CN114137418A - Storage battery performance identification method and device, computer equipment and storage medium - Google Patents
Storage battery performance identification method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a storage battery performance identification method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a battery internal resistance parameter data set of a to-be-detected storage battery in a first preset time period and a battery voltage parameter data set of a to-be-detected storage battery in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the to-be-detected storage battery in a floating charge mode according to a first sampling frequency, the battery voltage parameter data set is obtained by sampling the to-be-detected storage battery in an even charge mode according to a second sampling frequency, and the battery internal resistance parameter data set and the battery voltage data set are analyzed according to a preset multi-level data analysis rule and a CUSUM algorithm to obtain performance degradation storage battery identification results of different dimensions, so that a target performance degradation storage battery identification result is obtained. By adopting the method, the storage battery with degraded performance can be identified in advance before the performance of the storage battery is out of limit, so that the power accident caused by the failure of the storage battery is avoided, and the power supply safety is guaranteed.
Description
Technical Field
The present application relates to the field of battery performance identification technologies, and in particular, to a battery performance identification method, apparatus, computer device, storage medium, and computer program product.
Background
In a transformer substation and a converter station of a power system, a power supply is an important component, and a storage battery is used as a main device of the power supply, so that the storage battery has a very important significance for the safety and stability of power supply of secondary relay protection equipment, communication equipment and the like.
The storage battery is generally used as a backup power storage device of a UPS, a direct current power supply and a communication power supply, the storage battery is generally subjected to charge and discharge tests according to a period of one year or half a year, but in many cases, the storage battery with the performance exceeding the limit can be found when the tests are carried out, at the moment, the storage battery has been subjected to quality change, and even if the storage battery is charged again, the storage battery cannot be used continuously, so that the normal operation of a power system can be influenced, and certain influence is caused on the power supply safety.
Therefore, it is necessary to provide a solution for timely detecting the occurrence of a possible variation in the battery before the performance of the battery is out of limit.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for identifying battery performance with high accuracy.
In a first aspect, the application provides a battery performance identification method. The method comprises the following steps:
acquiring a battery internal resistance parameter data set of a storage battery to be tested in a first preset time period and a battery voltage parameter data set of the storage battery to be tested in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the storage battery to be tested in a floating charge mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the storage battery to be tested in an even charge mode according to a second sampling frequency;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
analyzing a battery internal resistance parameter data set and a battery voltage data set according to a CUSUM (computational sum) algorithm to obtain a second-dimension performance degradation storage battery identification result;
and combining the prediction result of the performance-degraded storage battery in the first dimension and the prediction result of the performance-degraded storage battery in the second dimension to obtain the identification result of the target performance-degraded storage battery.
In one embodiment, the step of analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain the first-dimension performance degradation storage battery identification result comprises the following steps:
dividing the battery internal resistance parameter data set into a plurality of battery internal resistance parameter subsets with equal time periods by taking the first time period as a unit, and dividing the battery voltage parameter data set into a plurality of battery voltage parameter subsets with equal time periods by taking the second time period as a unit;
analyzing each battery internal resistance parameter subset according to a preset multi-level data analysis rule to obtain a first performance degradation storage battery identification result, and analyzing each battery voltage parameter subset according to a preset multi-level data analysis rule to obtain a second performance degradation storage battery identification result;
and collecting the first performance degradation storage battery identification result and the second performance degradation storage battery identification result to obtain a first-dimension performance degradation storage battery identification result.
In one embodiment, the analyzing the subsets of the internal resistance parameters of the batteries according to a preset multi-level data analysis rule to obtain the identification result of the first performance-degraded storage battery comprises:
dividing each battery internal resistance parameter subset into a plurality of battery internal resistance parameter unit sets with equal time periods by taking the first sampling frequency as a unit;
according to the numerical value, sorting the internal resistance parameters of the batteries in the internal resistance parameter unit sets respectively to obtain a sorting result of the internal resistance parameter unit sets of the batteries, wherein the sorting result comprises the serial number of the storage battery equipment;
aiming at each internal resistance parameter subset of the batteries, comparing the sequencing results of each internal resistance parameter unit set of the batteries to obtain the initial identification result of the performance-degraded storage battery corresponding to each internal resistance parameter subset of the batteries, wherein the initial identification result of the performance-degraded storage battery is composed of the storage battery equipment numbers which are arranged in the sequencing results of each internal resistance parameter unit set of the batteries within the preset name range and have the frequency larger than the preset frequency threshold value;
and determining a first performance degradation storage battery identification result according to the performance degradation storage battery initial identification result corresponding to each battery internal resistance parameter subset.
