A testing method for a lithium battery of a vacuum cleaner

Abstract

This application relates to the technical field of battery performance testing, and specifically relates to a testing method for a lithium battery of a vacuum cleaner. The method includes: performing multiple charge and discharge processes on the lithium battery pack in the vacuum cleaner, and obtaining the test data of each test parameter of each lithium battery in the lithium battery pack at each moment through an intelligent sensor; determining the contact abnormality degree of the lithium battery pack at each moment; obtaining the effective test period and the deviation abnormality degree; obtaining the effective data set of each test parameter of each lithium battery; determining the consistency factor of each test parameter of the lithium battery pack; determining the consistency coefficient of the lithium battery pack, and detecting the performance of the lithium battery pack of the vacuum cleaner. This application avoids the phenomenon of contingency and singularity in test results, makes the performance test of the lithium battery more reliable, and improves the accuracy of the performance test of the lithium battery.

Description

Technical Field

[0001] The present application relates to the technical field of battery performance testing, and particularly relates to a method for testing a lithium battery of a vacuum cleaner. Background Art

[0002] As a new type of chemical energy source, lithium batteries are widely used in the electrical appliance market. Testing lithium batteries is to analyze the performance and quality of lithium batteries, so as to timely discover and solve potential safety hazards in the batteries, and ensure the high performance and safety of the products during use.

[0003] When testing the lithium battery of a vacuum cleaner, it is usually necessary to test whether the indicators such as the energy density, service life, self-discharge rate, charging efficiency, and internal resistance of the lithium battery are normal. Secondly, in a vacuum cleaner, usually a group of lithium batteries jointly maintain the normal operation of the vacuum cleaner. When the parameters such as voltage, internal resistance, and capacity of different lithium batteries in the lithium battery pack vary greatly, it will increase the loss of the lithium battery pack. Therefore, it is necessary to conduct a consistency test on the changes of battery parameters during the charging and discharging process of the lithium battery pack. When conducting a consistency test on the lithium battery pack of a vacuum cleaner, common test methods include internal resistance measurement method, capacity measurement method, and voltage measurement method. However, these methods only test a single parameter, and do not test the entire charging and discharging process of the lithium battery pack, but only measure the lithium battery pack at a certain moment after being fully charged, resulting in errors, contingency, and singularity in the test results, thus leading to low accuracy in the performance test of lithium batteries. Summary of the Invention

[0004] In order to solve the above technical problems, a method for testing a lithium battery of a vacuum cleaner is provided to solve the existing problems.

[0005] The solution of the present application to solve the technical problems is to provide a method for testing a lithium battery of a vacuum cleaner, including the following steps:

[0006] Perform multiple charging and discharging processes on the lithium battery pack in the vacuum cleaner, and obtain the test data of each test parameter of each lithium battery in the lithium battery pack at each moment through an intelligent sensor;

[0007] Analyze the fluctuation difference of the test data of each test parameter of each lithium battery before and after each moment, the difference of the test data of all test parameters of each lithium battery at different moments, and the abnormal proportion of the test data after each moment, and determine the contact abnormality degree of the lithium battery pack at each moment;

[0008] Based on the contact abnormality degree, obtain an effective test period; by analyzing the offset of the test data of each test parameter of each lithium battery at each moment within the effective test period, obtain the deviation abnormality degree of each lithium battery at each moment within the effective test period;

[0009] Based on the deviation abnormality degree, abnormal data rejection is performed on the test data at all times during the effective test period to obtain an effective data set for each test parameter of each lithium battery; analyze the correlation of the effective data sets among different lithium batteries under each test parameter, and determine the consistency factor of each test parameter of the lithium battery pack; based on the consistency factors of all test parameters of the lithium battery pack, determine the consistency coefficient of the lithium battery pack, and detect the performance of the lithium battery pack of the vacuum cleaner.

[0010] Preferably, each test parameter includes: voltage, current, temperature, capacity, internal resistance.

