coverage of the abnormal battery screening scene is high, and the data for judging the abnormal battery is enriched due to the adoption of a large amount of battery test data in the production process, and the screening precision of the abnormal battery can be improved.
In one embodiment, as shown in fig. 2, the battery test data includes target characteristic data, where the target characteristic data includes data corresponding to a target characteristic at each time in the pre-charging formation process and/or the volume fixing process, and in step 104, a battery quality data set of each battery to be detected is constructed according to the battery test data of each battery to be detected, where the method includes:
And 204, constructing a battery quality data set of the battery to be detected according to the detection data corresponding to the target characteristic data.
In the embodiments of the present application, the target feature is a category of the battery test data, such as current, voltage, temperature, and the like. One target feature data is a data set including data corresponding to target features acquired at a plurality of times during a pre-charging process and/or a constant volume process of a battery to be detected. For example, when the target characteristic is a voltage, the target characteristic data is voltage data, and the voltage data includes voltage values of the battery to be detected acquired at a plurality of moments in a pre-charging process and/or a constant volume process of the battery to be detected.
After the target characteristic data of the battery to be detected is obtained, for any target characteristic data, the detection data corresponding to the target characteristic data can be extracted from the target characteristic data, wherein the detection data is used for representing the data characteristics of the data corresponding to the target characteristic. One target feature data may correspond to one detection data or correspond to a plurality of detection data, which is not specifically limited in this embodiment of the present application.
For example, the manner of extracting the detection data corresponding to the obtained target feature data may include: summing all data in the target characteristic data, and taking the summation result as detection data of the target characteristic data; taking the mean value of each data in the target characteristic data, and taking the mean value as the detection data of the target characteristic data; and calculating the variance of each data in the target characteristic data, and using the variance as the detection data of the target characteristic data.
After the detection data corresponding to each target characteristic data of the battery to be detected is obtained, a battery quality data set of the battery to be detected can be constructed according to the detection data corresponding to each target characteristic data, so that abnormal batteries can be screened from the group to be detected according to the battery quality data set of each battery to be detected. Taking the target characteristics including voltage and current as an example, the detection data corresponding to the voltage data can be extracted from the voltage data: and voltage change rate, extracting detection data corresponding to the current data from the current data: and charging and/or discharging electric quantity, and further constructing a battery quality data set of the battery to be detected according to the voltage change rate and the charging and/or discharging electric quantity.
According to the abnormal battery determining method provided by the embodiment of the application, the detection data corresponding to the target characteristic data in the battery test data can be extracted, and then the battery quality data set is constructed according to the detection data corresponding to each target characteristic data. According to the embodiment of the application, the target characteristic data is integrated into the detection data, the data characteristics of the data corresponding to the target characteristic are kept, meanwhile, the quantity of battery test data needing to be added into the battery quality data set can be reduced, and the speed of screening abnormal batteries is increased.
In one embodiment, as shown in fig. 3, in step 202, extracting detection data corresponding to target feature data from target feature data of a battery to be detected includes:
In the embodiment of the application, the battery production process can be divided into a plurality of time periods according to the production strategy in the battery production process corresponding to the battery test data. Wherein the production strategy refers to the steps that need to be taken to produce the battery. Taking the target characteristic data as the battery test data generated in the pre-charging process of the battery to be detected as an example, the pre-charging process can be divided into time periods according to the specific steps of pre-charging the battery to be detected, so as to obtain the target data from the target characteristic data according to the target time period corresponding to the target characteristic data in each time period of the pre-charging process; taking the target characteristic data as the battery test data generated by the battery to be detected in the constant volume process as an example, time periods can be divided in the constant volume process according to the specific steps of the constant volume of the battery to be detected, so that the target data can be obtained from the target characteristic data according to the target time periods corresponding to the target characteristic data in each time period of the constant volume process. The target time period is one or more time periods obtained by time period division of the battery production process, the target time periods corresponding to different target characteristic data may be the same or different, and this is not specifically limited in this embodiment of the application.