In one embodiment, analyzing each battery voltage parameter subset according to a preset multi-level data analysis rule to obtain a second performance degradation storage battery identification result includes:
dividing each battery voltage parameter subset into a plurality of battery voltage parameter unit sets with equal time periods by taking the second sampling frequency as a unit;
summing and averaging the cell voltage parameter unit sets to obtain the average voltage of the cell voltage parameter unit sets;
comparing the battery voltage parameters in each battery voltage parameter unit set with the average voltage of each battery voltage parameter unit set to obtain a performance degradation storage battery identification result corresponding to each battery voltage parameter unit set;
aiming at each battery voltage parameter subset, comparing the initial identification results of the performance-degraded storage batteries corresponding to each battery voltage parameter unit set to obtain the intermediate identification results of the performance-degraded storage batteries corresponding to each battery voltage parameter subset;
and comparing the intermediate identification results of the performance-degraded storage batteries corresponding to the voltage parameter subsets of the batteries to obtain a second performance-degraded storage battery identification result.
In one embodiment, analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result includes:
according to a CUSUM algorithm, respectively carrying out mutation point identification on the battery internal resistance parameter data set and the battery voltage data set to obtain a third performance degradation storage battery identification result and a fourth performance degradation storage battery identification result;
and collecting the third performance degradation storage battery identification result and the fourth performance degradation storage battery identification result to obtain a second-dimension performance degradation storage battery identification result.
In one embodiment, after the target performance degradation storage battery identification result is obtained by combining the first-dimension performance degradation storage battery prediction result and the second-dimension performance degradation storage battery prediction result, the method further includes:
and sending a storage battery abnormity prompting message to the user terminal, wherein the storage battery abnormity prompting message carries a target performance degradation storage battery identification result.
In a second aspect, the application further provides a storage battery performance identification device. The device comprises:
the data acquisition module is used for acquiring a battery internal resistance parameter data set of the storage battery to be detected in a first preset time period and a battery voltage parameter data set of the storage battery to be detected in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the storage battery to be detected in a floating charge mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the storage battery to be detected in an even charge mode according to a second sampling frequency;
the first performance identification module is used for analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
the second performance identification module is used for analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result;
and the data processing module is used for combining the performance degradation storage battery prediction result of the first dimension and the performance degradation storage battery prediction result of the second dimension to obtain a target performance degradation storage battery identification result.
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 the following steps when executing the computer program:
acquiring a battery internal resistance parameter data set of a storage battery to be tested in a first preset time period and a battery voltage parameter data set of the storage battery to be tested in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the storage battery to be tested in a floating charge mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the storage battery to be tested in an even charge mode according to a second sampling frequency;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result;
and combining the prediction result of the performance-degraded storage battery in the first dimension and the prediction result of the performance-degraded storage battery in the second dimension to obtain the identification result of the target performance-degraded storage battery.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring a battery internal resistance parameter data set of a storage battery to be tested in a first preset time period and a battery voltage parameter data set of the storage battery to be tested in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the storage battery to be tested in a floating charge mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the storage battery to be tested in an even charge mode according to a second sampling frequency;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result;
and combining the prediction result of the performance-degraded storage battery in the first dimension and the prediction result of the performance-degraded storage battery in the second dimension to obtain the identification result of the target performance-degraded storage battery.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:
acquiring a battery internal resistance parameter data set of a storage battery to be tested in a first preset time period and a battery voltage parameter data set of the storage battery to be tested in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the storage battery to be tested in a floating charge mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the storage battery to be tested in an even charge mode according to a second sampling frequency;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result;
and combining the prediction result of the performance-degraded storage battery in the first dimension and the prediction result of the performance-degraded storage battery in the second dimension to obtain the identification result of the target performance-degraded storage battery.