[0011] Preferably, the determination of the contact abnormality degree of the lithium battery pack at each moment includes:

[0012] Analyze the degree of discrete distribution difference of the test data of each test parameter of each lithium battery before and after multiple moments, and calculate the abnormality characterization degree of each test parameter of each lithium battery at each moment;

[0013] Statistically analyze the frequency of the occurrence of 0 values in the test data at all times after each moment of each test parameter of each lithium battery;

[0014] Calculate the difference between the test data of each test parameter of each lithium battery at each moment and its adjacent moment, and denote it as the relative difference amount;

[0015] Take the sum of the abnormality characterization degree, the frequency, and the relative difference amount of all lithium batteries under each test parameter at each moment as the contact poor coefficient of each test parameter of the lithium battery pack at each moment;

[0016] The normalized result of the sum of the contact poor coefficients of all test parameters at each moment is used as the contact abnormality degree of the lithium battery pack at each moment.

[0017] Preferably, the calculation of the abnormality characterization degree of each test parameter of each lithium battery at each moment includes:

[0018] Calculate the degree of dispersion of the test data of each test parameter of each lithium battery at multiple moments before each moment, and denote it as the front-segment dispersion degree;

[0019] Calculate the degree of dispersion of the test data of each test parameter of each lithium battery at multiple moments after each moment, and denote it as the back-segment dispersion degree;

[0020] Calculate the difference between the back-segment dispersion degree and the front-segment dispersion degree, and denote it as the dispersion difference. Take the calculation result of the exponential function with the natural constant as the base and the dispersion difference as the exponent as the abnormality characterization degree of each test parameter of each lithium battery at each moment.

[0021] Preferably, the obtaining of the effective test period includes: marking the moment when the contact abnormality degree is greater than a preset first threshold as the poor contact moment; arranging all moments in chronological order, marking the first poor contact moment among all moments as the initial poor contact moment, and marking all moments before the initial poor contact moment as the effective test period.

[0022] Preferably, the obtaining of the deviation abnormality degree of each lithium battery at each moment within the effective test period includes:

[0023] Using the Z-score algorithm to calculate the Z-score of the test data of each test parameter of each lithium battery at each moment within the effective test period;

[0024] Marking the sum of the Z-scores of the test data corresponding to all test parameters of each lithium battery at each moment within the effective test period as the relative deviation coefficient;

[0025] Counting the number of the absolute values of the Z-scores of the test data corresponding to all test parameters of each lithium battery at each moment within the effective test period that are greater than a preset second threshold as the outlier number;

[0026] Taking the product of the relative deviation coefficient and the outlier number as the deviation abnormality degree of each lithium battery at each moment within the effective test period.

[0027] Preferably, the method for obtaining the effective data set of each test parameter of each lithium battery is:

[0028] Using the threshold segmentation algorithm to perform threshold segmentation on the deviation abnormality degree of each lithium battery at all moments within the effective test period to obtain the segmentation threshold;

[0029] Marking the moments when the deviation abnormality degree is greater than the segmentation threshold as the abnormal moments; removing the test data of each test parameter of each lithium battery at all the abnormal moments within the effective test period, and forming the effective data set of each test parameter of each lithium battery from the test data of the remaining all moments of each test parameter of each lithium battery within the effective test period.

[0030] Preferably, the consistency factor of each test parameter of the lithium battery pack is the average value of the correlation degrees of the effective data sets of all any two lithium batteries under each test parameter.

[0031] Preferably, the calculation method of the consistency coefficient Q of the lithium battery pack is: where α r is the preset parameter weight corresponding to the r-th test parameter of the lithium battery pack, U ris the consistency factor of the r-th test parameter of the lithium battery pack, and R is the number of all test parameters of the lithium battery pack.

[0032] Preferably, the performance of the lithium battery pack of the vacuum cleaner is detected, including: if the consistency coefficient is greater than a preset third threshold, the performance of the lithium battery pack of the vacuum cleaner is good; otherwise, the performance of the lithium battery pack of the vacuum cleaner is poor.

[0033] The present application has at least the following beneficial effects:

[0034] This application analyzes the fluctuation differences of the test data of each test parameter of each lithium battery before and after each moment, the differences of the test data of all test parameters of each lithium battery at different moments, and the abnormal proportion of the test data after each moment. Combining the abnormal characterization degree, it determines the contact abnormality degree of the lithium battery pack at each moment. The beneficial effect is that it considers the fluctuation difference degree of the test data corresponding to different moments before and after each moment to reflect the possible situation of poor contact of the corresponding sensor or tester at each moment, analyzes the degree of abnormality of the test data after each moment, and the difference of the test data of each test parameter of each lithium battery between adjacent moments to reflect the possibility of poor contact of the lithium battery pack at the corresponding moment; Based on the contact abnormality degree, an effective test period is obtained. The beneficial effect is that it considers the moment when poor contact first occurs and screens out the test data collected before the moment of poor contact, so as to perform abnormal detection and consistency detection on the screened data later, thereby reducing the error of the test result; By analyzing the deviation of the test data of each test parameter of each lithium battery at each moment within the effective test period, the deviation abnormality degree of each lithium battery at each moment within the effective test period is obtained. The beneficial effect is that it considers the possibility of abnormal test data at each moment within the effective test period to reflect the situation where the test data of the lithium battery is affected by environmental electromagnetic interference at the corresponding moment, indicating the possibility of error in the test data at the corresponding moment; Based on the deviation abnormality degree, the abnormal test data within all moments of the effective test period is removed, and an effective data set of each test parameter of each lithium battery is obtained. The beneficial effect is to remove the abnormal test data, thereby improving the accuracy of the performance test of the lithium battery; Analyze the correlation of the effective data sets between different lithium batteries under each test parameter, and determine the consistency factor of each test parameter of the lithium battery pack; Based on the consistency factors of all test parameters of the lithium battery pack, determine the consistency coefficient of the lithium battery pack, and detect the performance of the lithium battery pack of the vacuum cleaner. The beneficial effect is that it considers the similarity of the test data between different lithium batteries under each test parameter to reflect the degree of consistency of the change trends of the test data of all lithium batteries in the lithium battery pack under each test parameter, indicating the performance of the lithium battery pack. By analyzing multiple test parameters of the lithium battery pack during multiple charge and discharge processes, it avoids the phenomenon of contingency and singularity in the test results, makes the performance test of the lithium battery more reliable, and improves the accuracy of the performance test of the lithium battery. Description of the Drawings

[0035] The following further elaborates in detail a test method for a lithium battery of a vacuum cleaner according to this application with reference to the drawings.

[0036] Figure 1The flowchart of steps of a test method for a lithium battery of a vacuum cleaner provided by an embodiment of the present application;

[0037] Figure 2 The flowchart of steps of a method for obtaining the contact abnormality degree of a lithium battery pack at each moment provided by an embodiment of the present application;

[0038] Figure 3 The flowchart of steps of a method for obtaining the deviation abnormality degree of each lithium battery at each moment during the effective test period provided by an embodiment of the present application. Detailed implementation manners

[0039] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes in detail a test method for a lithium battery of a vacuum cleaner proposed by the present application in combination with the accompanying drawings and implementation examples. It should be understood that the specific implementation examples described herein are only used to explain the present application and are not used to limit the present application.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs.

[0041] Please refer to Figure 1 , which shows the flowchart of steps of a test method for a lithium battery of a vacuum cleaner provided by an embodiment of the present application. The method includes the following steps:

[0042] Step 1, perform multiple charge and discharge processes on the lithium battery pack in the vacuum cleaner, and obtain the test data of each test parameter of each lithium battery in the lithium battery pack at each moment.

[0043] Perform multiple charge and discharge processes on the lithium battery pack in the vacuum cleaner, and collect the test data of each test parameter of each lithium battery in the lithium battery pack during multiple charge and discharge processes through intelligent sensors. Among them, the intelligent sensors include a voltage sensor, a current sensor, a temperature sensor, a battery capacity tester, and an internal resistance tester. The collection time interval is t, and the test data of each test parameter of each lithium battery in the lithium battery pack in the vacuum cleaner at each moment is obtained. Among them, each test parameter includes: voltage, current, temperature, capacity, internal resistance, and all the collected data is normalized.

[0044] In this embodiment, the number of lithium batteries in the lithium battery pack of the vacuum cleaner is 8. The lithium battery pack in the vacuum cleaner is subjected to 10 charge and discharge processes, and the acquisition time interval is 1 minute. As other implementation manners, the implementer can set it according to the actual situation; secondly, the maximum-minimum normalization method is used for normalization processing. Among them, the maximum-minimum normalization method is a well-known technology and will not be elaborated here. As other implementation manners, the implementer can adopt other methods of the existing technology, for example, the Z-score normalization method, etc. This embodiment does not make special restrictions on this.

[0045] Thus, the test data of each test parameter of each lithium battery in the lithium battery pack of the vacuum cleaner at each moment is obtained.