For example, taking battery test data generated in a battery constant volume process as an example, the battery production process can be divided according to a constant volume strategy (that is, a specific step of constant volume of a battery to be detected in the constant volume process). For example, if the volume of the battery to be detected needs to be fixed, seven steps are needed: (1) charging a battery to be detected under constant current and constant voltage; (2) laying a battery to be detected; (3) enabling the battery to be detected to carry out constant current discharge; (4) laying the battery to be detected; (5) enabling the battery to be detected to carry out constant current discharge; (6) laying the battery to be detected; (7) the battery to be detected is charged under constant current and constant voltage, and when the constant volume process in the battery production process is divided into a plurality of time periods, the constant volume process can be divided into: (1) a first constant current and constant voltage charging section; (2) a first resting section; (3) a first constant current discharge section; (4) a second resting section; (5) a second constant current discharge section; (6) a third resting section; (7) and a second constant-current constant-voltage charging section.
And step 306, acquiring target data from the target characteristic data according to the target time period corresponding to the target characteristic data.
And 308, obtaining detection data corresponding to the target characteristic data according to the target data corresponding to the target characteristic data.
In the embodiment of the application, target time periods corresponding to the target characteristic data can be respectively determined, the target data can be obtained from the target characteristic data according to the target time periods, and then the detection data corresponding to the target characteristic data can be obtained through the target data. The target data acquired according to the target time period is a data set, and the data set comprises data corresponding to target features generated in the target time period.
It should be noted that only one piece of detected data may be obtained by one piece of target data, and a plurality of pieces of detected data may be obtained. For example, the mean of data in one target data may be one detection data, and the variance of data in the same target data may be another detection data. The embodiment of the present application is not particularly limited to this.
Illustratively, taking the example of performing the time-interval division on the battery production process in step 302 as an example, if the target characteristic data includes voltage data and current data, and the target time intervals corresponding to the voltage data are a first resting interval, a second resting interval and a third resting interval, the voltage data generated in the first resting interval is the first target data, the voltage data generated in the second resting interval is the second target data, and the voltage data generated in the third resting interval is the third target data. According to the first target data, the voltage change rate of the battery to be detected in the first shelving section can be obtained, according to the second target data, the voltage change rate of the battery to be detected in the second shelving section can be obtained, and according to the third target data, the voltage change rate of the battery to be detected in the third shelving section can be obtained. The obtained three voltage change rate data are detection data corresponding to the voltage data.
Similarly, if the target time periods corresponding to the current data are a first constant-current constant-voltage charging section, a second constant-current discharging section and a second constant-current constant-voltage charging section, the current data generated in the first constant-current constant-voltage charging section is first target data, the current data generated in the second constant-current discharging section is second target data, and the current data generated in the second constant-current constant-voltage charging section is third target data. According to the first target data, the charging electric quantity of the battery to be detected in the first constant-current constant-voltage charging section (the sum of the constant-current charging electric quantity of the battery to be detected in the constant-current charging and the constant-voltage charging electric quantity of the battery to be detected in the constant-voltage charging), according to the second target data, the discharging electric quantity of the battery to be detected in the second constant-current discharging section can be obtained, according to the third target data, the charging electric quantity of the battery to be detected in the second constant-current constant-voltage charging section can be obtained, and the constant-voltage charging electric quantity of the battery to be detected in the second constant-current constant-voltage charging section can be obtained. The obtained four electric quantity data are detection data corresponding to the voltage data.
According to the abnormal battery determining method provided by the embodiment of the application, the battery production process can be divided into a plurality of time periods according to the production strategy in the battery production process, the target time periods corresponding to the target characteristic data in the time periods are respectively determined, and then the detection data corresponding to the target characteristic data are obtained according to the target data in the target time periods. According to the embodiment of the application, the target characteristic data is integrated into the detection data, the data characteristics of the data corresponding to the target characteristic are kept, meanwhile, the quantity of battery test data needing to be added into the battery quality data set can be reduced, and the speed of screening abnormal batteries is increased.