The storage battery performance identification method, the device, the computer equipment, the storage medium and the computer program product fully consider the difference of the charging duration of the storage battery under different modes and the difference of the parameters capable of visually reflecting the performance of the storage battery by analyzing the internal resistance parameter data of the storage battery under the floating charge mode and the voltage parameter data under the uniform charge mode of the storage battery under different time periods, are favorable for obtaining more accurate performance degradation storage battery identification results, analyze and identify the storage battery with degraded performance from different dimensions according to the preset multi-level data analysis rule and the CUSUM algorithm, can obtain more comprehensive performance degradation storage battery identification results, can identify the storage battery with degraded performance in advance before the performance of the storage battery is over-limited by adopting the method, and ensure that a tester can carry out balanced charging and discharging on the storage battery with degraded performance in time, the normal operation of the storage battery is ensured, the electric power accident caused by the fault of the storage battery is avoided, and the power supply safety of the electric power system is guaranteed.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of a battery performance identification method;
FIG. 2 is a schematic flow chart of a battery performance identification method according to an embodiment;
FIG. 3 is a flowchart illustrating steps for obtaining a first performance degrading battery identification result in one embodiment;
FIG. 4 is a flowchart illustrating the steps for obtaining a second performance deterioration storage battery identification result in another embodiment;
FIG. 5 is a schematic diagram illustrating trends in the battery internal resistance parameter and the battery voltage parameter analyzed according to the CUSUM algorithm in one embodiment;
FIG. 6 is a block diagram showing the structure of a battery performance identifying apparatus according to an embodiment;
FIG. 7 is a block diagram showing the construction of a battery performance identifying apparatus according to another embodiment;
FIG. 8 is a diagram illustrating 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.
The storage battery performance identification method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server. Specifically, the storage battery inspector uploads the operation parameters (a battery internal resistance parameter data set in a first preset time period and a battery voltage parameter data set in a second preset time period) of the storage battery collected from a certain dc power supply device to the server 104 through the terminal 102 in real time, and then sends a performance identification message to the server 104 through the terminal 102, the server 104 responds to the performance identification message to obtain a battery internal resistance parameter data set of the storage battery to be inspected in the first preset time period and a battery voltage parameter data set in the second preset time period, the battery internal resistance parameter data set is obtained by sampling the storage battery to be inspected in the floating charge mode according to a first sampling frequency, the battery voltage parameter data set is obtained by sampling the storage battery to be inspected in the uniform charge mode according to a second sampling frequency, according to a preset multi-level data analysis rule, analyzing the internal resistance parameter data set and the voltage data set of the battery to obtain a first-dimension performance degradation storage battery identification result, analyzing the internal resistance parameter data set and the voltage data set of the battery according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result, and combining the first-dimension performance degradation storage battery prediction result and the second-dimension performance degradation storage battery prediction result to obtain a target performance degradation storage battery identification result. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In one embodiment, as shown in fig. 2, a method for identifying battery performance is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
In practical application, the data acquisition device can acquire the operating parameters of the storage battery from the running direct-current power supply equipment and periodically transmit the acquired operating parameters (including data such as battery temperature, battery internal resistance and battery voltage) of the storage battery to the computer of the application through the field bus. Because the charging time of the battery is different between the floating charging mode and the equalizing charging mode, the charging time of the floating charging mode is longer than that of the equalizing charging mode. Practical experience shows that the performance of the storage battery can be better reflected by observing the internal resistance parameters of the storage battery in the floating charge mode, and the performance of the storage battery can be better reflected by recording the voltage of the storage battery in the uniform charge mode. Therefore, the internal resistance parameter of the battery collected by the storage battery in the floating charge mode according to the sampling frequency once a week in a first preset time period, such as 2 months, can be extracted, and then the data of the storage battery number, the time and the like of each battery are combined to obtain the internal resistance parameter data set of the battery. And extracting battery voltage parameters acquired by the storage battery in a second preset time period, such as 1 day, in an equalizing charge mode according to the sampling frequency of once in 5 minutes, and combining the data of the storage battery number, the time and the like of each battery to obtain a battery voltage data set. Uploading a battery internal resistance parameter data set of the storage battery in a first preset time period and a battery voltage parameter data set of the storage battery in a second preset time period to a server, sending a performance detection message to the server by clicking a performance detection button on an operation interface of the terminal, and responding to the message by the server to obtain the battery internal resistance parameter data set of the storage battery in the first preset time period and the battery voltage parameter data set of the storage battery in the second preset time period for performance identification.
And 204, analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result.
After the battery internal resistance parameter data set of the storage battery in the first preset time period and the battery voltage parameter data set of the storage battery in the second preset time period are obtained, the battery internal resistance parameter data set and the battery voltage data set can be analyzed according to a preset multi-level data analysis rule, specifically, the multi-level data analysis rule can be that the first time period and the second time period are divided into multiple levels, such as minutes, hours, days, weeks, months or years, and then the battery internal resistance parameter data sets and the battery voltage data sets in all levels are analyzed one by one to obtain a first-dimension performance degradation storage battery identification result.