[0046] Step 2: Analyze the fluctuation difference of the test data of each test parameter of each lithium battery before and after each moment, the difference of the test data of all test parameters of each lithium battery at different moments, and the abnormal proportion of the test data after each moment, and combine the abnormal characterization degree to determine the contact abnormal degree of the lithium battery pack at each moment.

[0047] Inside the vacuum cleaner, generally multiple lithium batteries are connected in series and parallel to make the vacuum cleaner work normally. If the test parameters such as voltage and resistance between the lithium batteries are inconsistent, it will cause certain losses to the lithium battery pack. Therefore, when testing the lithium batteries, it is necessary not only to test the performance of a single lithium battery, but also to detect the performance consistency between the lithium batteries. The smaller the difference in the test data of each test parameter of each lithium battery at the same moment, the higher the consistency of the lithium battery pack; the greater the difference in the test data of each test parameter of each lithium battery at the same moment, the worse the consistency of the lithium battery pack.

[0048] However, during the process of collecting each test parameter, it will be interfered to a certain extent, such as the electromagnetic interference caused by household appliances to the sensor, or the poor contact between the sensor and the lithium battery due to the vibration of the vacuum cleaner, which may lead to the fact that the collected test data may not be the actual data of the lithium battery, and there will be a certain deviation in the test of the lithium battery. Therefore, before analyzing the consistency of each test parameter of the lithium battery pack, it is necessary to first detect the abnormal values in the test data of each test parameter.

[0049] When household appliances generate electromagnetic interference to the sensors, it will cause each test parameter of each lithium battery to shift and change at the same moment. The change may be an increase or a decrease. When the vacuum cleaner vibrates significantly during operation, it may cause poor contact between multiple sensors and the lithium battery. Then, the measured values of one or more sensors will mutate at the same moment, and the data after the mutation point will be significantly different from the data before the mutation point. Among them, the measured values after the mutation point will become more chaotic and there may be moments when the measured value is zero. When the sensor is neither affected by electromagnetic interference nor has poor contact, the data measured by the voltage sensor is the actual voltage of the lithium battery. In this case, the data collected by each sensor will show regular changes and there will be no mutation point. Based on the above analysis, to determine the degree of abnormal contact, the flowchart of the method for obtaining the degree of abnormal contact of the lithium battery pack at each moment provided by the embodiments of the present application is as Figure 2 shown.

[0050] First, according to the change trend of the test data of each test parameter of each lithium battery at each moment, determine the poor contact coefficient to reflect the possibility of poor contact between the sensor or tester corresponding to each test parameter and the lithium battery. Specifically:

[0051] Calculate the degree of dispersion of the test data of each test parameter of each lithium battery at multiple moments before each moment, denoted as the front-segment dispersion degree;

[0052] Calculate the degree of dispersion of the test data of each test parameter of each lithium battery at multiple moments after each moment, denoted as the back-segment dispersion degree;

[0053] In this embodiment, calculate the variance of the test data of each test parameter of each lithium battery at 8 moments before each moment, and calculate the variance of the test data of each test parameter of each lithium battery at 8 moments after each moment. As other implementation manners, the implementer can select by himself according to the actual situation the number of moments before or after each moment for calculating the test data. Secondly, the degree of dispersion is measured by variance. As other implementation manners, the implementer can adopt other methods in the prior art, such as standard deviation, coefficient of variation, etc. This embodiment does not make special restrictions on this. It should be noted that if the number of test data at all moments before or after a certain moment is less than 8, the degree of dispersion is not calculated for it.

[0054] Calculate the difference between the back-segment dispersion degree and the front-segment dispersion degree, denoted as the dispersion difference. Take the calculation result of the exponential function with the natural constant as the base and the dispersion difference as the exponent as the abnormal characterization degree of each test parameter of each lithium battery at each moment;

[0055] In this embodiment, the difference between the latter-stage dispersion and the former-stage dispersion is calculated and denoted as the dispersion difference. Secondly, the formula for the abnormality characterization degree of each test parameter of each lithium battery at each moment is: A i,r,t = exp(vh i,r,t - vq i,r,t ), where A i,r,t is the abnormality characterization degree of the r-th test parameter of the i-th lithium battery at the t-th moment, vh i,r,t is the latter-stage dispersion of the r-th test parameter of the i-th lithium battery at the t-th moment, vq i,r,t is the former-stage dispersion of the r-th test parameter of the i-th lithium battery at the t-th moment, exp() is the exponential function with the natural constant as the base. Secondly, vh i,r,t - vq i,r,t is the dispersion difference.