In one embodiment, the battery test data includes index data, and in step 104, the constructing a battery quality data set of each battery to be tested according to the battery test data of each battery to be tested includes:
and aiming at any battery to be detected, constructing a battery quality data set of the battery to be detected according to detection data corresponding to each target characteristic data of the battery to be detected and/or each index data of the battery to be detected.
In the embodiment of the present application, the index data is battery test data obtained only once in the battery production process, for example, battery capacity, battery internal resistance, open-circuit voltage after normal temperature aging of the battery, and the like. After the index data of the battery to be detected is obtained, a battery quality data set of the battery to be detected can be constructed according to the index data of the battery to be detected and/or the detection data corresponding to the target characteristic data of the battery to be detected obtained in the foregoing embodiment.
For example, taking the example in the foregoing embodiment as an example, for the voltage data in the target feature data, the embodiment of the present application may compare three detection data corresponding to the voltage data: adding the voltage change rate of the battery to be detected in the first shelving section, the voltage change rate of the battery to be detected in the second shelving section and the voltage change rate of the battery to be detected in the third shelving section into a battery quality data set; for the current data, the embodiment of the present application may be configured to obtain three detection data corresponding to the current data: charging electric quantity of the battery to be detected in the first constant-current constant-voltage charging section, discharging electric quantity of the battery to be detected in the second constant-current discharging section and total charging electric quantity of the battery to be detected in the second constant-current constant-voltage charging section are added into the battery quality data set. For the index data, the open-circuit voltage after the battery is aged at the normal temperature and the self-discharge rate after the battery is aged at the normal temperature can be added into the battery quality data set.
According to the abnormal battery determining method provided by the embodiment of the application, the battery quality data set can be constructed according to the detection data corresponding to the target characteristic data of the battery to be detected and/or the index data of the battery to be detected. According to the battery quality data set, the battery test data of different types generated in the battery production process are adopted to construct the battery quality data set, so that abnormal batteries can be screened according to the battery test data of different types, and the screening precision of the abnormal batteries is further improved.
In one embodiment, as shown in fig. 4, in step 106, determining abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected includes:
And step 404, regarding the battery to be detected with the abnormal value larger than the threshold value in the group to be detected as the abnormal battery.
In the embodiment of the application, an outlier corresponding to each battery to be detected can be determined through an outlier detection algorithm according to the battery quality data set of each battery to be detected, and the outlier is used for representing the difference degree between the battery test data of the battery to be detected and the battery test data of other batteries to be detected in the battery to be detected. If the abnormal value of a certain battery to be detected is greater than the threshold (a preset value, a specific value may be set by a person skilled in the art according to the requirement of detection accuracy, for example, when the requirement of detection accuracy is higher, the corresponding threshold may be set to be smaller), it may be determined that the battery to be detected is an abnormal battery. The outlier detection algorithm is an algorithm for detecting outlier data in a batch of data, and may include a local abnormal factor algorithm, a density-based clustering algorithm, an isolated forest algorithm, and the like.
For example, the outlier detection algorithm is used as a local abnormal factor algorithm, and the local abnormal factor algorithm calculates a local abnormal factor value (i.e., an abnormal value) of the battery to be detected according to all battery test data in the battery quality data set of the battery to be detected. When the local abnormal factor value is greater than 1, the larger the local abnormal factor value is, the higher the probability that the battery to be detected is an abnormal battery is. In the embodiment of the present application, the battery to be detected whose local abnormal factor value is greater than the threshold value may be determined as an abnormal battery. The specific value of the threshold in the embodiment of the present application is not specifically limited, and may be selected by a person skilled in the art according to experience.
According to the abnormal battery determining method provided by the embodiment of the application, the abnormal value corresponding to each battery to be detected can be determined through an outlier detection algorithm according to the battery quality data set of each battery to be detected, and then the battery to be detected with the abnormal value larger than the threshold value is determined as the abnormal battery. According to the embodiment of the application, a fixed abnormal threshold value is not set for each battery test data in advance, but the battery to be detected with the battery test data with larger difference with the battery test data of other batteries to be detected is determined as the abnormal battery according to the difference degree of the battery test data of each battery to be detected in the group to be detected, so that possible slight difference between the groups to be detected can be considered, and the screening precision of the abnormal battery is further improved.