And step 206, analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result.
The CUSUM algorithm is one of statistical algorithms, in this embodiment, the performance of each storage battery in the first time period and the second time period may be used as a monitoring object, the cumulative sum of the battery internal resistance parameter data set and the battery voltage data set of each battery is monitored, when the battery internal resistance or the battery voltage of a certain battery exceeds a preset threshold, it is described that the battery may have slight abnormality, and the battery may be marked as a performance-degraded battery. The performance degradation storage battery identification result of the second dimension is obtained in this way.
And step 208, combining the prediction result of the first-dimension performance degradation storage battery with the prediction result of the second-dimension performance degradation storage battery to obtain the identification result of the target performance degradation storage battery.
In a specific implementation, after obtaining the performance degradation storage battery prediction result of the first dimension and the performance degradation prediction result of the second dimension, the performance degradation storage battery prediction result of the first dimension and the performance degradation prediction result of the second dimension may be aggregated to obtain a final performance degradation storage battery identification result. In another embodiment, the first dimensional performance degradation storage battery prediction result and the second dimensional performance degradation prediction result may be integrated, and the first several storage battery numbers with the highest occurrence frequency in the prediction results may be screened out to obtain the final performance degradation storage battery identification result.
In the method for identifying the performance of the storage battery, the difference of the charging duration of the storage battery in different modes and the difference of parameters capable of visually reflecting the performance of the storage battery are fully considered by analyzing the internal resistance parameter data of the storage battery to be detected in different time periods in a floating charging mode and the voltage parameter data in an equalizing charging mode, so that a more accurate identification result of the storage battery with degraded performance can be obtained, the storage battery with degraded performance can be analyzed and identified from different dimensions according to the preset multi-level data analysis rule and the CUSUM algorithm, a more comprehensive identification result of the storage battery with degraded performance can be obtained, the storage battery with degraded performance can be identified in advance before the performance of the storage battery exceeds the limit by adopting the method, so that a tester can perform balanced charging and discharging on the storage battery with degraded performance in time, and the normal operation of the storage battery is ensured, the electric power accident caused by the fault of the storage battery is avoided, and the power supply safety of the electric power system is guaranteed.
In one embodiment, step 204 comprises:
dividing the battery internal resistance parameter data set into a plurality of battery internal resistance parameter subsets with equal time periods by taking the first time period as a unit, and dividing the battery voltage parameter data set into a plurality of battery voltage parameter subsets with equal time periods by taking the second time period as a unit;
analyzing each battery internal resistance parameter subset according to a preset multi-level data analysis rule to obtain a first performance degradation storage battery identification result, and analyzing each battery voltage parameter subset according to a preset multi-level data analysis rule to obtain a second performance degradation storage battery identification result;
and collecting the first performance degradation storage battery identification result and the second performance degradation storage battery identification result to obtain a first-dimension performance degradation storage battery identification result.
In this embodiment, the obtaining process of the battery identification result with degraded performance in the first dimension may be: assuming that the data set of the battery internal resistance parameter in the first time period is the battery internal resistance parameter of 3 months, the battery internal resistance parameter of 3 months may be divided into the battery internal resistance parameter of one month in one period, where one month is the first time period. Assuming that the battery voltage parameter data set in the second time period is a 3-hour battery voltage parameter subset, the one hour may be the second time period, and the 3-hour battery voltage parameter data set is divided into the one-hour battery voltage parameter subsets. Analyzing each cell internal resistance parameter subset according to a preset multi-level data analysis rule to obtain a first performance degradation storage battery identification result, analyzing each cell voltage parameter subset according to a preset multi-level data analysis rule to obtain a second performance degradation storage battery identification result, and then collecting the first performance degradation storage battery identification result and the second performance degradation storage battery identification result to obtain a first-dimension performance degradation storage battery identification result. In the embodiment, the battery internal resistance parameter data set and the battery voltage data set are divided, so that the data can be analyzed orderly and hierarchically, and a relatively accurate recognition result is obtained.
As shown in fig. 3, in one embodiment, analyzing the internal resistance parameter subsets of the cells according to the preset multi-level data analysis rule to obtain the first performance degradation storage battery identification result includes:
and 284, determining a first performance degradation storage battery identification result according to the performance degradation storage battery initial identification result corresponding to each battery internal resistance parameter subset.