[0056] Statistically analyze the frequency of the occurrence of 0 values in the test data of all moments after each moment for each test parameter of each lithium battery;

[0057] Calculate the difference between the test data of each test parameter of each lithium battery at each moment and the previous moment, and denote it as the relative difference amount;

[0058] In this embodiment, calculate the absolute value of the difference between the test data of each test parameter of each lithium battery at each moment and the previous moment, and denote it as the relative difference amount.

[0059] Take the sum of the abnormality characterization degree, the frequency, and the relative difference amount of each lithium battery under each test parameter at each moment as the poor contact coefficient of each test parameter of the lithium battery pack at each moment;

[0060] In this embodiment, the calculation method of the poor contact coefficient of each test parameter of the lithium battery pack at each moment is: where P r,t is the poor contact coefficient of the r-th test parameter of the lithium battery pack at the t-th moment, A i,r,t is the abnormality characterization degree of the r-th test parameter of the i-th lithium battery at the t-th moment, sh i,r,t is the frequency of the occurrence of 0 values in the test data of all moments after the t-th moment for the r-th test parameter of the i-th lithium battery, Δu i,r,t is the relative difference amount of the r-th test parameter of the i-th lithium battery at the t-th moment, and n is the number of all lithium batteries in the lithium battery pack in the vacuum cleaner.

[0061] It should be noted that when the abnormality characterization degree A i,r,tThe larger it is, it indicates that the fluctuation degree of the test data of the r-th test parameter of the i-th lithium battery after the t-th moment is greater than that before the t-th moment, indicating that the possibility of poor contact between the i-th lithium battery and the sensor or tester corresponding to the r-th test parameter at the t-th moment is greater; when the frequency sh i,r,t The larger it is, it indicates that the frequency of the zero value appearance of the i-th lithium battery after the t-th moment is higher, then the possibility of poor contact between the sensor or tester corresponding to the r-th test parameter of the i-th lithium battery at the t-th moment is greater; the relative difference amount Δu i,r,t The larger it is, it indicates that there is a large difference between the test data of the r-th test parameter of the i-th lithium battery at the t-th moment and the test data at the t-1 moment, then poor contact may occur at the t-th moment; the obtained poor contact coefficient P r,t The larger it is, it indicates that the possibility of poor contact between the sensor or tester corresponding to the r-th test parameter at the t-th moment and the lithium battery is greater.

[0062] Further, based on the poor contact coefficients of all test parameters of the lithium battery pack at each moment, determine the contact abnormality degree, specifically:

[0063] Take the normalized result of the sum of the poor contact coefficients of all test parameters of the lithium battery pack at each moment as the contact abnormality degree of the lithium battery pack at each moment;

[0064] In this embodiment, the sigmoid function is used for normalization processing. Among them, the sigmoid function is a well-known technology and will not be elaborated here. As other implementation manners, implementers can adopt other methods of the existing technology, such as the tanh function, etc. This embodiment does not make special restrictions on this.

[0065] It should be noted that the larger the contact abnormality degree is, it indicates that the possibility of poor contact between the sensor or tester and the lithium battery at the corresponding moment is greater.

[0066] So far, the contact abnormality degrees of the lithium battery pack at each moment are obtained.

[0067] Step 3, based on the contact abnormality degree, obtain the effective test period; by analyzing the offset situation of the test data of each test parameter of each lithium battery at each moment within the effective test period, obtain the deviation abnormality degree of each lithium battery at each moment within the effective test period.

[0068] Further, based on the contact abnormality degree, judge whether there is an abnormality in the contact between the sensor or tester and the lithium battery, and determine the effective test period, specifically:

[0069] The moment when the contact abnormality degree is greater than a preset first threshold is recorded as the poor contact moment; in chronological order, the first poor contact moment among all moments is recorded as the initial poor contact moment.

[0070] All moments before the initial poor contact moment are recorded as the effective test period.

[0071] In this embodiment, the value of the preset first threshold is 0.6. As other implementation manners, the implementer can set it according to the actual situation.