In order to make the embodiments of the present application better understood by those skilled in the art, the embodiments of the present application are described below by specific examples.
Referring to fig. 5 and 6, a flowchart of an abnormal battery determination method is shown. In the battery production process, the battery test data generated in the pre-charging process and/or the constant volume process of each battery to be detected can be extracted and obtained. The battery test data may include target characteristic data and index data, wherein the target characteristic data may be curve data.
Further, the detection data corresponding to the target characteristic data can be extracted and obtained through the target characteristic data. Taking the target characteristic data as curve data as an example, if the curve data is a voltage curve, the detection data may be one or more of a mean value, a variance, a kurtosis, a skewness and a change rate of the voltage curve in a certain time period; if the curve data is a current curve, the detection data can be the charge and/or discharge capacity of the battery, namely the integral of the current curve in a certain time period; if the curve data is a temperature curve, the detection data may be one or more of a mean, a variance, a kurtosis, a skewness, and a change rate of the temperature in a certain time period.
The embodiment of the application can further combine the one or more detection data and the one or more index data into a battery quality data set describing the battery quality. After the construction of the battery quality data set is completed for the n batteries to be detected in the group to be detected, the data of m × n dimensions in total can be obtained. After the data is input into an outlier detection algorithm for screening, abnormal batteries in the group to be detected can be determined.
Illustratively, as shown in fig. 7, a flow chart of an abnormal battery determination method is shown. The following examples of the present application will be described by taking a battery to be tested as a lithium iron phosphate battery as an example.
The embodiment of the application combines 8371 batteries to be detected in one production batch into a group to be detected. In the production process of the batteries, the battery test data of each battery to be detected in the group to be detected can be extracted and obtained, wherein the battery test data comprises voltage data and current data generated in the constant volume process, open-circuit voltage of the battery to be detected after normal temperature aging and self-discharge rate of the battery to be detected after normal temperature aging. The voltage data comprises voltage values of the battery to be detected, which are acquired at a plurality of moments in the constant volume process of the battery to be detected; the current data comprises current values of the battery to be detected, which are acquired at a plurality of moments in the constant volume process of the battery to be detected.
Further, the detection data corresponding to the voltage data and the detection data corresponding to the current data may be extracted from the voltage data and the current data. After the battery production process is divided according to a constant volume strategy, 7 time periods can be obtained: (1) a first constant current and constant voltage charging section; (2) a first resting section; (3) a first constant current discharge section; (4) a second resting section; (5) a second constant current discharge section; (6) a third resting section; (7) and a second constant current and constant voltage charging section. The target time periods corresponding to the voltage data can be a first shelving section, a second shelving section and a third shelving section, and the target time periods corresponding to the current data can be a first constant-current constant-voltage charging section, a second constant-current discharging section and a second constant-current constant-voltage charging section. The detection data extracted from the voltage data can be the voltage change rate of the battery to be detected in the first shelving section, the voltage change rate of the battery to be detected in the second shelving section and the voltage change rate of the battery to be detected in the third shelving section; the data to be detected extracted from the current data can be the charging electric quantity of the battery to be detected in the first constant-current constant-voltage charging section, the discharging electric quantity of the battery to be detected in the second constant-current discharging section and the charging electric quantity of the battery to be detected in the second constant-current constant-voltage charging section.
The embodiment of the application can further construct the battery quality data set of the battery to be detected according to the voltage change rate of the battery to be detected in the first shelving section, the voltage change rate of the battery to be detected in the second shelving section, the voltage change rate of the battery to be detected in the third shelving section, the charging capacity of the battery to be detected in the first constant-current constant-voltage charging section, the discharging capacity of the battery to be detected in the second constant-current discharging section, the charging capacity of the battery to be detected in the second constant-current constant-voltage charging section, the open-circuit voltage of the battery to be detected after normal-temperature aging, and the self-discharging rate of the battery to be detected after normal-temperature aging. That is, in the embodiment of the present application, there are 8 battery test data in the battery quality data set of each battery to be detected. After the battery quality data sets of all the batteries to be detected in the group to be detected are constructed, 8371 x 8 dimensional data can be obtained for inputting an outlier detection algorithm for abnormal battery screening.