Specifically, the battery internal resistance parameter is inversely proportional to the battery performance, i.e., the larger the value of the battery internal resistance parameter is, the worse the battery performance is. The analysis of the subset of internal resistance parameters of the battery may be: dividing the battery internal resistance parameter subset of each month into four battery internal resistance unit sets with one week period according to the sampling frequency of one week/time, then, sorting the internal resistances of the batteries concentrated by the internal resistance units of the batteries in each week in a descending order to obtain sorting results of the internal resistances of the batteries which are arranged from big to small, then, comparing the sorting results of the internal resistance of the batteries of 4 groups every week, screening out the storage battery numbers with the highest occurrence frequency in the first five groups, if the numbers of the first five groups in the sorting results of the first week are 1, 3, 5, 7 and 9, numbers 1, 3, 5, 7 and 9 are continuously more than 2 times, and the first five of the ranking results of the internal resistances of the batteries appearing in the following three weeks, it is indicated that the batteries of numbers 1, 3, 5, 7 and 9 have undergone a minute abnormality as the initial recognition result of the performance deterioration storage battery corresponding to the subset of the internal resistance parameters of the battery for each month. In the above method, after counting the initial identification result of the degraded performance storage battery of 3 months, the identification result of the degraded performance storage battery of 3 months may be compared frequently, that is, the storage battery number with the highest frequency of appearance in the identification result of the degraded performance storage battery of 3 months is compared, if there is a part of the storage battery numbers in the identification result of the degraded performance storage battery of the next 2 months in the storage battery number of the degraded performance identified in the first month, the storage battery number is marked, and finally, the storage battery numbers are collected to obtain the identification result of the degraded performance storage battery of the first performance. In this embodiment, the battery internal resistance parameter data set is analyzed according to a multi-level analysis rule, so that a multi-level comprehensive first performance degradation storage battery identification result can be obtained.
As shown in fig. 4, in one embodiment, analyzing each subset of the battery voltage parameters according to a preset multi-level data analysis rule to obtain a second performance degradation battery identification result includes:
and 294, comparing the intermediate identification results of the performance-degraded storage batteries corresponding to the voltage parameter subsets of the batteries to obtain a second performance-degraded storage battery identification result.
Specifically, analyzing the subset of battery voltage parameters may be: dividing a battery voltage parameter subset of each hour into 12 groups of battery voltage unit sets according to the sampling frequency of every 5 minutes/time, then summing and averaging the battery voltage parameter unit sets to obtain the average voltage of the battery voltage parameter unit sets, marking the voltage value smaller than the average voltage as a performance degradation storage battery identification result of each battery voltage parameter unit set, then frequently comparing the performance degradation storage battery identification results identified by the 12 groups of battery voltage parameter unit sets, wherein the comparison can be performed by comparing the storage battery numbers with the highest occurrence frequency arranged in the first five groups to obtain the performance degradation storage battery middle identification result corresponding to each battery voltage parameter subset of each hour, then frequently comparing the performance degradation storage battery middle identification results of each battery voltage parameter subset of 3 hours to obtain the storage battery number with the occurrence frequency of more than 2 times in the identification results of 3 hours, a second performance deterioration storage battery identification result is obtained. In this embodiment, the battery voltage parameter data set is analyzed according to a multi-level analysis rule, so that a multi-level comprehensive secondary performance degradation storage battery identification result can be obtained.
In one embodiment, analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result includes:
according to a CUSUM algorithm, respectively carrying out mutation point identification on the battery internal resistance parameter data set and the battery voltage data set to obtain a third performance degradation storage battery identification result and a fourth performance degradation storage battery identification result;
and collecting the third performance degradation storage battery identification result and the fourth performance degradation storage battery identification result to obtain a second-dimension performance degradation storage battery identification result.