[0072] It should be noted that the test data after the initial poor contact moment will have large errors due to the poor contact phenomenon. When performing consistency inspection on the lithium battery pack, it is necessary to perform the inspection based on the test data before the initial poor contact moment.

[0073] Furthermore, analyze the abnormality of the test data at all moments within the effective test period, specifically:

[0074] Adopt the Z-score algorithm to calculate the Z-score of the test data of each test parameter of each lithium battery at each moment within the effective test period.

[0075] The sum of the Z-scores of the test data corresponding to all test parameters of each lithium battery at each moment within the effective test period is recorded as the relative deviation coefficient.

[0076] Count the number of the absolute values of the Z-scores of the test data corresponding to all test parameters of each lithium battery at each moment within the effective test period that are greater than a preset second threshold, and record it as the outlier number.

[0077] In this embodiment, the value of the preset second threshold is 3. Among them, the Z-score algorithm is a well-known technology and will not be elaborated here. The Z-score Z corresponding to the test data at the t-th moment within the effective test period t The calculation formula is: x t is the test data at the t-th moment within the effective test period, μ is the mean value of the test data at all moments within the effective test period, σ is the standard deviation of the test data at all moments within the effective test period. Among them, the preset second threshold is 3, which is set according to the 3σ criterion, that is, count the number of values of x t that are not distributed in the range of (μ - 3σ, μ + 3σ). If the value of x t is not distributed in the range of (μ - 3σ, μ + 3σ), that is, the absolute value of Z t is greater than 3, it indicates that the test data at the corresponding moment is an abnormal point. Among them, the 3σ criterion is a well-known technology and will not be elaborated here.

[0078] Multiply the relative deviation coefficient by the number of outliers as the deviation abnormality of each lithium battery at each moment within the effective test period;

[0079] It should be noted that the larger the relative deviation coefficient, the greater the degree of deviation of the test data of all test parameters of each lithium battery at the corresponding moment compared to the overall data, indicating that the test data of each lithium battery at the corresponding moment is more likely to be affected by environmental electromagnetic interference, and thus the test data at that moment is more likely to have errors; the larger the number of outliers, the greater the deviation of the test data of multiple sensors at the corresponding moment, indicating that environmental interference may have occurred at the corresponding moment, and the greater the obtained deviation abnormality, the greater the possibility that the test data of the lithium battery pack at the corresponding moment has errors; the step flow chart of the method for obtaining the deviation abnormality of each lithium battery at each moment within the effective test period provided in this embodiment is as Figure 3 shown.

[0080] Thus, the deviation abnormality of each lithium battery at each moment within the effective test period is obtained.

[0081] Step 4: Based on the deviation abnormality, perform outlier rejection on the test data at all moments within the effective test period to obtain an effective data set for each test parameter of each lithium battery; analyze the correlation of the effective data sets between different lithium batteries under each test parameter, and determine the consistency factor for each test parameter of the lithium battery pack; based on the consistency factors of all test parameters of the lithium battery pack, determine the consistency coefficient of the lithium battery pack to detect the performance of the lithium battery pack of the vacuum cleaner.

[0082] Furthermore, based on the deviation abnormality, reject the abnormal data in the test data of each test parameter of each lithium battery at all moments within the effective test period, specifically:

[0083] Use the threshold segmentation algorithm to perform threshold segmentation on the deviation abnormality of each lithium battery at all moments within the effective test period to obtain a segmentation threshold;

[0084] Mark the moments when the deviation abnormality is greater than the segmentation threshold as abnormal moments;

[0085] In this embodiment, the Otsu threshold segmentation algorithm is used for threshold segmentation. The Otsu threshold segmentation algorithm is a well-known technology and will not be elaborated here.

[0086] Reject the test data of each test parameter of each lithium battery at all the abnormal moments within the effective test period, and form an effective data set for each test parameter of each lithium battery with the test data of each test parameter of each lithium battery at all other moments within the effective test period;

[0087] Furthermore, after eliminating the test data, the difference between any two valid data sets of lithium batteries under each test parameter is analyzed to determine the consistency factor of each test parameter, which is specifically:

[0088] Calculate the mean of the correlation degree of the valid data sets of all any two lithium batteries under each test parameter as the consistency factor of each test parameter of the lithium battery group;

[0089] In this embodiment, the mean cosine similarity of the valid data sets of all any two lithium batteries under each test parameter is calculated as the consistency factor of each test parameter of the lithium battery group, wherein the calculation of cosine similarity is a well-known technology and will not be repeated here. As other implementation methods, the implementer can adopt other methods of the prior art, for example, the inverse of the DTW distance for measurement, and this embodiment does not impose any special restrictions on this.