Further, abnormal battery screening can be performed on the batteries to be detected in the group to be detected through a local abnormal factor algorithm. After setting the threshold value of the local abnormal factor value and calculating the local abnormal factor value of each battery to be detected according to the battery quality data set of each battery to be detected, the battery to be detected with the local abnormal factor value larger than the threshold value can be determined as an abnormal battery. The results of the local abnormal factor algorithm screening are shown in fig. 7, in which the normal cells are marked with o and the abnormal cells are marked with x. The numbers in the figures refer to: f1: the charging electric quantity of the battery to be detected in the first constant-current constant-voltage charging section; f2: discharging electric quantity of the battery to be detected in the second constant current discharging section; f3: the charging electric quantity of the battery to be detected in the second constant-current constant-voltage charging section; f4: detecting the voltage change rate of the battery to be detected in the first laying section; f5: detecting the voltage change rate of the battery to be detected in the second laying section; f6: the voltage change rate of the battery to be detected in the third shelving section; f7: detecting the open-circuit voltage of the battery after normal temperature aging; f8: and (5) detecting the self-discharge rate of the battery after normal temperature aging.
According to the abnormal battery determining method provided by the embodiment of the application, the battery quality data sets of the batteries to be detected can be respectively constructed by acquiring the test data generated in the pre-charging process and/or the constant volume process of the batteries to be detected in the group to be detected, and then the abnormal batteries can be screened out from the group to be detected according to the battery quality data sets. The method and the device integrate the relevant data generated in the pre-charging formation process and/or the constant volume process of the battery to be detected in the group to be detected as the battery test data to judge the abnormal battery in the group to be detected, and the adopted battery test data is the data in the battery production process, so the method and the device can adapt to the screening of the abnormal battery under various practical production conditions, the coverage of an abnormal battery screening scene is high, and the data for judging the abnormal battery is enriched due to the adoption of a large amount of battery test data in the production process, and therefore the screening precision of the abnormal battery can be improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides an abnormal battery determination device for realizing the abnormal battery determination method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more embodiments of the abnormal battery determination device provided below may refer to the limitations on the abnormal battery determination method in the above description, and are not described herein again.
In one embodiment, as shown in fig. 9, there is provided an abnormal battery determining apparatus including: an obtaining module 902, a constructing module 904, and a determining module 906, wherein:
an obtaining module 902, configured to obtain, for any battery to be detected in a group to be detected, battery test data of the battery to be detected, where the battery test data is data generated in a pre-charging process and/or a constant volume process of the battery;
a constructing module 904, configured to construct a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected;
a determining module 906, configured to determine, according to the battery quality data set of each battery to be detected, an abnormal battery in the group to be detected.
The abnormal battery determining device provided by the embodiment of the application can respectively construct the battery quality data sets of the batteries to be detected by acquiring the test data generated in the pre-charging process and/or the constant volume process of the batteries to be detected in the group to be detected, and then screen out the abnormal batteries from the group to be detected according to the battery quality data sets. The embodiment of the application integrates the relevant data generated in the pre-charging process and/or the constant volume process of the battery to be detected in the group to be detected as the battery test data to judge the abnormal battery in the group to be detected, and the adopted battery test data is the data in the battery production process, so the embodiment of the application can adapt to the screening of the abnormal battery under various production practical conditions, the coverage of the abnormal battery screening scene is high, and the data for judging the abnormal battery is enriched due to the adoption of a large amount of battery test data in the production process, and the screening precision of the abnormal battery can be improved.
In one embodiment, the battery test data includes target feature data, where the target feature data includes data corresponding to a target feature at each time in the pre-charging process and/or the constant volume process, and the building module 904 is further configured to:
for any battery to be detected, extracting detection data corresponding to the target characteristic data through the target characteristic data of the battery to be detected;
and constructing a battery quality data set of the battery to be detected according to the detection data corresponding to each target characteristic data.