During specific implementation, the CUSUM algorithm is used for carrying out mutation point identification on the battery internal resistance parameter data set and the battery voltage data set acquired by the measurement loop, and the specific process is as follows:
(1-1) collecting a group of measured values Y with the length of N from the storage battery operation parameters at a certain moment in time sequenceNIs provided with YNIs a mean value of mu and a variance of sigma2Is independently and identically distributed random sequence of each point y in the sequencei(0<i ≦ N) probability density function P (y)i) Following a normal distribution, there are:
(1-2) calculating the difference Y of the measured values of the last time interval respectivelyNMean value of (a)0Sum variance
And the mean value mu of the measured value difference of the time interval1Sum variance
(1-3) calculating any point y in the time intervaliLog-likelihood ratio z (y) of preceding and following time windowsi)：
Wherein:
(1-4) calculating the cumulative sum S of log-likelihood ratios of the time intervalN：
(1-5) assume that the difference between the measured values in this time interval is abruptly changed at point v, that is, y1,y2,…yvThe distribution conforms to a known probability density function f, let yv+1,yv+2,…yNConforms to a probability density function g, and
v may be at any time. Two assumptions are made:
the original assumption is that: hk:v＝k,0≤k<N
The alternative assumption is that: h∞:v＝∞
The discrimination criteria of the test are:
if d is equal to 0, the measured value in the time interval is judged not to have mutation and is marked as H∞If d is equal to 1, the measured value in the time interval is judged to have mutation and is marked as Hk. Wherein H is the mutation threshold of the measured value, and H is the result of judgmentkIf the battery is abnormal, the battery is considered to be abnormal and marked. In this way, the third performance deterioration storage battery identification result and the fourth performance deterioration storage battery identification result can be obtained separately, and finally, the third performance deterioration storage battery identification result and the fourth performance deterioration storage battery identification result are collected to obtain the second-dimension performance deterioration storage battery identification result. Further, the battery internal resistance parameter data set and the battery voltage data set are analyzed through a CUSUM algorithm, and a trend chart of the battery internal resistance parameter and a trend chart of the battery voltage parameter of the storage battery can be obtained, and the trend chart is shown in fig. 5. In the embodiment, the mutation point identification is carried out on the battery internal resistance parameter data set and the battery voltage data set through the CUSUM algorithm, so that an accurate second-dimension performance degradation storage battery identification result can be obtained.
In one embodiment, after the target performance degradation storage battery identification result is obtained by combining the first-dimension performance degradation storage battery prediction result and the second-dimension performance degradation storage battery prediction result, the method further includes: and sending a storage battery abnormity prompting message to the user terminal, wherein the storage battery abnormity prompting message carries a target performance degradation storage battery identification result.
In this embodiment, after the final performance degradation storage battery identification result is obtained through identification, a storage battery abnormality prompt message carrying the target performance degradation storage battery identification result may be sent to the user terminal, so that a tester can visually obtain the performance degradation storage battery with slight abnormality, and charge and discharge of the battery in a balanced manner in time.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially 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 a storage battery performance identification device for realizing the storage battery performance identification method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so specific limitations in one or more embodiments of the battery performance identification device provided below can be referred to the limitations of the battery performance identification method in the foregoing, and details are not repeated herein.
In one embodiment, as shown in fig. 6, there is provided a battery performance identifying apparatus including: a data acquisition module 510, a first performance identification module 520, a second performance identification module 530, and a data processing module 540, wherein:
the data acquisition module 510 is configured to acquire a battery internal resistance parameter data set of the battery to be tested in a first preset time period and a battery voltage parameter data set of the battery to be tested in a second preset time period, where the battery internal resistance parameter data set is obtained by sampling the battery to be tested in the float charging mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the battery to be tested in the uniform charging mode according to a second sampling frequency;
the first performance identification module 520 is used for analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
the second performance identification module 530 is configured to analyze the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result;
and the data processing module 540 is used for combining the performance degradation storage battery prediction result of the first dimension and the performance degradation storage battery prediction result of the second dimension to obtain a target performance degradation storage battery identification result.
The storage battery performance identification device fully considers the difference of the charging duration of the storage batteries in different modes and the difference of parameters capable of visually reflecting the performance of the storage batteries by analyzing the internal resistance parameter data sets of the storage batteries to be detected in different time periods in the floating charging mode and the voltage parameter data in the uniform charging mode, is favorable for obtaining more accurate performance degradation storage battery identification results, analyzes and identifies the storage batteries with performance degradation from different dimensions according to the preset multi-level data analysis rule and the CUSUM algorithm, can obtain more comprehensive performance degradation storage battery identification results, can identify the storage batteries with performance degradation in advance before the performance of the storage batteries exceeds the limit by adopting the method, enables testers to perform balanced charging and discharging on the storage batteries with performance degradation in time, and ensures the normal operation of the storage batteries, the electric power accident caused by the fault of the storage battery is avoided, and the power supply safety of the electric power system is guaranteed.