[0090] It should be noted that the greater the degree of correlation, the more similar the effective data sets of the two lithium batteries are, and the larger the obtained consistency factor is, the stronger the consistency of the corresponding test parameters of the lithium battery group is.

[0091] Further, based on the consistency factor, the consistency factors of all test parameters of the lithium battery pack are fused by weighted fusion to determine the consistency coefficient, which is specifically:

[0092] The calculation method of the consistency coefficient of the lithium battery pack is: Among them, Q is the consistency coefficient of the lithium battery pack, α r is the preset parameter weight corresponding to the rth test parameter of the lithium battery pack, U r is the consistency factor of the rth test parameter of the lithium battery pack, R is the number of all test parameters of the lithium battery pack, norm() is the normalization function, and in this embodiment, the sigmoid function is used for normalization, wherein the sigmoid function is a well-known technology and will not be described here. As other implementation methods, the implementer may adopt other methods of the prior art, such as the tanh function, etc., and this embodiment does not impose any special restrictions on this.

[0093] In this embodiment, the number R of all test parameters of the lithium battery pack is 5, corresponding to voltage, current, temperature, capacity, and internal resistance respectively. Therefore, α1, α2, α3, α4, and α5 are the preset parameter weights corresponding to voltage, current, temperature, capacity, and internal resistance respectively. Then, α1 takes a value of 0.3, α2 takes a value of 0.1, α3 takes a value of 0.1, α4 takes a value of 0.3, and α5 takes a value of 0.2, wherein α1+α2+α3+α4+α5=1. As other implementation modes, the implementer can set them according to actual conditions.

[0094] It should be noted that the larger the obtained consistency coefficient is, the higher the degree of performance consistency of each lithium battery in the lithium battery pack of the vacuum cleaner during operation, which reflects better battery performance of the lithium battery pack.

[0095] Furthermore, based on the consistency coefficient, the performance of the lithium battery pack of the vacuum cleaner is detected. Specifically:

[0096] If the consistency coefficient is greater than a preset third threshold, the performance of the lithium battery pack of the vacuum cleaner is good; otherwise, the performance of the lithium battery pack of the vacuum cleaner is poor.

[0097] In this embodiment, the value of the preset third threshold is 0.6. As other implementation manners, the implementer can set it according to the actual situation.

[0098] It should be understood that although Figure 1 the steps in the flowchart of Figure 1 are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover,

[0099] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0100] The above-described embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limitations of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application all belong to the protection scope of the technical solution of the present application.

Claims

1. A test method for a lithium battery of a vacuum cleaner, characterized in that, The method includes the following steps: Perform multiple charge and discharge processes on the lithium battery pack in the vacuum cleaner, and obtain the test data of each test parameter of each lithium battery in the lithium battery pack at each moment through an intelligent sensor; Analyze the fluctuation difference of the test data of each test parameter of each lithium battery before and after each moment, the difference of the test data of all test parameters of each lithium battery at different moments, and the abnormal proportion of the test data after each moment, and determine the contact abnormality degree of the lithium battery pack at each moment; Based on the contact abnormality degree, obtain an effective test period; by analyzing the deviation of the test data of each test parameter of each lithium battery at each moment within the effective test period, obtain the deviation abnormality degree of each lithium battery at each moment within the effective test period; Based on the deviation abnormality degree, eliminate the anomalies from the test data at all moments within the effective test period, and obtain an effective data set of each test parameter of each lithium battery; analyze the correlation of the effective data set between different lithium batteries under each test parameter, and determine the consistency factor of each test parameter of the lithium battery pack; based on the consistency factors of all test parameters of the lithium battery pack, determine the consistency coefficient of the lithium battery pack, and detect the performance of the lithium battery pack of the vacuum cleaner.

2. The test method for a lithium battery of a vacuum cleaner according to claim 1, wherein Each test parameter includes: voltage, current, temperature, capacity, internal resistance.