In one embodiment, the building module 904 is further configured to:
dividing time periods of the battery production process according to the production strategy in the battery production process corresponding to the battery test data to obtain a plurality of time periods;
determining a target time period corresponding to the target characteristic data from the plurality of time periods;
acquiring target data from the target characteristic data according to a target time period corresponding to the target characteristic data;
and obtaining detection data corresponding to the target characteristic data according to the target data corresponding to the target characteristic data.
In one embodiment, the battery test data includes indicator data, and the constructing module 904 is further configured to:
and aiming at any battery to be detected, constructing a battery quality data set of the battery to be detected according to detection data corresponding to each target characteristic data of the battery to be detected and/or each index data of the battery to be detected.
In one embodiment, the determining module 906 is further configured to:
for any battery to be detected, determining an abnormal value corresponding to the battery to be detected by adopting an outlier detection algorithm according to the battery quality data set of the battery to be detected;
and taking the battery to be detected with the abnormal value larger than a threshold value in the group to be detected as an abnormal battery.
The respective modules in the abnormal battery determining apparatus described above may be entirely or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 10. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of automatic degradation of services.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. An abnormal battery determination method, characterized by comprising:
the method comprises the steps of acquiring battery test data of a battery to be detected aiming at any battery to be detected in a group to be detected, wherein the battery test data is data generated in the pre-charging process and/or the constant volume process of the battery;
respectively constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected;
and determining abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected.
2. The method according to claim 1, wherein the battery test data includes target characteristic data, the target characteristic data includes data corresponding to a target characteristic at each time in the pre-charging process and/or the constant volume process, and the constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected respectively includes:
for any battery to be detected, extracting detection data corresponding to the target characteristic data through the target characteristic data of the battery to be detected;
and constructing a battery quality data set of the battery to be detected according to the detection data corresponding to each target characteristic data.
3. The method according to claim 2, wherein the extracting, through the target characteristic data of the battery to be detected, detection data corresponding to the target characteristic data comprises:
dividing time periods of the battery production process according to the production strategy in the battery production process corresponding to the battery test data to obtain a plurality of time periods;
determining a target time period corresponding to the target characteristic data from the plurality of time periods;
acquiring target data from the target characteristic data according to a target time period corresponding to the target characteristic data;
and obtaining detection data corresponding to the target characteristic data according to the target data corresponding to the target characteristic data.
4. The method according to claim 2 or 3, wherein the battery test data comprises index data, and the constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected comprises:
and aiming at any battery to be detected, constructing a battery quality data set of the battery to be detected according to detection data corresponding to each target characteristic data of the battery to be detected and/or each index data of the battery to be detected.
5. The method of claim 1, wherein the determining abnormal batteries in the group to be detected according to the battery quality data set of each battery to be detected comprises:
for any battery to be detected, determining an abnormal value corresponding to the battery to be detected by adopting an outlier detection algorithm according to the battery quality data set of the battery to be detected;
and taking the battery to be detected with the abnormal value larger than a threshold value in the group to be detected as an abnormal battery.
6. An abnormal battery determination apparatus, characterized in that the apparatus comprises:
the battery testing data acquisition module is used for acquiring the battery testing data of the battery to be detected aiming at any battery to be detected in the group to be detected, wherein the battery testing data is data generated in the pre-charging process and/or the constant volume process of the battery;
the construction module is used for constructing a battery quality data set of each battery to be detected according to the battery test data of each battery to be detected;
and the determining module is used for determining the abnormal battery in the group to be detected according to the battery quality data set of each battery to be detected.
7. The apparatus of claim 6, wherein the battery test data comprises target characteristic data, the target characteristic data comprises data corresponding to a target characteristic at each time during the pre-charging process and/or the constant volume process, and the building module is further configured to:
for any battery to be detected, extracting detection data corresponding to the target characteristic data through the target characteristic data of the battery to be detected;
and constructing a battery quality data set of the battery to be detected according to the detection data corresponding to each target characteristic data.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 5.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 5 when executed by a processor.
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