In one embodiment, the first performance identification module 520 is further configured to divide the battery internal resistance parameter data set into a plurality of battery internal resistance parameter subsets with equal time periods by taking a first time period as a unit, divide the battery voltage parameter data set into a plurality of battery voltage parameter subsets with equal time periods by taking a second time period as a unit, analyze each battery internal resistance parameter subset according to a preset multi-level data analysis rule to obtain a first performance-degraded storage battery identification result, analyze each battery voltage parameter subset according to a preset multi-level data analysis rule to obtain a second performance-degraded storage battery identification result, and aggregate the first performance-degraded storage battery identification result and the second performance-degraded storage battery identification result to obtain a first-level performance-degraded storage battery identification result.
In one embodiment, the first performance identification module 520 is further configured to divide each internal resistance parameter subset of the batteries into a plurality of internal resistance parameter unit sets with equal time periods by using the first sampling frequency as a unit, sort the internal resistance parameters of the batteries in each internal resistance parameter unit set according to the values to obtain a sorting result of each internal resistance parameter unit set, where the sorting result includes the serial number of the storage battery device, compare the sorting results of each internal resistance parameter unit set with respect to each internal resistance parameter subset of the batteries to obtain an initial identification result of the storage battery with performance degradation corresponding to each internal resistance parameter subset, where the initial identification result of the storage battery with performance degradation is composed of the serial number of the storage battery device arranged in the sorting result of each internal resistance parameter unit set, where the frequency in a preset ranking range is greater than a preset frequency threshold, and according to the initial identification result of the storage battery with performance degradation corresponding to each internal resistance parameter subset of the batteries, a first performance degrading battery identification result is determined.
In one embodiment, the second performance identification module 530 is further configured to divide each cell voltage parameter subset into a plurality of cell voltage parameter unit sets with equal time periods by taking the second sampling frequency as a unit, sum and average each cell voltage parameter unit set to obtain an average voltage of each cell voltage parameter unit set, compare the cell voltage parameters in each cell voltage parameter unit set with the average voltage of each cell voltage parameter unit set to obtain a performance degradation storage battery identification result corresponding to each cell voltage parameter unit set, compare the performance degradation storage battery initial identification result corresponding to each cell voltage parameter unit set for each cell voltage parameter subset to obtain a performance degradation storage battery intermediate identification result corresponding to each cell voltage parameter subset, compare the performance degradation storage battery intermediate identification results corresponding to each cell voltage parameter subset, a second performance deterioration storage battery identification result is obtained.
In one embodiment, the second performance identification module 530 is further configured to perform mutation point identification on the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm, respectively, to obtain a third performance degradation storage battery identification result and a fourth performance degradation storage battery identification result, and collect the third performance degradation storage battery identification result and the fourth performance degradation storage battery identification result to obtain a second-dimensional performance degradation storage battery identification result.
As shown in fig. 7, in one embodiment, the apparatus further includes an abnormal message prompting module 550, configured to send a battery abnormal prompting message to the user terminal, where the battery abnormal prompting message carries the target performance degradation battery identification result.
The modules in the battery performance identification device can be wholly or partially realized 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 its internal structure diagram may be as shown in fig. 8. 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, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as a battery internal resistance parameter data set and a battery voltage data set. 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 battery performance identification method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 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, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the battery performance identification method when executing the computer program.
In one 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 battery performance identification method.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of the above battery performance identification method.
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 Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), 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. A battery performance identification method, the method comprising:
acquiring a battery internal resistance parameter data set of a to-be-detected storage battery in a first preset time period and a battery voltage parameter data set of a to-be-detected storage battery in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the to-be-detected storage battery in a floating charge mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the to-be-detected storage battery in an even charge mode according to a second sampling frequency;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result;
and combining the predicted result of the first-dimension performance degradation storage battery with the predicted result of the second-dimension performance degradation storage battery to obtain the target performance degradation storage battery identification result.
2. The method for identifying battery performance according to claim 1, wherein the analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multilevel data analysis rule to obtain the first-dimension performance degradation battery identification result comprises:
dividing the battery internal resistance parameter data set into a plurality of battery internal resistance parameter subsets with equal time periods by taking a first time period as a unit, and dividing the battery voltage parameter data set into a plurality of battery voltage parameter subsets with equal time periods by taking a second time period as a unit;
analyzing each battery internal resistance parameter subset according to a preset multi-level data analysis rule to obtain a first performance degradation storage battery identification result, and analyzing each battery voltage parameter subset according to a preset multi-level data analysis rule to obtain a second performance degradation storage battery identification result;
and collecting the first performance degradation storage battery identification result and the second performance degradation storage battery identification result to obtain a first-dimension performance degradation storage battery identification result.