3. The test method of a lithium battery for a vacuum cleaner according to claim 1, characterized in that, The determination of the contact abnormality degree of the lithium battery pack at each moment includes: Analyze the degree of discrete distribution difference of the test data of each test parameter of each lithium battery before and after multiple moments at each moment, and calculate the abnormal characterization degree of each test parameter of each lithium battery at each moment; Count the frequency of the appearance of 0 values in the test data of all moments after each moment of each test parameter of each lithium battery; Calculate the difference between the test data of each test parameter of each lithium battery between each moment and its adjacent moment, and record it as the relative difference amount; Take the sum of the abnormal characterization degree, the frequency and the relative difference amount of all lithium batteries under each test parameter at each moment as the contact bad coefficient of each test parameter of the lithium battery pack at each moment; The normalized result of the sum of the contact bad coefficients of all test parameters at each moment is used as the contact abnormality degree of the lithium battery pack at each moment.

4. The testing method of a lithium battery for a vacuum cleaner according to claim 3, characterized in that, The calculation of the abnormal characterization degree of each test parameter of each lithium battery at each moment includes: Calculate the degree of dispersion of the test data of each test parameter of each lithium battery at multiple moments before each moment, and record it as the front-segment dispersion degree; Calculate the degree of dispersion of the test data of each test parameter of each lithium battery at multiple moments after each moment, and record it as the back-segment dispersion degree; Calculate the difference between the back-segment dispersion degree and the front-segment dispersion degree, and record it as the dispersion difference. Take the calculation result of the exponential function with the natural constant as the base and the dispersion difference as the exponent as the abnormal characterization degree of each test parameter of each lithium battery at each moment.

5. The testing method of a lithium battery for a vacuum cleaner according to claim 1, characterized in that, The obtaining of the effective test period includes: recording the moment when the contact abnormality degree is greater than a preset first threshold as the poor contact moment; in chronological order, recording the first poor contact moment among all moments as the initial poor contact moment, and recording all moments before the initial poor contact moment as the effective test period.

6. The testing method of a lithium battery for a vacuum cleaner according to claim 1, wherein, The obtaining of the deviation abnormality degree of each lithium battery at each moment within the effective test period includes: Using the Z-score algorithm to calculate the Z-score of the test data of each test parameter of each lithium battery at each moment within the effective test period; Recording the sum of the Z-scores of the test data corresponding to all test parameters of each lithium battery at each moment within the effective test period as the relative deviation coefficient; Counting the number of the absolute values of the Z-scores of the test data corresponding to all test parameters of each lithium battery at each moment within the effective test period that are greater than a preset second threshold as the outlier number; Taking the product of the relative deviation coefficient and the outlier number as the deviation abnormality degree of each lithium battery at each moment within the effective test period.

7. The test method for a lithium battery of a vacuum cleaner according to claim 1, characterized in that, The obtaining method of the effective data set of each test parameter of each lithium battery is: Using the threshold segmentation algorithm to perform threshold segmentation on the deviation abnormality degree of each lithium battery at all moments within the effective test period to obtain the segmentation threshold; Recording the moment when the deviation abnormality degree is greater than the segmentation threshold as the abnormal moment; Excluding the test data of each test parameter of each lithium battery at all the abnormal moments within the effective test period, and forming the effective data set of each test parameter of each lithium battery with the test data of the remaining all moments of each test parameter of each lithium battery within the effective test period.

8. The test method for a lithium battery of a vacuum cleaner according to claim 1, wherein, The consistency factor of each test parameter of the lithium battery pack is the average value of the correlation degrees of the effective data sets of any two lithium batteries under each test parameter.

9. The test method of a lithium battery for a vacuum cleaner according to claim 1, wherein The calculation method of the consistency coefficient Q of the lithium battery pack is as follows: where α r is the preset parameter weight corresponding to the r-th test parameter of the lithium battery pack, U r is the consistency factor of the r-th test parameter of the lithium battery pack, and R is the number of all test parameters of the lithium battery pack.

10. The testing method of a lithium battery for a vacuum cleaner according to claim 1, characterized in that, The detecting of the performance of the lithium battery pack of the vacuum cleaner includes: if the consistency coefficient is greater than a preset third threshold, the performance of the lithium battery pack of the vacuum cleaner is good; otherwise, the performance of the lithium battery pack of the vacuum cleaner is poor.

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