3. The method for battery performance identification according to claim 2, wherein the analyzing each subset of internal resistance parameters of the battery according to the preset multilevel data analysis rule to obtain the first performance-degraded battery identification result comprises:
dividing each battery internal resistance parameter subset into a plurality of battery internal resistance parameter unit sets with equal time periods by taking the first sampling frequency as a unit;
according to the numerical value, sorting the internal resistance parameters of the batteries in the internal resistance parameter unit sets respectively to obtain a sorting result of the internal resistance parameter unit sets of the batteries, wherein the sorting result comprises the serial number of the storage battery equipment;
aiming at each internal resistance parameter subset of the batteries, comparing the sequencing results of each internal resistance parameter unit set of the batteries to obtain the initial identification result of the performance-degraded storage battery corresponding to each internal resistance parameter subset of the batteries, wherein the initial identification result of the performance-degraded storage battery is composed of the storage battery equipment numbers, which are arranged in the sequencing results of each internal resistance parameter unit set of the batteries within the preset name range and have the frequency larger than the preset frequency threshold value;
and determining a first performance degradation storage battery identification result according to the performance degradation storage battery initial identification result corresponding to each battery internal resistance parameter subset.
4. The battery performance identification method according to claim 2, wherein the analyzing each subset of battery voltage parameters according to the preset multi-level data analysis rule to obtain the second performance-degraded battery identification result comprises:
dividing each battery voltage parameter subset into a plurality of battery voltage parameter unit sets with equal time periods by taking the second sampling frequency as a unit;
summing and averaging the cell voltage parameter unit sets to obtain the average voltage of the cell voltage parameter unit sets;
comparing the battery voltage parameters in each battery voltage parameter unit set with the average voltage of each battery voltage parameter unit set to obtain a performance degradation storage battery identification result corresponding to each battery voltage parameter unit set;
aiming at each battery voltage parameter subset, comparing the initial identification results of the performance-degraded storage batteries corresponding to each battery voltage parameter unit set to obtain the intermediate identification results of the performance-degraded storage batteries corresponding to each battery voltage parameter subset;
and comparing the intermediate identification results of the performance-degraded storage batteries corresponding to the voltage parameter subsets of the batteries to obtain a second performance-degraded storage battery identification result.
5. The battery performance identification method according to any one of claims 1 to 4, wherein the analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain the identification result of the battery with the performance degradation of the second dimension comprises:
according to a CUSUM algorithm, respectively carrying out mutation point identification on the battery internal resistance parameter data set and the battery voltage data set to obtain a third performance degradation storage battery identification result and a fourth performance degradation storage battery identification result;
and collecting the third performance degradation storage battery identification result and the fourth performance degradation storage battery identification result to obtain a second-dimension performance degradation storage battery identification result.
6. The battery performance identification method according to any one of claims 1 to 4, wherein after the combination of the first-dimension performance degradation battery prediction result and the second-dimension performance degradation battery prediction result to obtain a target performance degradation battery identification result, the method further comprises:
and sending a storage battery abnormity prompting message to a user terminal, wherein the storage battery abnormity prompting message carries the target performance degradation storage battery identification result.
7. A battery performance identification apparatus, the apparatus comprising:
the data acquisition module is used for acquiring a battery internal resistance parameter data set of a to-be-detected storage battery in a first preset time period and a battery voltage parameter data set of a to-be-detected storage battery in a second preset time period, wherein the battery internal resistance parameter data set is obtained by sampling the to-be-detected storage battery in a floating charge mode according to a first sampling frequency, and the battery voltage parameter data set is obtained by sampling the to-be-detected storage battery in an even charge mode according to a second sampling frequency;
the first data analysis module is used for analyzing the battery internal resistance parameter data set and the battery voltage data set according to a preset multi-level data analysis rule to obtain a first-dimension performance degradation storage battery identification result;
the second data analysis module is used for analyzing the battery internal resistance parameter data set and the battery voltage data set according to a CUSUM algorithm to obtain a second-dimension performance degradation storage battery identification result;
and the data processing module is used for combining the prediction result of the first-dimension performance degradation storage battery and the prediction result of the second-dimension performance degradation storage battery to obtain the identification result of the target performance degradation storage battery.
8. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any of claims 1 to 6 when executing the computer program.
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 6.
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 6 when executed by a processor.
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